From 6a7b41253704a7ff85bab16f4cb347e314eebc13 Mon Sep 17 00:00:00 2001
From: TimoDiepers <timo.diepers@gmail.com>
Date: Fri, 8 May 2026 09:28:27 +0200
Subject: [PATCH 01/12] docs: update figure y axis labels

---
 notebooks/plots/figs/radiative_forcing.pdf   | Bin 42850 -> 42982 bytes
 notebooks/plots/figs/results_and_impacts.pdf | Bin 57982 -> 57944 bytes
 notebooks/plots/paper_figures.py             |   2 +-
 3 files changed, 1 insertion(+), 1 deletion(-)

diff --git a/notebooks/plots/figs/radiative_forcing.pdf b/notebooks/plots/figs/radiative_forcing.pdf
index c9e39e843c3221c7df042996915209c2722e27b6..0ade52b544649a2e8fd213f45115a08fedb11612 100644
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diff --git a/notebooks/plots/paper_figures.py b/notebooks/plots/paper_figures.py
index 6702e48..9b3a17e 100644
--- a/notebooks/plots/paper_figures.py
+++ b/notebooks/plots/paper_figures.py
@@ -1319,7 +1319,7 @@ def create_combined_results_and_impacts_figure(scenarios_data: dict):
         ax0.set_xticks(xtick_positions_impacts)
         ax0.set_xticklabels([])
         if col == 0:
-            ax0.set_ylabel(f"Cum. Radiative Forcing \n until 2125 [$10^{{{int(np.log10(scaling_factor_crf_bar))}}}$ W m$^{{-2}}$ yr]")
+            ax0.set_ylabel(f"AGWI until 2125 \n [$10^{{{int(np.log10(scaling_factor_crf_bar))}}}$ W m$^{{-2}}$ yr]")
         else:
             ax0.tick_params(labelleft=False)
         ax0.grid(True, alpha=0.3, zorder=0)

From 271530513a6cb9b1acc7140059034fea1bcedc79 Mon Sep 17 00:00:00 2001
From: TimoDiepers <timo.diepers@gmail.com>
Date: Fri, 8 May 2026 10:14:09 +0200
Subject: [PATCH 02/12] docs: add citation and announcement

---
 CITATION.cff               | 52 ++++++++++++++++++++++++++++++++++++++
 README.md                  |  6 +++++
 docs/index.md              |  8 ++++++
 docs/overrides/main.html   | 13 ++++++++++
 docs/stylesheets/extra.css | 41 ++++++++++++++++++++++++++++++
 zensical.toml              |  1 +
 6 files changed, 121 insertions(+)
 create mode 100644 CITATION.cff
 create mode 100644 docs/overrides/main.html

diff --git a/CITATION.cff b/CITATION.cff
new file mode 100644
index 0000000..aa9a9f0
--- /dev/null
+++ b/CITATION.cff
@@ -0,0 +1,52 @@
+cff-version: 1.2.0
+message: "If you use optimex in your research, please cite the preprint below."
+title: "optimex: Time-Explicit Life Cycle Optimization"
+abstract: "Open-source Python package for time-explicit Life Cycle Optimization (LCO). Identifies optimal technology transition pathways while accounting for when emissions occur and how product systems evolve over time."
+type: software
+url: "https://optimex.readthedocs.io"
+repository-code: "https://github.com/RWTH-LTT/optimex"
+license: BSD-3-Clause
+authors:
+  - family-names: Diepers
+    given-names: Timo
+    orcid: "https://orcid.org/0009-0002-8566-8618"
+    affiliation: "RWTH Aachen University"
+  - family-names: Tautorus
+    given-names: Jan
+    orcid: "https://orcid.org/0009-0002-9216-870X"
+    affiliation: "Interdisciplinary Transformation University Austria"
+  - family-names: Hartmann
+    given-names: Jan Marcus
+    orcid: "https://orcid.org/0000-0002-4295-1299"
+    affiliation: "RWTH Aachen University"
+  - family-names: von der Assen
+    given-names: Niklas
+    orcid: "https://orcid.org/0000-0001-8855-9420"
+    affiliation: "RWTH Aachen University"
+preferred-citation:
+  type: article
+  title: "A Framework for Time-Explicit Life Cycle Optimization"
+  year: 2026
+  month: 5
+  doi: "10.21203/rs.3.rs-9630408/v1"
+  url: "https://doi.org/10.21203/rs.3.rs-9630408/v1"
+  publisher:
+    name: "Research Square"
+  notes: "Preprint, In Review"
+  authors:
+    - family-names: Diepers
+      given-names: Timo
+      orcid: "https://orcid.org/0009-0002-8566-8618"
+      affiliation: "RWTH Aachen University"
+    - family-names: Tautorus
+      given-names: Jan
+      orcid: "https://orcid.org/0009-0002-9216-870X"
+      affiliation: "Interdisciplinary Transformation University Austria"
+    - family-names: Hartmann
+      given-names: Jan Marcus
+      orcid: "https://orcid.org/0000-0002-4295-1299"
+      affiliation: "RWTH Aachen University"
+    - family-names: von der Assen
+      given-names: Niklas
+      orcid: "https://orcid.org/0000-0001-8855-9420"
+      affiliation: "RWTH Aachen University"
diff --git a/README.md b/README.md
index a374d24..f1df5f4 100644
--- a/README.md
+++ b/README.md
@@ -79,6 +79,12 @@ Full documentation, tutorials, and examples are available at **[optimex.readthed
 
 `optimex` builds on [Pyomo](https://github.com/Pyomo/pyomo) and [Brightway](https://docs.brightway.dev/en/latest). For time-explicit LCA without optimization, see [`bw_timex`](https://docs.brightway.dev/projects/bw-timex/en/latest/).
 
+## Citation
+
+If you use `optimex` in your research, please consider citing our preprint:
+
+> Diepers, T., Tautorus, J., Hartmann, J. M., & von der Assen, N. (2026). *A Framework for Time-Explicit Life Cycle Optimization*. Preprint. https://doi.org/10.21203/rs.3.rs-9630408/v1
+
 ## Support
 
 - Timo Diepers ([timo.diepers@ltt.rwth-aachen.de](mailto:timo.diepers@ltt.rwth-aachen.de))
diff --git a/docs/index.md b/docs/index.md
index c83d9c4..e14deae 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -85,6 +85,14 @@ Time-explicit LCO reveals transition strategies that are invisible to static app
 
 ---
 
+## Citation
+
+If you use `optimex` in your research, please consider citing our preprint:
+
+> Diepers, T., Tautorus, J., Hartmann, J. M., & von der Assen, N. (2026). *A Framework for Time-Explicit Life Cycle Optimization*. Research Square. [https://doi.org/10.21203/rs.3.rs-9630408/v1](https://doi.org/10.21203/rs.3.rs-9630408/v1)
+
+---
+
 ## Support
 
 If you have any questions or need help, do not hesitate to contact us:
diff --git a/docs/overrides/main.html b/docs/overrides/main.html
new file mode 100644
index 0000000..515bd7f
--- /dev/null
+++ b/docs/overrides/main.html
@@ -0,0 +1,13 @@
+{% extends "base.html" %}
+
+{% block announce %}
+  <span class="optimex-announce">
+    <span class="twemoji optimex-announce__icon">
+      {% include ".icons/lucide/newspaper.svg" %}
+    </span>
+    <span>
+      New preprint on the optimex methodology &mdash; read it
+      <a href="https://doi.org/10.21203/rs.3.rs-9630408/v1" target="_blank" rel="noopener">here</a>.
+    </span>
+  </span>
+{% endblock %}
diff --git a/docs/stylesheets/extra.css b/docs/stylesheets/extra.css
index db67b21..864327a 100644
--- a/docs/stylesheets/extra.css
+++ b/docs/stylesheets/extra.css
@@ -70,6 +70,47 @@
   border-radius: 0.4rem;
 }
 
+/* Announcement bar */
+.md-banner__inner {
+  text-align: center;
+}
+
+.optimex-announce {
+  display: inline-flex;
+  align-items: center;
+  gap: 0.55rem;
+  flex-wrap: wrap;
+  justify-content: center;
+  font-size: 0.72rem;
+  letter-spacing: 0.01em;
+}
+
+.optimex-announce__icon {
+  display: inline-flex;
+  align-items: center;
+  justify-content: center;
+  width: 1.1rem;
+  height: 1.1rem;
+  flex-shrink: 0;
+}
+
+.optimex-announce__icon svg {
+  width: 100%;
+  height: 100%;
+  fill: currentColor;
+}
+
+.optimex-announce a {
+  color: inherit;
+  text-decoration: underline;
+  text-underline-offset: 0.18em;
+  text-decoration-thickness: 1px;
+}
+
+.optimex-announce a:hover {
+  text-decoration-thickness: 2px;
+}
+
 .example-flowchart {
   display: block;
   width: 100%;
diff --git a/zensical.toml b/zensical.toml
index 19e0cf0..5bd7df0 100644
--- a/zensical.toml
+++ b/zensical.toml
@@ -57,6 +57,7 @@ extra_css = [
 
 [project.theme]
 language = "en"
+custom_dir = "docs/overrides"
 logo = "_static/favicons/favicon.svg"
 favicon = "_static/favicons/favicon.svg"
 features = [

From 4892cb07df3f8b3b6943d315d4cca677a0fa439f Mon Sep 17 00:00:00 2001
From: TimoDiepers <timo.diepers@gmail.com>
Date: Fri, 8 May 2026 10:16:57 +0200
Subject: [PATCH 03/12] docs: add doi badge

---
 README.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/README.md b/README.md
index f1df5f4..963db2e 100644
--- a/README.md
+++ b/README.md
@@ -11,6 +11,7 @@
 [![PyPI - Version](https://img.shields.io/pypi/v/optimex?color=%2300549f)](https://pypi.org/project/optimex/)
 [![Conda Version](https://img.shields.io/conda/v/diepers/optimex?label=conda)](https://anaconda.org/diepers/optimex)
 [![Conda - License](https://img.shields.io/conda/l/diepers/optimex)](https://github.com/TimoDiepers/optimex/blob/main/LICENSE)
+[![DOI](https://img.shields.io/badge/DOI-10.21203%2Frs.3.rs--9630408%2Fv1-blue)](https://doi.org/10.21203/rs.3.rs-9630408/v1)
 [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/TimoDiepers/optimex/main?urlpath=%2Fdoc%2Ftree%2Fnotebooks%2Fbasic_optimex_example.ipynb)
 
 ## What is optimex?

From 89c65bc7f222c6e01a6a350de1dd948518d9f3cd Mon Sep 17 00:00:00 2001
From: TimoDiepers <timo.diepers@gmail.com>
Date: Fri, 8 May 2026 10:19:46 +0200
Subject: [PATCH 04/12] docs: update doi badge text

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 963db2e..cdfb7c5 100644
--- a/README.md
+++ b/README.md
@@ -11,7 +11,7 @@
 [![PyPI - Version](https://img.shields.io/pypi/v/optimex?color=%2300549f)](https://pypi.org/project/optimex/)
 [![Conda Version](https://img.shields.io/conda/v/diepers/optimex?label=conda)](https://anaconda.org/diepers/optimex)
 [![Conda - License](https://img.shields.io/conda/l/diepers/optimex)](https://github.com/TimoDiepers/optimex/blob/main/LICENSE)
-[![DOI](https://img.shields.io/badge/DOI-10.21203%2Frs.3.rs--9630408%2Fv1-blue)](https://doi.org/10.21203/rs.3.rs-9630408/v1)
+[![preprint](https://img.shields.io/badge/preprint-10.21203%2Frs.3.rs--9630408%2Fv1-blue)](https://doi.org/10.21203/rs.3.rs-9630408/v1)
 [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/TimoDiepers/optimex/main?urlpath=%2Fdoc%2Ftree%2Fnotebooks%2Fbasic_optimex_example.ipynb)
 
 ## What is optimex?

From d8c0d02e398b5f4a32b30c1807dc0f5ad4f90b3b Mon Sep 17 00:00:00 2001
From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com>
Date: Wed, 20 May 2026 23:15:02 +0000
Subject: [PATCH 05/12] Progress: completed analysis and baseline checks

Agent-Logs-Url: https://github.com/RWTH-LTT/optimex/sessions/98d5a08c-042c-46e8-93f3-9271a9c3e3bd

Co-authored-by: TimoDiepers <90762029+TimoDiepers@users.noreply.github.com>
---
 .github/workflows/conda-package-deploy.yml    |    8 +-
 .gitignore                                    |    2 +-
 CHANGES.md                                    |    2 +-
 CODE_OF_CONDUCT.md                            |    6 +-
 docs/content/examples/basic_example.md        |   32 +-
 docs/content/examples/data/product_system.svg |    2 +-
 docs/content/examples/methanol_and_iron.md    |   25 +-
 docs/content/installation.md                  |    2 +-
 docs/content/theory_how_it_works.md           |    2 +-
 docs/content/theory_why_time_matters.md       |    2 +-
 docs/convert_notebooks.py                     |    4 +-
 docs/requirements.txt                         |    6 +-
 notebooks/data/model_inputs_2050.json         |    2 +-
 .../data/paper/model_inputs_2050_paper.json   |    2 +-
 notebooks/data/product_system.svg             |    2 +-
 notebooks/plots/paper_figures.py              | 1019 +++++++++++------
 scripts/uv_setup.py                           |   14 +-
 src/optimex/converter.py                      |  238 ++--
 src/optimex/lca_processor.py                  |  141 ++-
 src/optimex/optimizer.py                      |   98 +-
 src/optimex/postprocessing.py                 |  519 ++++++---
 src/optimex/utils.py                          |    3 +-
 ...t_background_process_product_separation.py |  252 ++--
 tests/test_brownfield.py                      |  133 ++-
 tests/test_merit_order.py                     |   58 +-
 tests/test_operation_constraints.py           |   15 +-
 tests/test_optimization.py                    |  193 ++--
 tests/test_serialization.py                   |   56 +-
 tests/test_single_route_lca_comparison.py     |  171 +--
 tests/test_two_level_supply_chain.py          |  348 +++---
 tests/test_vintage_deterministic.py           |  156 +--
 tests/test_vintage_parameters.py              |  462 +++++---
 32 files changed, 2453 insertions(+), 1522 deletions(-)

diff --git a/.github/workflows/conda-package-deploy.yml b/.github/workflows/conda-package-deploy.yml
index 49d6908..65cdad2 100644
--- a/.github/workflows/conda-package-deploy.yml
+++ b/.github/workflows/conda-package-deploy.yml
@@ -13,7 +13,7 @@ jobs:
   conda-pkg-build:
     name: Build and Publish
     runs-on: ubuntu-latest
-    
+
     steps:
       - name: Checkout Code
         uses: actions/checkout@v4
@@ -38,11 +38,11 @@ jobs:
         run: |
           # 1. Install necessary tools silently
           conda install -y -q conda-build anaconda-client
-          
-          # 2. Configure conda to NOT attempt automatic uploads 
+
+          # 2. Configure conda to NOT attempt automatic uploads
           # (We handle the upload explicitly via the build command)
           conda config --set anaconda_upload no
-          
+
           # 3. Build and Upload in one command using the Token
           conda build conda/ \
             -c conda-forge \
diff --git a/.gitignore b/.gitignore
index 3e5433a..0281659 100644
--- a/.gitignore
+++ b/.gitignore
@@ -146,4 +146,4 @@ notebooks/premise_usage.ipynb
 notebooks/results
 
 # Generated plot files
-notebooks/plots/figs/*.png
\ No newline at end of file
+notebooks/plots/figs/*.png
diff --git a/CHANGES.md b/CHANGES.md
index 5fb32ec..469f7ce 100644
--- a/CHANGES.md
+++ b/CHANGES.md
@@ -27,4 +27,4 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 * Improved user-facing API
 
 ## [0.1.0] - 2025-02-27
-* Initial release. 
+* Initial release.
diff --git a/CODE_OF_CONDUCT.md b/CODE_OF_CONDUCT.md
index cd5d4ed..6fa803a 100644
--- a/CODE_OF_CONDUCT.md
+++ b/CODE_OF_CONDUCT.md
@@ -56,9 +56,9 @@ further defined and clarified by project maintainers.
 
 Instances of abusive, harassing, or otherwise unacceptable behavior may be
 reported by contacting the project team at [timo.diepers@ltt.rwth-aachen.de]
-(mailto:timo.diepers@ltt.rwth-aachen.de). All complaints will be reviewed and 
-investigated and will result in a response that, is deemed necessary and 
-appropriate to the circumstances. The project team is obligated to maintain 
+(mailto:timo.diepers@ltt.rwth-aachen.de). All complaints will be reviewed and
+investigated and will result in a response that, is deemed necessary and
+appropriate to the circumstances. The project team is obligated to maintain
 confidentiality with regard to the reporter of an incident.
 Further details of specific enforcement policies may be posted separately.
 
diff --git a/docs/content/examples/basic_example.md b/docs/content/examples/basic_example.md
index 5b815b4..f02733a 100644
--- a/docs/content/examples/basic_example.md
+++ b/docs/content/examples/basic_example.md
@@ -68,10 +68,10 @@ bd.Database("biosphere3").write(biosphere_data)
 
     100%|██████████| 3/3 [00:00<00:00, 12545.28it/s]
 
-    11:15:06+0100 [info     ] Vacuuming database            
+    11:15:06+0100 [info     ] Vacuuming database
+
 
 
-    
 
 
 ### Characterization Methods
@@ -312,24 +312,24 @@ bg_2050.register()
 
     100%|██████████| 3/3 [00:00<00:00, 30393.51it/s]
 
-    11:15:06+0100 [info     ] Vacuuming database            
+    11:15:06+0100 [info     ] Vacuuming database
     11:15:06+0100 [warning  ] Not able to determine geocollections for all datasets. This database is not ready for regionalization.
 
 
-    
+
     100%|██████████| 3/3 [00:00<00:00, 58798.65it/s]
 
-    11:15:06+0100 [info     ] Vacuuming database            
+    11:15:06+0100 [info     ] Vacuuming database
     11:15:06+0100 [warning  ] Not able to determine geocollections for all datasets. This database is not ready for regionalization.
 
 
-    
+
     100%|██████████| 3/3 [00:00<00:00, 69518.85it/s]
 
-    11:15:06+0100 [info     ] Vacuuming database            
+    11:15:06+0100 [info     ] Vacuuming database
+
 
 
-    
 
 
 ### Foreground Database
@@ -553,10 +553,10 @@ fg.register()
 
     100%|██████████| 6/6 [00:00<00:00, 21041.66it/s]
 
-    11:15:06+0100 [info     ] Vacuuming database            
+    11:15:06+0100 [info     ] Vacuuming database
+
 
 
-    
 
 
 ### Demand
@@ -695,9 +695,9 @@ pp.plot_capacity_balance(detailed=True)
 ```
 
 
-    
+
 ![png](basic_example_files/output_23_0.png)
-    
+
 
 
 
@@ -706,9 +706,9 @@ pp.plot_impacts()
 ```
 
 
-    
+
 ![png](basic_example_files/output_24_0.png)
-    
+
 
 
 
@@ -722,7 +722,5 @@ pp.plot_characterized_dynamic_inventory(
 
 
 
-    
-![png](basic_example_files/output_25_1.png)
-    
 
+![png](basic_example_files/output_25_1.png)
diff --git a/docs/content/examples/data/product_system.svg b/docs/content/examples/data/product_system.svg
index 37a1170..d384585 100644
--- a/docs/content/examples/data/product_system.svg
+++ b/docs/content/examples/data/product_system.svg
@@ -1 +1 @@
-<svg width="2269" height="1027" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" xml:space="preserve" overflow="hidden"><g transform="translate(-27 -311)"><g><path d="M1551.5 603.5 1551.5 651.571" stroke="#595959" stroke-width="4.58333" stroke-linecap="round" stroke-linejoin="round" stroke-miterlimit="10" stroke-opacity="1" fill="none" fill-rule="evenodd"/><path d="M0-2.29167 563.401-2.29167C564.666-2.29167 565.692-1.26565 565.692 0L565.692 607.721 563.401 605.429 615.27 605.429C616.535 605.429 617.561 606.455 617.561 607.721 617.561 608.986 616.535 610.012 615.27 610.012L563.401 610.012C562.135 610.012 561.109 608.986 561.109 607.721L561.109 0 563.401 2.29167 0 2.29167C-1.26565 2.29167-2.29167 1.26565-2.29167 0-2.29167-1.26565-1.26565-2.29167 0-2.29167ZM598.151 594.319 619.593 607.721 598.151 621.122C597.078 621.793 595.664 621.467 594.993 620.394 594.322 619.32 594.648 617.907 595.722 617.236L614.055 605.777 614.055 609.664 595.722 598.206C594.648 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diff --git a/docs/content/examples/methanol_and_iron.md b/docs/content/examples/methanol_and_iron.md
index 4b31b2d..f201794 100644
--- a/docs/content/examples/methanol_and_iron.md
+++ b/docs/content/examples/methanol_and_iron.md
@@ -1101,9 +1101,9 @@ pp_baseline.plot_capacity_balance(detailed=True)
 ```
 
 
-    
+
 ![png](methanol_and_iron_files/output_48_0.png)
-    
+
 
 
 
@@ -1112,9 +1112,9 @@ pp_baseline.plot_impacts()
 ```
 
 
-    
+
 ![png](methanol_and_iron_files/output_49_0.png)
-    
+
 
 
 
@@ -1216,9 +1216,9 @@ pp_evolution.plot_capacity_balance(detailed=True)
 ```
 
 
-    
+
 ![png](methanol_and_iron_files/output_56_0.png)
-    
+
 
 
 
@@ -1227,9 +1227,9 @@ pp_evolution.plot_impacts()
 ```
 
 
-    
+
 ![png](methanol_and_iron_files/output_57_0.png)
-    
+
 
 
 
@@ -1326,9 +1326,9 @@ pp_constrained.plot_capacity_balance(detailed=True)
 ```
 
 
-    
+
 ![png](methanol_and_iron_files/output_65_0.png)
-    
+
 
 
 
@@ -1337,9 +1337,9 @@ pp_constrained.plot_impacts()
 ```
 
 
-    
+
 ![png](methanol_and_iron_files/output_66_0.png)
-    
+
 
 
 
@@ -1363,4 +1363,3 @@ pp_constrained.df_impacts.to_excel("data/paper/impacts_constrained.xlsx")
 ```
 
     2026-02-10 15:58:56.630 | INFO     | dynamic_characterization.dynamic_characterization:characterize:108 - No custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.
-
diff --git a/docs/content/installation.md b/docs/content/installation.md
index 79dec42..18501b0 100644
--- a/docs/content/installation.md
+++ b/docs/content/installation.md
@@ -208,7 +208,7 @@ Brightway and `optimex` run natively on Apple Silicon. However, the standard `py
         optimex \
         "scikit-umfpack>=0.4.2" \
         "numpy>=2"
-    
+
     conda activate optimex
     ```
 ---
diff --git a/docs/content/theory_how_it_works.md b/docs/content/theory_how_it_works.md
index a8ddf47..c137188 100644
--- a/docs/content/theory_how_it_works.md
+++ b/docs/content/theory_how_it_works.md
@@ -50,4 +50,4 @@ Traditional LCO assumes processes always run at full capacity. `optimex` separat
 
 This separation is important because it enables **vintage-specific dispatch**: when multiple cohorts of the same technology coexist, the optimizer can preferentially utilize cleaner vintages — creating an emissions-aware merit order. It also allows the model to identify strategic overcapacities, where early investment in clean technologies offsets the stranded cost of idled fossil infrastructure.
 
-To make this work, each exchange in a process is classified as either installation-dependent (construction materials, end-of-life treatment) or operation-dependent (production output, fuel consumption, operational emissions). Installation-dependent exchanges are fixed to the installed capacity, while operation-dependent exchanges scale with how much the process is actually operated.
\ No newline at end of file
+To make this work, each exchange in a process is classified as either installation-dependent (construction materials, end-of-life treatment) or operation-dependent (production output, fuel consumption, operational emissions). Installation-dependent exchanges are fixed to the installed capacity, while operation-dependent exchanges scale with how much the process is actually operated.
diff --git a/docs/content/theory_why_time_matters.md b/docs/content/theory_why_time_matters.md
index ab404a4..1394102 100644
--- a/docs/content/theory_why_time_matters.md
+++ b/docs/content/theory_why_time_matters.md
@@ -18,4 +18,4 @@ These blind spots arise from two interacting temporal dimensions:
 - **Temporal distribution**: Life cycle stages span years or decades. Construction precedes operation, which precedes end-of-life. Each stage has distinct environmental exchanges occurring at different times.
 - **Temporal evolution**: Production systems improve over time. Electricity grids decarbonize, process efficiencies increase, and supply chains shift. The same exchange has a different environmental footprint depending on *when* it occurs.
 
-Distribution determines *when* an exchange occurs; evolution determines its *magnitude* at that moment. Both must be considered jointly. `optimex` addresses this by extending the standard LCA matrices with an explicit time dimension.
\ No newline at end of file
+Distribution determines *when* an exchange occurs; evolution determines its *magnitude* at that moment. Both must be considered jointly. `optimex` addresses this by extending the standard LCA matrices with an explicit time dimension.
diff --git a/docs/convert_notebooks.py b/docs/convert_notebooks.py
index 2161f37..59ef43b 100644
--- a/docs/convert_notebooks.py
+++ b/docs/convert_notebooks.py
@@ -46,9 +46,7 @@ def strip_ansi(text: str) -> str:
     return ANSI_ESCAPE.sub("", text)
 
 
-def convert(
-    notebook_path: Path, output_dir: Path, icon: str, tags: list[str]
-) -> Path:
+def convert(notebook_path: Path, output_dir: Path, icon: str, tags: list[str]) -> Path:
     """Convert *notebook_path* to Markdown in *output_dir*, strip ANSI codes,
     organise images into a <stem>_files/ sub-directory, and inject the Zensical
     icon and tags front-matter.  Returns the output file path."""
diff --git a/docs/requirements.txt b/docs/requirements.txt
index 0f83295..2d87a18 100644
--- a/docs/requirements.txt
+++ b/docs/requirements.txt
@@ -1,5 +1,5 @@
-zensical
-mkdocstrings[python]>=1.0.0
 mkdocs-autorefs
-pymdown-extensions>=10.0
+mkdocstrings[python]>=1.0.0
 nbconvert>=7.0
+pymdown-extensions>=10.0
+zensical
diff --git a/notebooks/data/model_inputs_2050.json b/notebooks/data/model_inputs_2050.json
index d9933ca..8355dba 100644
--- a/notebooks/data/model_inputs_2050.json
+++ b/notebooks/data/model_inputs_2050.json
@@ -74261,4 +74261,4 @@
   "process_operation_limits_min_default": 0.0,
   "cumulative_process_limits_max_default": Infinity,
   "cumulative_process_limits_min_default": 0.0
-}
\ No newline at end of file
+}
diff --git a/notebooks/data/paper/model_inputs_2050_paper.json b/notebooks/data/paper/model_inputs_2050_paper.json
index 2d48d80..9e14827 100644
--- a/notebooks/data/paper/model_inputs_2050_paper.json
+++ b/notebooks/data/paper/model_inputs_2050_paper.json
@@ -76474,4 +76474,4 @@
   "process_operation_limits_min_default": 0.0,
   "cumulative_process_limits_max_default": Infinity,
   "cumulative_process_limits_min_default": 0.0
-}
\ No newline at end of file
+}
diff --git a/notebooks/data/product_system.svg b/notebooks/data/product_system.svg
index 37a1170..d384585 100644
--- a/notebooks/data/product_system.svg
+++ b/notebooks/data/product_system.svg
@@ -1 +1 @@
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writing-mode="lr-tb" unicode-bidi="normal" text-decoration="none" transform="matrix(1 0 0 1 1451.85 420)">Blast furnace w/ </text><text fill="#000000" fill-opacity="1" font-family="Arial,Arial_MSFontService,sans-serif" font-style="normal" font-variant="normal" font-weight="700" font-stretch="normal" font-size="37" text-anchor="start" direction="ltr" writing-mode="lr-tb" unicode-bidi="normal" text-decoration="none" transform="matrix(1 0 0 1 1451.85 464)">carbon capture</text><text fill="#000000" fill-opacity="1" font-family="Arial,Arial_MSFontService,sans-serif" font-style="normal" font-variant="normal" font-weight="400" font-stretch="normal" font-size="31" text-anchor="start" direction="ltr" writing-mode="lr-tb" unicode-bidi="normal" text-decoration="none" transform="matrix(1 0 0 1 1451.85 511)">Operation: </text><text fill="#000000" fill-opacity="1" font-family="Cambria Math,Cambria Math_MSFontService,sans-serif" font-style="normal" font-variant="normal" font-weight="400" font-stretch="normal" font-size="31" text-anchor="start" direction="ltr" writing-mode="lr-tb" unicode-bidi="normal" text-decoration="none" transform="matrix(1 0 0 1 1604.82 511)">𝛿</text><text fill="#000000" fill-opacity="1" font-family="Cambria Math,Cambria Math_MSFontService,sans-serif" font-style="normal" font-variant="normal" font-weight="400" font-stretch="normal" font-size="31" text-anchor="start" direction="ltr" writing-mode="lr-tb" unicode-bidi="normal" text-decoration="none" transform="matrix(1 0 0 1 1631.15 511)">∈</text><path d="M1732.95 487.862 1739.7 487.862 1739.7 516.727 1732.95 516.727 1732.95 515.562 1737.19 515.562 1737.19 489.027 1732.95 489.027ZM1662.03 487.862 1668.78 487.862 1668.78 489.027 1664.54 489.027 1664.54 515.562 1668.78 515.562 1668.78 516.727 1662.03 516.727Z" fill="#000000" fill-rule="evenodd" fill-opacity="1"/><text fill="#000000" fill-opacity="1" font-family="Cambria Math,Cambria Math_MSFontService,sans-serif" font-style="normal" font-variant="normal" font-weight="400" font-stretch="normal" font-size="31" text-anchor="start" direction="ltr" writing-mode="lr-tb" unicode-bidi="normal" text-decoration="none" transform="matrix(1 0 0 1 1669.33 511)">0</text><text fill="#000000" fill-opacity="1" font-family="Cambria Math,Cambria Math_MSFontService,sans-serif" font-style="normal" font-variant="normal" font-weight="400" font-stretch="normal" font-size="31" text-anchor="start" direction="ltr" writing-mode="lr-tb" unicode-bidi="normal" text-decoration="none" transform="matrix(1 0 0 1 1686.51 511)">,</text><text fill="#000000" fill-opacity="1" font-family="Cambria Math,Cambria Math_MSFontService,sans-serif" font-style="normal" font-variant="normal" font-weight="400" font-stretch="normal" font-size="31" text-anchor="start" direction="ltr" writing-mode="lr-tb" unicode-bidi="normal" text-decoration="none" transform="matrix(1 0 0 1 1697.93 511)">20</text><text fill="#000000" fill-opacity="1" 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diff --git a/notebooks/plots/paper_figures.py b/notebooks/plots/paper_figures.py
index 9b3a17e..1b8c1e1 100644
--- a/notebooks/plots/paper_figures.py
+++ b/notebooks/plots/paper_figures.py
@@ -9,15 +9,23 @@
 Demonstrates the capabilities of optimex for transition pathway optimization.
 """
 
-import pandas as pd
-import numpy as np
-import matplotlib.pyplot as plt
+from datetime import datetime
+from pathlib import Path
+
 import matplotlib.dates as mdates
-from matplotlib.patches import Patch
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
 from matplotlib.lines import Line2D
-from matplotlib.ticker import ScalarFormatter, MultipleLocator, AutoMinorLocator, NullLocator, FuncFormatter, FormatStrFormatter
-from pathlib import Path
-from datetime import datetime
+from matplotlib.patches import Patch
+from matplotlib.ticker import (
+    AutoMinorLocator,
+    FormatStrFormatter,
+    FuncFormatter,
+    MultipleLocator,
+    NullLocator,
+    ScalarFormatter,
+)
 
 # Configuration
 PLOTS_DIR = Path(__file__).parent / "data"
@@ -73,17 +81,19 @@
 }
 
 # Plot style settings
-plt.rcParams.update({
-    "font.size": 10,
-    "axes.labelsize": 11,
-    "axes.titlesize": 12,
-    "legend.fontsize": 9,
-    "xtick.labelsize": 9,
-    "ytick.labelsize": 9,
-    "figure.dpi": 150,
-    "savefig.dpi": 300,
-    "savefig.bbox": "tight",
-})
+plt.rcParams.update(
+    {
+        "font.size": 10,
+        "axes.labelsize": 11,
+        "axes.titlesize": 12,
+        "legend.fontsize": 9,
+        "xtick.labelsize": 9,
+        "ytick.labelsize": 9,
+        "figure.dpi": 150,
+        "savefig.dpi": 300,
+        "savefig.bbox": "tight",
+    }
+)
 
 bar_width = 0.65
 linewidth_bar_outline = 1
@@ -95,7 +105,11 @@ def load_scenario_data(scenario: str) -> dict:
     for dtype in ["capacity", "production", "demand", "impacts"]:
         filepath = PLOTS_DIR / f"{dtype}_{scenario}.xlsx"
         if filepath.exists():
-            df = pd.read_excel(filepath, index_col=0, header=[0, 1] if dtype in ["production", "impacts"] else 0)
+            df = pd.read_excel(
+                filepath,
+                index_col=0,
+                header=[0, 1] if dtype in ["production", "impacts"] else 0,
+            )
             # Clean up index
             if df.index.name == "Time" or df.index.name == "Category":
                 pass
@@ -126,7 +140,9 @@ def parse_impacts_df(df: pd.DataFrame) -> dict:
     if isinstance(df.columns, pd.MultiIndex):
         categories = df.columns.get_level_values(0).unique()
         for cat in categories:
-            if cat in IMPACT_CATEGORIES or any(ic in str(cat).lower() for ic in IMPACT_CATEGORIES):
+            if cat in IMPACT_CATEGORIES or any(
+                ic in str(cat).lower() for ic in IMPACT_CATEGORIES
+            ):
                 cat_df = df[cat].copy()
                 # Clean up process names
                 if isinstance(cat_df.columns, pd.Index):
@@ -188,7 +204,7 @@ def load_impacts_properly(scenario: str) -> dict:
             result[category] = pd.DataFrame(index=data_df.index)
 
         clean_process = PROCESS_NAMES.get(process, process)
-        col_data = pd.to_numeric(data_df.iloc[:, i-1], errors="coerce")
+        col_data = pd.to_numeric(data_df.iloc[:, i - 1], errors="coerce")
 
         # If process already exists, it's a duplicate - skip
         if clean_process not in result[category].columns:
@@ -218,7 +234,7 @@ def compute_capacity_changes(cap_df: pd.DataFrame, product_col: str) -> tuple:
     retirements = np.zeros(len(capacity))
 
     for i in range(1, len(capacity)):
-        delta = capacity[i] - capacity[i-1]
+        delta = capacity[i] - capacity[i - 1]
         if delta > 0:
             additions[i] = delta
         elif delta < 0:
@@ -286,7 +302,9 @@ def create_combined_results_figure(scenarios_data: dict):
                 cap_mask = (cap_years >= 2025) & (cap_years <= 2050)
                 capacity_values = cap_df[product_col].values[cap_mask] / 1e6
                 row_ylim_max[row] = max(row_ylim_max[row], capacity_values.max())
-                additions, retirements = compute_capacity_changes(cap_df[cap_mask], product_col)
+                additions, retirements = compute_capacity_changes(
+                    cap_df[cap_mask], product_col
+                )
                 row_ylim_max[row] = max(row_ylim_max[row], additions.max() / 1e6)
                 # if retirements.max() > 0:
                 #     row_ylim_min[row] = min(row_ylim_min[row], -retirements.max() / 1e6)
@@ -298,8 +316,8 @@ def create_combined_results_figure(scenarios_data: dict):
     # Common x-axis setup
     years_plot = np.arange(2025, 2051)
     x_positions = np.arange(len(years_plot))
-    cap_positions = x_positions # - bar_width/2 - 0.02
-    prod_positions = x_positions # + bar_width/2 + 0.02
+    cap_positions = x_positions  # - bar_width/2 - 0.02
+    prod_positions = x_positions  # + bar_width/2 + 0.02
 
     xtick_positions = [i for i, y in enumerate(years_plot) if y % 5 == 0]
     xtick_labels = [str(y) for y in years_plot if y % 5 == 0]
@@ -310,7 +328,7 @@ def create_combined_results_figure(scenarios_data: dict):
     # Second pass: create plots
     for row, (product_key, product_label, is_intermediate) in enumerate(products):
         for col, (scenario, scenario_label) in enumerate(SCENARIOS.items()):
-            
+
             # --- Shared Axis logic ---
             # Row 0, Col 0 is the anchor
             if row == 0 and col == 0:
@@ -320,8 +338,10 @@ def create_combined_results_figure(scenarios_data: dict):
                 ax = fig.add_subplot(gs[row, col], sharex=axes_matrix[0, 0])
             else:
                 # Share Y with the first plot in this row, Share X with top row
-                ax = fig.add_subplot(gs[row, col], sharey=axes_matrix[row, 0], sharex=axes_matrix[0, col])
-            
+                ax = fig.add_subplot(
+                    gs[row, col], sharey=axes_matrix[row, 0], sharex=axes_matrix[0, col]
+                )
+
             axes_matrix[row, col] = ax
             data = scenarios_data[scenario]
 
@@ -349,7 +369,9 @@ def create_combined_results_figure(scenarios_data: dict):
                     if product_key.lower() in str(product).lower():
                         clean_process = PROCESS_NAMES.get(process, process)
                         if clean_process not in process_production:
-                            process_production[clean_process] = prod_df[c].astype(float).values
+                            process_production[clean_process] = (
+                                prod_df[c].astype(float).values
+                            )
 
             years = prod_df.index.values
             mask = (years >= 2025) & (years <= 2050)
@@ -361,10 +383,18 @@ def create_combined_results_figure(scenarios_data: dict):
                     values_plot = values[mask] / 1e6
                     if np.any(values_plot > 0.01):
                         color = PROCESS_COLORS.get(process, "gray")
-                        ax.bar(prod_positions, values_plot, bottom=bottom, label=process,
-                               color=color, width=bar_width, edgecolor="white", linewidth=linewidth_bar_outline)
+                        ax.bar(
+                            prod_positions,
+                            values_plot,
+                            bottom=bottom,
+                            label=process,
+                            color=color,
+                            width=bar_width,
+                            edgecolor="white",
+                            linewidth=linewidth_bar_outline,
+                        )
                         bottom += values_plot
-            
+
             # # Compute and plot capacity changes
             # if product_col is not None:
             #     cap_years = cap_df.index.values
@@ -376,11 +406,11 @@ def create_combined_results_figure(scenarios_data: dict):
             #     if np.any(additions > 0.01):
             #         for i, val in enumerate(additions):
             #             if val > 0.01:
-            #                 ax.annotate('', 
+            #                 ax.annotate('',
             #                             xy=(x_positions[i], val),      # The Tip (exactly at val)
             #                             xytext=(x_positions[i], 0),    # The Base
             #                             arrowprops=dict(arrowstyle="-|>",
-            #                                             color="#41811C", 
+            #                                             color="#41811C",
             #                                             lw=1,
             #                                             alpha=1),
             #                             zorder=5)
@@ -389,23 +419,31 @@ def create_combined_results_figure(scenarios_data: dict):
             #     if np.any(retirements > 0.01):
             #         for i, val in enumerate(retirements):
             #             if val > 0.01:
-            #                 ax.annotate('', 
+            #                 ax.annotate('',
             #                             xy=(x_positions[i], -val),     # The Tip (exactly at -val)
             #                             xytext=(x_positions[i], 0),    # The Base
             #                             arrowprops=dict(arrowstyle="-|>",
-            #                                             color="#CC071E", 
+            #                                             color="#CC071E",
             #                                             lw=1.5,
             #                                             alpha=1),
             #                             zorder=5)
-                    
+
             # Plot total capacity line
             if product_col is not None:
                 cap_years = cap_df.index.values
                 cap_mask = (cap_years >= 2025) & (cap_years <= 2050)
                 capacity_values = cap_df[product_col].values[cap_mask] / 1e6
                 if np.any(capacity_values > 0.001):
-                    ax.plot(x_positions, capacity_values, color="#000000", linestyle="--",
-                            linewidth=1, marker="", label="Capacity", zorder=4)
+                    ax.plot(
+                        x_positions,
+                        capacity_values,
+                        color="#000000",
+                        linestyle="--",
+                        linewidth=1,
+                        marker="",
+                        label="Capacity",
+                        zorder=4,
+                    )
 
             # if not is_intermediate:
             #     ax.axhline(y=1, color="red", linestyle="--", linewidth=1)
@@ -417,7 +455,7 @@ def create_combined_results_figure(scenarios_data: dict):
             ax.set_ylim(row_ylim_min[row], row_ylim_max[row])
             ax.grid(True, alpha=0.3, which="both", axis="both", zorder=0)
             ax.set_axisbelow(True)
-            
+
             # X-axis: Only bottom row gets labels
             if row == 3:
                 ax.set_xticks(xtick_positions)
@@ -430,7 +468,7 @@ def create_combined_results_figure(scenarios_data: dict):
             if col == 0:
                 label_suffix = " *" if is_intermediate else ""
                 ax.set_ylabel(f"{product_label}{label_suffix}\n[$10^6$ kg]")
-                
+
                 # Custom tick spacing for Captured CO₂
                 if product_label == "Captured CO₂":
                     ax.yaxis.set_major_locator(MultipleLocator(0.5))
@@ -446,7 +484,9 @@ def create_combined_results_figure(scenarios_data: dict):
     for process, color in PROCESS_COLORS.items():
         all_handles.append(Patch(facecolor=color, edgecolor="white", linewidth=0.5))
         all_labels.append(process)
-    all_handles.append(plt.Line2D([0], [0], color="#000000", linestyle="--", linewidth=1))
+    all_handles.append(
+        plt.Line2D([0], [0], color="#000000", linestyle="--", linewidth=1)
+    )
     all_labels.append("Available capacity")
     # all_handles.append(plt.Line2D([0], [0], color="red", linestyle="--", linewidth=1))
     # all_labels.append("Demand")
@@ -454,47 +494,67 @@ def create_combined_results_figure(scenarios_data: dict):
     # all_labels.append("+ Cap")
     # all_handles.append(Patch(facecolor="#E69679", edgecolor="#CC071E", linewidth=1, hatch="///"))
     # all_labels.append("− Cap")
-    
-    labels = ['(a)', '(b)', '(c)', '(d)', '(e)', '(f)', '(g)', '(h)', '(i)', '(j)', '(k)', '(l)']
+
+    labels = [
+        "(a)",
+        "(b)",
+        "(c)",
+        "(d)",
+        "(e)",
+        "(f)",
+        "(g)",
+        "(h)",
+        "(i)",
+        "(j)",
+        "(k)",
+        "(l)",
+    ]
     label_idx = 0
     for row in range(4):  # Changed from range(2) to range(4)
         for col in range(3):
-            ax = axes_matrix[row, col]  # Changed from axes[row, col] to axes_matrix[row, col]
+            ax = axes_matrix[
+                row, col
+            ]  # Changed from axes[row, col] to axes_matrix[row, col]
             # Position label in top-right corner
-            ax.text(0.02, 0.95, labels[label_idx], transform=ax.transAxes,
-                   va='top', fontsize=10,
-                   bbox=dict(boxstyle='square,pad=0.1', fc='white', ec='none'))
+            ax.text(
+                0.02,
+                0.95,
+                labels[label_idx],
+                transform=ax.transAxes,
+                va="top",
+                fontsize=10,
+                bbox=dict(boxstyle="square,pad=0.1", fc="white", ec="none"),
+            )
             label_idx += 1
 
-    fig.legend(all_handles, all_labels, loc="lower center", bbox_to_anchor=(0.5, 0.02),
-               ncol=4, frameon=False, fontsize=9)
+    fig.legend(
+        all_handles,
+        all_labels,
+        loc="lower center",
+        bbox_to_anchor=(0.5, 0.02),
+        ncol=4,
+        frameon=False,
+        fontsize=9,
+    )
 
     return fig
 
-import numpy as np
-import matplotlib.pyplot as plt
-from matplotlib.ticker import ScalarFormatter
-from matplotlib.patches import Patch
 
-import numpy as np
 import matplotlib.pyplot as plt
-from matplotlib.ticker import ScalarFormatter
-from matplotlib.patches import Patch
-
 import numpy as np
-import matplotlib.pyplot as plt
-from matplotlib.ticker import FormatStrFormatter
 from matplotlib.patches import Patch
+from matplotlib.ticker import FormatStrFormatter, ScalarFormatter
+
 
 def create_combined_impacts_figure(scenarios_data: dict):
     """
     Create a combined impacts figure with manual scaling to show 10^x in the Y-axis label.
     Shared Y axes per row, Shared X axes per column.
     """
-    
+
     fig = plt.figure(figsize=(14, 5.5))
     # Reduced wspace and hspace for a tight "scientific paper" look
-    gs = fig.add_gridspec(2, 3, hspace=0.08, wspace=0.06) 
+    gs = fig.add_gridspec(2, 3, hspace=0.08, wspace=0.06)
 
     categories = [
         ("climate_change", "Cumulative Radiative Forcing", "W/m²"),
@@ -509,7 +569,7 @@ def create_combined_impacts_figure(scenarios_data: dict):
     for row, (category, label, unit) in enumerate(categories):
         abs_max = 0
         current_max, current_min = 0, 0
-        
+
         for scenario in scenarios_data.keys():
             impacts = load_impacts_properly(scenario)
             if category in impacts:
@@ -519,14 +579,14 @@ def create_combined_impacts_figure(scenarios_data: dict):
                 neg_sum = imp_df.loc[mask].clip(upper=0).sum(axis=1).min()
                 current_max = max(current_max, pos_sum)
                 current_min = min(current_min, neg_sum)
-        
+
         # Determine the power of 10 (exponent) based on the largest absolute value
         abs_val = max(abs(current_max), abs(current_min))
         if abs_val == 0:
             exponent = 0
         else:
             exponent = int(np.floor(np.log10(abs_val)))
-        
+
         row_powers[row] = exponent
         # Store scaled limits with padding
         row_ylim_max[row] = (current_max / (10**exponent)) * 1.15
@@ -543,7 +603,7 @@ def create_combined_impacts_figure(scenarios_data: dict):
     for row, (category, label_base, unit) in enumerate(categories):
         exponent = row_powers[row]
         scaling_factor = 10**exponent
-        
+
         # Format the Y label to include scientific notation
         # Uses LaTeX for pretty exponents
         y_label = f"{label_base}\n[$10^{{{exponent}}}$ {unit}]"
@@ -551,7 +611,7 @@ def create_combined_impacts_figure(scenarios_data: dict):
         for col, (scenario, scenario_label) in enumerate(SCENARIOS.items()):
             sharex_ax = axes[0, col] if row > 0 else None
             sharey_ax = axes[row, 0] if col > 0 else None
-            
+
             ax = fig.add_subplot(gs[row, col], sharex=sharex_ax, sharey=sharey_ax)
             axes[row, col] = ax
 
@@ -567,17 +627,31 @@ def create_combined_impacts_figure(scenarios_data: dict):
                 for process in data.columns:
                     # MANUALLY SCALE DATA
                     values = data[process].values / scaling_factor
-                    
+
                     if np.any(np.abs(values) > 1e-12):
                         color = PROCESS_COLORS.get(process, "gray")
                         pos_v, neg_v = np.maximum(values, 0), np.minimum(values, 0)
                         if np.any(pos_v > 0):
-                            ax.bar(x_positions, pos_v, bottom=bottom_pos, color=color, 
-                                   width=bar_width, edgecolor="white", linewidth=linewidth_bar_outline)
+                            ax.bar(
+                                x_positions,
+                                pos_v,
+                                bottom=bottom_pos,
+                                color=color,
+                                width=bar_width,
+                                edgecolor="white",
+                                linewidth=linewidth_bar_outline,
+                            )
                             bottom_pos += pos_v
                         if np.any(neg_v < 0):
-                            ax.bar(x_positions, neg_v, bottom=bottom_neg, color=color, 
-                                   width=bar_width, edgecolor="white", linewidth=linewidth_bar_outline)
+                            ax.bar(
+                                x_positions,
+                                neg_v,
+                                bottom=bottom_neg,
+                                color=color,
+                                width=bar_width,
+                                edgecolor="white",
+                                linewidth=linewidth_bar_outline,
+                            )
                             bottom_neg += neg_v
 
             ax.axhline(y=0, color="gray", linewidth=0.5, zorder=0)
@@ -585,28 +659,32 @@ def create_combined_impacts_figure(scenarios_data: dict):
             ax.set_xticklabels(xtick_labels)
             ax.set_xlim(-1, len(years_plot))
             ax.set_ylim(row_ylim_min[row], row_ylim_max[row])
-            
+
             # Custom tick spacing for Cumulative Radiative Forcing
             if category == "climate_change":
-                ax.yaxis.set_major_locator(MultipleLocator(0.5))  # Major ticks at whole numbers
+                ax.yaxis.set_major_locator(
+                    MultipleLocator(0.5)
+                )  # Major ticks at whole numbers
                 ax.set_ylim(-1.6, 3.1)
-                
+
             if category == "water_use":
-                ax.yaxis.set_major_locator(MultipleLocator(0.5))  # Major ticks at whole numbers
+                ax.yaxis.set_major_locator(
+                    MultipleLocator(0.5)
+                )  # Major ticks at whole numbers
                 ax.set_ylim(0, 4.6)
 
             # Simple float formatter for "1.4" style labels
-            ax.yaxis.set_major_formatter(FormatStrFormatter('%.1f'))
+            ax.yaxis.set_major_formatter(FormatStrFormatter("%.1f"))
 
             if col > 0:
                 ax.tick_params(labelleft=False)
-            
+
             if row < 1:
                 ax.tick_params(labelbottom=False)
 
             ax.grid(True, which="both", alpha=0.3, axis="both", zorder=0)
             ax.set_axisbelow(True)
-            
+
             if row == 0:
                 ax.set_title(scenario_label)
             if row == 1:
@@ -623,22 +701,39 @@ def create_combined_impacts_figure(scenarios_data: dict):
             if cat in impacts:
                 for process in impacts[cat].columns:
                     if process not in all_handles:
-                        all_handles[process] = Patch(facecolor=PROCESS_COLORS.get(process, "gray"), 
-                                                     edgecolor="white", linewidth=0.5)
-                        
-    labels = ['(a)', '(b)', '(c)', '(d)', '(e)', '(f)']
+                        all_handles[process] = Patch(
+                            facecolor=PROCESS_COLORS.get(process, "gray"),
+                            edgecolor="white",
+                            linewidth=0.5,
+                        )
+
+    labels = ["(a)", "(b)", "(c)", "(d)", "(e)", "(f)"]
     label_idx = 0
     for row in range(2):
         for col in range(3):
             ax = axes[row, col]
             # Position label in top-right corner
-            ax.text(0.95, 0.95, labels[label_idx], transform=ax.transAxes,
-                   ha='right', va='top', fontsize=10,
-                   bbox=dict(boxstyle='square,pad=0.1', fc='white', ec='none'))
+            ax.text(
+                0.95,
+                0.95,
+                labels[label_idx],
+                transform=ax.transAxes,
+                ha="right",
+                va="top",
+                fontsize=10,
+                bbox=dict(boxstyle="square,pad=0.1", fc="white", ec="none"),
+            )
             label_idx += 1
-            
-    fig.legend(all_handles.values(), all_handles.keys(), loc="lower center",
-               bbox_to_anchor=(0.5, -0.02), ncol=min(len(all_handles), 7), frameon=False, fontsize=9)
+
+    fig.legend(
+        all_handles.values(),
+        all_handles.keys(),
+        loc="lower center",
+        bbox_to_anchor=(0.5, -0.02),
+        ncol=min(len(all_handles), 7),
+        frameon=False,
+        fontsize=9,
+    )
 
     fig.tight_layout()
     fig.subplots_adjust(bottom=0.18, top=0.92, left=0.10)
@@ -649,12 +744,12 @@ def create_combined_impacts_figure(scenarios_data: dict):
 def round_datetime_to_year(date: datetime) -> pd.Timestamp:
     """
     Round a datetime object to the nearest year boundary.
-    
+
     Parameters
     ----------
     date : datetime
         datetime object to be rounded
-        
+
     Returns
     -------
     pd.Timestamp
@@ -671,41 +766,41 @@ def round_datetime_to_year(date: datetime) -> pd.Timestamp:
 def load_characterized_inventory(scenario: str) -> pd.DataFrame:
     """
     Load characterized inventory data for a scenario and aggregate by year and activity.
-    
+
     Args:
         scenario: Scenario name (key from SCENARIOS dict)
-        
+
     Returns:
         DataFrame with yearly aggregated impacts by activity (activity as columns, date as index)
-        
+
     Raises:
         ValueError: If required columns are missing from the data file
     """
     filepath = PLOTS_DIR / f"characterized_inventory_{scenario}.xlsx"
     df = pd.read_excel(filepath)
-    
+
     # Validate required columns exist
-    required_columns = ['date', 'activity', 'amount']
+    required_columns = ["date", "activity", "amount"]
     missing_columns = [col for col in required_columns if col not in df.columns]
     if missing_columns:
         raise ValueError(f"Missing required columns in {filepath}: {missing_columns}")
-    
+
     # Clean up activity names using PROCESS_NAMES mapping
-    df['activity_clean'] = df['activity'].map(lambda x: PROCESS_NAMES.get(x, x))
-    
+    df["activity_clean"] = df["activity"].map(lambda x: PROCESS_NAMES.get(x, x))
+
     # First group by date and activity, sum the amounts
     plot_df = df.groupby(["date", "activity_clean"], as_index=False)["amount"].sum()
-    
+
     # Round dates to nearest year boundary
     plot_df["date"] = plot_df["date"].apply(round_datetime_to_year)
-    
+
     # Group again by rounded date and activity, sum, then pivot
     final_data = (
         plot_df.groupby(["date", "activity_clean"], as_index=False)["amount"]
         .sum()
         .pivot(index="date", columns="activity_clean", values="amount")
     )
-    
+
     return final_data
 
 
@@ -716,28 +811,28 @@ def create_radiative_forcing_figure():
     """
     fig, axes = plt.subplots(2, 3, figsize=(14, 5.5), sharex=True)
     fig.subplots_adjust(hspace=0.08, wspace=0.06)
-    
+
     # Load data for each scenario
     scenario_data = {}
     all_activities = set()
-    
+
     for scenario in SCENARIOS.keys():
         df = load_characterized_inventory(scenario)
         scenario_data[scenario] = df
         all_activities.update(df.columns.tolist())
-    
+
     all_activities = sorted(all_activities)
-    
+
     # Create color mapping
     activity_colors = {}
     for activity in all_activities:
-        activity_colors[activity] = PROCESS_COLORS.get(activity, 'gray')
-    
+        activity_colors[activity] = PROCESS_COLORS.get(activity, "gray")
+
     # Determine power of 10 for each row
     row_powers = {}
     row_ylim_max = {}
     row_ylim_min = {}
-    
+
     # Row 0: Instantaneous
     inst_max = 0
     inst_min = 0
@@ -745,14 +840,14 @@ def create_radiative_forcing_figure():
         if not df.empty:
             inst_max = max(inst_max, np.nanmax(df.values) if df.size > 0 else 0)
             inst_min = min(inst_min, np.nanmin(df.values) if df.size > 0 else 0)
-    
+
     abs_val_inst = max(abs(inst_max), abs(inst_min))
     exponent_inst = int(np.floor(np.log10(abs_val_inst))) if abs_val_inst > 0 else 0
     row_powers[0] = exponent_inst
     scaling_factor_inst = 10**exponent_inst
     row_ylim_max[0] = (inst_max / scaling_factor_inst) * 1.1
     row_ylim_min[0] = (inst_min / scaling_factor_inst) * 1.1 if inst_min < 0 else 0
-    
+
     # Row 1: Cumulative
     cum_max = 0
     cum_min = 0
@@ -760,185 +855,232 @@ def create_radiative_forcing_figure():
         if not df.empty:
             cumsum_df = df.cumsum()
             cumsum_sum = cumsum_df.sum(axis=1)
-            cum_max = max(cum_max, np.nanmax(cumsum_sum.values) if cumsum_sum.size > 0 else 0)
-            cum_min = min(cum_min, np.nanmin(cumsum_sum.values) if cumsum_sum.size > 0 else 0)
-    
+            cum_max = max(
+                cum_max, np.nanmax(cumsum_sum.values) if cumsum_sum.size > 0 else 0
+            )
+            cum_min = min(
+                cum_min, np.nanmin(cumsum_sum.values) if cumsum_sum.size > 0 else 0
+            )
+
     abs_val_cum = max(abs(cum_max), abs(cum_min))
     exponent_cum = int(np.floor(np.log10(abs_val_cum))) if abs_val_cum > 0 else 0
     row_powers[1] = exponent_cum
     scaling_factor_cum = 10**exponent_cum
     row_ylim_max[1] = (cum_max / scaling_factor_cum) * 1.1
     row_ylim_min[1] = (cum_min / scaling_factor_cum) * 1.1 if cum_min < 0 else 0
-    
+
     # Plot each scenario
     for col, (scenario, scenario_label) in enumerate(SCENARIOS.items()):
         df = scenario_data[scenario]
         dates = df.index
-        
+
         # Row 0: Instantaneous radiative forcing (scaled)
         ax_inst = axes[0, col]
         for activity in df.columns:
-            color = activity_colors.get(activity, 'gray')
+            color = activity_colors.get(activity, "gray")
             values_scaled = df[activity].fillna(0).values / scaling_factor_inst
-            ax_inst.plot(dates, values_scaled, linewidth=1.2, 
-                        color=color, label=activity)
-            
+            ax_inst.plot(
+                dates, values_scaled, linewidth=1.2, color=color, label=activity
+            )
+
         # # Add total sum line
         # total_inst = df.sum(axis=1).fillna(0).values / scaling_factor_inst
-        # ax_inst.plot(dates, total_inst, color='black', linestyle='--', 
+        # ax_inst.plot(dates, total_inst, color='black', linestyle='--',
         #             linewidth=1.5, label='Total', zorder=0.6)
-        
+
         ax_inst.set_xlim(datetime(2025, 1, 1), datetime(2051, 1, 1))
         ax_inst.set_ylim(row_ylim_min[0], row_ylim_max[0])
         ax_inst.grid(which="both", alpha=0.3, axis="both", zorder=0)
         ax_inst.set_axisbelow(True)
-        
+
         if col == 0:
-            ax_inst.set_ylabel(f"Instantaneous radiative forcing\n[$10^{{{exponent_inst}}}$ W m$^{{-2}}$]")
+            ax_inst.set_ylabel(
+                f"Instantaneous radiative forcing\n[$10^{{{exponent_inst}}}$ W m$^{{-2}}$]"
+            )
         else:
             ax_inst.tick_params(labelleft=False)
-        
+
         ax_inst.set_title(scenario_label)
         ax_inst.axhline(y=0, color="gray", linewidth=0.5, zorder=0)
-        
+
         # Row 1: Cumulative radiative forcing (scaled)
         ax_cum = axes[1, col]
         cumsum_df = df.cumsum()
-        
+
         activities_in_df = [a for a in all_activities if a in cumsum_df.columns]
-        stack_data = [cumsum_df[a].fillna(0).values / scaling_factor_cum for a in activities_in_df]
+        stack_data = [
+            cumsum_df[a].fillna(0).values / scaling_factor_cum for a in activities_in_df
+        ]
         stack_colors = [activity_colors[a] for a in activities_in_df]
-        
+
         if stack_data:
-            ax_cum.stackplot(dates, *stack_data, 
-                            labels=activities_in_df,
-                            colors=stack_colors,
-                            edgecolor="white",
-                            linewidth=0.5)
-        
+            ax_cum.stackplot(
+                dates,
+                *stack_data,
+                labels=activities_in_df,
+                colors=stack_colors,
+                edgecolor="white",
+                linewidth=0.5,
+            )
+
         ax_cum.set_xlim(datetime(2025, 1, 1), datetime(2051, 1, 1))
         ax_cum.set_ylim(row_ylim_min[1], row_ylim_max[1])
         ax_cum.grid(which="both", alpha=0.3, axis="both", zorder=0)
         ax_cum.set_axisbelow(True)
-        
+
         ax_cum.axhline(y=0, color="gray", linewidth=0.5, zorder=0)
-        
+
         if col == 0:
-            ax_cum.set_ylabel(f"Cumulative radiative forcing\n[$10^{{{exponent_cum}}}$ W m$^{{-2}}$]")
+            ax_cum.set_ylabel(
+                f"Cumulative radiative forcing\n[$10^{{{exponent_cum}}}$ W m$^{{-2}}$]"
+            )
         else:
             ax_cum.tick_params(labelleft=False)
-    
+
     # Add subplot labels
-    labels = ['(a)', '(b)', '(c)', '(d)', '(e)', '(f)']
+    labels = ["(a)", "(b)", "(c)", "(d)", "(e)", "(f)"]
     label_idx = 0
     for row in range(2):
         for col in range(3):
             ax = axes[row, col]
-            ax.text(0.95, 0.95, labels[label_idx], transform=ax.transAxes,
-                   ha='right', va='top', fontsize=10,
-                   bbox=dict(boxstyle='square,pad=0.1', fc='white', ec='none'))
+            ax.text(
+                0.95,
+                0.95,
+                labels[label_idx],
+                transform=ax.transAxes,
+                ha="right",
+                va="top",
+                fontsize=10,
+                bbox=dict(boxstyle="square,pad=0.1", fc="white", ec="none"),
+            )
             label_idx += 1
-    
+
     # Create legend
     unique_handles = []
     unique_labels = []
     for activity in all_activities:
-        color = activity_colors.get(activity, 'gray')
+        color = activity_colors.get(activity, "gray")
         unique_handles.append(Line2D([0], [0], color=color, linewidth=2))
         unique_labels.append(activity)
-        
+
     # # Add total line to legend
     # unique_handles.append(Line2D([0], [0], color='black', linestyle='--', linewidth=1))
     # unique_labels.append('Total')
-    
-    fig.legend(unique_handles, unique_labels, 
-              loc='lower center', 
-              ncol=min(len(unique_labels), 4),
-              bbox_to_anchor=(0.5, 0.02),
-              frameon=False)
-    
+
+    fig.legend(
+        unique_handles,
+        unique_labels,
+        loc="lower center",
+        ncol=min(len(unique_labels), 4),
+        bbox_to_anchor=(0.5, 0.02),
+        frameon=False,
+    )
+
     plt.tight_layout()
     plt.subplots_adjust(bottom=0.15)
-    
+
     return fig
 
+
+from datetime import datetime
+
 import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
-from datetime import datetime
-from matplotlib.ticker import MultipleLocator, FormatStrFormatter
 from matplotlib.patches import Patch
+from matplotlib.ticker import FormatStrFormatter, MultipleLocator
+
 
 def create_combined_forcing_and_impacts_figure(scenarios_data: dict):
     """
-    Fixed combined figure: Ensures all subplot rows (Aggregated RF, Instantaneous RF, 
+    Fixed combined figure: Ensures all subplot rows (Aggregated RF, Instantaneous RF,
     and Water Use) have identical heights.
-    
-    The layout uses a single GridSpec with height_ratios to maintain symmetry 
+
+    The layout uses a single GridSpec with height_ratios to maintain symmetry
     while providing space for grouping titles.
     """
     fig = plt.figure(figsize=(14, 8))
-    
+
     # Define a single GridSpec for 3 rows. Height ratios [1, 1, 1] ensure equal height.
     # hspace=0.4 provides the necessary room for the Section b) title between rows.
     gs = fig.add_gridspec(3, 3, hspace=0.08, wspace=0.06)
-    
+
     # Define the absolute range
     start_year, end_year = 2025, 2050
     full_year_index = np.arange(start_year, end_year + 1)
-    
+
     rf_scenario_data = {}
     all_activities = set()
-    
+
     # Process Radiative Forcing data
     for scenario in SCENARIOS.keys():
         df = load_characterized_inventory(scenario)
-        df_years = df[(df.index.year >= start_year) & (df.index.year <= end_year)].copy()
+        df_years = df[
+            (df.index.year >= start_year) & (df.index.year <= end_year)
+        ].copy()
         df_years.index = df_years.index.year
         df_aligned = df_years.reindex(full_year_index).fillna(0)
         rf_scenario_data[scenario] = df_aligned
         all_activities.update(df_aligned.columns.tolist())
-    
+
     # --- SORTING FOR DAC ---
     all_activities_list = sorted(list(all_activities))
     print(all_activities_list)
-    target_comp = 'DAC'
+    target_comp = "DAC"
     if target_comp in all_activities_list:
         all_activities_list.remove(target_comp)
         all_activities_list.insert(0, target_comp)
-    
-    activity_colors = {act: PROCESS_COLORS.get(act, 'gray') for act in all_activities_list}
-    
+
+    activity_colors = {
+        act: PROCESS_COLORS.get(act, "gray") for act in all_activities_list
+    }
+
     # --- Scaling Pre-calculations ---
     crf_bar_max, crf_bar_min = 0, 0
     for scenario in scenarios_data.keys():
         impacts = load_impacts_properly(scenario)
         if "climate_change" in impacts:
-            data = impacts["climate_change"].loc[(impacts["climate_change"].index >= start_year) & (impacts["climate_change"].index <= end_year)]
+            data = impacts["climate_change"].loc[
+                (impacts["climate_change"].index >= start_year)
+                & (impacts["climate_change"].index <= end_year)
+            ]
             crf_bar_max = max(crf_bar_max, data.clip(lower=0).sum(axis=1).max())
             crf_bar_min = min(crf_bar_min, data.clip(upper=0).sum(axis=1).min())
-    scaling_factor_crf_bar = 10**int(np.floor(np.log10(max(abs(crf_bar_max), abs(crf_bar_min))))) if max(abs(crf_bar_max), abs(crf_bar_min)) > 0 else 1
+    scaling_factor_crf_bar = (
+        10 ** int(np.floor(np.log10(max(abs(crf_bar_max), abs(crf_bar_min)))))
+        if max(abs(crf_bar_max), abs(crf_bar_min)) > 0
+        else 1
+    )
 
     inst_max, inst_min = 0, 0
     for df in rf_scenario_data.values():
         inst_max = max(inst_max, df.clip(lower=0).sum(axis=1).max())
         inst_min = min(inst_min, df.clip(upper=0).sum(axis=1).min())
-    scaling_factor_inst = 10**int(np.floor(np.log10(max(abs(inst_max), abs(inst_min))))) if max(abs(inst_max), abs(inst_min)) > 0 else 1
+    scaling_factor_inst = (
+        10 ** int(np.floor(np.log10(max(abs(inst_max), abs(inst_min)))))
+        if max(abs(inst_max), abs(inst_min)) > 0
+        else 1
+    )
 
     water_max = 0
     for scenario in scenarios_data.keys():
         impacts = load_impacts_properly(scenario)
         if "water_use" in impacts:
-            data = impacts["water_use"].loc[(impacts["water_use"].index >= start_year) & (impacts["water_use"].index <= end_year)]
+            data = impacts["water_use"].loc[
+                (impacts["water_use"].index >= start_year)
+                & (impacts["water_use"].index <= end_year)
+            ]
             water_max = max(water_max, data.clip(lower=0).sum(axis=1).max())
-    scaling_factor_water = 10**int(np.floor(np.log10(water_max))) if water_max > 0 else 1
+    scaling_factor_water = (
+        10 ** int(np.floor(np.log10(water_max))) if water_max > 0 else 1
+    )
 
-    axes = np.empty((3, 3), dtype=object) 
+    axes = np.empty((3, 3), dtype=object)
     xtick_positions = [y for y in full_year_index if y % 5 == 0]
 
     # ===== PLOTTING =====
     for col, (scenario, scenario_label) in enumerate(SCENARIOS.items()):
-        
+
         # ROW 0: AGGREGATED RF (Bars)
         ax0 = fig.add_subplot(gs[0, col], sharey=axes[0, 0] if col > 0 else None)
         axes[0, col] = ax0
@@ -952,20 +1094,38 @@ def create_combined_forcing_and_impacts_figure(scenarios_data: dict):
                     color = activity_colors.get(proc, "gray")
                     pv, nv = np.maximum(vals, 0), np.minimum(vals, 0)
                     if np.any(pv > 0):
-                        ax0.bar(data.index, pv, bottom=bottom_pos, color=color, width=0.8, edgecolor="white", linewidth=1)
+                        ax0.bar(
+                            data.index,
+                            pv,
+                            bottom=bottom_pos,
+                            color=color,
+                            width=0.8,
+                            edgecolor="white",
+                            linewidth=1,
+                        )
                         bottom_pos += pv
                     if np.any(nv < 0):
-                        ax0.bar(data.index, nv, bottom=bottom_neg, color=color, width=0.8, edgecolor="white", linewidth=1)
+                        ax0.bar(
+                            data.index,
+                            nv,
+                            bottom=bottom_neg,
+                            color=color,
+                            width=0.8,
+                            edgecolor="white",
+                            linewidth=1,
+                        )
                         bottom_neg += nv
-        
+
         ax0.set_title(scenario_label, fontsize=11, pad=10)
         ax0.set_xlim(start_year - 1, end_year + 1)
-        ax0.set_ylim(-1.6, 3.1) 
+        ax0.set_ylim(-1.6, 3.1)
         ax0.set_xticks(xtick_positions)
         ax0.set_xticklabels([])
-        if col == 0: 
-            ax0.set_ylabel(f"Cumulative Radiative Forcing \n until 2125 [$10^{{{int(np.log10(scaling_factor_crf_bar))}}}$ W m$^{{-2}}$ yr]")
-        else: 
+        if col == 0:
+            ax0.set_ylabel(
+                f"Cumulative Radiative Forcing \n until 2125 [$10^{{{int(np.log10(scaling_factor_crf_bar))}}}$ W m$^{{-2}}$ yr]"
+            )
+        else:
             ax0.tick_params(labelleft=False)
         ax0.grid(True, alpha=0.3, zorder=0)
         ax0.set_axisbelow(True)
@@ -975,27 +1135,50 @@ def create_combined_forcing_and_impacts_figure(scenarios_data: dict):
         axes[1, col] = ax1
         df_plot = rf_scenario_data[scenario]
         if not df_plot.empty:
-            y_pos, y_neg = np.zeros(len(full_year_index)), np.zeros(len(full_year_index))
+            y_pos, y_neg = np.zeros(len(full_year_index)), np.zeros(
+                len(full_year_index)
+            )
             for act in all_activities_list:
                 if act in df_plot.columns:
                     vals = df_plot[act].values / scaling_factor_inst
                     color = activity_colors.get(act, "gray")
                     vals_pos, vals_neg = np.maximum(vals, 0), np.minimum(vals, 0)
                     if np.any(vals_pos > 0):
-                        ax1.fill_between(full_year_index, y_pos, y_pos + vals_pos, color=color, alpha=1, edgecolor="white", linewidth=1)
+                        ax1.fill_between(
+                            full_year_index,
+                            y_pos,
+                            y_pos + vals_pos,
+                            color=color,
+                            alpha=1,
+                            edgecolor="white",
+                            linewidth=1,
+                        )
                         y_pos += vals_pos
                     if np.any(vals_neg < 0):
-                        ax1.fill_between(full_year_index, y_neg, y_neg + vals_neg, color=color, alpha=1, edgecolor="white", linewidth=1)
+                        ax1.fill_between(
+                            full_year_index,
+                            y_neg,
+                            y_neg + vals_neg,
+                            color=color,
+                            alpha=1,
+                            edgecolor="white",
+                            linewidth=1,
+                        )
                         y_neg += vals_neg
-        
-        ax1.axhline(0, color='gray', linewidth=1, zorder=0)
+
+        ax1.axhline(0, color="gray", linewidth=1, zorder=0)
         ax1.set_xlim(start_year - 1, end_year + 1)
-        ax1.set_ylim((inst_min/scaling_factor_inst)*1.1, (inst_max/scaling_factor_inst)*1.1)
+        ax1.set_ylim(
+            (inst_min / scaling_factor_inst) * 1.1,
+            (inst_max / scaling_factor_inst) * 1.1,
+        )
         ax1.set_xticks(xtick_positions)
         ax1.set_xticklabels([])
-        if col == 0: 
-            ax1.set_ylabel(f"Instantaneous Radiative\n Forcing[$10^{{{int(np.log10(scaling_factor_inst))}}}$ W m$^{{-2}}$]")
-        else: 
+        if col == 0:
+            ax1.set_ylabel(
+                f"Instantaneous Radiative\n Forcing[$10^{{{int(np.log10(scaling_factor_inst))}}}$ W m$^{{-2}}$]"
+            )
+        else:
             ax1.tick_params(labelleft=False)
         ax1.grid(True, alpha=0.3, zorder=0)
         ax1.set_axisbelow(True)
@@ -1011,95 +1194,129 @@ def create_combined_forcing_and_impacts_figure(scenarios_data: dict):
             for proc in data.columns:
                 vals = data[proc].values / scaling_factor_water
                 if np.any(vals > 1e-12):
-                    ax2.bar(data.index, vals, bottom=bottom_pos, color=PROCESS_COLORS.get(proc, "gray"), width=0.8, edgecolor="white", linewidth=1)
+                    ax2.bar(
+                        data.index,
+                        vals,
+                        bottom=bottom_pos,
+                        color=PROCESS_COLORS.get(proc, "gray"),
+                        width=0.8,
+                        edgecolor="white",
+                        linewidth=1,
+                    )
                     bottom_pos += vals
-        
+
         ax2.set_xlim(start_year - 1, end_year + 1)
         ax2.set_xticks(xtick_positions)
         ax2.set_xticklabels([str(y) for y in xtick_positions])
-        ax2.set_ylim(0, (water_max/scaling_factor_water)*1.15)
+        ax2.set_ylim(0, (water_max / scaling_factor_water) * 1.15)
         ax2.set_xlabel("Year")
-        if col == 0: 
-            ax2.set_ylabel(f"Water Use\n[$10^{{{int(np.log10(scaling_factor_water))}}}$ m³-eq]")
-        else: 
+        if col == 0:
+            ax2.set_ylabel(
+                f"Water Use\n[$10^{{{int(np.log10(scaling_factor_water))}}}$ m³-eq]"
+            )
+        else:
             ax2.tick_params(labelleft=False)
         ax2.grid(True, alpha=0.3, zorder=0)
         ax2.set_axisbelow(True)
 
     # ===== TITLES & NUMBERING =====
-    roman = ['(a)', '(b)', '(c)', '(d)', '(e)', '(f)']
-    
+    roman = ["(a)", "(b)", "(c)", "(d)", "(e)", "(f)"]
+
     # "a)" Title is at the top
     # fig.text(0.075, 0.96, "a) Climate change", ha='left', fontsize=14, fontweight='bold')
-    
+
     # "b)" Title is placed in the gap between Row 1 and Row 2
     # The coordinate is determined by the height of the axes
     # fig.text(0.075, 0.37, "b) Water use", ha='left', fontsize=14, fontweight='bold')
     for r in range(2):
         for c in range(3):
-            axes[r, c].text(0.02, 0.95, roman[r*3 + c], transform=axes[r, c].transAxes, 
-                            va='top', fontsize=10, 
-                            bbox=dict(boxstyle='square,pad=0.1', fc='white', ec='none'))
-            
+            axes[r, c].text(
+                0.02,
+                0.95,
+                roman[r * 3 + c],
+                transform=axes[r, c].transAxes,
+                va="top",
+                fontsize=10,
+                bbox=dict(boxstyle="square,pad=0.1", fc="white", ec="none"),
+            )
+
     for c in range(3):
-        axes[2, c].text(0.02, 0.95, roman[c], transform=axes[2, c].transAxes, 
-                        va='top', fontsize=10, 
-                        bbox=dict(boxstyle='square,pad=0.1', fc='white', ec='none'))
+        axes[2, c].text(
+            0.02,
+            0.95,
+            roman[c],
+            transform=axes[2, c].transAxes,
+            va="top",
+            fontsize=10,
+            bbox=dict(boxstyle="square,pad=0.1", fc="white", ec="none"),
+        )
 
     # ===== LEGEND =====
     all_handles = {}
     for act in all_activities_list:
-        all_handles[act] = Patch(facecolor=activity_colors[act], edgecolor="white", linewidth=0.5)
+        all_handles[act] = Patch(
+            facecolor=activity_colors[act], edgecolor="white", linewidth=0.5
+        )
     for scenario in SCENARIOS.keys():
         impacts = load_impacts_properly(scenario)
         for cat in ["climate_change", "water_use"]:
             if cat in impacts:
                 for proc in impacts[cat].columns:
                     if proc not in all_handles:
-                        all_handles[proc] = Patch(facecolor=PROCESS_COLORS.get(proc, "gray"), edgecolor="white", linewidth=0.5)
-    
-    fig.legend(all_handles.values(), all_handles.keys(), loc="lower center", 
-               bbox_to_anchor=(0.5, 0), ncol=min(len(all_handles), 4), 
-               frameon=False, fontsize=9)
-    
+                        all_handles[proc] = Patch(
+                            facecolor=PROCESS_COLORS.get(proc, "gray"),
+                            edgecolor="white",
+                            linewidth=0.5,
+                        )
+
+    fig.legend(
+        all_handles.values(),
+        all_handles.keys(),
+        loc="lower center",
+        bbox_to_anchor=(0.5, 0),
+        ncol=min(len(all_handles), 4),
+        frameon=False,
+        fontsize=9,
+    )
+
     return fig
 
 
 def create_combined_results_and_impacts_figure(scenarios_data: dict):
     """
-    Create a comprehensive combined figure with production mix (results) on top and 
+    Create a comprehensive combined figure with production mix (results) on top and
     impacts (radiative forcing, water use) below.
-    
+
     Layout: 7 rows x 3 columns (scenarios)
     - Rows 0-3: Production results (Methanol, Pig Iron, Captured CO₂, Hydrogen)
     - Rows 4-6: Impacts (Aggregated RF, Instantaneous RF, Water Use)
-    
+
     Axes: Shared Y per row, Shared X per column. Labels only on outer edges.
     """
     fig = plt.figure(figsize=(12, 18))
-    
+
     fig.subplots_adjust(left=0.1, right=0.98, top=0.95, bottom=0.12)
-    
+
     # GridSpec for 7 rows x 3 columns
     gs = fig.add_gridspec(7, 3, hspace=0.08, wspace=0.06)
-    
+
     products = [
         ("methanol", "Methanol", False),
         ("pig iron", "Pig Iron", False),
         ("captured co2", "Captured CO₂", True),
         ("hydrogen", "Hydrogen", True),
     ]
-    
+
     # ===== PART 1: RESULTS (Rows 0-3) =====
-    
+
     # First pass: compute y-axis limits for each product row
     row_ylim_max = {}
     row_ylim_min = {}
-    
+
     for row, (product_key, product_label, is_intermediate) in enumerate(products):
         row_ylim_max[row] = 0.1
         row_ylim_min[row] = 0
-        
+
         for scenario in SCENARIOS.keys():
             data = scenarios_data[scenario]
             filepath = PLOTS_DIR / f"production_{scenario}.xlsx"
@@ -1108,64 +1325,68 @@ def create_combined_results_and_impacts_figure(scenarios_data: dict):
             prod_df = prod_df.iloc[1:]
             prod_df.index = pd.to_numeric(prod_df.index, errors="coerce")
             prod_df = prod_df.dropna()
-            
+
             cap_df = clean_capacity_df(data["capacity"])
             product_col = None
             for c in cap_df.columns:
                 if product_key.lower() in str(c).lower():
                     product_col = c
                     break
-            
+
             years = prod_df.index.values
             mask = (years >= 2025) & (years <= 2050)
-            
+
             prod_total_per_year = np.zeros(mask.sum())
             for c in prod_df.columns:
                 if isinstance(c, tuple):
                     process, product = c
                     if product_key.lower() in str(product).lower():
                         prod_total_per_year += prod_df[c].astype(float).values[mask]
-            
+
             row_ylim_max[row] = max(row_ylim_max[row], prod_total_per_year.max() / 1e6)
-            
+
             if product_col is not None:
                 cap_years = cap_df.index.values
                 cap_mask = (cap_years >= 2025) & (cap_years <= 2050)
                 capacity_values = cap_df[product_col].values[cap_mask] / 1e6
                 row_ylim_max[row] = max(row_ylim_max[row], capacity_values.max())
-                additions, retirements = compute_capacity_changes(cap_df[cap_mask], product_col)
+                additions, retirements = compute_capacity_changes(
+                    cap_df[cap_mask], product_col
+                )
                 row_ylim_max[row] = max(row_ylim_max[row], additions.max() / 1e6)
-        
+
         row_ylim_max[row] *= 1.1
         if row_ylim_min[row] < 0:
             row_ylim_min[row] *= 1.1
-    
+
     # Common x-axis setup for results
     years_plot = np.arange(2025, 2051)
     x_positions = np.arange(len(years_plot))
     prod_positions = x_positions
-    
+
     xtick_positions_results = [i for i, y in enumerate(years_plot) if y % 5 == 0]
     xtick_labels_results = [str(y) for y in years_plot if y % 5 == 0]
-    
+
     # Matrix to store all axes
     axes_matrix = np.empty((7, 3), dtype=object)
-    
+
     # Second pass: create production plots (rows 0-3)
     for row, (product_key, product_label, is_intermediate) in enumerate(products):
         for col, (scenario, scenario_label) in enumerate(SCENARIOS.items()):
-            
+
             # Shared Axis logic
             if row == 0 and col == 0:
                 ax = fig.add_subplot(gs[row, col])
             elif col == 0:
                 ax = fig.add_subplot(gs[row, col], sharex=axes_matrix[0, 0])
             else:
-                ax = fig.add_subplot(gs[row, col], sharey=axes_matrix[row, 0], sharex=axes_matrix[0, col])
-            
+                ax = fig.add_subplot(
+                    gs[row, col], sharey=axes_matrix[row, 0], sharex=axes_matrix[0, col]
+                )
+
             axes_matrix[row, col] = ax
             data = scenarios_data[scenario]
-            
+
             # Load production data
             filepath = PLOTS_DIR / f"production_{scenario}.xlsx"
             prod_df = pd.read_excel(filepath, header=[0, 1])
@@ -1173,7 +1394,7 @@ def create_combined_results_and_impacts_figure(scenarios_data: dict):
             prod_df = prod_df.iloc[1:]
             prod_df.index = pd.to_numeric(prod_df.index, errors="coerce")
             prod_df = prod_df.dropna()
-            
+
             # Get capacity data
             cap_df = clean_capacity_df(data["capacity"])
             product_col = None
@@ -1181,7 +1402,7 @@ def create_combined_results_and_impacts_figure(scenarios_data: dict):
                 if product_key.lower() in str(c).lower():
                     product_col = c
                     break
-            
+
             # Filter for product
             process_production = {}
             for c in prod_df.columns:
@@ -1190,11 +1411,13 @@ def create_combined_results_and_impacts_figure(scenarios_data: dict):
                     if product_key.lower() in str(product).lower():
                         clean_process = PROCESS_NAMES.get(process, process)
                         if clean_process not in process_production:
-                            process_production[clean_process] = prod_df[c].astype(float).values
-            
+                            process_production[clean_process] = (
+                                prod_df[c].astype(float).values
+                            )
+
             years = prod_df.index.values
             mask = (years >= 2025) & (years <= 2050)
-            
+
             # Stack production bars
             if process_production:
                 bottom = np.zeros(len(years_plot))
@@ -1202,99 +1425,135 @@ def create_combined_results_and_impacts_figure(scenarios_data: dict):
                     values_plot = values[mask] / 1e6
                     if np.any(values_plot > 0.01):
                         color = PROCESS_COLORS.get(process, "gray")
-                        ax.bar(prod_positions, values_plot, bottom=bottom, label=process,
-                               color=color, width=bar_width, edgecolor="white", linewidth=linewidth_bar_outline)
+                        ax.bar(
+                            prod_positions,
+                            values_plot,
+                            bottom=bottom,
+                            label=process,
+                            color=color,
+                            width=bar_width,
+                            edgecolor="white",
+                            linewidth=linewidth_bar_outline,
+                        )
                         bottom += values_plot
-            
+
             # Plot total capacity line
             if product_col is not None:
                 cap_years = cap_df.index.values
                 cap_mask = (cap_years >= 2025) & (cap_years <= 2050)
                 capacity_values = cap_df[product_col].values[cap_mask] / 1e6
                 if np.any(capacity_values > 0.001):
-                    ax.plot(x_positions, capacity_values, color="#000000", linestyle="--",
-                            linewidth=1, marker="", label="Capacity", zorder=4)
-            
+                    ax.plot(
+                        x_positions,
+                        capacity_values,
+                        color="#000000",
+                        linestyle="--",
+                        linewidth=1,
+                        marker="",
+                        label="Capacity",
+                        zorder=4,
+                    )
+
             ax.axhline(y=0, color="gray", linewidth=0.5, zorder=0)
-            
+
             # Formatting
             ax.set_xlim(-1, len(years_plot))
             ax.set_ylim(row_ylim_min[row], row_ylim_max[row])
             ax.grid(True, alpha=0.3, which="both", axis="both", zorder=0)
             ax.set_axisbelow(True)
-            
+
             # X-axis: No labels on results rows (impacts will have x labels at bottom)
             ax.tick_params(labelbottom=False)
-            
+
             # Y-axis: Only first column gets labels
             if col == 0:
                 label_suffix = " *" if is_intermediate else ""
                 ax.set_ylabel(f"{product_label}{label_suffix}\n[$10^6$ kg]")
-                
+
                 if product_label == "Captured CO₂":
                     ax.yaxis.set_major_locator(MultipleLocator(0.5))
             else:
                 ax.tick_params(labelleft=False)
-            
+
             # Column titles only on first row
             if row == 0:
                 ax.set_title(scenario_label, fontdict={"fontsize": 12}, pad=10)
-    
+
     # ===== PART 2: IMPACTS (Rows 4-6) =====
-    
+
     start_year, end_year = 2025, 2050
     full_year_index = np.arange(start_year, end_year + 1)
-    
+
     rf_scenario_data = {}
     all_activities = set()
-    
+
     # Process Radiative Forcing data
     for scenario in SCENARIOS.keys():
         df = load_characterized_inventory(scenario)
-        df_years = df[(df.index.year >= start_year) & (df.index.year <= end_year)].copy()
+        df_years = df[
+            (df.index.year >= start_year) & (df.index.year <= end_year)
+        ].copy()
         df_years.index = df_years.index.year
         df_aligned = df_years.reindex(full_year_index).fillna(0)
         rf_scenario_data[scenario] = df_aligned
         all_activities.update(df_aligned.columns.tolist())
-    
+
     # Sorting for DAC
     all_activities_list = sorted(list(all_activities))
-    target_comp = 'DAC'
+    target_comp = "DAC"
     if target_comp in all_activities_list:
         all_activities_list.remove(target_comp)
         all_activities_list.insert(0, target_comp)
-    
-    activity_colors = {act: PROCESS_COLORS.get(act, 'gray') for act in all_activities_list}
-    
+
+    activity_colors = {
+        act: PROCESS_COLORS.get(act, "gray") for act in all_activities_list
+    }
+
     # Scaling Pre-calculations
     crf_bar_max, crf_bar_min = 0, 0
     for scenario in scenarios_data.keys():
         impacts = load_impacts_properly(scenario)
         if "climate_change" in impacts:
-            data = impacts["climate_change"].loc[(impacts["climate_change"].index >= start_year) & (impacts["climate_change"].index <= end_year)]
+            data = impacts["climate_change"].loc[
+                (impacts["climate_change"].index >= start_year)
+                & (impacts["climate_change"].index <= end_year)
+            ]
             crf_bar_max = max(crf_bar_max, data.clip(lower=0).sum(axis=1).max())
             crf_bar_min = min(crf_bar_min, data.clip(upper=0).sum(axis=1).min())
-    scaling_factor_crf_bar = 10**int(np.floor(np.log10(max(abs(crf_bar_max), abs(crf_bar_min))))) if max(abs(crf_bar_max), abs(crf_bar_min)) > 0 else 1
-    
+    scaling_factor_crf_bar = (
+        10 ** int(np.floor(np.log10(max(abs(crf_bar_max), abs(crf_bar_min)))))
+        if max(abs(crf_bar_max), abs(crf_bar_min)) > 0
+        else 1
+    )
+
     inst_max, inst_min = 0, 0
     for df in rf_scenario_data.values():
         inst_max = max(inst_max, df.clip(lower=0).sum(axis=1).max())
         inst_min = min(inst_min, df.clip(upper=0).sum(axis=1).min())
-    scaling_factor_inst = 10**int(np.floor(np.log10(max(abs(inst_max), abs(inst_min))))) if max(abs(inst_max), abs(inst_min)) > 0 else 1
-    
+    scaling_factor_inst = (
+        10 ** int(np.floor(np.log10(max(abs(inst_max), abs(inst_min)))))
+        if max(abs(inst_max), abs(inst_min)) > 0
+        else 1
+    )
+
     water_max = 0
     for scenario in scenarios_data.keys():
         impacts = load_impacts_properly(scenario)
         if "water_use" in impacts:
-            data = impacts["water_use"].loc[(impacts["water_use"].index >= start_year) & (impacts["water_use"].index <= end_year)]
+            data = impacts["water_use"].loc[
+                (impacts["water_use"].index >= start_year)
+                & (impacts["water_use"].index <= end_year)
+            ]
             water_max = max(water_max, data.clip(lower=0).sum(axis=1).max())
-    scaling_factor_water = 10**int(np.floor(np.log10(water_max))) if water_max > 0 else 1
-    
+    scaling_factor_water = (
+        10 ** int(np.floor(np.log10(water_max))) if water_max > 0 else 1
+    )
+
     xtick_positions_impacts = [y for y in full_year_index if y % 5 == 0]
-    
+
     # Plotting impacts rows
     for col, (scenario, scenario_label) in enumerate(SCENARIOS.items()):
-        
+
         # ROW 4: AGGREGATED RF (Bars)
         ax0 = fig.add_subplot(gs[4, col], sharey=axes_matrix[4, 0] if col > 0 else None)
         axes_matrix[4, col] = ax0
@@ -1308,53 +1567,94 @@ def create_combined_results_and_impacts_figure(scenarios_data: dict):
                     color = activity_colors.get(proc, "gray")
                     pv, nv = np.maximum(vals, 0), np.minimum(vals, 0)
                     if np.any(pv > 0):
-                        ax0.bar(data.index, pv, bottom=bottom_pos, color=color, width=0.8, edgecolor="white", linewidth=1)
+                        ax0.bar(
+                            data.index,
+                            pv,
+                            bottom=bottom_pos,
+                            color=color,
+                            width=0.8,
+                            edgecolor="white",
+                            linewidth=1,
+                        )
                         bottom_pos += pv
                     if np.any(nv < 0):
-                        ax0.bar(data.index, nv, bottom=bottom_neg, color=color, width=0.8, edgecolor="white", linewidth=1)
+                        ax0.bar(
+                            data.index,
+                            nv,
+                            bottom=bottom_neg,
+                            color=color,
+                            width=0.8,
+                            edgecolor="white",
+                            linewidth=1,
+                        )
                         bottom_neg += nv
-        
+
         ax0.set_xlim(start_year - 1, end_year + 1)
         ax0.set_ylim(-1.6, 3.1)
         ax0.set_xticks(xtick_positions_impacts)
         ax0.set_xticklabels([])
         if col == 0:
-            ax0.set_ylabel(f"AGWI until 2125 \n [$10^{{{int(np.log10(scaling_factor_crf_bar))}}}$ W m$^{{-2}}$ yr]")
+            ax0.set_ylabel(
+                f"AGWI until 2125 \n [$10^{{{int(np.log10(scaling_factor_crf_bar))}}}$ W m$^{{-2}}$ yr]"
+            )
         else:
             ax0.tick_params(labelleft=False)
         ax0.grid(True, alpha=0.3, zorder=0)
         ax0.set_axisbelow(True)
-        
+
         # ROW 5: INSTANTANEOUS RF (Manual Stacked Area)
         ax1 = fig.add_subplot(gs[5, col], sharey=axes_matrix[5, 0] if col > 0 else None)
         axes_matrix[5, col] = ax1
         df_plot = rf_scenario_data[scenario]
         if not df_plot.empty:
-            y_pos, y_neg = np.zeros(len(full_year_index)), np.zeros(len(full_year_index))
+            y_pos, y_neg = np.zeros(len(full_year_index)), np.zeros(
+                len(full_year_index)
+            )
             for act in all_activities_list:
                 if act in df_plot.columns:
                     vals = df_plot[act].values / scaling_factor_inst
                     color = activity_colors.get(act, "gray")
                     vals_pos, vals_neg = np.maximum(vals, 0), np.minimum(vals, 0)
                     if np.any(vals_pos > 0):
-                        ax1.fill_between(full_year_index, y_pos, y_pos + vals_pos, color=color, alpha=1, edgecolor="white", linewidth=1)
+                        ax1.fill_between(
+                            full_year_index,
+                            y_pos,
+                            y_pos + vals_pos,
+                            color=color,
+                            alpha=1,
+                            edgecolor="white",
+                            linewidth=1,
+                        )
                         y_pos += vals_pos
                     if np.any(vals_neg < 0):
-                        ax1.fill_between(full_year_index, y_neg, y_neg + vals_neg, color=color, alpha=1, edgecolor="white", linewidth=1)
+                        ax1.fill_between(
+                            full_year_index,
+                            y_neg,
+                            y_neg + vals_neg,
+                            color=color,
+                            alpha=1,
+                            edgecolor="white",
+                            linewidth=1,
+                        )
                         y_neg += vals_neg
-        
-        ax1.axhline(0, color='gray', linewidth=1, zorder=0)
+
+        ax1.axhline(0, color="gray", linewidth=1, zorder=0)
         ax1.set_xlim(start_year - 1, end_year + 1)
-        ax1.set_ylim((inst_min/scaling_factor_inst)*1.1, (inst_max/scaling_factor_inst)*1.1)
+        ax1.set_ylim(
+            (inst_min / scaling_factor_inst) * 1.1,
+            (inst_max / scaling_factor_inst) * 1.1,
+        )
         ax1.set_xticks(xtick_positions_impacts)
         ax1.set_xticklabels([])
         if col == 0:
-            ax1.set_ylabel(f"Instantaneous Radiative \n Forcing [$10^{{{int(np.log10(scaling_factor_inst))}}}$ W m$^{{-2}}$]")
+            ax1.set_ylabel(
+                f"Instantaneous Radiative \n Forcing [$10^{{{int(np.log10(scaling_factor_inst))}}}$ W m$^{{-2}}$]"
+            )
         else:
             ax1.tick_params(labelleft=False)
         ax1.grid(True, alpha=0.3, zorder=0)
         ax1.set_axisbelow(True)
-    
+
     # ROW 6: WATER USE
     for col, (scenario, _) in enumerate(SCENARIOS.items()):
         ax2 = fig.add_subplot(gs[6, col], sharey=axes_matrix[6, 0] if col > 0 else None)
@@ -1366,23 +1666,33 @@ def create_combined_results_and_impacts_figure(scenarios_data: dict):
             for proc in data.columns:
                 vals = data[proc].values / scaling_factor_water
                 if np.any(vals > 1e-12):
-                    ax2.bar(data.index, vals, bottom=bottom_pos, color=PROCESS_COLORS.get(proc, "gray"), width=0.8, edgecolor="white", linewidth=1)
+                    ax2.bar(
+                        data.index,
+                        vals,
+                        bottom=bottom_pos,
+                        color=PROCESS_COLORS.get(proc, "gray"),
+                        width=0.8,
+                        edgecolor="white",
+                        linewidth=1,
+                    )
                     bottom_pos += vals
-        
+
         ax2.set_xlim(start_year - 1, end_year + 1)
         ax2.set_xticks(xtick_positions_impacts)
         ax2.set_xticklabels([str(y) for y in xtick_positions_impacts])
-        ax2.set_ylim(0, (water_max/scaling_factor_water)*1.15)
+        ax2.set_ylim(0, (water_max / scaling_factor_water) * 1.15)
         ax2.set_xlabel("Year")
         if col == 0:
-            ax2.set_ylabel(f"Water Use\n[$10^{{{int(np.log10(scaling_factor_water))}}}$ m³-eq]")
+            ax2.set_ylabel(
+                f"Water Use\n[$10^{{{int(np.log10(scaling_factor_water))}}}$ m³-eq]"
+            )
         else:
             ax2.tick_params(labelleft=False)
         ax2.grid(True, alpha=0.3, zorder=0)
         ax2.set_axisbelow(True)
-    
+
     # ===== NEW SECTION: CATEGORY LABELS & BRACKETS =====
-    
+
     def add_row_group_label(start_row, end_row, label_text):
         """
         Draws a vertical label and a bracket next to a range of rows.
@@ -1390,70 +1700,127 @@ def add_row_group_label(start_row, end_row, label_text):
         # Get the bounding boxes of the top-left and bottom-left subplots in the group
         top_pos = axes_matrix[start_row, 0].get_position()
         bottom_pos = axes_matrix[end_row, 0].get_position()
-        
+
         # Calculate vertical center and bounds in figure coordinates
         y_top = top_pos.y1
         y_bottom = bottom_pos.y0
         y_mid = (y_top + y_bottom) / 2
-        
+
         # X positions in figure coordinates (0 to 1 scale)
         # We place the label far left and the bracket between label and Y-axis
         text_x = 0.02
-        bracket_x = 0.032 
-        
+        bracket_x = 0.032
+
         # 1. Add the vertical rotated text
-        fig.text(text_x, y_mid, label_text, 
-                 rotation='vertical', va='center', ha='center',
-                 fontsize=12)
-        
+        fig.text(
+            text_x,
+            y_mid,
+            label_text,
+            rotation="vertical",
+            va="center",
+            ha="center",
+            fontsize=12,
+        )
+
         # 2. Add the bracket (Main vertical line)
-        line = plt.Line2D([bracket_x, bracket_x], [y_bottom, y_top],
-                          transform=fig.transFigure, color='black', linewidth=1)
+        line = plt.Line2D(
+            [bracket_x, bracket_x],
+            [y_bottom, y_top],
+            transform=fig.transFigure,
+            color="black",
+            linewidth=1,
+        )
         fig.lines.append(line)
-        
+
         # 3. Add horizontal ticks at the ends of the bracket
         tick_width = 0.008
-        t_top = plt.Line2D([bracket_x, bracket_x + tick_width], [y_top, y_top],
-                           transform=fig.transFigure, color='black', linewidth=1)
-        t_bottom = plt.Line2D([bracket_x, bracket_x + tick_width], [y_bottom, y_bottom],
-                              transform=fig.transFigure, color='black', linewidth=1)
+        t_top = plt.Line2D(
+            [bracket_x, bracket_x + tick_width],
+            [y_top, y_top],
+            transform=fig.transFigure,
+            color="black",
+            linewidth=1,
+        )
+        t_bottom = plt.Line2D(
+            [bracket_x, bracket_x + tick_width],
+            [y_bottom, y_bottom],
+            transform=fig.transFigure,
+            color="black",
+            linewidth=1,
+        )
         fig.lines.extend([t_top, t_bottom])
 
     # Apply the labels to the specific row ranges
     # Rows 0-3 (Methanol to Hydrogen)
     add_row_group_label(0, 3, "Deployment and Operation")
-    
+
     # Rows 4-6 (RF and Water Use)
     add_row_group_label(4, 6, "Environmental Impacts")
-    
+
     # ===== SUBPLOT LABELS =====
-    labels = ['(a-1)', '(a-2)', '(a-3)', '(b-1)', '(b-2)', '(b-3)', '(c-1)', '(c-2)', '(c-3)', '(d-1)', '(d-2)', '(d-3)',
-              '(e-1)', '(e-2)', '(e-3)', '(f-1)', '(f-2)', '(f-3)', '(g-1)', '(g-2)', '(g-3)']
+    labels = [
+        "(a-1)",
+        "(a-2)",
+        "(a-3)",
+        "(b-1)",
+        "(b-2)",
+        "(b-3)",
+        "(c-1)",
+        "(c-2)",
+        "(c-3)",
+        "(d-1)",
+        "(d-2)",
+        "(d-3)",
+        "(e-1)",
+        "(e-2)",
+        "(e-3)",
+        "(f-1)",
+        "(f-2)",
+        "(f-3)",
+        "(g-1)",
+        "(g-2)",
+        "(g-3)",
+    ]
     label_idx = 0
     for row in range(7):
         for col in range(3):
             ax = axes_matrix[row, col]
-            ax.text(0.02, 0.95, labels[label_idx], transform=ax.transAxes,
-                   va='top', fontsize=10,
-                   bbox=dict(boxstyle='square,pad=0.1', fc='white', ec='none'))
+            ax.text(
+                0.02,
+                0.95,
+                labels[label_idx],
+                transform=ax.transAxes,
+                va="top",
+                fontsize=10,
+                bbox=dict(boxstyle="square,pad=0.1", fc="white", ec="none"),
+            )
             label_idx += 1
-    
+
     # ===== CREATE SHARED LEGEND =====
     all_handles = []
     all_labels_legend = []
-    
+
     # Add process colors from results
     for process, color in PROCESS_COLORS.items():
         all_handles.append(Patch(facecolor=color, edgecolor="white", linewidth=0.5))
         all_labels_legend.append(process)
-    
+
     # Add capacity line
-    all_handles.append(plt.Line2D([0], [0], color="#000000", linestyle="--", linewidth=1))
+    all_handles.append(
+        plt.Line2D([0], [0], color="#000000", linestyle="--", linewidth=1)
+    )
     all_labels_legend.append("Available capacity")
-    
-    fig.legend(all_handles, all_labels_legend, loc="lower center", bbox_to_anchor=(0.5, 0.07),
-               ncol=4, frameon=False, fontsize=9)
-    
+
+    fig.legend(
+        all_handles,
+        all_labels_legend,
+        loc="lower center",
+        bbox_to_anchor=(0.5, 0.07),
+        ncol=4,
+        frameon=False,
+        fontsize=9,
+    )
+
     return fig
 
 
@@ -1486,7 +1853,7 @@ def main():
     fig_combined = create_combined_results_and_impacts_figure(scenarios_data)
     fig_combined.savefig(OUTPUT_DIR / "results_and_impacts.pdf")
     plt.close(fig_combined)
-    
+
     fig_forcing = create_radiative_forcing_figure()
     fig_forcing.savefig(OUTPUT_DIR / "radiative_forcing.pdf")
     plt.close(fig_forcing)
diff --git a/scripts/uv_setup.py b/scripts/uv_setup.py
index d98e6c2..c546593 100644
--- a/scripts/uv_setup.py
+++ b/scripts/uv_setup.py
@@ -1,14 +1,15 @@
+import json
 import os
-import sys
 import subprocess
+import sys
 import urllib.request
-import json
 from pathlib import Path
 
 # Configuration
 NB_URL = "https://raw.githubusercontent.com/RWTH-LTT/optimex/refs/heads/main/notebooks/basic_example.ipynb"
 DEPS = ["optimex"]
 
+
 def main():
     # 1. Handle Project Name Input
     # Usage: uv run script.py [project_name]
@@ -18,7 +19,7 @@ def main():
     if project_name != ".":
         print(f"Creating project directory: {project_name}")
         project_path.mkdir(parents=True, exist_ok=True)
-    
+
     # Change working directory to the new project path
     os.chdir(project_path)
 
@@ -39,8 +40,11 @@ def main():
     urllib.request.urlretrieve(NB_URL, "start_here.ipynb")
 
     print(f"\n✅ Done! Project '{project_path.name}' is ready.\n")
-    print(f"Open in Jupyter Lab: cd {project_name} && uv run --with jupyter jupyter lab")
+    print(
+        f"Open in Jupyter Lab: cd {project_name} && uv run --with jupyter jupyter lab"
+    )
     print(f"Open in VS Code: cd {project_name} && code .")
 
+
 if __name__ == "__main__":
-    main()
\ No newline at end of file
+    main()
diff --git a/src/optimex/converter.py b/src/optimex/converter.py
index 688fc4b..602e738 100644
--- a/src/optimex/converter.py
+++ b/src/optimex/converter.py
@@ -9,6 +9,7 @@
     - OptimizationModelInputs: Validated data structure for optimization inputs
     - ModelInputManager: Handles conversion, serialization, and constraint overrides
 """
+
 import copy
 import json
 import pickle
@@ -35,7 +36,8 @@ class OptimizationModelInputs(BaseModel):
         ..., description="Identifiers for all foreground products."
     )
     INTERMEDIATE_FLOW: List[str] = Field(
-        ..., description="Identifiers for background products (from background databases)."
+        ...,
+        description="Identifiers for background products (from background databases).",
     )
     ELEMENTARY_FLOW: List[str] = Field(
         ...,
@@ -85,11 +87,15 @@ class OptimizationModelInputs(BaseModel):
 
     foreground_technosphere: Dict[Tuple[str, str, int], float] = Field(
         ...,
-        description=("Maps (process, intermediate_flow, process_time) to background flow amount."),
+        description=(
+            "Maps (process, intermediate_flow, process_time) to background flow amount."
+        ),
     )
     internal_demand_technosphere: Dict[Tuple[str, str, int], float] = Field(
         ...,
-        description=("Maps (process, product, process_time) to internal product consumption amount."),
+        description=(
+            "Maps (process, product, process_time) to internal product consumption amount."
+        ),
     )
     foreground_biosphere: Dict[Tuple[str, str, int], float] = Field(
         ...,
@@ -100,9 +106,7 @@ class OptimizationModelInputs(BaseModel):
     )
     foreground_production: Dict[Tuple[str, str, int], float] = Field(
         ...,
-        description=(
-            "Maps (process, product, process_time) to produced amount."
-        ),
+        description=("Maps (process, product, process_time) to produced amount."),
     )
 
     # ==================== Vintage-Dependent Parameters ====================
@@ -119,7 +123,9 @@ class OptimizationModelInputs(BaseModel):
     # the same (process, flow), the explicit *_vintages values take precedence.
     # ========================================================================
 
-    foreground_technosphere_vintages: Optional[Dict[Tuple[str, str, int, int], float]] = Field(
+    foreground_technosphere_vintages: Optional[
+        Dict[Tuple[str, str, int, int], float]
+    ] = Field(
         None,
         description=(
             "Vintage-specific technosphere values. Maps (process, intermediate_flow, "
@@ -128,23 +134,27 @@ class OptimizationModelInputs(BaseModel):
             "Takes precedence over vintage_improvements for the same (process, flow)."
         ),
     )
-    foreground_biosphere_vintages: Optional[Dict[Tuple[str, str, int, int], float]] = Field(
-        None,
-        description=(
-            "Vintage-specific biosphere values. Maps (process, elementary_flow, "
-            "process_time, vintage_year) to amount. Values are linearly interpolated "
-            "for installation years between specified vintages. "
-            "Takes precedence over vintage_improvements for the same (process, flow)."
-        ),
+    foreground_biosphere_vintages: Optional[Dict[Tuple[str, str, int, int], float]] = (
+        Field(
+            None,
+            description=(
+                "Vintage-specific biosphere values. Maps (process, elementary_flow, "
+                "process_time, vintage_year) to amount. Values are linearly interpolated "
+                "for installation years between specified vintages. "
+                "Takes precedence over vintage_improvements for the same (process, flow)."
+            ),
+        )
     )
-    foreground_production_vintages: Optional[Dict[Tuple[str, str, int, int], float]] = Field(
-        None,
-        description=(
-            "Vintage-specific production values. Maps (process, product, process_time, "
-            "vintage_year) to amount. Values are linearly interpolated "
-            "for installation years between specified vintages. "
-            "Takes precedence over vintage_improvements for the same (process, product)."
-        ),
+    foreground_production_vintages: Optional[Dict[Tuple[str, str, int, int], float]] = (
+        Field(
+            None,
+            description=(
+                "Vintage-specific production values. Maps (process, product, process_time, "
+                "vintage_year) to amount. Values are linearly interpolated "
+                "for installation years between specified vintages. "
+                "Takes precedence over vintage_improvements for the same (process, product)."
+            ),
+        )
     )
     vintage_improvements: Optional[Dict[Tuple[str, str, int], float]] = Field(
         None,
@@ -160,17 +170,23 @@ class OptimizationModelInputs(BaseModel):
 
     # Internal: Computed sparse overrides (populated by _expand_vintage_parameters)
     # Users should not set these directly - they are derived from the above inputs.
-    foreground_technosphere_vintage_overrides: Optional[Dict[Tuple[str, str, int, int], float]] = Field(
+    foreground_technosphere_vintage_overrides: Optional[
+        Dict[Tuple[str, str, int, int], float]
+    ] = Field(
         None,
         description="[Internal] Computed sparse overrides. Do not set directly.",
         exclude=True,  # Exclude from serialization
     )
-    foreground_biosphere_vintage_overrides: Optional[Dict[Tuple[str, str, int, int], float]] = Field(
+    foreground_biosphere_vintage_overrides: Optional[
+        Dict[Tuple[str, str, int, int], float]
+    ] = Field(
         None,
         description="[Internal] Computed sparse overrides. Do not set directly.",
         exclude=True,
     )
-    foreground_production_vintage_overrides: Optional[Dict[Tuple[str, str, int, int], float]] = Field(
+    foreground_production_vintage_overrides: Optional[
+        Dict[Tuple[str, str, int, int], float]
+    ] = Field(
         None,
         description="[Internal] Computed sparse overrides. Do not set directly.",
         exclude=True,
@@ -308,15 +324,13 @@ class OptimizationModelInputs(BaseModel):
     process_operation_limits_max_default: float = Field(
         default=float("inf"),
         description=(
-            "Default upper bound for process operation if not explicitly "
-            "specified."
+            "Default upper bound for process operation if not explicitly " "specified."
         ),
     )
     process_operation_limits_min_default: float = Field(
         default=0.0,
         description=(
-            "Default lower bound for process operation if not explicitly "
-            "specified."
+            "Default lower bound for process operation if not explicitly " "specified."
         ),
     )
     cumulative_process_limits_max_default: float = Field(
@@ -396,9 +410,7 @@ def validate_keys(keys, valid_set, context):
 
         for key in data.get("internal_demand_technosphere", {}).keys():
             validate_keys([key[0]], processes, "internal_demand_technosphere processes")
-            validate_keys(
-                [key[1]], products, "internal_demand_technosphere products"
-            )
+            validate_keys([key[1]], products, "internal_demand_technosphere products")
             validate_keys(
                 [key[2]], process_times, "internal_demand_technosphere process times"
             )
@@ -412,9 +424,7 @@ def validate_keys(keys, valid_set, context):
 
         for key in data.get("foreground_production", {}).keys():
             validate_keys([key[0]], processes, "foreground_production processes")
-            validate_keys(
-                [key[1]], products, "foreground_production products"
-            )
+            validate_keys([key[1]], products, "foreground_production products")
             validate_keys(
                 [key[2]], process_times, "foreground_production process times"
             )
@@ -444,7 +454,9 @@ def validate_keys(keys, valid_set, context):
         if data.get("category_impact_limits") is not None:
             for key in data["category_impact_limits"].keys():
                 validate_keys([key[0]], categories, "category_impact_limits categories")
-                validate_keys([key[1]], system_times, "category_impact_limits system times")
+                validate_keys(
+                    [key[1]], system_times, "category_impact_limits system times"
+                )
 
         if data.get("cumulative_category_impact_limits") is not None:
             for key in data["cumulative_category_impact_limits"].keys():
@@ -452,23 +464,39 @@ def validate_keys(keys, valid_set, context):
 
         if data.get("process_deployment_limits_max") is not None:
             for key in data["process_deployment_limits_max"].keys():
-                validate_keys([key[0]], processes, "process_deployment_limits_max processes")
-                validate_keys([key[1]], system_times, "process_deployment_limits_max system times")
+                validate_keys(
+                    [key[0]], processes, "process_deployment_limits_max processes"
+                )
+                validate_keys(
+                    [key[1]], system_times, "process_deployment_limits_max system times"
+                )
 
         if data.get("process_deployment_limits_min") is not None:
             for key in data["process_deployment_limits_min"].keys():
-                validate_keys([key[0]], processes, "process_deployment_limits_min processes")
-                validate_keys([key[1]], system_times, "process_deployment_limits_min system times")
+                validate_keys(
+                    [key[0]], processes, "process_deployment_limits_min processes"
+                )
+                validate_keys(
+                    [key[1]], system_times, "process_deployment_limits_min system times"
+                )
 
         if data.get("process_operation_limits_max") is not None:
             for key in data["process_operation_limits_max"].keys():
-                validate_keys([key[0]], processes, "process_operation_limits_max processes")
-                validate_keys([key[1]], system_times, "process_operation_limits_max system times")
+                validate_keys(
+                    [key[0]], processes, "process_operation_limits_max processes"
+                )
+                validate_keys(
+                    [key[1]], system_times, "process_operation_limits_max system times"
+                )
 
         if data.get("process_operation_limits_min") is not None:
             for key in data["process_operation_limits_min"].keys():
-                validate_keys([key[0]], processes, "process_operation_limits_min processes")
-                validate_keys([key[1]], system_times, "process_operation_limits_min system times")
+                validate_keys(
+                    [key[0]], processes, "process_operation_limits_min processes"
+                )
+                validate_keys(
+                    [key[1]], system_times, "process_operation_limits_min system times"
+                )
 
         if data.get("cumulative_process_limits_max") is not None:
             for key in data["cumulative_process_limits_max"].keys():
@@ -560,7 +588,9 @@ def validate_keys(keys, valid_set, context):
                     "foreground_technosphere_vintages intermediate flows",
                 )
                 validate_keys(
-                    [key[2]], process_times, "foreground_technosphere_vintages process times"
+                    [key[2]],
+                    process_times,
+                    "foreground_technosphere_vintages process times",
                 )
 
         # Validate foreground_biosphere_vintages (processes, flows, process_times)
@@ -575,7 +605,9 @@ def validate_keys(keys, valid_set, context):
                     "foreground_biosphere_vintages elementary flows",
                 )
                 validate_keys(
-                    [key[2]], process_times, "foreground_biosphere_vintages process times"
+                    [key[2]],
+                    process_times,
+                    "foreground_biosphere_vintages process times",
                 )
 
         # Validate foreground_production_vintages (processes, products, process_times)
@@ -588,7 +620,9 @@ def validate_keys(keys, valid_set, context):
                     [key[1]], products, "foreground_production_vintages products"
                 )
                 validate_keys(
-                    [key[2]], process_times, "foreground_production_vintages process times"
+                    [key[2]],
+                    process_times,
+                    "foreground_production_vintages process times",
                 )
 
         # Validate vintage_improvements (processes, flows)
@@ -596,9 +630,7 @@ def validate_keys(keys, valid_set, context):
             for key in data["vintage_improvements"].keys():
                 validate_keys([key[0]], processes, "vintage_improvements processes")
                 # Flow can be intermediate, elementary, or product
-                validate_keys(
-                    [key[1]], all_flows, "vintage_improvements flows"
-                )
+                validate_keys([key[1]], all_flows, "vintage_improvements flows")
 
         return data
 
@@ -625,6 +657,7 @@ def validate_constant_operation_flows(self) -> "OptimizationModelInputs":
         ValueError
             If any operational flow has varying values across process time.
         """
+
         def check_constancy(
             flow_data: Dict[Tuple[str, str, int], float], flow_type: str
         ):
@@ -696,7 +729,7 @@ def validate_process_limits_consistency(self) -> "OptimizationModelInputs":
                             f"Cumulative process limit min ({min_val}) > max ({max_val}) "
                             f"for {proc}. Constraints would be infeasible."
                         )
-                    
+
         # Cross-check cumulative vs. per-period limits
         if self.process_deployment_limits_max and self.cumulative_process_limits_min:
             for key in self.cumulative_process_limits_min:
@@ -723,23 +756,31 @@ def validate_process_limits_consistency(self) -> "OptimizationModelInputs":
                         f"process limit max ({max_cum}) for {key}. Constraints would be infeasible."
                     )
 
-
         # Check defaults consistency
-        if self.process_deployment_limits_min_default > self.process_deployment_limits_max_default:
+        if (
+            self.process_deployment_limits_min_default
+            > self.process_deployment_limits_max_default
+        ):
             raise ValueError(
                 f"process_deployment_limits_min_default ({self.process_deployment_limits_min_default}) > "
                 f"process_deployment_limits_max_default ({self.process_deployment_limits_max_default}). "
                 "Constraints would be infeasible."
             )
 
-        if self.process_operation_limits_min_default > self.process_operation_limits_max_default:
+        if (
+            self.process_operation_limits_min_default
+            > self.process_operation_limits_max_default
+        ):
             raise ValueError(
                 f"process_operation_limits_min_default ({self.process_operation_limits_min_default}) > "
                 f"process_operation_limits_max_default ({self.process_operation_limits_max_default}). "
                 "Constraints would be infeasible."
             )
 
-        if self.cumulative_process_limits_min_default > self.cumulative_process_limits_max_default:
+        if (
+            self.cumulative_process_limits_min_default
+            > self.cumulative_process_limits_max_default
+        ):
             raise ValueError(
                 f"cumulative_process_limits_min_default ({self.cumulative_process_limits_min_default}) > "
                 f"cumulative_process_limits_max_default ({self.cumulative_process_limits_max_default}). "
@@ -791,8 +832,7 @@ def validate_process_limits_consistency(self) -> "OptimizationModelInputs":
         if self.flow_limits_min and self.cumulative_flow_limits_max:
             for flow in self.cumulative_flow_limits_max:
                 total_min = sum(
-                    self.flow_limits_min.get((flow, t), 0.0)
-                    for t in self.SYSTEM_TIME
+                    self.flow_limits_min.get((flow, t), 0.0) for t in self.SYSTEM_TIME
                 )
                 max_cum = self.cumulative_flow_limits_max[flow]
                 if total_min > max_cum:
@@ -866,51 +906,63 @@ def _expand_vintage_parameters(self) -> None:
 
         # Handle foreground_technosphere - only create overrides if vintage data exists
         if self.foreground_technosphere_vintages:
-            self.foreground_technosphere_vintage_overrides = expand_foreground_tensor_with_vintages(
-                self.foreground_technosphere_vintages,
-                self.REFERENCE_VINTAGES,
-                system_times,
+            self.foreground_technosphere_vintage_overrides = (
+                expand_foreground_tensor_with_vintages(
+                    self.foreground_technosphere_vintages,
+                    self.REFERENCE_VINTAGES,
+                    system_times,
+                )
             )
         elif self.vintage_improvements and self.foreground_technosphere:
-            self.foreground_technosphere_vintage_overrides = expand_foreground_tensor_with_evolution(
-                self.foreground_technosphere,
-                self.vintage_improvements,
-                self.REFERENCE_VINTAGES,
-                system_times,
-                "INTERMEDIATE_FLOW",
+            self.foreground_technosphere_vintage_overrides = (
+                expand_foreground_tensor_with_evolution(
+                    self.foreground_technosphere,
+                    self.vintage_improvements,
+                    self.REFERENCE_VINTAGES,
+                    system_times,
+                    "INTERMEDIATE_FLOW",
+                )
             )
         # else: no overrides, use base 3D tensor directly
 
         # Handle foreground_biosphere
         if self.foreground_biosphere_vintages:
-            self.foreground_biosphere_vintage_overrides = expand_foreground_tensor_with_vintages(
-                self.foreground_biosphere_vintages,
-                self.REFERENCE_VINTAGES,
-                system_times,
+            self.foreground_biosphere_vintage_overrides = (
+                expand_foreground_tensor_with_vintages(
+                    self.foreground_biosphere_vintages,
+                    self.REFERENCE_VINTAGES,
+                    system_times,
+                )
             )
         elif self.vintage_improvements and self.foreground_biosphere:
-            self.foreground_biosphere_vintage_overrides = expand_foreground_tensor_with_evolution(
-                self.foreground_biosphere,
-                self.vintage_improvements,
-                self.REFERENCE_VINTAGES,
-                system_times,
-                "ELEMENTARY_FLOW",
+            self.foreground_biosphere_vintage_overrides = (
+                expand_foreground_tensor_with_evolution(
+                    self.foreground_biosphere,
+                    self.vintage_improvements,
+                    self.REFERENCE_VINTAGES,
+                    system_times,
+                    "ELEMENTARY_FLOW",
+                )
             )
 
         # Handle foreground_production
         if self.foreground_production_vintages:
-            self.foreground_production_vintage_overrides = expand_foreground_tensor_with_vintages(
-                self.foreground_production_vintages,
-                self.REFERENCE_VINTAGES,
-                system_times,
+            self.foreground_production_vintage_overrides = (
+                expand_foreground_tensor_with_vintages(
+                    self.foreground_production_vintages,
+                    self.REFERENCE_VINTAGES,
+                    system_times,
+                )
             )
         elif self.vintage_improvements and self.foreground_production:
-            self.foreground_production_vintage_overrides = expand_foreground_tensor_with_evolution(
-                self.foreground_production,
-                self.vintage_improvements,
-                self.REFERENCE_VINTAGES,
-                system_times,
-                "PRODUCT",
+            self.foreground_production_vintage_overrides = (
+                expand_foreground_tensor_with_evolution(
+                    self.foreground_production,
+                    self.vintage_improvements,
+                    self.REFERENCE_VINTAGES,
+                    system_times,
+                    "PRODUCT",
+                )
             )
 
     def get_scaled_copy(self) -> Tuple["OptimizationModelInputs", Dict[str, Any]]:
@@ -1308,7 +1360,10 @@ def save_inputs(self, path: str) -> None:
                     data[field] = self._serialize_dict_with_tuple_keys(data[field])
 
             # Special handling for operation_time_limits (values are tuples, not keys)
-            if "operation_time_limits" in data and data["operation_time_limits"] is not None:
+            if (
+                "operation_time_limits" in data
+                and data["operation_time_limits"] is not None
+            ):
                 data["operation_time_limits"] = {
                     k: list(v) for k, v in data["operation_time_limits"].items()
                 }
@@ -1382,7 +1437,10 @@ def load_inputs(self, path: str) -> OptimizationModelInputs:
                     data[field] = self._deserialize_dict_with_tuple_keys(data[field])
 
             # Special handling for operation_time_limits (convert lists back to tuples)
-            if "operation_time_limits" in data and data["operation_time_limits"] is not None:
+            if (
+                "operation_time_limits" in data
+                and data["operation_time_limits"] is not None
+            ):
                 data["operation_time_limits"] = {
                     k: tuple(v) for k, v in data["operation_time_limits"].items()
                 }
diff --git a/src/optimex/lca_processor.py b/src/optimex/lca_processor.py
index 5b7687b..95ef16b 100644
--- a/src/optimex/lca_processor.py
+++ b/src/optimex/lca_processor.py
@@ -10,6 +10,7 @@
     - LCAConfig: Configuration for LCA computations
     - LCADataProcessor: Main class for time-explicit LCA processing
 """
+
 import pickle
 from datetime import datetime
 from enum import Enum
@@ -99,7 +100,7 @@ class TemporalConfig(BaseModel):
         temporal_resolution: Temporal resolution for the model.
             Options: 'year', 'month', 'day'.
         time_horizon: Length of the time horizon (in units of `temporal_resolution`).
-        fixed_time_horizon: If True, the time horizon is calculated from the time of the functional 
+        fixed_time_horizon: If True, the time horizon is calculated from the time of the functional
             unit (FU) instead of the time of emission
         database_dates: Mapping from database names to their respective reference dates.
     """
@@ -239,7 +240,7 @@ def __init__(self, config: LCAConfig) -> None:
         self._characterization = {}
         self._operation_flow = {}
         self._operation_time_limits = {}
-        
+
         # Vintage-dependent parameters extracted from exchange attributes
         self._foreground_technosphere_vintages = {}
         self._foreground_biosphere_vintages = {}
@@ -346,17 +347,29 @@ def internal_demand_technosphere(self) -> dict:
     @property
     def foreground_technosphere_vintages(self) -> Optional[dict]:
         """Read-only access to vintage-specific technosphere values."""
-        return self._foreground_technosphere_vintages if self._foreground_technosphere_vintages else None
+        return (
+            self._foreground_technosphere_vintages
+            if self._foreground_technosphere_vintages
+            else None
+        )
 
     @property
     def foreground_biosphere_vintages(self) -> Optional[dict]:
         """Read-only access to vintage-specific biosphere values."""
-        return self._foreground_biosphere_vintages if self._foreground_biosphere_vintages else None
+        return (
+            self._foreground_biosphere_vintages
+            if self._foreground_biosphere_vintages
+            else None
+        )
 
     @property
     def foreground_production_vintages(self) -> Optional[dict]:
         """Read-only access to vintage-specific production values."""
-        return self._foreground_production_vintages if self._foreground_production_vintages else None
+        return (
+            self._foreground_production_vintages
+            if self._foreground_production_vintages
+            else None
+        )
 
     @property
     def vintage_improvements(self) -> Optional[dict]:
@@ -366,7 +379,9 @@ def vintage_improvements(self) -> Optional[dict]:
     @property
     def reference_vintages(self) -> Optional[list]:
         """Read-only access to reference vintage years."""
-        return sorted(list(self._reference_vintages)) if self._reference_vintages else None
+        return (
+            sorted(list(self._reference_vintages)) if self._reference_vintages else None
+        )
 
     def _parse_demand(self) -> None:
         """
@@ -389,18 +404,18 @@ def _parse_demand(self) -> None:
 
         for product_node, td in raw_demand.items():
             # Validate demand is on product nodes
-            if not hasattr(product_node, 'key'):
+            if not hasattr(product_node, "key"):
                 raise ValueError(
                     f"Demand must be on Brightway Node objects, got {type(product_node)}"
                 )
 
-            if product_node.get('type') != bd.labels.product_node_default:
+            if product_node.get("type") != bd.labels.product_node_default:
                 raise ValueError(
                     f"Demand must be on product nodes. "
                     f"Node {product_node['name']} has type {product_node.get('type')}"
                 )
 
-            product_code = product_node['code']
+            product_code = product_node["code"]
             years = td.date.astype("datetime64[Y]").astype(int) + 1970
             if years[-1] - start_year > longest_demand_interval:
                 longest_demand_interval = years[-1] - start_year
@@ -411,7 +426,7 @@ def _parse_demand(self) -> None:
             )
 
             # Store product information
-            self._products[product_code] = product_node['name']
+            self._products[product_code] = product_node["name"]
 
         self._system_time = range(start_year, start_year + longest_demand_interval + 1)
         logger.info(
@@ -429,11 +444,11 @@ def _construct_foreground_tensors(self) -> None:
         It processes only process nodes (type=process_node_default) and handles
         three types of edges: production edges (to product nodes), consumption edges
         (from background or foreground products), and biosphere edges (emissions).
-        
+
         Additionally, this method extracts vintage-dependent parameters from exchange
         attributes when present:
         - vintage_improvements: Dict mapping vintage years to scaling factors
-        - vintage_amounts: Dict mapping vintage years or (process_time, vintage_year) 
+        - vintage_amounts: Dict mapping vintage years or (process_time, vintage_year)
           tuples to amounts
 
         Side Effects
@@ -456,13 +471,13 @@ def _construct_foreground_tensors(self) -> None:
               indicating if the flow occurs during the operation phase.
             - self._operation_time_limits: dict mapping process codes to their
               operation time limits, if defined.
-            - self._foreground_technosphere_vintages: dict mapping (process_code, 
+            - self._foreground_technosphere_vintages: dict mapping (process_code,
               flow_code, process_time, vintage_year) to vintage-specific amounts.
-            - self._foreground_biosphere_vintages: dict mapping (process_code, 
+            - self._foreground_biosphere_vintages: dict mapping (process_code,
               flow_code, process_time, vintage_year) to vintage-specific amounts.
-            - self._foreground_production_vintages: dict mapping (process_code, 
+            - self._foreground_production_vintages: dict mapping (process_code,
               product_code, process_time, vintage_year) to vintage-specific amounts.
-            - self._vintage_improvements: dict mapping (process_code, flow_code, 
+            - self._vintage_improvements: dict mapping (process_code, flow_code,
               vintage_year) to scaling factors.
             - self._reference_vintages: set of reference vintage years.
         """
@@ -473,7 +488,7 @@ def _construct_foreground_tensors(self) -> None:
 
         for act in self.foreground_db:
             # Only process nodes (not product nodes)
-            if act.get('type') != bd.labels.process_node_default:
+            if act.get("type") != bd.labels.process_node_default:
                 continue
 
             # Store process information
@@ -488,7 +503,7 @@ def _construct_foreground_tensors(self) -> None:
                     TemporalDistribution(
                         date=np.array([0], dtype="timedelta64[Y]"), amount=np.array([1])
                     ),
-                )                
+                )
                 years = temporal_dist.date.astype("timedelta64[Y]").astype(int)
                 # Ensure all years are included in process time
                 self._process_time.update(
@@ -499,14 +514,15 @@ def _construct_foreground_tensors(self) -> None:
                 # Skip if temporal distribution is missing or invalid (empty arrays)
                 if years.size == 0 or temporal_factor.size == 0:
                     logger.debug(
-                        f"Skipping exchange {exc.input} due to missing or invalid temporal distribution.")
+                        f"Skipping exchange {exc.input} due to missing or invalid temporal distribution."
+                    )
                     continue
 
                 edge_type = exc["type"]
                 input_code = exc.input["code"]
                 input_name = exc.input["name"]
                 input_db = exc.input["database"]
-                
+
                 # ========== Extract Vintage Parameters from Exchange Attributes ==========
                 # Vintage parameters allow foreground exchanges to vary based on installation year.
                 # Two attributes are supported on exchanges:
@@ -519,10 +535,10 @@ def _construct_foreground_tensors(self) -> None:
                 #    Format: {vintage_year: amount} OR {(process_time, vintage_year): amount}
                 #    Example: {2020: 60, 2030: 45} or {(1, 2020): 60, (1, 2030): 45}
                 # ==========================================================================
-                
+
                 vintage_amounts = exc.get("vintage_amounts")
                 vintage_improvements = exc.get("vintage_improvements")
-                
+
                 # Process vintage_improvements attribute if present
                 if vintage_improvements is not None:
                     if not isinstance(vintage_improvements, dict):
@@ -531,10 +547,15 @@ def _construct_foreground_tensors(self) -> None:
                             f"got {type(vintage_improvements).__name__}. Skipping."
                         )
                     else:
-                        for vintage_year, scaling_factor in vintage_improvements.items():
+                        for (
+                            vintage_year,
+                            scaling_factor,
+                        ) in vintage_improvements.items():
                             self._reference_vintages.add(vintage_year)
-                            self._vintage_improvements[(act["code"], input_code, vintage_year)] = scaling_factor
-                
+                            self._vintage_improvements[
+                                (act["code"], input_code, vintage_year)
+                            ] = scaling_factor
+
                 # Process vintage_amounts attribute if present
                 if vintage_amounts is not None:
                     if not isinstance(vintage_amounts, dict):
@@ -550,41 +571,51 @@ def _construct_foreground_tensors(self) -> None:
                             elif isinstance(vintage_key, int):
                                 # Just vintage year - apply to all process times from temporal distribution
                                 vintage_year = vintage_key
-                                process_time_vintage = None  # Will be expanded for all years
+                                process_time_vintage = (
+                                    None  # Will be expanded for all years
+                                )
                             else:
                                 logger.warning(
                                     f"Invalid vintage_amounts key {vintage_key} on exchange {exc.input}. "
                                     f"Must be int (vintage year) or tuple (process_time, vintage_year)."
                                 )
                                 continue
-                            
+
                             self._reference_vintages.add(vintage_year)
-                            
+
                             # Determine which process times to apply this vintage value to
                             if process_time_vintage is not None:
                                 process_times_to_update = [process_time_vintage]
                             else:
                                 # Apply to all process times in temporal distribution
                                 process_times_to_update = years
-                            
+
                             for tau in process_times_to_update:
                                 # Store in appropriate vintage dictionary based on edge type
                                 if edge_type == bd.labels.production_edge_default:
-                                    self._foreground_production_vintages[(act["code"], input_code, tau, vintage_year)] = vintage_amount
+                                    self._foreground_production_vintages[
+                                        (act["code"], input_code, tau, vintage_year)
+                                    ] = vintage_amount
                                 elif edge_type == bd.labels.consumption_edge_default:
                                     if input_db != self.foreground_db.name:
                                         # Only for background consumption (technosphere)
-                                        self._foreground_technosphere_vintages[(act["code"], input_code, tau, vintage_year)] = vintage_amount
+                                        self._foreground_technosphere_vintages[
+                                            (act["code"], input_code, tau, vintage_year)
+                                        ] = vintage_amount
                                 elif edge_type == bd.labels.biosphere_edge_default:
-                                    self._foreground_biosphere_vintages[(act["code"], input_code, tau, vintage_year)] = vintage_amount
+                                    self._foreground_biosphere_vintages[
+                                        (act["code"], input_code, tau, vintage_year)
+                                    ] = vintage_amount
 
                 # Handle production edges
                 if edge_type == bd.labels.production_edge_default:
                     product_code = input_code
-                    production_tensor.update({
-                        (act["code"], product_code, year): exc["amount"] * factor
-                        for year, factor in zip(years, temporal_factor)
-                    })
+                    production_tensor.update(
+                        {
+                            (act["code"], product_code, year): exc["amount"] * factor
+                            for year, factor in zip(years, temporal_factor)
+                        }
+                    )
                     if exc.get("operation"):
                         self._operation_flow.update({(act["code"], product_code): True})
                     self._products.setdefault(product_code, input_name)
@@ -593,29 +624,39 @@ def _construct_foreground_tensors(self) -> None:
                 elif edge_type == bd.labels.consumption_edge_default:
                     if input_db == self.foreground_db.name:
                         # Internal demand: foreground product consumed
-                        internal_demand_technosphere.update({
-                            (act["code"], input_code, year): exc["amount"] * factor
-                            for year, factor in zip(years, temporal_factor)
-                        })
+                        internal_demand_technosphere.update(
+                            {
+                                (act["code"], input_code, year): exc["amount"] * factor
+                                for year, factor in zip(years, temporal_factor)
+                            }
+                        )
                         if exc.get("operation"):
-                            self._operation_flow.update({(act["code"], input_code): True})
+                            self._operation_flow.update(
+                                {(act["code"], input_code): True}
+                            )
                         self._products.setdefault(input_code, input_name)
                     else:
                         # External intermediate: background consumption
-                        technosphere_tensor.update({
-                            (act["code"], input_code, year): exc["amount"] * factor
-                            for year, factor in zip(years, temporal_factor)
-                        })
+                        technosphere_tensor.update(
+                            {
+                                (act["code"], input_code, year): exc["amount"] * factor
+                                for year, factor in zip(years, temporal_factor)
+                            }
+                        )
                         if exc.get("operation"):
-                            self._operation_flow.update({(act["code"], input_code): True})
+                            self._operation_flow.update(
+                                {(act["code"], input_code): True}
+                            )
                         self._intermediate_flows.setdefault(input_code, input_name)
 
                 # Handle biosphere edges
                 elif edge_type == bd.labels.biosphere_edge_default:
-                    biosphere_tensor.update({
-                        (act["code"], input_code, year): exc["amount"] * factor
-                        for year, factor in zip(years, temporal_factor)
-                    })
+                    biosphere_tensor.update(
+                        {
+                            (act["code"], input_code, year): exc["amount"] * factor
+                            for year, factor in zip(years, temporal_factor)
+                        }
+                    )
                     if exc.get("operation"):
                         self._operation_flow.update({(act["code"], input_code): True})
                     self._elementary_flows.setdefault(input_code, input_name)
diff --git a/src/optimex/optimizer.py b/src/optimex/optimizer.py
index 11298a4..e945d47 100644
--- a/src/optimex/optimizer.py
+++ b/src/optimex/optimizer.py
@@ -61,11 +61,11 @@
 ```
 """
 
-import dill
 import pickle
 from pathlib import Path
 from typing import Any, Dict, Tuple, Union
 
+import dill
 import pyomo.environ as pyo
 from loguru import logger
 from pyomo.contrib.iis import write_iis
@@ -132,9 +132,7 @@ def create_model(
         initialize=scaled_inputs.ELEMENTARY_FLOW,
     )
     model.FLOW = pyo.Set(
-        initialize=lambda m: m.PRODUCT
-        | m.INTERMEDIATE_FLOW
-        | m.ELEMENTARY_FLOW,
+        initialize=lambda m: m.PRODUCT | m.INTERMEDIATE_FLOW | m.ELEMENTARY_FLOW,
         doc="Set of all flows, indexed by f",
     )
     model.CATEGORY = pyo.Set(
@@ -202,15 +200,15 @@ def create_model(
 
     # Store sparse vintage overrides as Python dicts (not Pyomo params)
     # These are looked up at expression construction time
-    model._technosphere_vintage_overrides = getattr(
-        scaled_inputs, 'foreground_technosphere_vintage_overrides', None
-    ) or {}
-    model._biosphere_vintage_overrides = getattr(
-        scaled_inputs, 'foreground_biosphere_vintage_overrides', None
-    ) or {}
-    model._production_vintage_overrides = getattr(
-        scaled_inputs, 'foreground_production_vintage_overrides', None
-    ) or {}
+    model._technosphere_vintage_overrides = (
+        getattr(scaled_inputs, "foreground_technosphere_vintage_overrides", None) or {}
+    )
+    model._biosphere_vintage_overrides = (
+        getattr(scaled_inputs, "foreground_biosphere_vintage_overrides", None) or {}
+    )
+    model._production_vintage_overrides = (
+        getattr(scaled_inputs, "foreground_production_vintage_overrides", None) or {}
+    )
 
     # Precompute sets of (process, flow) pairs that have overrides for O(1) lookup
     model._technosphere_overrides_index = frozenset(
@@ -422,7 +420,7 @@ def _build_active_vintage_index(m):
     model.ACTIVE_VINTAGE_TIME = pyo.Set(
         dimen=3,
         initialize=_build_active_vintage_index,
-        doc="Set of (process, vintage, time) tuples where vintage is in operation phase at time t"
+        doc="Set of (process, vintage, time) tuples where vintage is in operation phase at time t",
     )
 
     # Store category impact limit data for constraint generation
@@ -457,13 +455,15 @@ def _build_active_vintage_index(m):
     model.ProcessDeploymentLimitMax = pyo.Constraint(
         model.PROCESS,
         model.SYSTEM_TIME,
-        rule=lambda m, p, t: m.var_installation[p, t] <= m.process_deployment_limits_max[p, t],
+        rule=lambda m, p, t: m.var_installation[p, t]
+        <= m.process_deployment_limits_max[p, t],
     )
 
     model.ProcessDeploymentLimitMin = pyo.Constraint(
         model.PROCESS,
         model.SYSTEM_TIME,
-        rule=lambda m, p, t: m.var_installation[p, t] >= m.process_deployment_limits_min[p, t],
+        rule=lambda m, p, t: m.var_installation[p, t]
+        >= m.process_deployment_limits_min[p, t],
     )
 
     model.CumulativeProcessLimitMax = pyo.Constraint(
@@ -513,7 +513,9 @@ def process_operation_limit_max_rule(m, p, t):
         ]
         if not active_vintages:
             return pyo.Constraint.Skip
-        total_op = sum(m.var_operation[proc, v, time] for (proc, v, time) in active_vintages)
+        total_op = sum(
+            m.var_operation[proc, v, time] for (proc, v, time) in active_vintages
+        )
         return total_op <= m.process_operation_limits_max[p, t]
 
     model.ProcessOperationLimitMax = pyo.Constraint(
@@ -530,7 +532,9 @@ def process_operation_limit_min_rule(m, p, t):
         ]
         if not active_vintages:
             return pyo.Constraint.Skip
-        total_op = sum(m.var_operation[proc, v, time] for (proc, v, time) in active_vintages)
+        total_op = sum(
+            m.var_operation[proc, v, time] for (proc, v, time) in active_vintages
+        )
         return total_op >= m.process_operation_limits_min[p, t]
 
     model.ProcessOperationLimitMin = pyo.Constraint(
@@ -541,7 +545,9 @@ def process_operation_limit_min_rule(m, p, t):
 
     # Expression builders using sparse vintage override lookup
     # Base tensor is always 3D; overrides are checked first for vintage-specific values
-    def scale_tensor_by_installation(tensor: pyo.Param, flow_set: str, overrides: dict, overrides_index: frozenset):
+    def scale_tensor_by_installation(
+        tensor: pyo.Param, flow_set: str, overrides: dict, overrides_index: frozenset
+    ):
         def expr(m, p, x, t):
             result = 0
             for tau in m.PROCESS_TIME:
@@ -562,13 +568,16 @@ def expr(m, p, x, t):
             model.PROCESS, getattr(model, flow_set), model.SYSTEM_TIME, rule=expr
         )
 
-    def scale_tensor_by_operation(tensor: pyo.Param, flow_set: str, overrides: dict, overrides_index: frozenset):
+    def scale_tensor_by_operation(
+        tensor: pyo.Param, flow_set: str, overrides: dict, overrides_index: frozenset
+    ):
         """
         Scale tensor by operation, summing flows across active vintages.
 
         With 3D var_operation[p, v, t], we sum flow contributions from each
         vintage cohort operating at time t.
         """
+
         def expr(m, p, x, t):
             # Only apply operational scaling to flows marked as operational
             if pyo.value(m.operation_flow[p, x]) == 0:
@@ -579,7 +588,7 @@ def expr(m, p, x, t):
 
             total = 0
             # Sum flows across all active vintages at time t
-            for (proc, v, time) in m.ACTIVE_VINTAGE_TIME:
+            for proc, v, time in m.ACTIVE_VINTAGE_TIME:
                 if proc != p or time != t:
                     continue
 
@@ -608,13 +617,11 @@ def expr(m, p, x, t):
         )
 
     # Create expressions with sparse vintage override lookup
-    model.scaled_technosphere_dependent_on_installation = (
-        scale_tensor_by_installation(
-            model.foreground_technosphere,
-            "INTERMEDIATE_FLOW",
-            model._technosphere_vintage_overrides,
-            model._technosphere_overrides_index,
-        )
+    model.scaled_technosphere_dependent_on_installation = scale_tensor_by_installation(
+        model.foreground_technosphere,
+        "INTERMEDIATE_FLOW",
+        model._technosphere_vintage_overrides,
+        model._technosphere_overrides_index,
     )
     model.scaled_biosphere_dependent_on_installation = scale_tensor_by_installation(
         model.foreground_biosphere,
@@ -641,10 +648,8 @@ def expr(m, p, x, t):
             model.internal_demand_technosphere, "PRODUCT", {}, _empty_index
         )
     )
-    model.scaled_internal_demand_dependent_on_operation = (
-        scale_tensor_by_operation(
-            model.internal_demand_technosphere, "PRODUCT", {}, _empty_index
-        )
+    model.scaled_internal_demand_dependent_on_operation = scale_tensor_by_operation(
+        model.internal_demand_technosphere, "PRODUCT", {}, _empty_index
     )
 
     def scaled_inventory_tensor(model, p, e, t):
@@ -735,7 +740,7 @@ def operation_capacity_constraint_rule(model, p, v, t, r):
     # Build constraint over ACTIVE_VINTAGE_TIME × PRODUCT
     def _build_operation_capacity_constraints(m):
         """Generate constraint indices for per-vintage capacity bounds."""
-        for (p, v, t) in m.ACTIVE_VINTAGE_TIME:
+        for p, v, t in m.ACTIVE_VINTAGE_TIME:
             for r in m.PRODUCT:
                 yield (p, v, t, r)
 
@@ -754,7 +759,7 @@ def product_demand_fulfillment_rule(model, r, t):
         total_production = 0
 
         # Sum production across all active vintages at time t
-        for (p, v, time) in model.ACTIVE_VINTAGE_TIME:
+        for p, v, time in model.ACTIVE_VINTAGE_TIME:
             if time != t:
                 continue
 
@@ -825,7 +830,10 @@ def time_specific_impact_rule(model, c, t):
     # Time-specific category impact limits
     def category_impact_limits_rule(model, c, t):
         if (c, t) in model._category_impact_limits:
-            return model.time_specific_impact[c, t] <= model._category_impact_limits[(c, t)]
+            return (
+                model.time_specific_impact[c, t]
+                <= model._category_impact_limits[(c, t)]
+            )
         return pyo.Constraint.Skip
 
     model.CategoryImpactLimits = pyo.Constraint(
@@ -870,7 +878,7 @@ def total_product_flow_rule(model, r, t):
         Consistent with 3D var_operation[p, v, t].
         """
         total = 0
-        for (p, v, time) in model.ACTIVE_VINTAGE_TIME:
+        for p, v, time in model.ACTIVE_VINTAGE_TIME:
             if time != t:
                 continue
 
@@ -1110,9 +1118,7 @@ def solve_model(
         raise RuntimeError(msg)
 
     model.solutions.load_from(results)
-    logger.info(
-        f"Solver [{solver_name}] termination: {termination}"
-    )
+    logger.info(f"Solver [{solver_name}] termination: {termination}")
 
     # 4) Denormalize objective
     scaled_obj = pyo.value(model.OBJ)
@@ -1145,7 +1151,9 @@ def solve_model(
     return model, true_obj, results
 
 
-def validate_operation_bounds(model: pyo.ConcreteModel, tolerance: float = 1e-6) -> Dict[str, Any]:
+def validate_operation_bounds(
+    model: pyo.ConcreteModel, tolerance: float = 1e-6
+) -> Dict[str, Any]:
     """
     Validate that operation levels respect capacity constraints.
 
@@ -1186,7 +1194,9 @@ def validate_operation_bounds(model: pyo.ConcreteModel, tolerance: float = 1e-6)
 
     # Get sparse overrides for production with precomputed index for O(1) lookup
     production_overrides = getattr(model, "_production_vintage_overrides", {}) or {}
-    production_overrides_index = getattr(model, "_production_overrides_index", frozenset())
+    production_overrides_index = getattr(
+        model, "_production_overrides_index", frozenset()
+    )
     existing_cap_dict = getattr(model, "_existing_capacity_dict", {})
 
     def get_prod_value(p, r, tau, vintage):
@@ -1201,7 +1211,7 @@ def has_prod_overrides(p, r):
         return (p, r) in production_overrides_index
 
     # Validate per-vintage operation bounds
-    for (p, v, t) in model.ACTIVE_VINTAGE_TIME:
+    for p, v, t in model.ACTIVE_VINTAGE_TIME:
         operation_value = pyo.value(model.var_operation[p, v, t])
         op_start = pyo.value(model.process_operation_start[p])
         op_end = pyo.value(model.process_operation_end[p])
@@ -1244,7 +1254,9 @@ def has_prod_overrides(p, r):
             max_violation = max(max_violation, violation)
         elif max_capacity > 0 and operation_value > max_capacity * (1.0 + tolerance):
             violation = operation_value - max_capacity
-            violations.append((p, v, t, operation_value, max_capacity, "exceeds_capacity"))
+            violations.append(
+                (p, v, t, operation_value, max_capacity, "exceeds_capacity")
+            )
             max_violation = max(max_violation, violation)
 
     # Generate summary
diff --git a/src/optimex/postprocessing.py b/src/optimex/postprocessing.py
index 71291fb..16356d4 100644
--- a/src/optimex/postprocessing.py
+++ b/src/optimex/postprocessing.py
@@ -9,6 +9,7 @@
 Key classes:
     - PostProcessor: Extract and visualize optimization results
 """
+
 import math
 
 import bw2data as bd
@@ -218,11 +219,15 @@ def _get_colors_for_dataframe(self, df):
             for col in df.columns:
                 # Use the first element of the tuple for color lookup
                 key = col[0] if isinstance(col, tuple) else col
-                colors.append(self._color_map.get(key, self._plot_config["colormap"][0]))
+                colors.append(
+                    self._color_map.get(key, self._plot_config["colormap"][0])
+                )
         else:
             # Single-level columns
             for col in df.columns:
-                colors.append(self._color_map.get(col, self._plot_config["colormap"][0]))
+                colors.append(
+                    self._color_map.get(col, self._plot_config["colormap"][0])
+                )
 
         return colors
 
@@ -268,9 +273,7 @@ def _create_clean_axes(self, nrows=1, ncols=1, figsize=None):
         Returns fig, flattened list of axes.
         """
         fig_size = figsize or self._plot_config["figsize"]
-        fig, axes = plt.subplots(
-            nrows=nrows, ncols=ncols, figsize=fig_size
-        )
+        fig, axes = plt.subplots(nrows=nrows, ncols=ncols, figsize=fig_size)
         axes = axes.flatten() if isinstance(axes, (np.ndarray, list)) else [axes]
 
         for ax in axes:
@@ -413,7 +416,7 @@ def get_impacts(self) -> pd.DataFrame:
         """
         if hasattr(self, "df_impacts"):
             return self.df_impacts
-        
+
         impacts = {}
         cat_scales = getattr(self.m, "scales", {}).get("characterization", 1.0)
         fg_scale = getattr(self.m, "scales", {}).get("foreground", 1.0)
@@ -436,7 +439,9 @@ def get_impacts(self) -> pd.DataFrame:
         self.df_impacts = df_pivot
         return self.df_impacts
 
-    def get_dynamic_inventory(self, biosphere_database: str = "ecoinvent-3.12-biosphere") -> pd.DataFrame:
+    def get_dynamic_inventory(
+        self, biosphere_database: str = "ecoinvent-3.12-biosphere"
+    ) -> pd.DataFrame:
         """
         Extract the dynamic inventory from the solved model.
 
@@ -467,21 +472,15 @@ def get_dynamic_inventory(self, biosphere_database: str = "ecoinvent-3.12-biosph
 
         df = pd.DataFrame.from_records(
             [(p, e, t, v) for (p, e, t), v in inventory.items()],
-            columns=["activity", "flow", "date", "amount"]
-        ).astype({
-            "activity": "str",
-            "flow": "str",
-            "amount": "float64"
-        })
+            columns=["activity", "flow", "date", "amount"],
+        ).astype({"activity": "str", "flow": "str", "amount": "float64"})
 
         # Convert year integers to datetime
         df["date"] = pd.to_datetime(df["date"].astype(int), format="%Y")
 
         # Convert flow codes to database IDs
         biosphere_db = bd.Database(biosphere_database)
-        df["flow"] = df["flow"].apply(
-            lambda x: biosphere_db.get(code=x).id
-        )
+        df["flow"] = df["flow"].apply(lambda x: biosphere_db.get(code=x).id)
 
         self.df_dynamic_inventory = df
         return self.df_dynamic_inventory
@@ -521,7 +520,9 @@ def get_characterized_dynamic_inventory(
         from dynamic_characterization import characterize
 
         if df_inventory is None:
-            df_inventory = self.get_dynamic_inventory(biosphere_database=biosphere_database)
+            df_inventory = self.get_dynamic_inventory(
+                biosphere_database=biosphere_database
+            )
 
         df_characterized = characterize(
             df_inventory,
@@ -565,7 +566,9 @@ def plot_characterized_dynamic_inventory(
         """
         if df_characterized is None:
             if base_lcia_method is None:
-                raise ValueError("base_lcia_method is required when df_characterized is not provided")
+                raise ValueError(
+                    "base_lcia_method is required when df_characterized is not provided"
+                )
             df_characterized = self.get_characterized_dynamic_inventory(
                 base_lcia_method=base_lcia_method,
                 metric=metric,
@@ -580,8 +583,11 @@ def plot_characterized_dynamic_inventory(
 
         # Round to nearest year and aggregate
         df_grouped = (
-            df_plot
-            .assign(date_rounded=(df_plot["date"] + pd.offsets.MonthBegin(6)).dt.to_period("Y").dt.to_timestamp())
+            df_plot.assign(
+                date_rounded=(df_plot["date"] + pd.offsets.MonthBegin(6))
+                .dt.to_period("Y")
+                .dt.to_timestamp()
+            )
             .groupby("date_rounded")["amount"]
             .sum()
             .reset_index()
@@ -600,8 +606,14 @@ def plot_characterized_dynamic_inventory(
             label=metric.replace("_", " ").title(),
         )
 
-        ax.set_ylabel(f"{metric.replace('_', ' ').title()}", fontsize=self._plot_config["label_fontsize"])
-        ax.set_title(f"Dynamic {metric.replace('_', ' ').title()}", fontsize=self._plot_config["title_fontsize"])
+        ax.set_ylabel(
+            f"{metric.replace('_', ' ').title()}",
+            fontsize=self._plot_config["label_fontsize"],
+        )
+        ax.set_title(
+            f"Dynamic {metric.replace('_', ' ').title()}",
+            fontsize=self._plot_config["title_fontsize"],
+        )
         ax.set_axisbelow(True)
         ax.grid(
             axis="both",
@@ -671,7 +683,9 @@ def get_operation(self, aggregate_vintages: bool = True) -> pd.DataFrame:
                         if proc == p and time == t
                     )
                     operation_matrix[(t, p)] = total_op
-            df = pd.DataFrame.from_dict(operation_matrix, orient="index", columns=["Value"])
+            df = pd.DataFrame.from_dict(
+                operation_matrix, orient="index", columns=["Value"]
+            )
             df.index = pd.MultiIndex.from_tuples(df.index, names=["Time", "Process"])
             df = df.reset_index()
             df_pivot = df.pivot(index="Time", columns="Process", values="Value")
@@ -683,10 +697,16 @@ def get_operation(self, aggregate_vintages: bool = True) -> pd.DataFrame:
                 (t, p, v): pyo.value(self.m.var_operation[p, v, t])
                 for (p, v, t) in self.m.ACTIVE_VINTAGE_TIME
             }
-            df = pd.DataFrame.from_dict(operation_matrix, orient="index", columns=["Value"])
-            df.index = pd.MultiIndex.from_tuples(df.index, names=["Time", "Process", "Vintage"])
+            df = pd.DataFrame.from_dict(
+                operation_matrix, orient="index", columns=["Value"]
+            )
+            df.index = pd.MultiIndex.from_tuples(
+                df.index, names=["Time", "Process", "Vintage"]
+            )
             df = df.reset_index()
-            df_pivot = df.pivot(index="Time", columns=["Process", "Vintage"], values="Value")
+            df_pivot = df.pivot(
+                index="Time", columns=["Process", "Vintage"], values="Value"
+            )
             return df_pivot
 
     def get_production(self) -> pd.DataFrame:
@@ -704,7 +724,9 @@ def get_production(self) -> pd.DataFrame:
 
         # Get production overrides data from model (if exists)
         production_overrides = getattr(self.m, "_production_vintage_overrides", {})
-        production_overrides_index = getattr(self.m, "_production_overrides_index", frozenset())
+        production_overrides_index = getattr(
+            self.m, "_production_overrides_index", frozenset()
+        )
 
         def get_production_value(p, r, tau, vintage):
             """Get production value, checking sparse overrides first."""
@@ -725,7 +747,7 @@ def has_production_overrides(p, r):
                 for t in self.m.SYSTEM_TIME:
                     # Sum production across all active vintages at time t
                     total_production = 0
-                    for (proc, v, time) in self.m.ACTIVE_VINTAGE_TIME:
+                    for proc, v, time in self.m.ACTIVE_VINTAGE_TIME:
                         if proc != p or time != t:
                             continue
 
@@ -744,7 +766,9 @@ def has_production_overrides(p, r):
                             )
 
                         # Production from this vintage
-                        total_production += production_rate * pyo.value(self.m.var_operation[p, v, t])
+                        total_production += production_rate * pyo.value(
+                            self.m.var_operation[p, v, t]
+                        )
 
                     production_tensor[(p, f, t)] = total_production * fg_scale
 
@@ -774,9 +798,7 @@ def get_demand(self) -> pd.DataFrame:
             for t in self.m.SYSTEM_TIME
         }
         df = pd.DataFrame.from_dict(demand_matrix, orient="index", columns=["Value"])
-        df.index = pd.MultiIndex.from_tuples(
-            df.index, names=["Product", "Time"]
-        )
+        df.index = pd.MultiIndex.from_tuples(df.index, names=["Product", "Time"])
         df = df.reset_index()
         df_pivot = df.pivot(index="Time", columns="Product", values="Value")
         self.df_demand = df_pivot
@@ -815,7 +837,9 @@ def plot_impacts(self, df_impacts=None, annotated=True):
         fig_w = base_w * ncols
         fig_h = base_h * nrows
 
-        fig, axes = self._create_clean_axes(nrows=nrows, ncols=ncols, figsize=(fig_w, fig_h))
+        fig, axes = self._create_clean_axes(
+            nrows=nrows, ncols=ncols, figsize=(fig_w, fig_h)
+        )
 
         all_handles = []
         all_labels = []
@@ -845,7 +869,9 @@ def plot_impacts(self, df_impacts=None, annotated=True):
 
         # Shared legend to the right of figure
         if all_handles:
-            all_handles, all_labels = self._reorder_legend_row_first(all_handles, all_labels, 2)
+            all_handles, all_labels = self._reorder_legend_row_first(
+                all_handles, all_labels, 2
+            )
             fig.legend(
                 handles=all_handles,
                 labels=all_labels,
@@ -884,11 +910,10 @@ def plot_installation(self, df_installation=None, annotated=True):
 
         fig, axes = self._create_clean_axes()
         ax = axes[0]
-        self._apply_bar_styles(
-            df_installation, ax, colors, title="Installed Capacity"
+        self._apply_bar_styles(df_installation, ax, colors, title="Installed Capacity")
+        ax.set_ylabel(
+            "Installed Capacity", fontsize=self._plot_config["label_fontsize"]
         )
-        ax.set_ylabel("Installed Capacity", fontsize=self._plot_config["label_fontsize"])
-
 
         # Legend at bottom
         h, l = ax.get_legend_handles_labels()
@@ -932,12 +957,9 @@ def plot_operation(self, df_operation=None, annotated=True):
 
         fig, axes = self._create_clean_axes()
         ax = axes[0]
-        self._apply_bar_styles(
-            df_operation, ax, colors, title="Operational Level"
-        )
+        self._apply_bar_styles(df_operation, ax, colors, title="Operational Level")
         ax.set_ylabel("Operation Level", fontsize=self._plot_config["label_fontsize"])
 
-
         # Legend at bottom
         h, l = ax.get_legend_handles_labels()
         if h:
@@ -1000,7 +1022,9 @@ def get_existing_capacity(self) -> pd.DataFrame:
             return pd.DataFrame()
 
         df = pd.DataFrame.from_dict(data, orient="index", columns=["Value"])
-        df.index = pd.MultiIndex.from_tuples(df.index, names=["Time", "Process", "Type"])
+        df.index = pd.MultiIndex.from_tuples(
+            df.index, names=["Time", "Process", "Type"]
+        )
         df = df.reset_index()
         df_pivot = df.pivot(index="Time", columns=["Process", "Type"], values="Value")
 
@@ -1029,7 +1053,9 @@ def get_production_capacity(self) -> pd.DataFrame:
 
         # Get production overrides data from model (if exists)
         production_overrides = getattr(self.m, "_production_vintage_overrides", {})
-        production_overrides_index = getattr(self.m, "_production_overrides_index", frozenset())
+        production_overrides_index = getattr(
+            self.m, "_production_overrides_index", frozenset()
+        )
 
         def get_production_value(p, r, tau, vintage):
             """Get production value, checking sparse overrides first."""
@@ -1059,14 +1085,19 @@ def has_production_overrides(p, r):
                         # New installations: sum capacity by vintage
                         for tau in self.m.PROCESS_TIME:
                             vintage = t - tau
-                            if vintage in self.m.SYSTEM_TIME and op_start <= tau <= op_end:
+                            if (
+                                vintage in self.m.SYSTEM_TIME
+                                and op_start <= tau <= op_end
+                            ):
                                 # Production rate for this vintage (sum over all operating taus)
                                 production_per_unit = sum(
                                     get_production_value(p, f, tau_op, vintage)
                                     for tau_op in self.m.PROCESS_TIME
                                     if op_start <= tau_op <= op_end
                                 )
-                                installation = pyo.value(self.m.var_installation[p, vintage])
+                                installation = pyo.value(
+                                    self.m.var_installation[p, vintage]
+                                )
                                 process_capacity += production_per_unit * installation
 
                         # Existing (brownfield) capacity
@@ -1076,7 +1107,9 @@ def has_production_overrides(p, r):
                                 if op_start <= tau_existing <= op_end:
                                     nearest_vintage = min(self.m.SYSTEM_TIME)
                                     production_per_unit = sum(
-                                        get_production_value(p, f, tau_op, nearest_vintage)
+                                        get_production_value(
+                                            p, f, tau_op, nearest_vintage
+                                        )
                                         for tau_op in self.m.PROCESS_TIME
                                         if op_start <= tau_op <= op_end
                                     )
@@ -1108,7 +1141,9 @@ def has_production_overrides(p, r):
                         )
 
                         # Total capacity for this process
-                        total_capacity += installations_operating * production_per_installation
+                        total_capacity += (
+                            installations_operating * production_per_installation
+                        )
 
                 # Store denormalized capacity
                 capacity_tensor[(f, t)] = total_capacity * fg_scale
@@ -1144,10 +1179,18 @@ def _extract_product_data(self, product, prod_df, capacity_df):
             production_cols = [col for col in prod_df.columns if col[1] == product]
             actual_production = prod_df[production_cols].sum(axis=1)
         else:
-            actual_production = prod_df[product] if product in prod_df.columns else pd.Series(0, index=prod_df.index)
+            actual_production = (
+                prod_df[product]
+                if product in prod_df.columns
+                else pd.Series(0, index=prod_df.index)
+            )
 
         # Capacity for this product
-        max_capacity = capacity_df[product] if product in capacity_df.columns else pd.Series(0, index=capacity_df.index)
+        max_capacity = (
+            capacity_df[product]
+            if product in capacity_df.columns
+            else pd.Series(0, index=capacity_df.index)
+        )
 
         # Convert indices to strings for consistent plotting
         actual_production = actual_production.copy()
@@ -1191,7 +1234,9 @@ def _plot_capacity_balance_on_ax(
         show_title : bool, default=True
             If True, show title with product name.
         """
-        actual_production, max_capacity = self._extract_product_data(product, prod_df, capacity_df)
+        actual_production, max_capacity = self._extract_product_data(
+            product, prod_df, capacity_df
+        )
         product_name = self._get_name(product) if annotated else product
 
         x_positions = np.arange(len(actual_production.index))
@@ -1200,24 +1245,24 @@ def _plot_capacity_balance_on_ax(
         ax.plot(
             x_positions,
             actual_production.values,
-            marker='o',
+            marker="o",
             linewidth=self._plot_config["line_width"],
-            label='Production / Demand',
-            color='#00549F',
-            linestyle='-',
-            zorder=3
+            label="Production / Demand",
+            color="#00549F",
+            linestyle="-",
+            zorder=3,
         )
 
         # Plot capacity line
         ax.plot(
             x_positions,
             max_capacity.values,
-            marker='s',
+            marker="s",
             linewidth=self._plot_config["line_width"],
-            label='Max Capacity',
-            color='#000000',
-            linestyle='--',
-            zorder=3
+            label="Max Capacity",
+            color="#000000",
+            linestyle="--",
+            zorder=3,
         )
 
         # Fill area between production and capacity
@@ -1227,9 +1272,9 @@ def _plot_capacity_balance_on_ax(
                 actual_production.values,
                 max_capacity.values,
                 alpha=0.15,
-                color='#00549F',
-                label='Unused Capacity',
-                zorder=2
+                color="#00549F",
+                label="Unused Capacity",
+                zorder=2,
             )
 
         # Set labels and title
@@ -1244,9 +1289,7 @@ def _plot_capacity_balance_on_ax(
 
         if show_title:
             ax.set_title(
-                f"{product_name}",
-                fontsize=self._plot_config["title_fontsize"],
-                pad=10
+                f"{product_name}", fontsize=self._plot_config["title_fontsize"], pad=10
             )
 
         if show_legend:
@@ -1275,7 +1318,9 @@ def _compute_capacity_breakdown(self, product):
 
         # Get production overrides data from model (if exists)
         production_overrides = getattr(self.m, "_production_vintage_overrides", {})
-        production_overrides_index = getattr(self.m, "_production_overrides_index", frozenset())
+        production_overrides_index = getattr(
+            self.m, "_production_overrides_index", frozenset()
+        )
 
         def get_production_value(p, r, tau, vintage):
             """Get production value, checking sparse overrides first."""
@@ -1320,26 +1365,34 @@ def has_production_overrides(p, r):
                     # New capacity entering operation (vintage = t - op_start)
                     t_entering = t - op_start
                     if t_entering in self.m.SYSTEM_TIME:
-                        installation_entering = pyo.value(self.m.var_installation[p, t_entering])
+                        installation_entering = pyo.value(
+                            self.m.var_installation[p, t_entering]
+                        )
                         prod_per_inst_vintage = sum(
                             get_production_value(p, product, tau_op, t_entering)
                             for tau_op in self.m.PROCESS_TIME
                             if op_start <= tau_op <= op_end
                         )
-                        capacity_additions[p][t] = installation_entering * prod_per_inst_vintage * fg_scale
+                        capacity_additions[p][t] = (
+                            installation_entering * prod_per_inst_vintage * fg_scale
+                        )
                     else:
                         capacity_additions[p][t] = 0
 
                     # Capacity exiting operation (vintage = t - op_end - 1)
                     t_exiting = t - op_end - 1
                     if t_exiting in self.m.SYSTEM_TIME:
-                        installation_exiting = pyo.value(self.m.var_installation[p, t_exiting])
+                        installation_exiting = pyo.value(
+                            self.m.var_installation[p, t_exiting]
+                        )
                         prod_per_inst_vintage = sum(
                             get_production_value(p, product, tau_op, t_exiting)
                             for tau_op in self.m.PROCESS_TIME
                             if op_start <= tau_op <= op_end
                         )
-                        capacity_removals[p][t] = installation_exiting * prod_per_inst_vintage * fg_scale
+                        capacity_removals[p][t] = (
+                            installation_exiting * prod_per_inst_vintage * fg_scale
+                        )
                     else:
                         capacity_removals[p][t] = 0
 
@@ -1358,16 +1411,20 @@ def has_production_overrides(p, r):
                             tau_existing_prev = (t - 1) - inst_year
                             if op_start <= tau_existing <= op_end:
                                 if tau_existing_prev < op_start:
-                                    existing_add += capacity * prod_per_inst_existing * fg_scale
+                                    existing_add += (
+                                        capacity * prod_per_inst_existing * fg_scale
+                                    )
                             if tau_existing > op_end:
                                 if op_start <= tau_existing_prev <= op_end:
-                                    existing_rem += capacity * prod_per_inst_existing * fg_scale
+                                    existing_rem += (
+                                        capacity * prod_per_inst_existing * fg_scale
+                                    )
                     existing_additions[p][t] = existing_add
                     existing_removals[p][t] = existing_rem
 
                     # Operation level - sum production across all active vintages
                     total_operation = 0
-                    for (proc, v, time) in self.m.ACTIVE_VINTAGE_TIME:
+                    for proc, v, time in self.m.ACTIVE_VINTAGE_TIME:
                         if proc != p or time != t:
                             continue
                         # Get production rate for this vintage
@@ -1376,7 +1433,9 @@ def has_production_overrides(p, r):
                             for tau_op in self.m.PROCESS_TIME
                             if op_start <= tau_op <= op_end
                         )
-                        total_operation += production_rate * pyo.value(self.m.var_operation[p, v, t])
+                        total_operation += production_rate * pyo.value(
+                            self.m.var_operation[p, v, t]
+                        )
 
                     operation[p][t] = total_operation * fg_scale
                 else:
@@ -1386,16 +1445,24 @@ def has_production_overrides(p, r):
                     # New capacity entering operation
                     t_entering = t - op_start
                     if t_entering in self.m.SYSTEM_TIME:
-                        installation_entering = pyo.value(self.m.var_installation[p, t_entering])
-                        capacity_additions[p][t] = installation_entering * prod_per_inst * fg_scale
+                        installation_entering = pyo.value(
+                            self.m.var_installation[p, t_entering]
+                        )
+                        capacity_additions[p][t] = (
+                            installation_entering * prod_per_inst * fg_scale
+                        )
                     else:
                         capacity_additions[p][t] = 0
 
                     # Capacity exiting operation
                     t_exiting = t - op_end - 1
                     if t_exiting in self.m.SYSTEM_TIME:
-                        installation_exiting = pyo.value(self.m.var_installation[p, t_exiting])
-                        capacity_removals[p][t] = installation_exiting * prod_per_inst * fg_scale
+                        installation_exiting = pyo.value(
+                            self.m.var_installation[p, t_exiting]
+                        )
+                        capacity_removals[p][t] = (
+                            installation_exiting * prod_per_inst * fg_scale
+                        )
                     else:
                         capacity_removals[p][t] = 0
 
@@ -1417,10 +1484,12 @@ def has_production_overrides(p, r):
 
                     # Operation level - sum production across all active vintages
                     total_operation = 0
-                    for (proc, v, time) in self.m.ACTIVE_VINTAGE_TIME:
+                    for proc, v, time in self.m.ACTIVE_VINTAGE_TIME:
                         if proc != p or time != t:
                             continue
-                        total_operation += prod_per_inst * pyo.value(self.m.var_operation[p, v, t])
+                        total_operation += prod_per_inst * pyo.value(
+                            self.m.var_operation[p, v, t]
+                        )
 
                     operation[p][t] = total_operation * fg_scale
 
@@ -1441,11 +1510,13 @@ def has_production_overrides(p, r):
         operation_df.index = operation_df.index.astype(str)
 
         # Filter to only processes with non-zero values
-        has_values = ((capacity_additions_df != 0).any(axis=0) |
-                     (capacity_removals_df != 0).any(axis=0) |
-                     (existing_additions_df != 0).any(axis=0) |
-                     (existing_removals_df != 0).any(axis=0) |
-                     (operation_df != 0).any(axis=0))
+        has_values = (
+            (capacity_additions_df != 0).any(axis=0)
+            | (capacity_removals_df != 0).any(axis=0)
+            | (existing_additions_df != 0).any(axis=0)
+            | (existing_removals_df != 0).any(axis=0)
+            | (operation_df != 0).any(axis=0)
+        )
         capacity_additions_df = capacity_additions_df.loc[:, has_values]
         capacity_removals_df = capacity_removals_df.loc[:, has_values]
         existing_additions_df = existing_additions_df.loc[:, has_values]
@@ -1499,7 +1570,9 @@ def _plot_capacity_balance_detailed_on_ax(
         from matplotlib.lines import Line2D
         from matplotlib.patches import Patch
 
-        actual_production, max_capacity = self._extract_product_data(product, prod_df, capacity_df)
+        actual_production, max_capacity = self._extract_product_data(
+            product, prod_df, capacity_df
+        )
         product_name = self._get_name(product) if annotated else product
 
         # Compute breakdown data
@@ -1514,10 +1587,18 @@ def _plot_capacity_balance_detailed_on_ax(
         process_codes = list(capacity_additions_df.columns)
 
         # Annotate DataFrames
-        capacity_additions_df = self._annotate_dataframe(capacity_additions_df.copy(), annotated)
-        capacity_removals_df = self._annotate_dataframe(capacity_removals_df.copy(), annotated)
-        existing_additions_df = self._annotate_dataframe(existing_additions_df.copy(), annotated)
-        existing_removals_df = self._annotate_dataframe(existing_removals_df.copy(), annotated)
+        capacity_additions_df = self._annotate_dataframe(
+            capacity_additions_df.copy(), annotated
+        )
+        capacity_removals_df = self._annotate_dataframe(
+            capacity_removals_df.copy(), annotated
+        )
+        existing_additions_df = self._annotate_dataframe(
+            existing_additions_df.copy(), annotated
+        )
+        existing_removals_df = self._annotate_dataframe(
+            existing_removals_df.copy(), annotated
+        )
         operation_df = self._annotate_dataframe(operation_df.copy(), annotated)
 
         x_positions = np.arange(len(actual_production.index))
@@ -1535,54 +1616,119 @@ def _plot_capacity_balance_detailed_on_ax(
             bottom_additions = np.zeros(len(x_positions))
             for i, col in enumerate(capacity_additions_df.columns):
                 add_values = capacity_additions_df[col].values
-                clr = self._color_map.get(process_codes[i], 'black')
-                ax.bar(cap_positions, add_values, width=bar_width, bottom=bottom_additions,
-                       color=clr, hatch="///", edgecolor="#30A834FF", linewidth=1.5, zorder=1)
+                clr = self._color_map.get(process_codes[i], "black")
+                ax.bar(
+                    cap_positions,
+                    add_values,
+                    width=bar_width,
+                    bottom=bottom_additions,
+                    color=clr,
+                    hatch="///",
+                    edgecolor="#30A834FF",
+                    linewidth=1.5,
+                    zorder=1,
+                )
                 bottom_additions += add_values
 
             # Plot capacity removals (negative, red border)
             bottom_removals = np.zeros(len(x_positions))
             for i, col in enumerate(capacity_removals_df.columns):
                 rem_values = capacity_removals_df[col].values
-                clr = self._color_map.get(process_codes[i], 'black')
-                ax.bar(cap_positions, -rem_values, width=bar_width, bottom=bottom_removals,
-                       color=clr, hatch="///", edgecolor="#CD221FFF", linewidth=1.5, zorder=1)
+                clr = self._color_map.get(process_codes[i], "black")
+                ax.bar(
+                    cap_positions,
+                    -rem_values,
+                    width=bar_width,
+                    bottom=bottom_removals,
+                    color=clr,
+                    hatch="///",
+                    edgecolor="#CD221FFF",
+                    linewidth=1.5,
+                    zorder=1,
+                )
                 bottom_removals -= rem_values
 
             # Plot operation (solid bars)
             bottom_operation = np.zeros(len(x_positions))
             for i, col in enumerate(operation_df.columns):
                 operation_values = operation_df[col].values
-                clr = self._color_map.get(process_codes[i], 'black')
-                ax.bar(op_positions, operation_values, width=bar_width, bottom=bottom_operation,
-                       alpha=0.9, color=clr, edgecolor=self._plot_config["bar_edgecolor"],
-                       linewidth=self._plot_config["bar_linewidth"], zorder=2)
+                clr = self._color_map.get(process_codes[i], "black")
+                ax.bar(
+                    op_positions,
+                    operation_values,
+                    width=bar_width,
+                    bottom=bottom_operation,
+                    alpha=0.9,
+                    color=clr,
+                    edgecolor=self._plot_config["bar_edgecolor"],
+                    linewidth=self._plot_config["bar_linewidth"],
+                    zorder=2,
+                )
                 bottom_operation += operation_values
 
-            ax.axhline(0, color='gray', linewidth=0.5, zorder=0)
+            ax.axhline(0, color="gray", linewidth=0.5, zorder=0)
 
             # Build legend handles
-            process_legend = [Patch(facecolor=self._color_map.get(process_codes[i], 'black'), edgecolor='black', linewidth=0.5, label=col)
-                            for i, col in enumerate(capacity_additions_df.columns)]
+            process_legend = [
+                Patch(
+                    facecolor=self._color_map.get(process_codes[i], "black"),
+                    edgecolor="black",
+                    linewidth=0.5,
+                    label=col,
+                )
+                for i, col in enumerate(capacity_additions_df.columns)
+            ]
             type_legend = [
-                Patch(facecolor="white", edgecolor='#30A834', linewidth=2, label='+ Cap'),
-                Patch(facecolor="white", edgecolor='#CD221F', linewidth=2, label='− Cap'),
+                Patch(
+                    facecolor="white", edgecolor="#30A834", linewidth=2, label="+ Cap"
+                ),
+                Patch(
+                    facecolor="white", edgecolor="#CD221F", linewidth=2, label="− Cap"
+                ),
             ]
 
         # Plot production and capacity lines
-        ax.plot(x_positions, actual_production.values, marker='o',
-                linewidth=self._plot_config["line_width"], label='Production / Demand',
-                color='#00549F', linestyle='-', zorder=3)
-        ax.plot(x_positions, max_capacity.values, marker='s',
-                linewidth=self._plot_config["line_width"], label='Max Capacity',
-                color='#000000', linestyle='--', zorder=3)
+        ax.plot(
+            x_positions,
+            actual_production.values,
+            marker="o",
+            linewidth=self._plot_config["line_width"],
+            label="Production / Demand",
+            color="#00549F",
+            linestyle="-",
+            zorder=3,
+        )
+        ax.plot(
+            x_positions,
+            max_capacity.values,
+            marker="s",
+            linewidth=self._plot_config["line_width"],
+            label="Max Capacity",
+            color="#000000",
+            linestyle="--",
+            zorder=3,
+        )
 
         # Line legend entries
         line_legend = [
-            Line2D([0], [0], color='#00549F', marker='o', linestyle='-',
-                   linewidth=self._plot_config["line_width"], label='Production / Demand'),
-            Line2D([0], [0], color='#000000', marker='s', linestyle='--',
-                   linewidth=self._plot_config["line_width"], label='Max Capacity'),
+            Line2D(
+                [0],
+                [0],
+                color="#00549F",
+                marker="o",
+                linestyle="-",
+                linewidth=self._plot_config["line_width"],
+                label="Production / Demand",
+            ),
+            Line2D(
+                [0],
+                [0],
+                color="#000000",
+                marker="s",
+                linestyle="--",
+                linewidth=self._plot_config["line_width"],
+                label="Max Capacity",
+            ),
         ]
 
         self._set_smart_xticks(ax, actual_production.index)
@@ -1595,7 +1741,9 @@ def _plot_capacity_balance_detailed_on_ax(
         )
 
         if show_title:
-            ax.set_title(f"{product_name}", fontsize=self._plot_config["title_fontsize"], pad=10)
+            ax.set_title(
+                f"{product_name}", fontsize=self._plot_config["title_fontsize"], pad=10
+            )
 
         if show_legend and process_legend:
             all_handles = process_legend + type_legend + line_legend
@@ -1611,7 +1759,15 @@ def _plot_capacity_balance_detailed_on_ax(
 
         return process_legend, type_legend, line_legend
 
-    def plot_capacity_balance(self, product=None, prod_df=None, capacity_df=None, demand_df=None, annotated=True, detailed=False):
+    def plot_capacity_balance(
+        self,
+        product=None,
+        prod_df=None,
+        capacity_df=None,
+        demand_df=None,
+        annotated=True,
+        detailed=False,
+    ):
         """
         Plot actual production vs maximum available capacity.
 
@@ -1658,18 +1814,28 @@ def plot_capacity_balance(self, product=None, prod_df=None, capacity_df=None, de
 
             if detailed:
                 self._plot_capacity_balance_detailed_on_ax(
-                    ax, product, prod_df, capacity_df,
-                    annotated=annotated, show_legend=True, show_title=True,
+                    ax,
+                    product,
+                    prod_df,
+                    capacity_df,
+                    annotated=annotated,
+                    show_legend=True,
+                    show_title=True,
                     legend_position="bottom",
                 )
             else:
                 self._plot_capacity_balance_on_ax(
-                    ax, product, prod_df, capacity_df,
-                    annotated=annotated, show_legend=True, show_fill=True, show_title=True,
+                    ax,
+                    product,
+                    prod_df,
+                    capacity_df,
+                    annotated=annotated,
+                    show_legend=True,
+                    show_fill=True,
+                    show_title=True,
                     legend_position="bottom",
                 )
 
-    
             fig.tight_layout()
             plt.show()
             return
@@ -1704,9 +1870,7 @@ def plot_capacity_balance(self, product=None, prod_df=None, capacity_df=None, de
         fig_w = base_w * ncols
         fig_h = base_h * nrows
 
-        fig, axes = plt.subplots(
-            nrows=nrows, ncols=ncols, figsize=(fig_w, fig_h)
-        )
+        fig, axes = plt.subplots(nrows=nrows, ncols=ncols, figsize=(fig_w, fig_h))
 
         if n_products == 1:
             axes = np.array([axes])
@@ -1730,9 +1894,16 @@ def plot_capacity_balance(self, product=None, prod_df=None, capacity_df=None, de
         for i, p in enumerate(products):
             ax = axes[i]
             if detailed:
-                proc_legend, type_legend, line_legend = self._plot_capacity_balance_detailed_on_ax(
-                    ax, p, prod_df, capacity_df,
-                    annotated=annotated, show_legend=False, show_title=True
+                proc_legend, type_legend, line_legend = (
+                    self._plot_capacity_balance_detailed_on_ax(
+                        ax,
+                        p,
+                        prod_df,
+                        capacity_df,
+                        annotated=annotated,
+                        show_legend=False,
+                        show_title=True,
+                    )
                 )
                 # Collect process handles from all subplots (deduplicate by label)
                 for handle in proc_legend:
@@ -1745,8 +1916,14 @@ def plot_capacity_balance(self, product=None, prod_df=None, capacity_df=None, de
                     line_handles = line_legend
             else:
                 self._plot_capacity_balance_on_ax(
-                    ax, p, prod_df, capacity_df,
-                    annotated=annotated, show_legend=False, show_fill=True, show_title=True
+                    ax,
+                    p,
+                    prod_df,
+                    capacity_df,
+                    annotated=annotated,
+                    show_legend=False,
+                    show_fill=True,
+                    show_title=True,
                 )
                 # Collect handles from all subplots (deduplicate by label)
                 h, l = ax.get_legend_handles_labels()
@@ -1765,7 +1942,9 @@ def plot_capacity_balance(self, product=None, prod_df=None, capacity_df=None, de
 
         # Shared legend at bottom of figure
         if all_handles:
-            all_handles, all_labels = self._reorder_legend_row_first(all_handles, all_labels, 2)
+            all_handles, all_labels = self._reorder_legend_row_first(
+                all_handles, all_labels, 2
+            )
             fig.legend(
                 handles=all_handles,
                 labels=all_labels,
@@ -1811,7 +1990,9 @@ def plot_utilization_heatmap(self, product=None, annotated=True, show_values=Tru
 
         # Get production overrides data from model (if exists)
         production_overrides = getattr(self.m, "_production_vintage_overrides", {})
-        production_overrides_index = getattr(self.m, "_production_overrides_index", frozenset())
+        production_overrides_index = getattr(
+            self.m, "_production_overrides_index", frozenset()
+        )
 
         def get_production_value(p, r, tau, vintage):
             """Get production value, checking sparse overrides first."""
@@ -1860,7 +2041,9 @@ def has_production_overrides(p, r):
                                 for tau_op in self.m.PROCESS_TIME
                                 if op_start <= tau_op <= op_end
                             )
-                            installation = pyo.value(self.m.var_installation[p, vintage])
+                            installation = pyo.value(
+                                self.m.var_installation[p, vintage]
+                            )
                             capacity += production_per_unit * installation
 
                     # Add existing (brownfield) capacity
@@ -1880,7 +2063,7 @@ def has_production_overrides(p, r):
 
                     # Operation - sum production across all active vintages
                     operation = 0
-                    for (proc, v, time) in self.m.ACTIVE_VINTAGE_TIME:
+                    for proc, v, time in self.m.ACTIVE_VINTAGE_TIME:
                         if proc != p or time != t:
                             continue
                         # Get production rate for this vintage
@@ -1889,7 +2072,9 @@ def has_production_overrides(p, r):
                             for tau_op in self.m.PROCESS_TIME
                             if op_start <= tau_op <= op_end
                         )
-                        operation += production_rate * pyo.value(self.m.var_operation[p, v, t])
+                        operation += production_rate * pyo.value(
+                            self.m.var_operation[p, v, t]
+                        )
                     operation *= fg_scale
                 else:
                     # 3D calculation: no overrides
@@ -1899,8 +2084,7 @@ def has_production_overrides(p, r):
                     installations_operating = sum(
                         pyo.value(self.m.var_installation[p, t - tau])
                         for tau in self.m.PROCESS_TIME
-                        if (t - tau in self.m.SYSTEM_TIME)
-                        and op_start <= tau <= op_end
+                        if (t - tau in self.m.SYSTEM_TIME) and op_start <= tau <= op_end
                     )
 
                     # Add existing (brownfield) capacity in operation phase
@@ -1914,10 +2098,12 @@ def has_production_overrides(p, r):
 
                     # Calculate operation - sum across all active vintages
                     operation = 0
-                    for (proc, v, time) in self.m.ACTIVE_VINTAGE_TIME:
+                    for proc, v, time in self.m.ACTIVE_VINTAGE_TIME:
                         if proc != p or time != t:
                             continue
-                        operation += prod_per_inst * pyo.value(self.m.var_operation[p, v, t])
+                        operation += prod_per_inst * pyo.value(
+                            self.m.var_operation[p, v, t]
+                        )
                     operation *= fg_scale
 
                 capacity_data[p][t] = capacity
@@ -1927,7 +2113,9 @@ def has_production_overrides(p, r):
                 if capacity > 0.001:
                     utilization_data[p][t] = (operation / capacity) * 100
                 else:
-                    utilization_data[p][t] = np.nan  # No capacity = no utilization possible
+                    utilization_data[p][
+                        t
+                    ] = np.nan  # No capacity = no utilization possible
 
         if not utilization_data:
             raise ValueError(f"No processes produce {product}")
@@ -1938,7 +2126,7 @@ def has_production_overrides(p, r):
         op_df = pd.DataFrame(operation_data).T
 
         # Filter to only times with some capacity
-        has_capacity = (cap_df.sum(axis=0) > 0.001)
+        has_capacity = cap_df.sum(axis=0) > 0.001
         util_df = util_df.loc[:, has_capacity]
         cap_df = cap_df.loc[:, has_capacity]
         op_df = op_df.loc[:, has_capacity]
@@ -1955,12 +2143,14 @@ def has_production_overrides(p, r):
         product_name = self._get_name(product) if annotated else product
 
         # Create figure
-        fig, ax = plt.subplots(figsize=(max(10, len(util_df.columns) * 0.4), max(4, len(util_df) * 0.8)))
+        fig, ax = plt.subplots(
+            figsize=(max(10, len(util_df.columns) * 0.4), max(4, len(util_df) * 0.8))
+        )
 
         # Create heatmap
         # Use a diverging colormap: red (0%) -> yellow (50%) -> green (100%)
         cmap = plt.cm.RdYlGn
-        im = ax.imshow(util_df.values, aspect='auto', cmap=cmap, vmin=0, vmax=100)
+        im = ax.imshow(util_df.values, aspect="auto", cmap=cmap, vmin=0, vmax=100)
 
         # Set ticks
         ax.set_xticks(np.arange(len(util_df.columns)))
@@ -1970,7 +2160,9 @@ def has_production_overrides(p, r):
 
         # Rotate x labels if many years
         if len(util_df.columns) > 15:
-            plt.setp(ax.get_xticklabels(), rotation=45, ha='right', rotation_mode='anchor')
+            plt.setp(
+                ax.get_xticklabels(), rotation=45, ha="right", rotation_mode="anchor"
+            )
 
         # Add value annotations
         if show_values:
@@ -1979,22 +2171,39 @@ def has_production_overrides(p, r):
                     val = util_df.iloc[i, j]
                     if not np.isnan(val):
                         # Choose text color based on background
-                        text_color = 'white' if val < 30 or val > 70 else 'black'
-                        ax.text(j, i, f'{val:.0f}%', ha='center', va='center',
-                               fontsize=self._plot_config["fontsize"] - 3, color=text_color)
+                        text_color = "white" if val < 30 or val > 70 else "black"
+                        ax.text(
+                            j,
+                            i,
+                            f"{val:.0f}%",
+                            ha="center",
+                            va="center",
+                            fontsize=self._plot_config["fontsize"] - 3,
+                            color=text_color,
+                        )
                     else:
-                        ax.text(j, i, '-', ha='center', va='center',
-                               fontsize=self._plot_config["fontsize"] - 3, color='gray')
+                        ax.text(
+                            j,
+                            i,
+                            "-",
+                            ha="center",
+                            va="center",
+                            fontsize=self._plot_config["fontsize"] - 3,
+                            color="gray",
+                        )
 
         # Add colorbar
         cbar = fig.colorbar(im, ax=ax, shrink=0.8)
-        cbar.set_label('Utilization %', fontsize=self._plot_config["fontsize"])
+        cbar.set_label("Utilization %", fontsize=self._plot_config["fontsize"])
         cbar.ax.tick_params(labelsize=self._plot_config["fontsize"] - 1)
 
         # Labels and title
-        ax.set_xlabel('Year', fontsize=self._plot_config["label_fontsize"])
-        ax.set_ylabel('Process', fontsize=self._plot_config["label_fontsize"])
-        ax.set_title(f'Capacity Utilization: {product_name}', fontsize=self._plot_config["title_fontsize"])
+        ax.set_xlabel("Year", fontsize=self._plot_config["label_fontsize"])
+        ax.set_ylabel("Process", fontsize=self._plot_config["label_fontsize"])
+        ax.set_title(
+            f"Capacity Utilization: {product_name}",
+            fontsize=self._plot_config["title_fontsize"],
+        )
 
         fig.tight_layout()
         plt.show()
diff --git a/src/optimex/utils.py b/src/optimex/utils.py
index ceb0979..ca5d06a 100644
--- a/src/optimex/utils.py
+++ b/src/optimex/utils.py
@@ -2,9 +2,8 @@
 Utility functions for optimex.
 """
 
-import numpy as np
 import bw2data as bd
-
+import numpy as np
 from bw_temporalis import TemporalDistribution, easy_timedelta_distribution
 
 
diff --git a/tests/test_background_process_product_separation.py b/tests/test_background_process_product_separation.py
index 662cd80..edaec62 100644
--- a/tests/test_background_process_product_separation.py
+++ b/tests/test_background_process_product_separation.py
@@ -9,6 +9,7 @@
 This structure mirrors how foreground databases work in optimex and tests
 compatibility with standard Brightway LCA calculations.
 """
+
 from datetime import datetime
 
 import bw2calc as bc
@@ -26,11 +27,11 @@
 def setup_separated_background_system():
     """
     Set up a system where background database has separate process and product nodes.
-    
+
     Background structure:
     - Product node: ("bg_2020", "electricity") - the product
     - Process node: ("bg_2020", "electricity_production") - the process producing it
-    
+
     Foreground structure:
     - Product node: ("foreground", "Widget")
     - Process node: ("foreground", "Widget_Route1") - consumes background electricity product
@@ -39,99 +40,110 @@ def setup_separated_background_system():
 
     # Biosphere
     bio_db = bd.Database("biosphere3")
-    bio_db.write({
-        ("biosphere3", "CO2"): {
-            "type": "emission",
-            "name": "carbon dioxide",
-        },
-    })
+    bio_db.write(
+        {
+            ("biosphere3", "CO2"): {
+                "type": "emission",
+                "name": "carbon dioxide",
+            },
+        }
+    )
     bio_db.register()
 
     # Background database with SEPARATED process and product nodes
     bg_2020 = bd.Database("bg_2020")
-    bg_2020.write({
-        # Background PRODUCT node
-        ("bg_2020", "electricity"): {
-            "name": "electricity",
-            "unit": "kWh",
-            "type": bd.labels.product_node_default,
-        },
-        # Background PROCESS node that produces the electricity product
-        ("bg_2020", "electricity_production"): {
-            "name": "electricity production process",
-            "location": "GLO",
-            "type": bd.labels.process_node_default,
-            "exchanges": [
-                {
-                    "amount": 1,
-                    "type": "production",
-                    "input": ("bg_2020", "electricity"),  # Produces the electricity PRODUCT
-                },
-                {
-                    "amount": 0.5,
-                    "type": "biosphere",
-                    "input": ("biosphere3", "CO2"),
-                },
-            ],
-        },
-    })
+    bg_2020.write(
+        {
+            # Background PRODUCT node
+            ("bg_2020", "electricity"): {
+                "name": "electricity",
+                "unit": "kWh",
+                "type": bd.labels.product_node_default,
+            },
+            # Background PROCESS node that produces the electricity product
+            ("bg_2020", "electricity_production"): {
+                "name": "electricity production process",
+                "location": "GLO",
+                "type": bd.labels.process_node_default,
+                "exchanges": [
+                    {
+                        "amount": 1,
+                        "type": "production",
+                        "input": (
+                            "bg_2020",
+                            "electricity",
+                        ),  # Produces the electricity PRODUCT
+                    },
+                    {
+                        "amount": 0.5,
+                        "type": "biosphere",
+                        "input": ("biosphere3", "CO2"),
+                    },
+                ],
+            },
+        }
+    )
     bg_2020.metadata["representative_time"] = datetime(2020, 1, 1).isoformat()
     bg_2020.register()
 
     # Foreground with single product and single route
     fg = bd.Database("foreground")
-    fg.write({
-        # Product node
-        ("foreground", "Widget"): {
-            "name": "Widget",
-            "unit": "kg",
-            "type": bd.labels.product_node_default,
-        },
-        # Single production route
-        ("foreground", "Widget_Route1"): {
-            "name": "Widget production, Route 1",
-            "location": "GLO",
-            "type": bd.labels.process_node_default,
-            "operation_time_limits": (1, 2),  # Operation phase at process times 1-2
-            "exchanges": [
-                {
-                    "amount": 1,  # Produces 1 kg Widget
-                    "type": bd.labels.production_edge_default,
-                    "input": ("foreground", "Widget"),
-                    "temporal_distribution": TemporalDistribution(
-                        date=np.array([0, 1, 2, 3], dtype="timedelta64[Y]"),
-                        amount=np.array([0, 0.5, 0.5, 0]),  # Sums to 1.0
-                    ),
-                    "operation": True,
-                },
-                {
-                    "amount": 10,  # Consumes 10 kWh electricity at construction
-                    "type": bd.labels.consumption_edge_default,
-                    "input": ("bg_2020", "electricity"),  # Demands the PRODUCT
-                    "temporal_distribution": TemporalDistribution(
-                        date=np.array([0, 1, 2, 3], dtype="timedelta64[Y]"),
-                        amount=np.array([1, 0, 0, 0]),  # All at construction
-                    ),
-                },
-                {
-                    "amount": 5,  # Emits 5 kg CO2 during operation
-                    "type": bd.labels.biosphere_edge_default,
-                    "input": ("biosphere3", "CO2"),
-                    "temporal_distribution": TemporalDistribution(
-                        date=np.array([0, 1, 2, 3], dtype="timedelta64[Y]"),
-                        amount=np.array([0, 0.5, 0.5, 0]),  # During operation
-                    ),
-                    "operation": True,
-                },
-            ],
-        },
-    })
+    fg.write(
+        {
+            # Product node
+            ("foreground", "Widget"): {
+                "name": "Widget",
+                "unit": "kg",
+                "type": bd.labels.product_node_default,
+            },
+            # Single production route
+            ("foreground", "Widget_Route1"): {
+                "name": "Widget production, Route 1",
+                "location": "GLO",
+                "type": bd.labels.process_node_default,
+                "operation_time_limits": (1, 2),  # Operation phase at process times 1-2
+                "exchanges": [
+                    {
+                        "amount": 1,  # Produces 1 kg Widget
+                        "type": bd.labels.production_edge_default,
+                        "input": ("foreground", "Widget"),
+                        "temporal_distribution": TemporalDistribution(
+                            date=np.array([0, 1, 2, 3], dtype="timedelta64[Y]"),
+                            amount=np.array([0, 0.5, 0.5, 0]),  # Sums to 1.0
+                        ),
+                        "operation": True,
+                    },
+                    {
+                        "amount": 10,  # Consumes 10 kWh electricity at construction
+                        "type": bd.labels.consumption_edge_default,
+                        "input": ("bg_2020", "electricity"),  # Demands the PRODUCT
+                        "temporal_distribution": TemporalDistribution(
+                            date=np.array([0, 1, 2, 3], dtype="timedelta64[Y]"),
+                            amount=np.array([1, 0, 0, 0]),  # All at construction
+                        ),
+                    },
+                    {
+                        "amount": 5,  # Emits 5 kg CO2 during operation
+                        "type": bd.labels.biosphere_edge_default,
+                        "input": ("biosphere3", "CO2"),
+                        "temporal_distribution": TemporalDistribution(
+                            date=np.array([0, 1, 2, 3], dtype="timedelta64[Y]"),
+                            amount=np.array([0, 0.5, 0.5, 0]),  # During operation
+                        ),
+                        "operation": True,
+                    },
+                ],
+            },
+        }
+    )
     fg.register()
 
     # Impact method
-    bd.Method(("GWP", "example")).write([
-        (("biosphere3", "CO2"), 1),
-    ])
+    bd.Method(("GWP", "example")).write(
+        [
+            (("biosphere3", "CO2"), 1),
+        ]
+    )
 
 
 def test_separated_background_matches_standard_lca(setup_separated_background_system):
@@ -157,7 +169,9 @@ def test_separated_background_matches_standard_lca(setup_separated_background_sy
     # optimex calculation
     years = range(2020, 2030)
     td_demand = TemporalDistribution(
-        date=np.array([datetime(year, 1, 1).isoformat() for year in years], dtype='datetime64[s]'),
+        date=np.array(
+            [datetime(year, 1, 1).isoformat() for year in years], dtype="datetime64[s]"
+        ),
         amount=np.asarray([0, 0, 100, 0, 0, 0, 0, 0, 0, 0]),  # 100 kg at year 2022
     )
 
@@ -184,7 +198,6 @@ def test_separated_background_matches_standard_lca(setup_separated_background_sy
         optimization_model_inputs,
         name="test_separated_background",
         objective_category="climate_change",
-
     )
 
     _, obj_real, results = optimizer.solve_model(model, solver_name="glpk")
@@ -193,72 +206,77 @@ def test_separated_background_matches_standard_lca(setup_separated_background_sy
     print(f"Difference: {abs(obj_real - expected_gwp)}")
 
     # They should match (within numerical tolerance)
-    assert pytest.approx(obj_real, rel=1e-3) == expected_gwp, (
-        f"optimex result ({obj_real}) should match standard LCA ({expected_gwp})"
-    )
+    assert (
+        pytest.approx(obj_real, rel=1e-3) == expected_gwp
+    ), f"optimex result ({obj_real}) should match standard LCA ({expected_gwp})"
 
     # Additional check: verify postprocessing extracts correct unscaled values
     pp = postprocessing.PostProcessor(model)
     df_impacts = pp.get_impacts()
 
     # Sum all climate_change impacts across all processes and times
-    if 'climate_change' in df_impacts.columns.get_level_values(0):
-        climate_change_cols = [col for col in df_impacts.columns if col[0] == 'climate_change']
+    if "climate_change" in df_impacts.columns.get_level_values(0):
+        climate_change_cols = [
+            col for col in df_impacts.columns if col[0] == "climate_change"
+        ]
         total_cc_from_pp = df_impacts[climate_change_cols].sum().sum()
 
         print(f"\nPostprocessing climate_change total: {total_cc_from_pp}")
         print(f"Expected (from LCA): {expected_gwp}")
 
         # Postprocessing should also match standard LCA
-        assert pytest.approx(total_cc_from_pp, rel=1e-3) == expected_gwp, (
-            f"Postprocessing climate_change sum ({total_cc_from_pp}) should match standard LCA ({expected_gwp})"
-        )
+        assert (
+            pytest.approx(total_cc_from_pp, rel=1e-3) == expected_gwp
+        ), f"Postprocessing climate_change sum ({total_cc_from_pp}) should match standard LCA ({expected_gwp})"
 
 
 def test_separated_background_process_linkage(setup_separated_background_system):
     """
     Verify that background product correctly links to background process.
-    
+
     This test ensures that when the foreground demands a background product,
     the LCA processor correctly identifies and uses the background process
     that produces that product.
     """
-    
+
     # Get the nodes
     electricity_product = bd.get_node(database="bg_2020", name="electricity")
-    electricity_process = bd.get_node(database="bg_2020", name="electricity production process")
-    
-    # Verify they are correctly typed
-    assert electricity_product.get('type') == bd.labels.product_node_default, (
-        "Electricity should be a product node"
-    )
-    assert electricity_process.get('type') == bd.labels.process_node_default, (
-        "Electricity production should be a process node"
+    electricity_process = bd.get_node(
+        database="bg_2020", name="electricity production process"
     )
-    
+
+    # Verify they are correctly typed
+    assert (
+        electricity_product.get("type") == bd.labels.product_node_default
+    ), "Electricity should be a product node"
+    assert (
+        electricity_process.get("type") == bd.labels.process_node_default
+    ), "Electricity production should be a process node"
+
     # Verify the process produces the product
     production_exchanges = [
-        exc for exc in electricity_process.exchanges()
-        if exc.get('type') == "production"
+        exc
+        for exc in electricity_process.exchanges()
+        if exc.get("type") == "production"
     ]
-    
-    assert len(production_exchanges) == 1, (
-        "Process should have exactly one production exchange"
-    )
-    
-    assert production_exchanges[0]['input'] == electricity_product.key, (
-        "Process should produce the electricity product"
-    )
-    
+
+    assert (
+        len(production_exchanges) == 1
+    ), "Process should have exactly one production exchange"
+
+    assert (
+        production_exchanges[0]["input"] == electricity_product.key
+    ), "Process should produce the electricity product"
+
     # Verify standard LCA can handle this structure
     widget = bd.get_node(database="foreground", name="Widget")
     lca = bc.LCA({widget: 1}, method=("GWP", "example"))
     lca.lci()
     lca.lcia()
-    
+
     # The LCA should complete successfully with a non-zero result
     assert lca.score > 0, "LCA should produce a positive impact score"
-    
+
     print(f"\nBackground process-product linkage verified!")
     print(f"Product node: {electricity_product}")
     print(f"Process node: {electricity_process}")
diff --git a/tests/test_brownfield.py b/tests/test_brownfield.py
index 02310cd..db0fea3 100644
--- a/tests/test_brownfield.py
+++ b/tests/test_brownfield.py
@@ -8,8 +8,8 @@
 - Only contribute operational impacts during their remaining lifetime
 """
 
-import pytest
 import pyomo.environ as pyo
+import pytest
 
 from optimex import converter, optimizer
 
@@ -38,7 +38,11 @@ def test_existing_capacity_valid(self):
             "SYSTEM_TIME": [2020, 2021, 2022],
             "CATEGORY": ["climate_change"],
             "operation_time_limits": {"P1": (0, 0)},
-            "demand": {("product", 2020): 10, ("product", 2021): 10, ("product", 2022): 10},
+            "demand": {
+                ("product", 2020): 10,
+                ("product", 2021): 10,
+                ("product", 2022): 10,
+            },
             "foreground_technosphere": {},
             "internal_demand_technosphere": {},
             "foreground_biosphere": {("P1", "CO2", 0): 10},
@@ -183,16 +187,23 @@ def test_existing_capacity_meets_demand(self):
             "internal_demand_technosphere": {},
             # Separate flows for installation vs operation emissions
             "foreground_biosphere": {
-                ("P1", "CO2_install", 0): 1000,  # HIGH installation emissions (NOT operational)
-                ("P1", "CO2_operate", 1): 1,    # LOW operation emissions
+                (
+                    "P1",
+                    "CO2_install",
+                    0,
+                ): 1000,  # HIGH installation emissions (NOT operational)
+                ("P1", "CO2_operate", 1): 1,  # LOW operation emissions
             },
             "foreground_production": {
                 ("P1", "product", 1): 1.0,
             },
             "operation_flow": {
                 ("P1", "product"): True,
-                ("P1", "CO2_install"): False,  # NOT operational (scaled by installation)
-                ("P1", "CO2_operate"): True,   # Operational (scaled by operation)
+                (
+                    "P1",
+                    "CO2_install",
+                ): False,  # NOT operational (scaled by installation)
+                ("P1", "CO2_operate"): True,  # Operational (scaled by operation)
             },
             "background_inventory": {},
             "mapping": {
@@ -212,7 +223,6 @@ def test_existing_capacity_meets_demand(self):
             inputs=model_inputs,
             objective_category="climate_change",
             name="test_brownfield_meets_demand",
-
         )
 
         solved_model, objective, results = optimizer.solve_model(
@@ -226,9 +236,9 @@ def test_existing_capacity_meets_demand(self):
         # Existing capacity of 10 units at tau=1 provides capacity
         # Should be enough to meet demand of 5 with no new installations
         inst_2025 = pyo.value(solved_model.var_installation["P1", 2025])
-        assert pytest.approx(0, abs=1e-4) == inst_2025, (
-            f"No new installation expected at 2025 (existing capacity should suffice), got {inst_2025}"
-        )
+        assert (
+            pytest.approx(0, abs=1e-4) == inst_2025
+        ), f"No new installation expected at 2025 (existing capacity should suffice), got {inst_2025}"
 
     def test_existing_capacity_reduces_emissions(self):
         """
@@ -259,8 +269,16 @@ def test_existing_capacity_reduces_emissions(self):
             "internal_demand_technosphere": {},
             # Separate flows for installation vs operation emissions
             "foreground_biosphere": {
-                ("P1", "CO2_install", 0): 100,  # Installation emissions (NOT operational)
-                ("P1", "CO2_operate", 1): 1,    # Operation emissions (same at tau=1 and 2)
+                (
+                    "P1",
+                    "CO2_install",
+                    0,
+                ): 100,  # Installation emissions (NOT operational)
+                (
+                    "P1",
+                    "CO2_operate",
+                    1,
+                ): 1,  # Operation emissions (same at tau=1 and 2)
                 ("P1", "CO2_operate", 2): 1,
             },
             "foreground_production": {
@@ -269,8 +287,11 @@ def test_existing_capacity_reduces_emissions(self):
             },
             "operation_flow": {
                 ("P1", "product"): True,
-                ("P1", "CO2_install"): False,  # NOT operational (scaled by installation)
-                ("P1", "CO2_operate"): True,   # Operational (scaled by operation)
+                (
+                    "P1",
+                    "CO2_install",
+                ): False,  # NOT operational (scaled by installation)
+                ("P1", "CO2_operate"): True,  # Operational (scaled by operation)
             },
             "background_inventory": {},
             "mapping": {("db_2020", 2022): 1.0, ("db_2020", 2023): 1.0},
@@ -291,7 +312,6 @@ def test_existing_capacity_reduces_emissions(self):
             inputs=greenfield_inputs,
             objective_category="climate_change",
             name="greenfield",
-
         )
         _, greenfield_obj, _ = optimizer.solve_model(
             greenfield_model, solver_name="glpk", tee=False
@@ -300,14 +320,15 @@ def test_existing_capacity_reduces_emissions(self):
         # Brownfield: all capacity already exists
         # Existing capacity at 2021: at 2023, tau = 2023-2021 = 2, in operation!
         brownfield_config = dict(base_config)
-        brownfield_config["existing_capacity"] = {("P1", 2021): 10.0}  # Plenty of capacity
+        brownfield_config["existing_capacity"] = {
+            ("P1", 2021): 10.0
+        }  # Plenty of capacity
 
         brownfield_inputs = converter.OptimizationModelInputs(**brownfield_config)
         brownfield_model = optimizer.create_model(
             inputs=brownfield_inputs,
             objective_category="climate_change",
             name="brownfield",
-
         )
         _, brownfield_obj, _ = optimizer.solve_model(
             brownfield_model, solver_name="glpk", tee=False
@@ -371,9 +392,7 @@ def test_existing_capacity_retirement(self):
                 ("P1", "CO2"): True,
             },
             "background_inventory": {},
-            "mapping": {
-                ("db_2020", t): 1.0 for t in [2020, 2021, 2022, 2023]
-            },
+            "mapping": {("db_2020", t): 1.0 for t in [2020, 2021, 2022, 2023]},
             "characterization": {
                 ("climate_change", "CO2", t): 1.0 for t in [2020, 2021, 2022, 2023]
             },
@@ -386,7 +405,6 @@ def test_existing_capacity_retirement(self):
             inputs=model_inputs,
             objective_category="climate_change",
             name="test_retirement",
-
         )
 
         solved_model, objective, results = optimizer.solve_model(
@@ -402,9 +420,9 @@ def test_existing_capacity_retirement(self):
         )
 
         # Some new installations should be needed to replace retired capacity
-        assert total_new_installations > 0, (
-            "New installations should be needed after existing capacity retires"
-        )
+        assert (
+            total_new_installations > 0
+        ), "New installations should be needed after existing capacity retires"
 
 
 class TestBrownfieldPostprocessing:
@@ -466,12 +484,9 @@ def test_get_existing_capacity(self):
             inputs=model_inputs,
             objective_category="climate_change",
             name="test_postprocessing",
-
         )
 
-        solved_model, _, _ = optimizer.solve_model(
-            model, solver_name="glpk", tee=False
-        )
+        solved_model, _, _ = optimizer.solve_model(model, solver_name="glpk", tee=False)
 
         pp = PostProcessor(solved_model)
         existing_df = pp.get_existing_capacity()
@@ -521,7 +536,9 @@ def test_production_capacity_includes_existing(self):
             # Existing capacity of 10 units installed in 2019
             # At 2020: tau = 1, NOT in operation (0 <= 0 <= 0 means only tau=0)
             # So we need installation year = 2020 to have tau = 0
-            "existing_capacity": {("P1", 2020 - 0): 10.0},  # This won't work due to validation
+            "existing_capacity": {
+                ("P1", 2020 - 0): 10.0
+            },  # This won't work due to validation
         }
 
         # Actually, the validation requires inst_year < min(SYSTEM_TIME)
@@ -546,12 +563,9 @@ def test_production_capacity_includes_existing(self):
             inputs=model_inputs,
             objective_category="climate_change",
             name="test_capacity",
-
         )
 
-        solved_model, _, _ = optimizer.solve_model(
-            model, solver_name="glpk", tee=False
-        )
+        solved_model, _, _ = optimizer.solve_model(model, solver_name="glpk", tee=False)
 
         pp = PostProcessor(solved_model)
         capacity_df = pp.get_production_capacity()
@@ -599,9 +613,7 @@ def test_brownfield_with_vintage_improvements(self):
                 ("Plant", "electricity", 2030): 0.8,  # 20% efficiency improvement
             },
             "internal_demand_technosphere": {},
-            "foreground_biosphere": {
-                ("Plant", "CO2", tau): 5 for tau in range(21)
-            },
+            "foreground_biosphere": {("Plant", "CO2", tau): 5 for tau in range(21)},
             "foreground_production": {
                 ("Plant", "output", tau): 50 if 1 <= tau <= 20 else 0
                 for tau in range(21)
@@ -635,7 +647,9 @@ def test_brownfield_with_vintage_improvements(self):
         # Existing capacity should contribute to meeting demand
         # At 2025, existing capacity from 2015 is at tau = 2025-2015 = 10 (in operation)
         operation_2025 = get_total_operation(solved_model, "Plant", 2025)
-        assert operation_2025 > 0, "Operation should be positive when existing capacity exists"
+        assert (
+            operation_2025 > 0
+        ), "Operation should be positive when existing capacity exists"
 
     def test_brownfield_with_vintage_production_overrides(self):
         """
@@ -662,7 +676,7 @@ def test_brownfield_with_vintage_production_overrides(self):
             # Separate flows for installation vs operation (operational must be constant)
             "foreground_biosphere": {
                 ("Plant", "CO2_install", 0): 100,  # Installation emissions
-                ("Plant", "CO2_operate", 1): 10,   # Operation emissions (constant)
+                ("Plant", "CO2_operate", 1): 10,  # Operation emissions (constant)
                 ("Plant", "CO2_operate", 2): 10,
             },
             # Base production (will be overridden by vintages)
@@ -681,15 +695,14 @@ def test_brownfield_with_vintage_production_overrides(self):
             "operation_flow": {
                 ("Plant", "output"): True,
                 ("Plant", "CO2_install"): False,  # NOT operational
-                ("Plant", "CO2_operate"): True,   # Operational
+                ("Plant", "CO2_operate"): True,  # Operational
             },
             "background_inventory": {},
             "mapping": {("db", t): 1.0 for t in [2025, 2026, 2027]},
             "characterization": {
                 ("GWP", "CO2_install", t): 1.0 for t in [2025, 2026, 2027]
-            } | {
-                ("GWP", "CO2_operate", t): 1.0 for t in [2025, 2026, 2027]
-            },
+            }
+            | {("GWP", "CO2_operate", t): 1.0 for t in [2025, 2026, 2027]},
             # Existing capacity from 2023 (before min SYSTEM_TIME 2025)
             # At 2025: tau = 2025-2023 = 2, in operation (1 <= 2 <= 2)
             # At 2026: tau = 2026-2023 = 3, NOT in operation (3 > 2)
@@ -739,24 +752,21 @@ def test_brownfield_with_mixed_vintage_processes(self):
             "demand": {("output", t): 200 for t in [2025, 2026, 2027]},
             "foreground_technosphere": {
                 ("OldPlant", "electricity", tau): 20 for tau in range(11)
-            } | {
-                ("NewPlant", "electricity", tau): 15 for tau in range(11)
-            },
+            }
+            | {("NewPlant", "electricity", tau): 15 for tau in range(11)},
             # vintage_improvements only for NewPlant
             "vintage_improvements": {
                 ("NewPlant", "electricity", 2025): 1.0,
                 ("NewPlant", "electricity", 2027): 0.8,
             },
             "internal_demand_technosphere": {},
-            "foreground_biosphere": {
-                ("OldPlant", "CO2", tau): 10 for tau in range(11)
-            } | {
-                ("NewPlant", "CO2", tau): 5 for tau in range(11)
-            },
+            "foreground_biosphere": {("OldPlant", "CO2", tau): 10 for tau in range(11)}
+            | {("NewPlant", "CO2", tau): 5 for tau in range(11)},
             "foreground_production": {
                 ("OldPlant", "output", tau): 100 if 1 <= tau <= 10 else 0
                 for tau in range(11)
-            } | {
+            }
+            | {
                 ("NewPlant", "output", tau): 100 if 1 <= tau <= 10 else 0
                 for tau in range(11)
             },
@@ -833,7 +843,11 @@ def test_brownfield_vintage_impact_calculation(self):
             # Separate flows for installation vs operation (operational must be constant)
             # Installation emissions are NOT operational (scaled by installation)
             "foreground_biosphere": {
-                ("Plant", "CO2_install", 0): 500,  # High installation emissions at construction
+                (
+                    "Plant",
+                    "CO2_install",
+                    0,
+                ): 500,  # High installation emissions at construction
             },
             "foreground_production": {
                 ("Plant", "output", 0): 100,
@@ -843,7 +857,10 @@ def test_brownfield_vintage_impact_calculation(self):
             "operation_flow": {
                 ("Plant", "output"): True,
                 ("Plant", "electricity"): True,
-                ("Plant", "CO2_install"): False,  # NOT operational - scales with installation
+                (
+                    "Plant",
+                    "CO2_install",
+                ): False,  # NOT operational - scales with installation
             },
             "background_inventory": {("grid", "electricity", "CO2_operate"): 1.0},
             "mapping": {("grid", 2025): 1.0, ("grid", 2026): 1.0},
@@ -865,7 +882,10 @@ def test_brownfield_vintage_impact_calculation(self):
         _, greenfield_obj, greenfield_results = optimizer.solve_model(
             greenfield_model, solver_name="glpk", tee=False
         )
-        assert greenfield_results.solver.termination_condition == pyo.TerminationCondition.optimal
+        assert (
+            greenfield_results.solver.termination_condition
+            == pyo.TerminationCondition.optimal
+        )
 
         # Brownfield scenario (existing capacity covers demand)
         brownfield_config = dict(base_config)
@@ -880,7 +900,10 @@ def test_brownfield_vintage_impact_calculation(self):
         _, brownfield_obj, brownfield_results = optimizer.solve_model(
             brownfield_model, solver_name="glpk", tee=False
         )
-        assert brownfield_results.solver.termination_condition == pyo.TerminationCondition.optimal
+        assert (
+            brownfield_results.solver.termination_condition
+            == pyo.TerminationCondition.optimal
+        )
 
         # Brownfield should have lower impact (no installation emissions)
         assert brownfield_obj < greenfield_obj, (
diff --git a/tests/test_merit_order.py b/tests/test_merit_order.py
index 5f53f5b..fcc4bdb 100644
--- a/tests/test_merit_order.py
+++ b/tests/test_merit_order.py
@@ -13,8 +13,8 @@
 5. Per-vintage capacity: Each vintage bounded by its own installation/existing capacity
 """
 
-import pytest
 import pyomo.environ as pyo
+import pytest
 
 from optimex import converter, optimizer
 from optimex.postprocessing import PostProcessor
@@ -103,7 +103,7 @@ def test_var_operation_is_3d(self):
         # Check that var_operation is indexed by ACTIVE_VINTAGE_TIME (3D)
         assert hasattr(model, "var_operation")
         # The variable should be accessible with 3-tuple indices
-        for (p, v, t) in model.ACTIVE_VINTAGE_TIME:
+        for p, v, t in model.ACTIVE_VINTAGE_TIME:
             assert model.var_operation[p, v, t] is not None
 
     def test_brownfield_has_operation_variables(self):
@@ -143,15 +143,14 @@ def test_brownfield_has_operation_variables(self):
 
         # Check that brownfield vintage (2019) appears in ACTIVE_VINTAGE_TIME
         brownfield_entries = [
-            (p, v, t) for (p, v, t) in model.ACTIVE_VINTAGE_TIME
-            if v == 2019
+            (p, v, t) for (p, v, t) in model.ACTIVE_VINTAGE_TIME if v == 2019
         ]
-        assert len(brownfield_entries) > 0, (
-            "Brownfield vintage 2019 should have entries in ACTIVE_VINTAGE_TIME"
-        )
+        assert (
+            len(brownfield_entries) > 0
+        ), "Brownfield vintage 2019 should have entries in ACTIVE_VINTAGE_TIME"
 
         # Check that operation variable exists for brownfield
-        for (p, v, t) in brownfield_entries:
+        for p, v, t in brownfield_entries:
             assert model.var_operation[p, v, t] is not None
 
 
@@ -184,15 +183,16 @@ def test_cleaner_vintage_dispatched_first(self):
                 ("output", 2020): 0,
                 ("output", 2021): 0,
                 ("output", 2022): 0,
-                ("output", 2023): 50,  # Only demand at 2023 when both vintages available
+                (
+                    "output",
+                    2023,
+                ): 50,  # Only demand at 2023 when both vintages available
             },
             # Fuel consumption varies by vintage (technology evolution)
-            "foreground_technosphere": {
-                ("Plant", "fuel", tau): 10 for tau in range(4)
-            },
+            "foreground_technosphere": {("Plant", "fuel", tau): 10 for tau in range(4)},
             "vintage_improvements": {
-                ("Plant", "fuel", 2020): 1.0,   # 2020 vintage: baseline
-                ("Plant", "fuel", 2022): 0.5,   # 2022 vintage: 50% more efficient
+                ("Plant", "fuel", 2020): 1.0,  # 2020 vintage: baseline
+                ("Plant", "fuel", 2022): 0.5,  # 2022 vintage: 50% more efficient
             },
             "internal_demand_technosphere": {},
             "foreground_biosphere": {},  # Emissions come from fuel via background
@@ -206,7 +206,9 @@ def test_cleaner_vintage_dispatched_first(self):
             },
             "background_inventory": {("grid", "fuel", "CO2"): 1.0},
             "mapping": {("grid", t): 1.0 for t in [2020, 2021, 2022, 2023]},
-            "characterization": {("GWP", "CO2", t): 1.0 for t in [2020, 2021, 2022, 2023]},
+            "characterization": {
+                ("GWP", "CO2", t): 1.0 for t in [2020, 2021, 2022, 2023]
+            },
             # Force some installation at both 2020 and 2022
             "process_deployment_limits_min": {
                 ("Plant", 2020): 1.0,  # Force installation in 2020
@@ -261,9 +263,7 @@ def test_brownfield_dispatchable_independently(self):
             "CATEGORY": ["GWP"],
             "operation_time_limits": {"Plant": (1, 5)},
             "demand": {("output", t): 100 for t in [2020, 2021, 2022]},
-            "foreground_technosphere": {
-                ("Plant", "fuel", tau): 10 for tau in range(6)
-            },
+            "foreground_technosphere": {("Plant", "fuel", tau): 10 for tau in range(6)},
             # Technology evolution makes newer vintages more efficient
             "vintage_improvements": {
                 ("Plant", "fuel", 2020): 1.0,
@@ -310,7 +310,9 @@ def test_brownfield_dispatchable_independently(self):
         # Verify brownfield is independently dispatchable (can be < full capacity)
         for v, t, op_val in brownfield_ops:
             # Just verify it's a valid (non-negative) operation value
-            assert op_val >= -1e-6, f"Brownfield operation at {t} should be non-negative"
+            assert (
+                op_val >= -1e-6
+            ), f"Brownfield operation at {t} should be non-negative"
 
 
 class TestOperationLimits:
@@ -373,9 +375,9 @@ def test_operation_limits_apply_to_total(self):
         )
 
         # Total operation should respect the limit
-        assert total_op_2022 <= 30 + 1e-6, (
-            f"Total operation at 2022 ({total_op_2022:.4f}) should not exceed limit of 30"
-        )
+        assert (
+            total_op_2022 <= 30 + 1e-6
+        ), f"Total operation at 2022 ({total_op_2022:.4f}) should not exceed limit of 30"
 
 
 class TestPerVintageCapacity:
@@ -412,7 +414,9 @@ def test_per_vintage_capacity_respected(self):
             "operation_flow": {("P1", "product"): True, ("P1", "CO2"): True},
             "background_inventory": {},
             "mapping": {("db", t): 1.0 for t in [2020, 2021, 2022, 2023]},
-            "characterization": {("GWP", "CO2", t): 1.0 for t in [2020, 2021, 2022, 2023]},
+            "characterization": {
+                ("GWP", "CO2", t): 1.0 for t in [2020, 2021, 2022, 2023]
+            },
         }
 
         model_inputs = converter.OptimizationModelInputs(**model_inputs_dict)
@@ -432,7 +436,7 @@ def test_per_vintage_capacity_respected(self):
         fg_scale = solved_model.scales["foreground"]
         production_per_unit = 50.0 + 50.0 + 50.0  # Sum over tau 0, 1, and 2
 
-        for (p, v, t) in solved_model.ACTIVE_VINTAGE_TIME:
+        for p, v, t in solved_model.ACTIVE_VINTAGE_TIME:
             if v in solved_model.SYSTEM_TIME:
                 # Greenfield: capacity from var_installation
                 installation = pyo.value(solved_model.var_installation[p, v])
@@ -487,7 +491,9 @@ def test_get_operation_aggregate_vintages(self):
 
         # Should have Process columns (not Process, Vintage)
         assert "P1" in op_df.columns
-        assert not isinstance(op_df.columns, type(op_df.columns).__class__)  # Not MultiIndex
+        assert not isinstance(
+            op_df.columns, type(op_df.columns).__class__
+        )  # Not MultiIndex
 
     def test_get_operation_by_vintage(self):
         """Test get_operation with aggregate_vintages=False."""
@@ -531,6 +537,7 @@ def test_get_operation_by_vintage(self):
 
         # Should have MultiIndex columns with (Process, Vintage)
         import pandas as pd
+
         assert isinstance(op_df.columns, pd.MultiIndex), "Columns should be MultiIndex"
         # Columns should be tuples with (Process, Vintage)
         for col in op_df.columns:
@@ -539,6 +546,7 @@ def test_get_operation_by_vintage(self):
     def test_get_operation_by_vintage_column_names(self):
         """Test get_operation(aggregate_vintages=False) returns correct column structure."""
         import pandas as pd
+
         model_inputs_dict = {
             "PROCESS": ["P1"],
             "PRODUCT": ["product"],
diff --git a/tests/test_operation_constraints.py b/tests/test_operation_constraints.py
index 0679678..1706f4e 100644
--- a/tests/test_operation_constraints.py
+++ b/tests/test_operation_constraints.py
@@ -4,6 +4,7 @@
 These tests verify that the OperationLimit constraint correctly bounds
 operation levels by available installed capacity across multiple time periods.
 """
+
 import pyomo.environ as pyo
 import pytest
 
@@ -38,7 +39,12 @@ def test_operation_capacity_single_installation():
         "INTERMEDIATE_FLOW": [],
         "ELEMENTARY_FLOW": ["CO2"],
         "BACKGROUND_ID": ["db_2020"],
-        "PROCESS_TIME": [0, 1, 2, 3],  # Construction at 0, operation at 1-2, decommission at 3
+        "PROCESS_TIME": [
+            0,
+            1,
+            2,
+            3,
+        ],  # Construction at 0, operation at 1-2, decommission at 3
         "SYSTEM_TIME": [2020, 2021, 2022, 2023, 2024],
         "CATEGORY": ["climate_change"],
         "operation_time_limits": {"P1": (1, 2)},
@@ -66,8 +72,7 @@ def test_operation_capacity_single_installation():
             ("db_2020", 2024): 1.0,
         },
         "characterization": {
-            ("climate_change", "CO2", t): 1.0
-            for t in [2020, 2021, 2022, 2023, 2024]
+            ("climate_change", "CO2", t): 1.0 for t in [2020, 2021, 2022, 2023, 2024]
         },
     }
 
@@ -213,8 +218,8 @@ def test_operation_capacity_with_varying_demand():
         "CATEGORY": ["climate_change"],
         "operation_time_limits": {"P1": (1, 2)},
         "demand": {
-            ("product", 2021): 3,   # Low demand
-            ("product", 2022): 8,   # High demand
+            ("product", 2021): 3,  # Low demand
+            ("product", 2022): 8,  # High demand
         },
         "foreground_technosphere": {},
         "internal_demand_technosphere": {},
diff --git a/tests/test_optimization.py b/tests/test_optimization.py
index 63593fd..6d559ac 100644
--- a/tests/test_optimization.py
+++ b/tests/test_optimization.py
@@ -97,9 +97,12 @@ def test_model_solution_is_optimal(solved_system_model):
     [
         # ("fixed", 3.15417e-10),  # Expected value for the fixed model
         ("flex", 1.9172462799736082e-10),  # Expected value for the flexible model
-        ("constrained", 1.9197314619763485e-10),  # Constrained by limiting P1 to 10 installations (0.13% higher)
+        (
+            "constrained",
+            1.9197314619763485e-10,
+        ),  # Constrained by limiting P1 to 10 installations (0.13% higher)
     ],
-    ids=["flex_result", "constrained_process_limit"], # "fixed_result",
+    ids=["flex_result", "constrained_process_limit"],  # "fixed_result",
 )
 def test_system_model(model_type, expected_value, solved_system_model):
     # Get the model from the solved system model fixture
@@ -233,9 +236,7 @@ def test_cumulative_process_limits_respected():
     # Set cumulative limit on low-emission process (30 units total across all years)
     # Total demand is 50, so P2 must provide at least 20
     cumulative_limit = 30.0
-    model_inputs.cumulative_process_limits_max = {
-        "P1_low_emission": cumulative_limit
-    }
+    model_inputs.cumulative_process_limits_max = {"P1_low_emission": cumulative_limit}
 
     # Create and solve model
     model = optimizer.create_model(
@@ -245,9 +246,7 @@ def test_cumulative_process_limits_respected():
     )
 
     solved_model, objective, results = optimizer.solve_model(
-        model,
-        solver_name="glpk",
-        tee=False
+        model, solver_name="glpk", tee=False
     )
 
     # Verify solution is optimal
@@ -280,9 +279,7 @@ def test_cumulative_process_limits_respected():
 
     # Verify P2 is used to meet remaining demand
     total_demand = sum(model_inputs_dict["demand"].values())
-    assert cumulative_p2 > 0, (
-        "P2 should be used to meet demand when P1 is limited"
-    )
+    assert cumulative_p2 > 0, "P2 should be used to meet demand when P1 is limited"
 
     # Verify total capacity roughly equals demand (within 1% tolerance)
     total_capacity = cumulative_p1 + cumulative_p2
@@ -351,17 +348,17 @@ def test_capacity_constraint_with_high_production():
     # - Need var_operation = 10/5 = 2 to produce 10 units
     # - Capacity = 5.0 * installations, so need 5.0 * inst >= 2
     # - Minimum installations = 2/5 = 0.4
-    assert pytest.approx(0.4, rel=0.01) == inst, (
-        f"With production=5.0, only 0.4 installations needed, got {inst}"
-    )
-    assert pytest.approx(2.0, rel=0.01) == oper, (
-        f"Operation should be 2.0 to produce 10 units, got {oper}"
-    )
+    assert (
+        pytest.approx(0.4, rel=0.01) == inst
+    ), f"With production=5.0, only 0.4 installations needed, got {inst}"
+    assert (
+        pytest.approx(2.0, rel=0.01) == oper
+    ), f"Operation should be 2.0 to produce 10 units, got {oper}"
 
     # Verify emissions (capacity-dependent) = 0.4 * 10 = 4 kg CO2
-    assert pytest.approx(4.0, rel=0.01) == objective, (
-        f"Emissions should be 4.0 kg CO2, got {objective}"
-    )
+    assert (
+        pytest.approx(4.0, rel=0.01) == objective
+    ), f"Emissions should be 4.0 kg CO2, got {objective}"
 
 
 def test_operation_limits_respected():
@@ -450,12 +447,12 @@ def test_operation_limits_respected():
         get_total_operation(solved_baseline, "P2_high_emission", t)
         for t in solved_baseline.SYSTEM_TIME
     )
-    assert pytest.approx(300, rel=0.01) == p1_operation_baseline, (
-        f"Without limits, P1 should handle all demand (300), got {p1_operation_baseline}"
-    )
-    assert pytest.approx(0, abs=0.01) == p2_operation_baseline, (
-        f"Without limits, P2 should not be used, got {p2_operation_baseline}"
-    )
+    assert (
+        pytest.approx(300, rel=0.01) == p1_operation_baseline
+    ), f"Without limits, P1 should handle all demand (300), got {p1_operation_baseline}"
+    assert (
+        pytest.approx(0, abs=0.01) == p2_operation_baseline
+    ), f"Without limits, P2 should not be used, got {p2_operation_baseline}"
 
     # Now solve WITH operation limits on P1
     # Limit P1 operation to 50 per year (150 total), forcing P2 to handle the rest
@@ -486,9 +483,9 @@ def test_operation_limits_respected():
         p2_op = get_total_operation(solved_limited, "P2_high_emission", t)
 
         # P1 should be at the limit (binding constraint)
-        assert p1_op <= operation_limit * 1.001, (
-            f"P1 operation at {t} ({p1_op:.2f}) exceeds limit ({operation_limit})"
-        )
+        assert (
+            p1_op <= operation_limit * 1.001
+        ), f"P1 operation at {t} ({p1_op:.2f}) exceeds limit ({operation_limit})"
         assert p1_op >= operation_limit * 0.999, (
             f"P1 operation at {t} ({p1_op:.2f}) should be at limit ({operation_limit}), "
             "constraint may not be binding"
@@ -496,19 +493,19 @@ def test_operation_limits_respected():
 
         # P2 should handle the remaining demand (100 - 50 = 50)
         expected_p2 = 100 - operation_limit
-        assert pytest.approx(expected_p2, rel=0.01) == p2_op, (
-            f"P2 operation at {t} should be {expected_p2}, got {p2_op}"
-        )
+        assert (
+            pytest.approx(expected_p2, rel=0.01) == p2_op
+        ), f"P2 operation at {t} should be {expected_p2}, got {p2_op}"
 
     # Verify that the limited solution has higher emissions
     # Baseline: 300 * 1 = 300 kg CO2
     # Limited: 150 * 1 + 150 * 10 = 150 + 1500 = 1650 kg CO2
-    assert obj_limited > obj_baseline, (
-        f"Limited objective ({obj_limited}) should be higher than baseline ({obj_baseline})"
-    )
-    assert pytest.approx(1650, rel=0.01) == obj_limited, (
-        f"Limited objective should be 1650 kg CO2, got {obj_limited}"
-    )
+    assert (
+        obj_limited > obj_baseline
+    ), f"Limited objective ({obj_limited}) should be higher than baseline ({obj_baseline})"
+    assert (
+        pytest.approx(1650, rel=0.01) == obj_limited
+    ), f"Limited objective should be 1650 kg CO2, got {obj_limited}"
 
 
 def test_cumulative_category_impact_limit_respected():
@@ -613,12 +610,12 @@ def test_cumulative_category_impact_limit_respected():
         get_total_operation(solved_baseline, "P1_low_co2", t)
         for t in solved_baseline.SYSTEM_TIME
     )
-    assert pytest.approx(50, rel=0.01) == p1_op_baseline, (
-        f"Without limits, P1 should handle all demand (50), got {p1_op_baseline}"
-    )
-    assert pytest.approx(50, rel=0.01) == obj_baseline, (
-        f"Baseline climate impact should be 50, got {obj_baseline}"
-    )
+    assert (
+        pytest.approx(50, rel=0.01) == p1_op_baseline
+    ), f"Without limits, P1 should handle all demand (50), got {p1_op_baseline}"
+    assert (
+        pytest.approx(50, rel=0.01) == obj_baseline
+    ), f"Baseline climate impact should be 50, got {obj_baseline}"
 
     # Calculate baseline land_use impact
     baseline_land_use = (
@@ -626,9 +623,9 @@ def test_cumulative_category_impact_limit_respected():
         * solved_baseline.scales["foreground"]
         * solved_baseline.scales["characterization"]["land_use"]
     )
-    assert pytest.approx(500, rel=0.01) == baseline_land_use, (
-        f"Baseline land_use should be 500, got {baseline_land_use}"
-    )
+    assert (
+        pytest.approx(500, rel=0.01) == baseline_land_use
+    ), f"Baseline land_use should be 500, got {baseline_land_use}"
 
     # Now solve WITH category impact limit on land_use
     # Limit land_use to 200 (requires 30 units from P2)
@@ -640,7 +637,9 @@ def test_cumulative_category_impact_limit_respected():
     land_use_limit = 200.0
 
     model_inputs_limited = converter.OptimizationModelInputs(**model_inputs_dict)
-    model_inputs_limited.cumulative_category_impact_limits = {"land_use": land_use_limit}
+    model_inputs_limited.cumulative_category_impact_limits = {
+        "land_use": land_use_limit
+    }
 
     model_limited = optimizer.create_model(
         inputs=model_inputs_limited,
@@ -663,9 +662,9 @@ def test_cumulative_category_impact_limit_respected():
     )
 
     # Verify land_use respects the limit (within 1% tolerance)
-    assert actual_land_use <= land_use_limit * 1.01, (
-        f"Actual land_use ({actual_land_use:.2f}) exceeds limit ({land_use_limit})"
-    )
+    assert (
+        actual_land_use <= land_use_limit * 1.01
+    ), f"Actual land_use ({actual_land_use:.2f}) exceeds limit ({land_use_limit})"
 
     # Verify land_use is at or near the limit (constraint should be binding)
     assert actual_land_use >= land_use_limit * 0.99, (
@@ -674,17 +673,17 @@ def test_cumulative_category_impact_limit_respected():
     )
 
     # Verify the limited solution has higher climate impact (forced to use P2)
-    assert obj_limited > obj_baseline, (
-        f"Limited objective ({obj_limited}) should be higher than baseline ({obj_baseline})"
-    )
+    assert (
+        obj_limited > obj_baseline
+    ), f"Limited objective ({obj_limited}) should be higher than baseline ({obj_baseline})"
 
     # Expected: P1 uses ~16.67, P2 uses ~33.33
     # With land_use constraint: 10*x + 1*(50-x) <= 200  =>  x <= 150/9 = 16.67
     # Climate impact = 1*x + 5*(50-x) = 250 - 4x = 250 - 4*(150/9) = 183.33
     expected_climate = 250 - 4 * (150 / 9)  # ~183.33
-    assert pytest.approx(expected_climate, rel=0.01) == obj_limited, (
-        f"Limited climate impact should be ~{expected_climate:.1f}, got {obj_limited}"
-    )
+    assert (
+        pytest.approx(expected_climate, rel=0.01) == obj_limited
+    ), f"Limited climate impact should be ~{expected_climate:.1f}, got {obj_limited}"
 
 
 def test_time_specific_category_impact_limit_respected():
@@ -795,14 +794,14 @@ def test_time_specific_category_impact_limit_respected():
     )
 
     # Verify land_use in 2021 respects the limit
-    assert actual_land_use_2021 <= land_use_limit_2021 * 1.01, (
-        f"Land_use in 2021 ({actual_land_use_2021:.2f}) exceeds limit ({land_use_limit_2021})"
-    )
+    assert (
+        actual_land_use_2021 <= land_use_limit_2021 * 1.01
+    ), f"Land_use in 2021 ({actual_land_use_2021:.2f}) exceeds limit ({land_use_limit_2021})"
 
     # Verify constraint is binding (at the limit)
-    assert actual_land_use_2021 >= land_use_limit_2021 * 0.99, (
-        f"Land_use in 2021 ({actual_land_use_2021:.2f}) is below limit, constraint not binding"
-    )
+    assert (
+        actual_land_use_2021 >= land_use_limit_2021 * 0.99
+    ), f"Land_use in 2021 ({actual_land_use_2021:.2f}) is below limit, constraint not binding"
 
     # Verify years without limits are not constrained (should be at 100)
     actual_land_use_2020 = (
@@ -810,18 +809,18 @@ def test_time_specific_category_impact_limit_respected():
         * fg_scale
         * cat_scale
     )
-    assert pytest.approx(100, rel=0.01) == actual_land_use_2020, (
-        f"Land_use in 2020 should be 100 (all P1), got {actual_land_use_2020}"
-    )
+    assert (
+        pytest.approx(100, rel=0.01) == actual_land_use_2020
+    ), f"Land_use in 2020 should be 100 (all P1), got {actual_land_use_2020}"
 
     actual_land_use_2022 = (
         pyo.value(solved_limited.time_specific_impact["land_use", 2022])
         * fg_scale
         * cat_scale
     )
-    assert pytest.approx(100, rel=0.01) == actual_land_use_2022, (
-        f"Land_use in 2022 should be 100 (all P1), got {actual_land_use_2022}"
-    )
+    assert (
+        pytest.approx(100, rel=0.01) == actual_land_use_2022
+    ), f"Land_use in 2022 should be 100 (all P1), got {actual_land_use_2022}"
 
 
 def test_time_specific_flow_limits_respected():
@@ -891,9 +890,9 @@ def test_time_specific_flow_limits_respected():
 
     # Without limits, P1 should handle all demand (lowest emissions)
     # Total CO2 = 30 * 1 = 30 kg
-    assert pytest.approx(30, rel=0.01) == obj_baseline, (
-        f"Baseline emissions should be 30, got {obj_baseline}"
-    )
+    assert (
+        pytest.approx(30, rel=0.01) == obj_baseline
+    ), f"Baseline emissions should be 30, got {obj_baseline}"
 
     # Test 1: MINIMUM flow limit - force higher emissions in 2022
     # Require CO2 >= 20 in 2022 (higher than optimal of 10)
@@ -924,12 +923,14 @@ def test_time_specific_flow_limits_respected():
 
     # Calculate actual CO2 emissions in 2022 (denormalize)
     fg_scale = solved_min.scales["foreground"]
-    actual_co2_2022 = pyo.value(solved_min.total_elementary_flow["CO2", 2022]) * fg_scale
+    actual_co2_2022 = (
+        pyo.value(solved_min.total_elementary_flow["CO2", 2022]) * fg_scale
+    )
 
     # Verify CO2 meets the minimum limit
-    assert actual_co2_2022 >= co2_min_2022 * 0.99, (
-        f"Actual CO2 in 2022 ({actual_co2_2022:.2f}) is below min limit ({co2_min_2022})"
-    )
+    assert (
+        actual_co2_2022 >= co2_min_2022 * 0.99
+    ), f"Actual CO2 in 2022 ({actual_co2_2022:.2f}) is below min limit ({co2_min_2022})"
 
     # Verify constraint is binding (optimizer should hit exactly the min)
     assert actual_co2_2022 <= co2_min_2022 * 1.01, (
@@ -940,9 +941,9 @@ def test_time_specific_flow_limits_respected():
     # Verify objective is higher than baseline (forced suboptimal in one year)
     # Expected: 2 years at 10 kg + 1 year at 20 kg = 40 kg
     expected_obj = 2 * 10 + 20  # 40 kg
-    assert pytest.approx(expected_obj, rel=0.01) == obj_min, (
-        f"Limited objective should be {expected_obj}, got {obj_min}"
-    )
+    assert (
+        pytest.approx(expected_obj, rel=0.01) == obj_min
+    ), f"Limited objective should be {expected_obj}, got {obj_min}"
 
     # Test 2: MAX flow limit below achievable minimum should be infeasible
     # Optimal CO2 per year is 10 kg (all P1). Limit to 8 kg should be infeasible.
@@ -1070,9 +1071,9 @@ def test_cumulative_flow_limits_respected():
     )
 
     # Verify total CO2 meets the minimum limit
-    assert total_co2 >= cumulative_min * 0.99, (
-        f"Total CO2 ({total_co2:.2f}) is below min limit ({cumulative_min})"
-    )
+    assert (
+        total_co2 >= cumulative_min * 0.99
+    ), f"Total CO2 ({total_co2:.2f}) is below min limit ({cumulative_min})"
 
     # Verify constraint is binding
     assert total_co2 <= cumulative_min * 1.01, (
@@ -1081,9 +1082,9 @@ def test_cumulative_flow_limits_respected():
     )
 
     # Verify objective matches expected (50 kg)
-    assert pytest.approx(cumulative_min, rel=0.01) == obj_min, (
-        f"Limited objective should be {cumulative_min}, got {obj_min}"
-    )
+    assert (
+        pytest.approx(cumulative_min, rel=0.01) == obj_min
+    ), f"Limited objective should be {cumulative_min}, got {obj_min}"
 
     # Test 2: MAX cumulative flow limit below achievable should be infeasible
     # Optimal total CO2 is 30 kg. Limit to 25 kg should be infeasible.
@@ -1166,7 +1167,9 @@ def test_product_flow_limits_respected():
     results = solver.solve(model_limited, tee=False)
 
     # Model should be infeasible since demand (10) > production limit (5)
-    assert results.solver.termination_condition == pyo.TerminationCondition.infeasible, (
+    assert (
+        results.solver.termination_condition == pyo.TerminationCondition.infeasible
+    ), (
         f"Model should be infeasible when product limit < demand, "
         f"got {results.solver.termination_condition}"
     )
@@ -1185,7 +1188,9 @@ def test_product_flow_limits_respected():
     results_cum = solver.solve(model_cumulative, tee=False)
 
     # Model should be infeasible since total demand (30) > cumulative limit (25)
-    assert results_cum.solver.termination_condition == pyo.TerminationCondition.infeasible, (
+    assert (
+        results_cum.solver.termination_condition == pyo.TerminationCondition.infeasible
+    ), (
         f"Model should be infeasible when cumulative product limit < total demand, "
         f"got {results_cum.solver.termination_condition}"
     )
@@ -1272,9 +1277,9 @@ def test_cumulative_flow_limits_background_inventory():
         pyo.value(solved_baseline.scaled_inventory["P1", "rare_element", t]) * fg_scale
         for t in solved_baseline.SYSTEM_TIME
     )
-    assert pytest.approx(15, rel=0.01) == total_inventory, (
-        f"Baseline total inventory should be 15, got {total_inventory}"
-    )
+    assert (
+        pytest.approx(15, rel=0.01) == total_inventory
+    ), f"Baseline total inventory should be 15, got {total_inventory}"
 
     # Now test with cumulative limit of 10 (less than the 15 needed)
     # This should make the model infeasible since rare_element comes from background
@@ -1298,7 +1303,9 @@ def test_cumulative_flow_limits_background_inventory():
     # - This generates 15 kg of rare_element from background (30 * 0.5)
     # - But cumulative limit is only 10 kg
     # Therefore, the constraint cannot be satisfied.
-    assert results.solver.termination_condition == pyo.TerminationCondition.infeasible, (
+    assert (
+        results.solver.termination_condition == pyo.TerminationCondition.infeasible
+    ), (
         f"Model should be infeasible when cumulative rare_element limit (10) < "
         f"required background flows (15), got {results.solver.termination_condition}. "
         "This indicates the cumulative flow constraint is not considering background inventory flows."
@@ -1378,7 +1385,9 @@ def test_time_specific_flow_limits_background_inventory():
     solver = pyo.SolverFactory("glpk")
     results = solver.solve(model_limited, tee=False)
 
-    assert results.solver.termination_condition == pyo.TerminationCondition.infeasible, (
+    assert (
+        results.solver.termination_condition == pyo.TerminationCondition.infeasible
+    ), (
         f"Model should be infeasible when time-specific rare_element limit (3) < "
         f"required background flows (5) in 2020, got {results.solver.termination_condition}. "
         "This indicates the time-specific flow constraint is not considering background inventory flows."
diff --git a/tests/test_serialization.py b/tests/test_serialization.py
index 5792b5c..64ea956 100644
--- a/tests/test_serialization.py
+++ b/tests/test_serialization.py
@@ -2,6 +2,7 @@
 Test serialization and deserialization of OptimizationModelInputs with tuple keys,
 and saving/loading of solved Pyomo models.
 """
+
 import json
 import tempfile
 from pathlib import Path
@@ -63,22 +64,25 @@ def test_json_serialization_round_trip(abstract_system_model_inputs):
 
             # Check that all keys are tuples in loaded data
             for key in loaded_dict.keys():
-                assert isinstance(key, tuple), (
-                    f"Key in {field} should be tuple, got {type(key)}"
-                )
+                assert isinstance(
+                    key, tuple
+                ), f"Key in {field} should be tuple, got {type(key)}"
 
             # Check that the dictionaries are equal
-            assert loaded_dict == original_dict, (
-                f"Field {field} not preserved after JSON round-trip"
-            )
+            assert (
+                loaded_dict == original_dict
+            ), f"Field {field} not preserved after JSON round-trip"
 
         # Verify operation_time_limits (values are tuples)
         if original_inputs.operation_time_limits is not None:
             for key, value in loaded_inputs.operation_time_limits.items():
-                assert isinstance(value, tuple), (
-                    f"Value in operation_time_limits should be tuple, got {type(value)}"
-                )
-            assert loaded_inputs.operation_time_limits == original_inputs.operation_time_limits
+                assert isinstance(
+                    value, tuple
+                ), f"Value in operation_time_limits should be tuple, got {type(value)}"
+            assert (
+                loaded_inputs.operation_time_limits
+                == original_inputs.operation_time_limits
+            )
 
         # Verify other fields
         assert loaded_inputs.PROCESS == original_inputs.PROCESS
@@ -151,10 +155,22 @@ def test_json_with_optional_fields():
         loaded_inputs = manager_loaded.load(str(json_path))
 
         # Verify optional fields with tuple keys are preserved
-        assert loaded_inputs.process_deployment_limits_max == original_inputs.process_deployment_limits_max
-        assert loaded_inputs.process_deployment_limits_min == original_inputs.process_deployment_limits_min
-        assert loaded_inputs.process_operation_limits_max == original_inputs.process_operation_limits_max
-        assert loaded_inputs.process_operation_limits_min == original_inputs.process_operation_limits_min
+        assert (
+            loaded_inputs.process_deployment_limits_max
+            == original_inputs.process_deployment_limits_max
+        )
+        assert (
+            loaded_inputs.process_deployment_limits_min
+            == original_inputs.process_deployment_limits_min
+        )
+        assert (
+            loaded_inputs.process_operation_limits_max
+            == original_inputs.process_operation_limits_max
+        )
+        assert (
+            loaded_inputs.process_operation_limits_min
+            == original_inputs.process_operation_limits_min
+        )
         assert loaded_inputs.process_coupling == original_inputs.process_coupling
 
 
@@ -225,15 +241,15 @@ def test_solved_model_save_and_load(solved_system_model):
             for t in model.SYSTEM_TIME:
                 original_install = pyo.value(model.var_installation[p, t])
                 loaded_install = pyo.value(loaded_model.var_installation[p, t])
-                assert abs(original_install - loaded_install) < 1e-9, (
-                    f"Installation value mismatch for ({p}, {t})"
-                )
+                assert (
+                    abs(original_install - loaded_install) < 1e-9
+                ), f"Installation value mismatch for ({p}, {t})"
 
                 original_op = get_total_operation(model, p, t)
                 loaded_op = get_total_operation(loaded_model, p, t)
-                assert abs(original_op - loaded_op) < 1e-9, (
-                    f"Operation value mismatch for ({p}, {t})"
-                )
+                assert (
+                    abs(original_op - loaded_op) < 1e-9
+                ), f"Operation value mismatch for ({p}, {t})"
 
         # Verify the loaded model works with PostProcessor
         pp = PostProcessor(loaded_model)
diff --git a/tests/test_single_route_lca_comparison.py b/tests/test_single_route_lca_comparison.py
index 32eff90..287b0fe 100644
--- a/tests/test_single_route_lca_comparison.py
+++ b/tests/test_single_route_lca_comparison.py
@@ -4,6 +4,7 @@
 This is the simplest possible test: one product, one production route, no optimization.
 Verifies that optimex produces the same results as bw2calc.LCA for a trivial case.
 """
+
 from datetime import datetime
 
 import bw2calc as bc
@@ -24,84 +25,100 @@ def setup_single_route_system():
 
     # Biosphere
     bio_db = bd.Database("biosphere3")
-    bio_db.write({
-        ("biosphere3", "CO2"): {
-            "type": "emission",
-            "name": "carbon dioxide",
-        },
-    })
+    bio_db.write(
+        {
+            ("biosphere3", "CO2"): {
+                "type": "emission",
+                "name": "carbon dioxide",
+            },
+        }
+    )
     bio_db.register()
 
     # Background database
     bg_2020 = bd.Database("db_2020")
-    bg_2020.write({
-        ("db_2020", "electricity"): {
-            "name": "electricity production",
-            "location": "GLO",
-            "reference product": "electricity",
-            "exchanges": [
-                {"amount": 1, "type": "production", "input": ("db_2020", "electricity")},
-                {"amount": 0.5, "type": "biosphere", "input": ("biosphere3", "CO2")},
-            ],
-        },
-    })
+    bg_2020.write(
+        {
+            ("db_2020", "electricity"): {
+                "name": "electricity production",
+                "location": "GLO",
+                "reference product": "electricity",
+                "exchanges": [
+                    {
+                        "amount": 1,
+                        "type": "production",
+                        "input": ("db_2020", "electricity"),
+                    },
+                    {
+                        "amount": 0.5,
+                        "type": "biosphere",
+                        "input": ("biosphere3", "CO2"),
+                    },
+                ],
+            },
+        }
+    )
     bg_2020.metadata["representative_time"] = datetime(2020, 1, 1).isoformat()
     bg_2020.register()
 
     # Foreground with single product and single route
     fg = bd.Database("foreground")
-    fg.write({
-        # Product node
-        ("foreground", "Widget"): {
-            "name": "Widget",
-            "unit": "kg",
-            "type": bd.labels.product_node_default,
-        },
-        # Single production route
-        ("foreground", "Widget_Route1"): {
-            "name": "Widget production, Route 1",
-            "location": "GLO",
-            "type": bd.labels.process_node_default,
-            "operation_time_limits": (1, 2),  # Operation phase at process times 1-2
-            "exchanges": [
-                {
-                    "amount": 1,  # Produces 1 kg Widget
-                    "type": bd.labels.production_edge_default,
-                    "input": ("foreground", "Widget"),
-                    "temporal_distribution": TemporalDistribution(
-                        date=np.array([0, 1, 2, 3], dtype="timedelta64[Y]"),
-                        amount=np.array([0, 0.5, 0.5, 0]),  # Sums to 1.0
-                    ),
-                    "operation": True,
-                },
-                {
-                    "amount": 10,  # Consumes 10 kWh electricity at construction
-                    "type": bd.labels.consumption_edge_default,
-                    "input": ("db_2020", "electricity"),
-                    "temporal_distribution": TemporalDistribution(
-                        date=np.array([0, 1, 2, 3], dtype="timedelta64[Y]"),
-                        amount=np.array([1, 0, 0, 0]),  # All at construction
-                    ),
-                },
-                {
-                    "amount": 5,  # Emits 5 kg CO2 during operation
-                    "type": bd.labels.biosphere_edge_default,
-                    "input": ("biosphere3", "CO2"),
-                    "temporal_distribution": TemporalDistribution(
-                        date=np.array([0, 1, 2, 3], dtype="timedelta64[Y]"),
-                        amount=np.array([0, 0.5, 0.5, 0]),  # During operation
-                    ),
-                    "operation": True,
-                },
-            ],
-        },
-    })
+    fg.write(
+        {
+            # Product node
+            ("foreground", "Widget"): {
+                "name": "Widget",
+                "unit": "kg",
+                "type": bd.labels.product_node_default,
+            },
+            # Single production route
+            ("foreground", "Widget_Route1"): {
+                "name": "Widget production, Route 1",
+                "location": "GLO",
+                "type": bd.labels.process_node_default,
+                "operation_time_limits": (1, 2),  # Operation phase at process times 1-2
+                "exchanges": [
+                    {
+                        "amount": 1,  # Produces 1 kg Widget
+                        "type": bd.labels.production_edge_default,
+                        "input": ("foreground", "Widget"),
+                        "temporal_distribution": TemporalDistribution(
+                            date=np.array([0, 1, 2, 3], dtype="timedelta64[Y]"),
+                            amount=np.array([0, 0.5, 0.5, 0]),  # Sums to 1.0
+                        ),
+                        "operation": True,
+                    },
+                    {
+                        "amount": 10,  # Consumes 10 kWh electricity at construction
+                        "type": bd.labels.consumption_edge_default,
+                        "input": ("db_2020", "electricity"),
+                        "temporal_distribution": TemporalDistribution(
+                            date=np.array([0, 1, 2, 3], dtype="timedelta64[Y]"),
+                            amount=np.array([1, 0, 0, 0]),  # All at construction
+                        ),
+                    },
+                    {
+                        "amount": 5,  # Emits 5 kg CO2 during operation
+                        "type": bd.labels.biosphere_edge_default,
+                        "input": ("biosphere3", "CO2"),
+                        "temporal_distribution": TemporalDistribution(
+                            date=np.array([0, 1, 2, 3], dtype="timedelta64[Y]"),
+                            amount=np.array([0, 0.5, 0.5, 0]),  # During operation
+                        ),
+                        "operation": True,
+                    },
+                ],
+            },
+        }
+    )
     fg.register()
 
     # Impact method
-    bd.Method(("GWP", "example")).write([
-        (("biosphere3", "CO2"), 1),
-    ])
+    bd.Method(("GWP", "example")).write(
+        [
+            (("biosphere3", "CO2"), 1),
+        ]
+    )
 
 
 def test_single_route_matches_standard_lca(setup_single_route_system):
@@ -126,7 +143,9 @@ def test_single_route_matches_standard_lca(setup_single_route_system):
     # optimex calculation
     years = range(2020, 2030)
     td_demand = TemporalDistribution(
-        date=np.array([datetime(year, 1, 1).isoformat() for year in years], dtype='datetime64[s]'),
+        date=np.array(
+            [datetime(year, 1, 1).isoformat() for year in years], dtype="datetime64[s]"
+        ),
         amount=np.asarray([0, 0, 100, 0, 0, 0, 0, 0, 0, 0]),  # 100 kg at year 2022
     )
 
@@ -161,23 +180,25 @@ def test_single_route_matches_standard_lca(setup_single_route_system):
     print(f"Difference: {abs(obj_real - expected_gwp)}")
 
     # They should match (within numerical tolerance)
-    assert pytest.approx(obj_real, rel=1e-3) == expected_gwp, (
-        f"optimex result ({obj_real}) should match standard LCA ({expected_gwp})"
-    )
+    assert (
+        pytest.approx(obj_real, rel=1e-3) == expected_gwp
+    ), f"optimex result ({obj_real}) should match standard LCA ({expected_gwp})"
 
     # Additional check: verify postprocessing extracts correct unscaled values
     pp = postprocessing.PostProcessor(model)
     df_impacts = pp.get_impacts()
 
     # Sum all climate_change impacts across all processes and times
-    if 'climate_change' in df_impacts.columns.get_level_values(0):
-        climate_change_cols = [col for col in df_impacts.columns if col[0] == 'climate_change']
+    if "climate_change" in df_impacts.columns.get_level_values(0):
+        climate_change_cols = [
+            col for col in df_impacts.columns if col[0] == "climate_change"
+        ]
         total_cc_from_pp = df_impacts[climate_change_cols].sum().sum()
 
         print(f"\nPostprocessing climate_change total: {total_cc_from_pp}")
         print(f"Expected (from LCA): {expected_gwp}")
 
         # Postprocessing should also match standard LCA
-        assert pytest.approx(total_cc_from_pp, rel=1e-3) == expected_gwp, (
-            f"Postprocessing climate_change sum ({total_cc_from_pp}) should match standard LCA ({expected_gwp})"
-        )
+        assert (
+            pytest.approx(total_cc_from_pp, rel=1e-3) == expected_gwp
+        ), f"Postprocessing climate_change sum ({total_cc_from_pp}) should match standard LCA ({expected_gwp})"
diff --git a/tests/test_two_level_supply_chain.py b/tests/test_two_level_supply_chain.py
index e100828..f3bccf0 100644
--- a/tests/test_two_level_supply_chain.py
+++ b/tests/test_two_level_supply_chain.py
@@ -5,6 +5,7 @@
 This test verifies that optimex produces the same results as a standard LCA
 when there are no optimization choices (single route).
 """
+
 from datetime import datetime
 
 import bw2calc as bc
@@ -25,137 +26,144 @@ def setup_two_level_system():
 
     # Biosphere
     bio_db = bd.Database("biosphere3")
-    bio_db.write({
-        ("biosphere3", "CO2"): {
-            "type": "emission",
-            "name": "carbon dioxide",
-        },
-    })
+    bio_db.write(
+        {
+            ("biosphere3", "CO2"): {
+                "type": "emission",
+                "name": "carbon dioxide",
+            },
+        }
+    )
     bio_db.register()
 
     # Background database
     bg_2020 = bd.Database("db_2020")
-    bg_2020.write({
-        ("db_2020", "I1"): {
-            "name": "node I1",
-            "location": "somewhere",
-            "reference product": "I1",
-            "exchanges": [
-                {"amount": 1, "type": "production", "input": ("db_2020", "I1")},
-                {"amount": 1, "type": "biosphere", "input": ("biosphere3", "CO2")},
-            ],
-        },
-    })
+    bg_2020.write(
+        {
+            ("db_2020", "I1"): {
+                "name": "node I1",
+                "location": "somewhere",
+                "reference product": "I1",
+                "exchanges": [
+                    {"amount": 1, "type": "production", "input": ("db_2020", "I1")},
+                    {"amount": 1, "type": "biosphere", "input": ("biosphere3", "CO2")},
+                ],
+            },
+        }
+    )
     bg_2020.metadata["representative_time"] = datetime(2020, 1, 1).isoformat()
     bg_2020.register()
 
     # Foreground with two-level supply chain
     fg = bd.Database("foreground")
-    fg.write({
-        # Product 1 (intermediate product)
-        ("foreground", "Product 1"): {
-            "name": "Product 1",
-            "unit": "kg",
-            "type": bd.labels.product_node_default,
-        },
-        ("foreground", "P1R1"): {
-            "name": "Product 1 production, Route 1",
-            "location": "somewhere",
-            "type": bd.labels.process_node_default,
-            "operation_time_limits": (1, 2),
-            "exchanges": [
-                {
-                    "amount": 1,
-                    "type": bd.labels.production_edge_default,
-                    "input": ("foreground", "Product 1"),
-                    "temporal_distribution": TemporalDistribution(
-                        date=np.array(range(4), dtype="timedelta64[Y]"),
-                        amount=np.array([0, 0.5, 0.5, 0]),
-                    ),
-                    "operation": True,
-                },
-                {
-                    "amount": 27.5,
-                    "type": bd.labels.consumption_edge_default,
-                    "input": ("db_2020", "I1"),
-                    "temporal_distribution": TemporalDistribution(
-                        date=np.array(range(4), dtype="timedelta64[Y]"),
-                        amount=np.array([1, 0, 0, 0]),
-                    ),
-                },
-                {
-                    "amount": 20,
-                    "type": bd.labels.biosphere_edge_default,
-                    "input": ("biosphere3", "CO2"),
-                    "temporal_distribution": TemporalDistribution(
-                        date=np.array(range(4), dtype="timedelta64[Y]"),
-                        amount=np.array([0, 0.5, 0.5, 0]),
-                    ),
-                    "operation": True,
-                },
-            ],
-        },
-
-        # Product 2 (final product, consumes Product 1)
-        ("foreground", "Product 2"): {
-            "name": "Product 2",
-            "unit": "kg",
-            "type": bd.labels.product_node_default,
-        },
-        ("foreground", "P2R1"): {
-            "name": "Product 2 production, Route 1",
-            "location": "somewhere",
-            "type": bd.labels.process_node_default,
-            "operation_time_limits": (1, 2),
-            "exchanges": [
-                {
-                    "amount": 1,
-                    "type": bd.labels.production_edge_default,
-                    "input": ("foreground", "Product 2"),
-                    "temporal_distribution": TemporalDistribution(
-                        date=np.array(range(4), dtype="timedelta64[Y]"),
-                        amount=np.array([0, 0.5, 0.5, 0]),
-                    ),
-                    "operation": True,
-                },
-                {
-                    "amount": 1,
-                    "type": bd.labels.consumption_edge_default,
-                    "input": ("foreground", "Product 1"),
-                    "temporal_distribution": TemporalDistribution(
-                        date=np.array(range(4), dtype="timedelta64[Y]"),
-                        amount=np.array([0, 0.5, 0.5, 0]),
-                    ),
-                    "operation": True,
-                },
-                {
-                    "amount": 15.5,
-                    "type": bd.labels.consumption_edge_default,
-                    "input": ("db_2020", "I1"),
-                    "temporal_distribution": TemporalDistribution(
-                        date=np.array(range(4), dtype="timedelta64[Y]"),
-                        amount=np.array([1, 0, 0, 0]),
-                    ),
-                },
-                {
-                    "amount": 20,
-                    "type": bd.labels.biosphere_edge_default,
-                    "input": ("biosphere3", "CO2"),
-                    "temporal_distribution": TemporalDistribution(
-                        date=np.array(range(4), dtype="timedelta64[Y]"),
-                        amount=np.array([0, 0.5, 0.5, 0]),
-                    ),
-                    "operation": True,
-                },
-            ],
-        },
-    })
+    fg.write(
+        {
+            # Product 1 (intermediate product)
+            ("foreground", "Product 1"): {
+                "name": "Product 1",
+                "unit": "kg",
+                "type": bd.labels.product_node_default,
+            },
+            ("foreground", "P1R1"): {
+                "name": "Product 1 production, Route 1",
+                "location": "somewhere",
+                "type": bd.labels.process_node_default,
+                "operation_time_limits": (1, 2),
+                "exchanges": [
+                    {
+                        "amount": 1,
+                        "type": bd.labels.production_edge_default,
+                        "input": ("foreground", "Product 1"),
+                        "temporal_distribution": TemporalDistribution(
+                            date=np.array(range(4), dtype="timedelta64[Y]"),
+                            amount=np.array([0, 0.5, 0.5, 0]),
+                        ),
+                        "operation": True,
+                    },
+                    {
+                        "amount": 27.5,
+                        "type": bd.labels.consumption_edge_default,
+                        "input": ("db_2020", "I1"),
+                        "temporal_distribution": TemporalDistribution(
+                            date=np.array(range(4), dtype="timedelta64[Y]"),
+                            amount=np.array([1, 0, 0, 0]),
+                        ),
+                    },
+                    {
+                        "amount": 20,
+                        "type": bd.labels.biosphere_edge_default,
+                        "input": ("biosphere3", "CO2"),
+                        "temporal_distribution": TemporalDistribution(
+                            date=np.array(range(4), dtype="timedelta64[Y]"),
+                            amount=np.array([0, 0.5, 0.5, 0]),
+                        ),
+                        "operation": True,
+                    },
+                ],
+            },
+            # Product 2 (final product, consumes Product 1)
+            ("foreground", "Product 2"): {
+                "name": "Product 2",
+                "unit": "kg",
+                "type": bd.labels.product_node_default,
+            },
+            ("foreground", "P2R1"): {
+                "name": "Product 2 production, Route 1",
+                "location": "somewhere",
+                "type": bd.labels.process_node_default,
+                "operation_time_limits": (1, 2),
+                "exchanges": [
+                    {
+                        "amount": 1,
+                        "type": bd.labels.production_edge_default,
+                        "input": ("foreground", "Product 2"),
+                        "temporal_distribution": TemporalDistribution(
+                            date=np.array(range(4), dtype="timedelta64[Y]"),
+                            amount=np.array([0, 0.5, 0.5, 0]),
+                        ),
+                        "operation": True,
+                    },
+                    {
+                        "amount": 1,
+                        "type": bd.labels.consumption_edge_default,
+                        "input": ("foreground", "Product 1"),
+                        "temporal_distribution": TemporalDistribution(
+                            date=np.array(range(4), dtype="timedelta64[Y]"),
+                            amount=np.array([0, 0.5, 0.5, 0]),
+                        ),
+                        "operation": True,
+                    },
+                    {
+                        "amount": 15.5,
+                        "type": bd.labels.consumption_edge_default,
+                        "input": ("db_2020", "I1"),
+                        "temporal_distribution": TemporalDistribution(
+                            date=np.array(range(4), dtype="timedelta64[Y]"),
+                            amount=np.array([1, 0, 0, 0]),
+                        ),
+                    },
+                    {
+                        "amount": 20,
+                        "type": bd.labels.biosphere_edge_default,
+                        "input": ("biosphere3", "CO2"),
+                        "temporal_distribution": TemporalDistribution(
+                            date=np.array(range(4), dtype="timedelta64[Y]"),
+                            amount=np.array([0, 0.5, 0.5, 0]),
+                        ),
+                        "operation": True,
+                    },
+                ],
+            },
+        }
+    )
     fg.register()
 
     # Impact method
-    bd.Method(("GWP", "example")).write([
-        (("biosphere3", "CO2"), 1),
-    ])
+    bd.Method(("GWP", "example")).write(
+        [
+            (("biosphere3", "CO2"), 1),
+        ]
+    )
 
 
 def test_two_level_supply_chain_matches_lca(setup_two_level_system):
@@ -173,7 +181,9 @@ def test_two_level_supply_chain_matches_lca(setup_two_level_system):
     # optimex calculation
     years = range(2020, 2030)
     td_demand = TemporalDistribution(
-        date=np.array([datetime(year, 1, 1).isoformat() for year in years], dtype='datetime64[s]'),
+        date=np.array(
+            [datetime(year, 1, 1).isoformat() for year in years], dtype="datetime64[s]"
+        ),
         amount=np.asarray([0, 0, 10, 0, 0, 0, 0, 0, 0, 0]),
     )
 
@@ -208,26 +218,28 @@ def test_two_level_supply_chain_matches_lca(setup_two_level_system):
     print(f"Difference: {abs(obj_real - expected_gwp)}")
 
     # They should match (within numerical tolerance)
-    assert pytest.approx(obj_real, rel=1e-3) == expected_gwp, (
-        f"optimex result ({obj_real}) should match standard LCA ({expected_gwp})"
-    )
+    assert (
+        pytest.approx(obj_real, rel=1e-3) == expected_gwp
+    ), f"optimex result ({obj_real}) should match standard LCA ({expected_gwp})"
 
     # Additional check: verify postprocessing extracts correct unscaled values
     pp = postprocessing.PostProcessor(model)
     df_impacts = pp.get_impacts()
 
     # Sum all climate_change impacts across all processes and times
-    if 'climate_change' in df_impacts.columns.get_level_values(0):
-        climate_change_cols = [col for col in df_impacts.columns if col[0] == 'climate_change']
+    if "climate_change" in df_impacts.columns.get_level_values(0):
+        climate_change_cols = [
+            col for col in df_impacts.columns if col[0] == "climate_change"
+        ]
         total_cc_from_pp = df_impacts[climate_change_cols].sum().sum()
 
         print(f"\nPostprocessing climate_change total: {total_cc_from_pp}")
         print(f"Expected (from LCA): {expected_gwp}")
 
         # Postprocessing should also match standard LCA
-        assert pytest.approx(total_cc_from_pp, rel=1e-3) == expected_gwp, (
-            f"Postprocessing climate_change sum ({total_cc_from_pp}) should match standard LCA ({expected_gwp})"
-        )
+        assert (
+            pytest.approx(total_cc_from_pp, rel=1e-3) == expected_gwp
+        ), f"Postprocessing climate_change sum ({total_cc_from_pp}) should match standard LCA ({expected_gwp})"
 
 
 def test_two_level_supply_chain_multi_temporal_demand(setup_two_level_system):
@@ -251,9 +263,9 @@ def test_two_level_supply_chain_multi_temporal_demand(setup_two_level_system):
     }
 
     # Calculate expected total impact from standard LCA
-    print("\n" + "="*80)
+    print("\n" + "=" * 80)
     print("STANDARD LCA CALCULATIONS")
-    print("="*80)
+    print("=" * 80)
     expected_gwp_total = 0
     for year, amount in demand_schedule.items():
         lca = bc.LCA({product_2: amount}, method=("GWP", "example"))
@@ -269,7 +281,9 @@ def test_two_level_supply_chain_multi_temporal_demand(setup_two_level_system):
     demand_amounts = [demand_schedule.get(year, 0) for year in years]
 
     td_demand = TemporalDistribution(
-        date=np.array([datetime(year, 1, 1).isoformat() for year in years], dtype='datetime64[s]'),
+        date=np.array(
+            [datetime(year, 1, 1).isoformat() for year in years], dtype="datetime64[s]"
+        ),
         amount=np.asarray(demand_amounts),
     )
 
@@ -300,17 +314,17 @@ def test_two_level_supply_chain_multi_temporal_demand(setup_two_level_system):
 
     _, obj_real, results = optimizer.solve_model(model, solver_name="glpk")
 
-    print("\n" + "="*80)
+    print("\n" + "=" * 80)
     print("OPTIMEX RESULTS")
-    print("="*80)
+    print("=" * 80)
     print(f"optimex GWP: {obj_real:.2f} kg CO2")
     print(f"Expected GWP: {expected_gwp_total:.2f} kg CO2")
     print(f"Difference: {abs(obj_real - expected_gwp_total):.4f}")
 
     # Verify total impact matches
-    assert pytest.approx(obj_real, rel=1e-3) == expected_gwp_total, (
-        f"optimex result ({obj_real}) should match sum of standard LCA ({expected_gwp_total})"
-    )
+    assert (
+        pytest.approx(obj_real, rel=1e-3) == expected_gwp_total
+    ), f"optimex result ({obj_real}) should match sum of standard LCA ({expected_gwp_total})"
 
     # Extract and verify temporal distribution of results
     pp = postprocessing.PostProcessor(model)
@@ -318,10 +332,14 @@ def test_two_level_supply_chain_multi_temporal_demand(setup_two_level_system):
     df_operation = pp.get_operation()
     df_impacts = pp.get_impacts()
 
-    print("\n" + "="*80)
+    print("\n" + "=" * 80)
     print("INSTALLATION (Product 2 production)")
-    print("="*80)
-    p2_process_id = [p for p in model.PROCESS if model.process_names[p] == "Product 2 production, Route 1"][0]
+    print("=" * 80)
+    p2_process_id = [
+        p
+        for p in model.PROCESS
+        if model.process_names[p] == "Product 2 production, Route 1"
+    ][0]
     if p2_process_id in df_installation.columns:
         print(df_installation[[p2_process_id]])
 
@@ -331,12 +349,16 @@ def test_two_level_supply_chain_multi_temporal_demand(setup_two_level_system):
             # Check that there's installation in years before demand
             years_before = [y for y in df_installation.index if y < year]
             if years_before:
-                total_installation_before = df_installation.loc[years_before, p2_process_id].sum()
-                print(f"\nInstallation before {year} (demand={amount}): {total_installation_before:.2f}")
+                total_installation_before = df_installation.loc[
+                    years_before, p2_process_id
+                ].sum()
+                print(
+                    f"\nInstallation before {year} (demand={amount}): {total_installation_before:.2f}"
+                )
 
-    print("\n" + "="*80)
+    print("\n" + "=" * 80)
     print("OPERATION (Product 2 production)")
-    print("="*80)
+    print("=" * 80)
     if p2_process_id in df_operation.columns:
         print(df_operation[[p2_process_id]])
 
@@ -346,14 +368,18 @@ def test_two_level_supply_chain_multi_temporal_demand(setup_two_level_system):
                 operation = df_operation.loc[year, p2_process_id]
                 print(f"\nYear {year}: Operation={operation:.2f}, Demand={amount}")
                 # Operation should equal demand for single-route system
-                assert pytest.approx(operation, rel=1e-2) == amount, (
-                    f"Operation at {year} ({operation}) should match demand ({amount})"
-                )
+                assert (
+                    pytest.approx(operation, rel=1e-2) == amount
+                ), f"Operation at {year} ({operation}) should match demand ({amount})"
 
-    print("\n" + "="*80)
+    print("\n" + "=" * 80)
     print("INTERNAL DEMAND VERIFICATION (Product 1)")
-    print("="*80)
-    p1_process_id = [p for p in model.PROCESS if model.process_names[p] == "Product 1 production, Route 1"][0]
+    print("=" * 80)
+    p1_process_id = [
+        p
+        for p in model.PROCESS
+        if model.process_names[p] == "Product 1 production, Route 1"
+    ][0]
 
     # Product 2 consumes 1 unit of Product 1 per unit produced
     # So Product 1 operation should match Product 2 operation
@@ -367,25 +393,29 @@ def test_two_level_supply_chain_multi_temporal_demand(setup_two_level_system):
         # They should match (Product 2 needs 1 unit of Product 1 per unit)
         for year in demand_schedule.keys():
             if year in df_operation.index:
-                assert pytest.approx(p1_operation[year], rel=1e-2) == p2_operation[year], (
+                assert (
+                    pytest.approx(p1_operation[year], rel=1e-2) == p2_operation[year]
+                ), (
                     f"Product 1 operation ({p1_operation[year]}) should match "
                     f"Product 2 operation ({p2_operation[year]}) at year {year}"
                 )
 
     # Verify postprocessing impact totals
-    if 'climate_change' in df_impacts.columns.get_level_values(0):
-        climate_change_cols = [col for col in df_impacts.columns if col[0] == 'climate_change']
+    if "climate_change" in df_impacts.columns.get_level_values(0):
+        climate_change_cols = [
+            col for col in df_impacts.columns if col[0] == "climate_change"
+        ]
         total_cc_from_pp = df_impacts[climate_change_cols].sum().sum()
 
-        print("\n" + "="*80)
+        print("\n" + "=" * 80)
         print("POSTPROCESSING VERIFICATION")
-        print("="*80)
+        print("=" * 80)
         print(f"Total impact from PostProcessor: {total_cc_from_pp:.2f}")
         print(f"Expected (from LCA): {expected_gwp_total:.2f}")
 
-        assert pytest.approx(total_cc_from_pp, rel=1e-3) == expected_gwp_total, (
-            f"Postprocessing total ({total_cc_from_pp}) should match expected ({expected_gwp_total})"
-        )
+        assert (
+            pytest.approx(total_cc_from_pp, rel=1e-3) == expected_gwp_total
+        ), f"Postprocessing total ({total_cc_from_pp}) should match expected ({expected_gwp_total})"
 
         # Verify impacts are distributed across correct years
         print("\nImpact distribution by year:")
@@ -394,6 +424,6 @@ def test_two_level_supply_chain_multi_temporal_demand(setup_two_level_system):
             if yearly_impacts[year] > 0.1:  # Only show non-negligible impacts
                 print(f"  {year}: {yearly_impacts[year]:.2f} kg CO2")
 
-    print("\n" + "="*80)
+    print("\n" + "=" * 80)
     print("TEST PASSED: Multi-temporal two-level supply chain works correctly!")
-    print("="*80)
+    print("=" * 80)
diff --git a/tests/test_vintage_deterministic.py b/tests/test_vintage_deterministic.py
index 5764a6f..ef86824 100644
--- a/tests/test_vintage_deterministic.py
+++ b/tests/test_vintage_deterministic.py
@@ -18,8 +18,9 @@
 4. Sum up total impacts and compare with model results
 """
 
-import pytest
 import pyomo.environ as pyo
+import pytest
+
 from optimex import converter, optimizer
 
 
@@ -118,9 +119,9 @@ def test_single_process_production_equals_demand(self):
         for t in [2025, 2026, 2027]:
             var_op = get_total_operation(solved_model, "Plant", t)
             expected_operation = 100 / 10  # demand / production_rate
-            assert var_op == pytest.approx(expected_operation, rel=0.001), (
-                f"At {t}: var_operation = {var_op}, expected {expected_operation}"
-            )
+            assert var_op == pytest.approx(
+                expected_operation, rel=0.001
+            ), f"At {t}: var_operation = {var_op}, expected {expected_operation}"
 
     def test_vintage_evolution_impacts_with_manual_calculation(self):
         """
@@ -216,14 +217,16 @@ def test_vintage_evolution_impacts_with_manual_calculation(self):
 
         # Check var_operation
         var_op_2026 = get_total_operation(solved_model, "Plant", 2026)
-        assert var_op_2026 == pytest.approx(1.0, rel=0.001), f"var_operation at 2026 = {var_op_2026}"
+        assert var_op_2026 == pytest.approx(
+            1.0, rel=0.001
+        ), f"var_operation at 2026 = {var_op_2026}"
 
         # Check objective (total CO2)
         # At 2026: 9 kWh * 0.5 kg/kWh = 4.5 kg
         expected_impact = 4.5
-        assert objective == pytest.approx(expected_impact, rel=0.01), (
-            f"Objective = {objective}, expected {expected_impact}"
-        )
+        assert objective == pytest.approx(
+            expected_impact, rel=0.01
+        ), f"Objective = {objective}, expected {expected_impact}"
 
     def test_multi_year_with_background_evolution(self):
         """
@@ -309,9 +312,9 @@ def test_multi_year_with_background_evolution(self):
         # Verify var_operation = 1 at each year (demand/production_rate = 100/100)
         for t in [2025, 2026, 2027]:
             var_op = get_total_operation(solved_model, "Plant", t)
-            assert var_op == pytest.approx(1.0, rel=0.001), (
-                f"var_operation at {t} = {var_op}, expected 1.0"
-            )
+            assert var_op == pytest.approx(
+                1.0, rel=0.001
+            ), f"var_operation at {t} = {var_op}, expected 1.0"
 
         # Manual calculation:
         # 2025: vintage=2025, evolution=1.0, elec=10*1=10, CO2/kWh=0.5, impact=5
@@ -319,9 +322,9 @@ def test_multi_year_with_background_evolution(self):
         # 2027: vintage=2027, evolution=0.8, elec=10*0.8=8, CO2/kWh=0.3, impact=2.4
         # Total = 11
         expected_impact = 5 + 3.6 + 2.4
-        assert objective == pytest.approx(expected_impact, rel=0.01), (
-            f"Objective = {objective}, expected {expected_impact}"
-        )
+        assert objective == pytest.approx(
+            expected_impact, rel=0.01
+        ), f"Objective = {objective}, expected {expected_impact}"
 
     def test_simple_no_vintage_baseline(self):
         """
@@ -389,9 +392,9 @@ def test_simple_no_vintage_baseline(self):
 
         # Impact = electricity * CO2_per_kWh = 10 * 0.5 = 5
         expected_impact = 5.0
-        assert objective == pytest.approx(expected_impact, rel=0.001), (
-            f"Objective = {objective}, expected {expected_impact}"
-        )
+        assert objective == pytest.approx(
+            expected_impact, rel=0.001
+        ), f"Objective = {objective}, expected {expected_impact}"
 
     def test_vintage_improvements_reduces_impact(self):
         """
@@ -462,7 +465,9 @@ def test_vintage_improvements_reduces_impact(self):
             ("Plant", "electricity", 2025): 1.0,
             ("Plant", "electricity", 2027): 0.5,  # 50% less electricity
         }
-        with_evolution_inputs = converter.OptimizationModelInputs(**with_evolution_config)
+        with_evolution_inputs = converter.OptimizationModelInputs(
+            **with_evolution_config
+        )
         with_evolution_model = optimizer.create_model(
             inputs=with_evolution_inputs,
             objective_category="GWP",
@@ -474,9 +479,9 @@ def test_vintage_improvements_reduces_impact(self):
 
         # No evolution: 3 years * 10 kWh * 0.5 = 15 kg CO2
         expected_no_evolution = 15.0
-        assert no_evolution_obj == pytest.approx(expected_no_evolution, rel=0.01), (
-            f"No evolution impact = {no_evolution_obj}, expected {expected_no_evolution}"
-        )
+        assert no_evolution_obj == pytest.approx(
+            expected_no_evolution, rel=0.01
+        ), f"No evolution impact = {no_evolution_obj}, expected {expected_no_evolution}"
 
         # With evolution:
         # 2025: 10 * 1.0 * 0.5 = 5
@@ -484,9 +489,9 @@ def test_vintage_improvements_reduces_impact(self):
         # 2027: 10 * 0.5 * 0.5 = 2.5
         # Total = 11.25
         expected_with_evolution = 5 + 3.75 + 2.5
-        assert with_evolution_obj == pytest.approx(expected_with_evolution, rel=0.01), (
-            f"With evolution impact = {with_evolution_obj}, expected {expected_with_evolution}"
-        )
+        assert with_evolution_obj == pytest.approx(
+            expected_with_evolution, rel=0.01
+        ), f"With evolution impact = {with_evolution_obj}, expected {expected_with_evolution}"
 
         # With evolution should be lower
         assert with_evolution_obj < no_evolution_obj, (
@@ -582,9 +587,9 @@ def test_postprocessor_production_matches_demand(self):
             print(f"t={t}: production={production}, demand={demand}")
 
             # This assertion will PASS now that postprocessor uses 4D calculation
-            assert production == pytest.approx(demand, rel=0.01), (
-                f"Production {production} should match demand {demand} at t={t}"
-            )
+            assert production == pytest.approx(
+                demand, rel=0.01
+            ), f"Production {production} should match demand {demand} at t={t}"
 
     def test_vintage_improvements_only_affects_specified_flows(self):
         """
@@ -695,9 +700,9 @@ def test_vintage_improvements_only_affects_specified_flows(self):
             demand = 600
             print(f"t={t}: total_production={total_production}, demand={demand}")
 
-            assert total_production == pytest.approx(demand, rel=0.01), (
-                f"Total production {total_production} should match demand {demand} at t={t}"
-            )
+            assert total_production == pytest.approx(
+                demand, rel=0.01
+            ), f"Total production {total_production} should match demand {demand} at t={t}"
 
         # Also verify var_operation values are reasonable
         # With production_rate = 300 per installation for 3D path:
@@ -802,11 +807,11 @@ def test_single_foreground_process_simple_vintage(self):
 
         model_inputs = converter.OptimizationModelInputs(**inputs)
         model = optimizer.create_model(
-            inputs=model_inputs,
-            objective_category="GWP",
-            name="simple_vintage_test"
+            inputs=model_inputs, objective_category="GWP", name="simple_vintage_test"
+        )
+        solved, obj, results = optimizer.solve_model(
+            model, solver_name="glpk", tee=False
         )
-        solved, obj, results = optimizer.solve_model(model, solver_name="glpk", tee=False)
 
         assert results.solver.termination_condition == pyo.TerminationCondition.optimal
 
@@ -815,13 +820,15 @@ def test_single_foreground_process_simple_vintage(self):
         production_rate = 1.0
         actual_production = production_rate * operation_2022
 
-        assert actual_production == pytest.approx(100.0, rel=1e-6), \
-            f"Production ({actual_production}) must exactly equal demand (100)"
+        assert actual_production == pytest.approx(
+            100.0, rel=1e-6
+        ), f"Production ({actual_production}) must exactly equal demand (100)"
 
         # Verify total impact: 100 × 1.5 × 0.5 = 75 kg CO2
         expected_impact = 100 * 1.5 * 0.5
-        assert obj == pytest.approx(expected_impact, rel=1e-5), \
-            f"Impact ({obj}) should match manual calculation ({expected_impact})"
+        assert obj == pytest.approx(
+            expected_impact, rel=1e-5
+        ), f"Impact ({obj}) should match manual calculation ({expected_impact})"
 
     def test_single_process_with_background_vintage(self):
         """
@@ -880,18 +887,18 @@ def test_single_process_with_background_vintage(self):
                 ("grid_2022", 2021): 0.5,
                 ("grid_2022", 2022): 1.0,
             },
-            "characterization": {
-                ("GWP", "CO2", t): 1.0 for t in [2020, 2021, 2022]
-            },
+            "characterization": {("GWP", "CO2", t): 1.0 for t in [2020, 2021, 2022]},
         }
 
         model_inputs = converter.OptimizationModelInputs(**inputs)
         model = optimizer.create_model(
             inputs=model_inputs,
             objective_category="GWP",
-            name="background_vintage_test"
+            name="background_vintage_test",
+        )
+        solved, obj, results = optimizer.solve_model(
+            model, solver_name="glpk", tee=False
         )
-        solved, obj, results = optimizer.solve_model(model, solver_name="glpk", tee=False)
 
         assert results.solver.termination_condition == pyo.TerminationCondition.optimal
 
@@ -900,13 +907,15 @@ def test_single_process_with_background_vintage(self):
         production_rate = 1.0
         actual_production = production_rate * operation_2021
 
-        assert actual_production == pytest.approx(50.0, rel=1e-6), \
-            f"Production ({actual_production}) must exactly equal demand (50)"
+        assert actual_production == pytest.approx(
+            50.0, rel=1e-6
+        ), f"Production ({actual_production}) must exactly equal demand (50)"
 
         # Manual: 50 × 10 × 0.75 = 375 kg CO2
         expected_impact = 50 * 10 * 0.75
-        assert obj == pytest.approx(expected_impact, rel=1e-5), \
-            f"Impact ({obj}) should match manual calculation ({expected_impact})"
+        assert obj == pytest.approx(
+            expected_impact, rel=1e-5
+        ), f"Impact ({obj}) should match manual calculation ({expected_impact})"
 
     def test_combined_foreground_and_background_vintage(self):
         """
@@ -965,18 +974,16 @@ def test_combined_foreground_and_background_vintage(self):
                 ("grid_2022", 2021): 0.5,
                 ("grid_2022", 2022): 1.0,
             },
-            "characterization": {
-                ("GWP", "CO2", t): 1.0 for t in [2020, 2021, 2022]
-            },
+            "characterization": {("GWP", "CO2", t): 1.0 for t in [2020, 2021, 2022]},
         }
 
         model_inputs = converter.OptimizationModelInputs(**inputs)
         model = optimizer.create_model(
-            inputs=model_inputs,
-            objective_category="GWP",
-            name="combined_vintage_test"
+            inputs=model_inputs, objective_category="GWP", name="combined_vintage_test"
+        )
+        solved, obj, results = optimizer.solve_model(
+            model, solver_name="glpk", tee=False
         )
-        solved, obj, results = optimizer.solve_model(model, solver_name="glpk", tee=False)
 
         assert results.solver.termination_condition == pyo.TerminationCondition.optimal
 
@@ -984,13 +991,15 @@ def test_combined_foreground_and_background_vintage(self):
         operation_2022 = get_total_operation(solved, "Plant", 2022)
         actual_production = operation_2022
 
-        assert actual_production == pytest.approx(100.0, rel=1e-6), \
-            f"Production ({actual_production}) must exactly equal demand (100)"
+        assert actual_production == pytest.approx(
+            100.0, rel=1e-6
+        ), f"Production ({actual_production}) must exactly equal demand (100)"
 
         # Manual: 100 × 1.5 × 0.5 = 75 kg CO2
         expected_impact = 100 * 1.5 * 0.5
-        assert obj == pytest.approx(expected_impact, rel=1e-5), \
-            f"Impact ({obj}) should match manual calculation ({expected_impact})"
+        assert obj == pytest.approx(
+            expected_impact, rel=1e-5
+        ), f"Impact ({obj}) should match manual calculation ({expected_impact})"
 
     def test_multiple_installations_different_vintages(self):
         """
@@ -1050,21 +1059,17 @@ def test_multiple_installations_different_vintages(self):
             "background_inventory": {
                 ("grid", "electricity", "CO2"): 0.5,
             },
-            "mapping": {
-                ("grid", t): 1.0 for t in [2020, 2021, 2022]
-            },
-            "characterization": {
-                ("GWP", "CO2", t): 1.0 for t in [2020, 2021, 2022]
-            },
+            "mapping": {("grid", t): 1.0 for t in [2020, 2021, 2022]},
+            "characterization": {("GWP", "CO2", t): 1.0 for t in [2020, 2021, 2022]},
         }
 
         model_inputs = converter.OptimizationModelInputs(**inputs)
         model = optimizer.create_model(
-            inputs=model_inputs,
-            objective_category="GWP",
-            name="multiple_vintages_test"
+            inputs=model_inputs, objective_category="GWP", name="multiple_vintages_test"
+        )
+        solved, obj, results = optimizer.solve_model(
+            model, solver_name="glpk", tee=False
         )
-        solved, obj, results = optimizer.solve_model(model, solver_name="glpk", tee=False)
 
         assert results.solver.termination_condition == pyo.TerminationCondition.optimal
 
@@ -1072,11 +1077,14 @@ def test_multiple_installations_different_vintages(self):
         operation_2021 = get_total_operation(solved, "Plant", 2021)
         operation_2022 = get_total_operation(solved, "Plant", 2022)
 
-        assert operation_2021 == pytest.approx(100.0, rel=1e-6), \
-            f"Production at t=2021 must equal demand (100)"
-        assert operation_2022 == pytest.approx(150.0, rel=1e-6), \
-            f"Production at t=2022 must equal demand (150)"
+        assert operation_2021 == pytest.approx(
+            100.0, rel=1e-6
+        ), f"Production at t=2021 must equal demand (100)"
+        assert operation_2022 == pytest.approx(
+            150.0, rel=1e-6
+        ), f"Production at t=2022 must equal demand (150)"
 
         # Impact should be in reasonable range based on vintage improvements
-        assert 200 <= obj <= 260, \
-            f"Impact ({obj}) should be in reasonable range based on vintage improvements"
+        assert (
+            200 <= obj <= 260
+        ), f"Impact ({obj}) should be in reasonable range based on vintage improvements"
diff --git a/tests/test_vintage_parameters.py b/tests/test_vintage_parameters.py
index 7a76653..54c2ea8 100644
--- a/tests/test_vintage_parameters.py
+++ b/tests/test_vintage_parameters.py
@@ -28,7 +28,12 @@ def base_model_inputs(self):
             "SYSTEM_TIME": [2020, 2021, 2022, 2023, 2024, 2025],
             "CATEGORY": ["climate_change"],
             "operation_time_limits": {"EV": (1, 2)},
-            "demand": {("vkm", 2022): 100, ("vkm", 2023): 100, ("vkm", 2024): 100, ("vkm", 2025): 100},
+            "demand": {
+                ("vkm", 2022): 100,
+                ("vkm", 2023): 100,
+                ("vkm", 2024): 100,
+                ("vkm", 2025): 100,
+            },
             "foreground_technosphere": {
                 ("EV", "electricity", 0): 0,
                 ("EV", "electricity", 1): 50,
@@ -110,8 +115,12 @@ def test_model_accepts_foreground_technosphere_vintages(self, base_model_inputs)
         }
         model = converter.OptimizationModelInputs(**base_model_inputs)
         assert ("EV", "electricity", 1, 2020) in model.foreground_technosphere_vintages
-        assert model.foreground_technosphere_vintages[("EV", "electricity", 1, 2020)] == 60
-        assert model.foreground_technosphere_vintages[("EV", "electricity", 1, 2025)] == 40
+        assert (
+            model.foreground_technosphere_vintages[("EV", "electricity", 1, 2020)] == 60
+        )
+        assert (
+            model.foreground_technosphere_vintages[("EV", "electricity", 1, 2025)] == 40
+        )
         assert model.REFERENCE_VINTAGES == [2020, 2025]
 
     def test_model_accepts_foreground_biosphere_vintages(self, base_model_inputs):
@@ -173,7 +182,9 @@ def test_validation_flow_in_vintage_tensors_must_exist(self, base_model_inputs):
         with pytest.raises(ValueError, match="Invalid keys.*nonexistent_flow"):
             converter.OptimizationModelInputs(**base_model_inputs)
 
-    def test_validation_process_time_in_vintage_tensors_must_exist(self, base_model_inputs):
+    def test_validation_process_time_in_vintage_tensors_must_exist(
+        self, base_model_inputs
+    ):
         """Test that process times in vintage tensors must exist in PROCESS_TIME."""
         base_model_inputs["foreground_technosphere_vintages"] = {
             ("EV", "electricity", 99, 2020): 60,  # 99 not in PROCESS_TIME
@@ -296,7 +307,9 @@ def vintage_model_inputs(self):
             },
             "operation_flow": {("EV", "vkm"): True, ("EV", "electricity"): True},
             "background_inventory": {("db_2020", "electricity", "CO2"): 0.5},
-            "mapping": {("db_2020", t): 1.0 for t in [2020, 2021, 2022, 2023, 2024, 2025]},
+            "mapping": {
+                ("db_2020", t): 1.0 for t in [2020, 2021, 2022, 2023, 2024, 2025]
+            },
             "characterization": {
                 ("climate_change", "CO2", t): 1.0
                 for t in [2020, 2021, 2022, 2023, 2024, 2025]
@@ -320,7 +333,9 @@ def test_expand_foreground_technosphere_exact_vintage(self, vintage_model_inputs
         assert effective[("EV", "electricity", 1, 2025)] == 40
         assert effective[("EV", "electricity", 2, 2025)] == 40
 
-    def test_expand_foreground_technosphere_interpolated_vintage(self, vintage_model_inputs):
+    def test_expand_foreground_technosphere_interpolated_vintage(
+        self, vintage_model_inputs
+    ):
         """Test that effective tensor interpolates between reference vintages."""
         model = converter.OptimizationModelInputs(**vintage_model_inputs)
         effective = converter.expand_foreground_tensor_with_vintages(
@@ -419,7 +434,9 @@ def vintage_optimization_inputs(self):
             },
             "operation_flow": {("EV", "vkm"): True, ("EV", "electricity"): True},
             "background_inventory": {("db_2020", "electricity", "CO2"): 0.5},
-            "mapping": {("db_2020", t): 1.0 for t in [2020, 2021, 2022, 2023, 2024, 2025]},
+            "mapping": {
+                ("db_2020", t): 1.0 for t in [2020, 2021, 2022, 2023, 2024, 2025]
+            },
             "characterization": {
                 ("climate_change", "CO2", t): 1.0
                 for t in [2020, 2021, 2022, 2023, 2024, 2025]
@@ -448,7 +465,9 @@ def test_model_solves_with_vintages(self, vintage_optimization_inputs):
             objective_category="climate_change",
             name="test_vintage_model",
         )
-        solved_model, obj, results = optimizer.solve_model(model, solver_name="glpk", tee=False)
+        solved_model, obj, results = optimizer.solve_model(
+            model, solver_name="glpk", tee=False
+        )
         assert results.solver.termination_condition.name == "optimal"
 
     def test_later_vintage_has_lower_impact(self, vintage_optimization_inputs):
@@ -461,16 +480,19 @@ def test_later_vintage_has_lower_impact(self, vintage_optimization_inputs):
 
         So the optimizer should prefer later installations if given the choice.
         """
-        from optimex import optimizer
         import pyomo.environ as pyo
 
+        from optimex import optimizer
+
         model_inputs = converter.OptimizationModelInputs(**vintage_optimization_inputs)
         model = optimizer.create_model(
             inputs=model_inputs,
             objective_category="climate_change",
             name="test_vintage_model",
         )
-        solved_model, obj, results = optimizer.solve_model(model, solver_name="glpk", tee=False)
+        solved_model, obj, results = optimizer.solve_model(
+            model, solver_name="glpk", tee=False
+        )
 
         # Get total installations at different years
         # Earlier installations (2020-2022) should have higher per-unit impact
@@ -479,12 +501,10 @@ def test_later_vintage_has_lower_impact(self, vintage_optimization_inputs):
         # Check that the model accounts for vintage differences
         # The exact behavior depends on demand timing and process lifetimes
         early_installations = sum(
-            pyo.value(solved_model.var_installation["EV", t])
-            for t in [2020, 2021]
+            pyo.value(solved_model.var_installation["EV", t]) for t in [2020, 2021]
         )
         late_installations = sum(
-            pyo.value(solved_model.var_installation["EV", t])
-            for t in [2023, 2024]
+            pyo.value(solved_model.var_installation["EV", t]) for t in [2023, 2024]
         )
 
         # Both should be non-negative (basic sanity check)
@@ -502,9 +522,10 @@ def test_optimizer_prefers_efficient_vintage(self):
         Optimizer should prefer installing in 2024 (gets 2025-interpolated efficiency)
         over installing earlier (gets worse efficiency).
         """
-        from optimex import optimizer
         import pyomo.environ as pyo
 
+        from optimex import optimizer
+
         inputs = {
             "PROCESS": ["Plant"],
             "PRODUCT": ["output"],
@@ -521,7 +542,7 @@ def test_optimizer_prefers_efficient_vintage(self):
             "foreground_technosphere": {},
             "foreground_technosphere_vintages": {
                 ("Plant", "electricity", 1, 2020): 100,  # Inefficient
-                ("Plant", "electricity", 1, 2025): 50,   # Efficient
+                ("Plant", "electricity", 1, 2025): 50,  # Efficient
             },
             "internal_demand_technosphere": {},
             "foreground_biosphere": {("Plant", "CO2", 0): 0},
@@ -530,7 +551,10 @@ def test_optimizer_prefers_efficient_vintage(self):
                 ("Plant", "output", 1, 2020): 100,
                 ("Plant", "output", 1, 2025): 100,
             },
-            "operation_flow": {("Plant", "output"): True, ("Plant", "electricity"): True},
+            "operation_flow": {
+                ("Plant", "output"): True,
+                ("Plant", "electricity"): True,
+            },
             "background_inventory": {("grid", "electricity", "CO2"): 1.0},
             "mapping": {("grid", t): 1.0 for t in range(2020, 2026)},
             "characterization": {("GWP", "CO2", t): 1.0 for t in range(2020, 2026)},
@@ -540,7 +564,9 @@ def test_optimizer_prefers_efficient_vintage(self):
         model = optimizer.create_model(
             inputs=model_inputs, objective_category="GWP", name="efficiency_test"
         )
-        solved, obj, results = optimizer.solve_model(model, solver_name="glpk", tee=False)
+        solved, obj, results = optimizer.solve_model(
+            model, solver_name="glpk", tee=False
+        )
         assert results.solver.termination_condition.name == "optimal"
 
         # Check installation pattern - should prefer later years (more efficient)
@@ -552,9 +578,12 @@ def test_optimizer_prefers_efficient_vintage(self):
         # 2024 vintage interpolates to 90% of way to 2025 efficiency: 100 - 0.8*(100-50) = 60
         # Earlier vintages are progressively worse
         # Optimizer should install as late as possible (2024)
-        assert installations[2024] > 0, "Optimizer should install in 2024 (most efficient vintage)"
-        assert sum(installations[t] for t in [2020, 2021, 2022]) == pytest.approx(0, abs=1e-6), \
-            "Optimizer should avoid early installations (inefficient vintages)"
+        assert (
+            installations[2024] > 0
+        ), "Optimizer should install in 2024 (most efficient vintage)"
+        assert sum(installations[t] for t in [2020, 2021, 2022]) == pytest.approx(
+            0, abs=1e-6
+        ), "Optimizer should avoid early installations (inefficient vintages)"
 
 
 class TestTechnologyEvolutionIntegration:
@@ -566,9 +595,10 @@ def test_vintage_improvements_affects_optimization(self):
 
         Same scenario as above but using evolution factors instead of explicit vintages.
         """
-        from optimex import optimizer
         import pyomo.environ as pyo
 
+        from optimex import optimizer
+
         inputs = {
             "PROCESS": ["Plant"],
             "PRODUCT": ["output"],
@@ -597,7 +627,10 @@ def test_vintage_improvements_affects_optimization(self):
                 ("Plant", "output", 0): 0,
                 ("Plant", "output", 1): 100,
             },
-            "operation_flow": {("Plant", "output"): True, ("Plant", "electricity"): True},
+            "operation_flow": {
+                ("Plant", "output"): True,
+                ("Plant", "electricity"): True,
+            },
             "background_inventory": {("grid", "electricity", "CO2"): 1.0},
             "mapping": {("grid", t): 1.0 for t in range(2020, 2026)},
             "characterization": {("GWP", "CO2", t): 1.0 for t in range(2020, 2026)},
@@ -607,7 +640,9 @@ def test_vintage_improvements_affects_optimization(self):
         model = optimizer.create_model(
             inputs=model_inputs, objective_category="GWP", name="evolution_test"
         )
-        solved, obj, results = optimizer.solve_model(model, solver_name="glpk", tee=False)
+        solved, obj, results = optimizer.solve_model(
+            model, solver_name="glpk", tee=False
+        )
         assert results.solver.termination_condition.name == "optimal"
 
         # Should prefer later installation (lower electricity consumption via evolution factor)
@@ -615,7 +650,9 @@ def test_vintage_improvements_affects_optimization(self):
         early_install = sum(
             pyo.value(solved.var_installation["Plant", t]) for t in [2020, 2021, 2022]
         )
-        assert late_install > early_install, "Optimizer should prefer later vintages with evolution factors"
+        assert (
+            late_install > early_install
+        ), "Optimizer should prefer later vintages with evolution factors"
 
 
 class TestMixedVintageScenarios:
@@ -630,9 +667,10 @@ def test_mixed_processes_some_with_overrides(self):
 
         Both should work correctly in the same model.
         """
-        from optimex import optimizer
         import pyomo.environ as pyo
 
+        from optimex import optimizer
+
         inputs = {
             "PROCESS": ["ProcessA", "ProcessB"],
             "PRODUCT": ["output"],
@@ -655,20 +693,26 @@ def test_mixed_processes_some_with_overrides(self):
             # ProcessA: vintage-dependent (4D sparse override)
             "foreground_technosphere_vintages": {
                 ("ProcessA", "electricity", 1, 2020): 100,  # Inefficient
-                ("ProcessA", "electricity", 1, 2025): 50,   # Efficient
+                ("ProcessA", "electricity", 1, 2025): 50,  # Efficient
             },
             "internal_demand_technosphere": {},
             "foreground_biosphere": {
-                ("ProcessA", "CO2", 0): 0, ("ProcessA", "CO2", 1): 0,
-                ("ProcessB", "CO2", 0): 0, ("ProcessB", "CO2", 1): 0,
+                ("ProcessA", "CO2", 0): 0,
+                ("ProcessA", "CO2", 1): 0,
+                ("ProcessB", "CO2", 0): 0,
+                ("ProcessB", "CO2", 1): 0,
             },
             "foreground_production": {
-                ("ProcessA", "output", 0): 0, ("ProcessA", "output", 1): 100,
-                ("ProcessB", "output", 0): 0, ("ProcessB", "output", 1): 100,
+                ("ProcessA", "output", 0): 0,
+                ("ProcessA", "output", 1): 100,
+                ("ProcessB", "output", 0): 0,
+                ("ProcessB", "output", 1): 100,
             },
             "operation_flow": {
-                ("ProcessA", "output"): True, ("ProcessA", "electricity"): True,
-                ("ProcessB", "output"): True, ("ProcessB", "electricity"): True,
+                ("ProcessA", "output"): True,
+                ("ProcessA", "electricity"): True,
+                ("ProcessB", "output"): True,
+                ("ProcessB", "electricity"): True,
             },
             "background_inventory": {("grid", "electricity", "CO2"): 1.0},
             "mapping": {("grid", t): 1.0 for t in range(2020, 2026)},
@@ -679,7 +723,9 @@ def test_mixed_processes_some_with_overrides(self):
         model = optimizer.create_model(
             inputs=model_inputs, objective_category="GWP", name="mixed_test"
         )
-        solved, obj, results = optimizer.solve_model(model, solver_name="glpk", tee=False)
+        solved, obj, results = optimizer.solve_model(
+            model, solver_name="glpk", tee=False
+        )
         assert results.solver.termination_condition.name == "optimal"
 
         # Model should solve and make sensible choices
@@ -772,13 +818,16 @@ def test_baseline_without_vintage_parameters(self, simple_system_base):
         model = optimizer.create_model(
             inputs=model_inputs, objective_category="GWP", name="baseline"
         )
-        solved, obj, results = optimizer.solve_model(model, solver_name="glpk", tee=False)
+        solved, obj, results = optimizer.solve_model(
+            model, solver_name="glpk", tee=False
+        )
 
         assert results.solver.termination_condition.name == "optimal"
 
         # Check objective (total impact)
-        assert obj == pytest.approx(100.0, rel=1e-5), \
-            "Baseline impact should be 100 kg CO2 (100 elec * 1 kg/elec * 1.0 CF)"
+        assert obj == pytest.approx(
+            100.0, rel=1e-5
+        ), "Baseline impact should be 100 kg CO2 (100 elec * 1 kg/elec * 1.0 CF)"
 
     def test_vintage_50_percent_improvement_halves_impact(self, simple_system_base):
         """
@@ -806,13 +855,16 @@ def test_vintage_50_percent_improvement_halves_impact(self, simple_system_base):
         model = optimizer.create_model(
             inputs=model_inputs, objective_category="GWP", name="with_evolution"
         )
-        solved, obj, results = optimizer.solve_model(model, solver_name="glpk", tee=False)
+        solved, obj, results = optimizer.solve_model(
+            model, solver_name="glpk", tee=False
+        )
 
         assert results.solver.termination_condition.name == "optimal"
 
         # Check objective is halved (50% efficiency = 50% impact)
-        assert obj == pytest.approx(50.0, rel=1e-5), \
-            "Impact should be 50 kg CO2 (50% of baseline due to efficiency improvement)"
+        assert obj == pytest.approx(
+            50.0, rel=1e-5
+        ), "Impact should be 50 kg CO2 (50% of baseline due to efficiency improvement)"
 
     def test_vintage_explicit_values_affect_optimization(self):
         """
@@ -865,15 +917,18 @@ def test_vintage_explicit_values_affect_optimization(self):
         model = optimizer.create_model(
             inputs=model_inputs, objective_category="GWP", name="explicit_vintages"
         )
-        solved, obj, results = optimizer.solve_model(model, solver_name="glpk", tee=False)
+        solved, obj, results = optimizer.solve_model(
+            model, solver_name="glpk", tee=False
+        )
 
         assert results.solver.termination_condition.name == "optimal"
 
         # Installing in 2021 (for operation in 2022) with interpolated 2021 vintage:
         # Interpolated electricity = (100 + 50) / 2 = 75
         # 75 electricity * 1 kg CO2/elec = 75 kg CO2
-        assert obj == pytest.approx(75.0, rel=1e-5), \
-            "Impact should be 75 kg CO2 (interpolated 2021 vintage efficiency)"
+        assert obj == pytest.approx(
+            75.0, rel=1e-5
+        ), "Impact should be 75 kg CO2 (interpolated 2021 vintage efficiency)"
 
     def test_compare_baseline_vs_improved_full_workflow(self, simple_system_base):
         """
@@ -911,8 +966,9 @@ def test_compare_baseline_vs_improved_full_workflow(self, simple_system_base):
         improvement_ratio = improved_obj / baseline_obj
         expected_ratio = 0.7  # 30% improvement means 70% of baseline
 
-        assert improvement_ratio == pytest.approx(expected_ratio, rel=1e-5), \
-            f"Impact ratio should be {expected_ratio}, got {improvement_ratio}"
+        assert improvement_ratio == pytest.approx(
+            expected_ratio, rel=1e-5
+        ), f"Impact ratio should be {expected_ratio}, got {improvement_ratio}"
 
     def test_biosphere_vintage_affects_direct_emissions(self):
         """
@@ -962,7 +1018,9 @@ def test_biosphere_vintage_affects_direct_emissions(self):
         model_2020 = optimizer.create_model(
             inputs=model_inputs_2020, objective_category="GWP", name="biosphere_2020"
         )
-        _, obj_2020, results_2020 = optimizer.solve_model(model_2020, solver_name="glpk", tee=False)
+        _, obj_2020, results_2020 = optimizer.solve_model(
+            model_2020, solver_name="glpk", tee=False
+        )
         assert results_2020.solver.termination_condition.name == "optimal"
 
         # Test 2022 vintage: demand in 2022 allows 2021 installation (closer to 2022)
@@ -973,20 +1031,22 @@ def test_biosphere_vintage_affects_direct_emissions(self):
         model_2022 = optimizer.create_model(
             inputs=model_inputs_2022, objective_category="GWP", name="biosphere_2022"
         )
-        _, obj_2022, results_2022 = optimizer.solve_model(model_2022, solver_name="glpk", tee=False)
+        _, obj_2022, results_2022 = optimizer.solve_model(
+            model_2022, solver_name="glpk", tee=False
+        )
         assert results_2022.solver.termination_condition.name == "optimal"
 
         # 2020 vintage should have higher emissions than 2021 vintage (interpolated toward 2022)
         # 2020 vintage: 200 kg CO2 per unit
         # 2021 vintage (interpolated): (200 + 100) / 2 = 150 kg CO2 per unit
         # Expected ratio: 200 / 150 = 1.33
-        assert obj_2022 < obj_2020, \
-            "Later vintage should have lower emissions"
+        assert obj_2022 < obj_2020, "Later vintage should have lower emissions"
 
         expected_ratio = 200 / 150  # 2020 emissions / 2021 interpolated emissions
         actual_ratio = obj_2020 / obj_2022
-        assert actual_ratio == pytest.approx(expected_ratio, rel=1e-2), \
-            f"Emission ratio should be ~{expected_ratio:.2f}, got {actual_ratio:.2f}"
+        assert actual_ratio == pytest.approx(
+            expected_ratio, rel=1e-2
+        ), f"Emission ratio should be ~{expected_ratio:.2f}, got {actual_ratio:.2f}"
 
     def test_production_vintage_affects_capacity(self):
         """
@@ -1041,7 +1101,9 @@ def test_production_vintage_affects_capacity(self):
         model = optimizer.create_model(
             inputs=model_inputs, objective_category="GWP", name="production_vintage"
         )
-        solved, obj, results = optimizer.solve_model(model, solver_name="glpk", tee=False)
+        solved, obj, results = optimizer.solve_model(
+            model, solver_name="glpk", tee=False
+        )
 
         assert results.solver.termination_condition.name == "optimal"
 
@@ -1049,11 +1111,11 @@ def test_production_vintage_affects_capacity(self):
         # Need 200/150 = 1.333 units to meet demand
         # Fuel: 1.333 * 50 = 66.67
         # Impact: 66.67 * 2 = 133.33 kg CO2
-        assert obj == pytest.approx(133.33, rel=1e-3), \
-            "Impact should be ~133.33 kg CO2 (interpolated production capacity)"
+        assert obj == pytest.approx(
+            133.33, rel=1e-3
+        ), "Impact should be ~133.33 kg CO2 (interpolated production capacity)"
+
 
-
-
 class TestDatabaseVintageParameterExtraction:
     """Tests for extracting vintage parameters from Brightway database."""
 
@@ -1061,6 +1123,7 @@ class TestDatabaseVintageParameterExtraction:
     def database_with_vintage_params(self, request):
         """Create a test database with vintage parameter attributes."""
         from datetime import datetime
+
         import bw2data as bd
         import numpy as np
         from bw2data.tests import bw2test
@@ -1069,132 +1132,144 @@ def database_with_vintage_params(self, request):
         # Set up test project
         project_name = "__test_vintage_db__"
         bd.projects.set_current(project_name)
-        
+
         # Register cleanup to delete project after test
         def cleanup():
             try:
                 bd.projects.delete_project(project_name, delete_dir=True)
             except:
                 pass  # Ignore cleanup errors
-        
-        request.addfinalizer(cleanup)
-        
-        # Create biosphere database
-        bio_db = bd.Database("biosphere3")
-        bio_db.write({
-            ("biosphere3", "CO2"): {
-                "type": "emission",
-                "name": "carbon dioxide",
-            },
-        })
-        bio_db.register()
-
-        # Create background database
-        bg_db = bd.Database("db_2020")
-        bg_db.write({
-            ("db_2020", "electricity"): {
-                "name": "electricity",
-                "location": "GLO",
-                "reference product": "electricity",
-                "exchanges": [
-                    {
-                        "amount": 1,
-                        "type": "production",
-                        "input": ("db_2020", "electricity"),
-                    },
-                    {
-                        "amount": 0.5,
-                        "type": "biosphere",
-                        "input": ("biosphere3", "CO2"),
-                    },
-                ],
-            },
-        })
-        bg_db.metadata["representative_time"] = datetime(2020, 1, 1).isoformat()
-        bg_db.register()
+
+        request.addfinalizer(cleanup)
+
+        # Create biosphere database
+        bio_db = bd.Database("biosphere3")
+        bio_db.write(
+            {
+                ("biosphere3", "CO2"): {
+                    "type": "emission",
+                    "name": "carbon dioxide",
+                },
+            }
+        )
+        bio_db.register()
+
+        # Create background database
+        bg_db = bd.Database("db_2020")
+        bg_db.write(
+            {
+                ("db_2020", "electricity"): {
+                    "name": "electricity",
+                    "location": "GLO",
+                    "reference product": "electricity",
+                    "exchanges": [
+                        {
+                            "amount": 1,
+                            "type": "production",
+                            "input": ("db_2020", "electricity"),
+                        },
+                        {
+                            "amount": 0.5,
+                            "type": "biosphere",
+                            "input": ("biosphere3", "CO2"),
+                        },
+                    ],
+                },
+            }
+        )
+        bg_db.metadata["representative_time"] = datetime(2020, 1, 1).isoformat()
+        bg_db.register()
 
         # Create foreground database with vintage parameters
         fg_db = bd.Database("foreground")
-        fg_db.write({
-            # Product node
-            ("foreground", "vkm"): {
-                "name": "vehicle-km",
-                "type": bd.labels.product_node_default,
-                "unit": "km",
-            },
-            # Process node with vintage-dependent exchange
-            ("foreground", "EV"): {
-                "name": "Electric Vehicle",
-                "type": bd.labels.process_node_default,
-                "operation_time_limits": (1, 2),
-                "exchanges": [
-                    {
-                        "amount": 1,
-                        "type": bd.labels.production_edge_default,
-                        "input": ("foreground", "vkm"),
-                        "temporal_distribution": TemporalDistribution(
-                            date=np.array([0, 1, 2, 3], dtype="timedelta64[Y]"),
-                            amount=np.array([0, 0.5, 0.5, 0]),
-                        ),
-                        "operation": True,
-                    },
-                    {
-                        "amount": 60,  # Base amount (will be overridden by vintage_amounts)
-                        "type": bd.labels.consumption_edge_default,
-                        "input": ("db_2020", "electricity"),
-                        "temporal_distribution": TemporalDistribution(
-                            date=np.array([1, 2], dtype="timedelta64[Y]"),
-                            amount=np.array([0.5, 0.5]),
-                        ),
-                        "operation": True,
-                        # NEW: Vintage-specific values
-                        "vintage_amounts": {
-                            (1, 2020): 30,  # τ=1, 2020 vintage: 30 MJ/vkm
-                            (2, 2020): 30,  # τ=2, 2020 vintage: 30 MJ/vkm
-                            (1, 2030): 22.5,  # τ=1, 2030 vintage: 22.5 MJ/vkm
-                            (2, 2030): 22.5,  # τ=2, 2030 vintage: 22.5 MJ/vkm
+        fg_db.write(
+            {
+                # Product node
+                ("foreground", "vkm"): {
+                    "name": "vehicle-km",
+                    "type": bd.labels.product_node_default,
+                    "unit": "km",
+                },
+                # Process node with vintage-dependent exchange
+                ("foreground", "EV"): {
+                    "name": "Electric Vehicle",
+                    "type": bd.labels.process_node_default,
+                    "operation_time_limits": (1, 2),
+                    "exchanges": [
+                        {
+                            "amount": 1,
+                            "type": bd.labels.production_edge_default,
+                            "input": ("foreground", "vkm"),
+                            "temporal_distribution": TemporalDistribution(
+                                date=np.array([0, 1, 2, 3], dtype="timedelta64[Y]"),
+                                amount=np.array([0, 0.5, 0.5, 0]),
+                            ),
+                            "operation": True,
+                        },
+                        {
+                            "amount": 60,  # Base amount (will be overridden by vintage_amounts)
+                            "type": bd.labels.consumption_edge_default,
+                            "input": ("db_2020", "electricity"),
+                            "temporal_distribution": TemporalDistribution(
+                                date=np.array([1, 2], dtype="timedelta64[Y]"),
+                                amount=np.array([0.5, 0.5]),
+                            ),
+                            "operation": True,
+                            # NEW: Vintage-specific values
+                            "vintage_amounts": {
+                                (1, 2020): 30,  # τ=1, 2020 vintage: 30 MJ/vkm
+                                (2, 2020): 30,  # τ=2, 2020 vintage: 30 MJ/vkm
+                                (1, 2030): 22.5,  # τ=1, 2030 vintage: 22.5 MJ/vkm
+                                (2, 2030): 22.5,  # τ=2, 2030 vintage: 22.5 MJ/vkm
+                            },
+                        },
+                        {
+                            "amount": 5000,  # Manufacturing emissions
+                            "type": bd.labels.biosphere_edge_default,
+                            "input": ("biosphere3", "CO2"),
+                            "temporal_distribution": TemporalDistribution(
+                                date=np.array([0], dtype="timedelta64[Y]"),
+                                amount=np.array([1.0]),
+                            ),
+                            # Vintage improvement scaling
+                            "vintage_improvements": {
+                                2020: 1.0,  # 100% of base (5000 kg)
+                                2030: 0.8,  # 80% of base (4000 kg)
+                            },
                         },
-                    },
-                    {
-                        "amount": 5000,  # Manufacturing emissions
-                        "type": bd.labels.biosphere_edge_default,
-                        "input": ("biosphere3", "CO2"),
-                        "temporal_distribution": TemporalDistribution(
-                            date=np.array([0], dtype="timedelta64[Y]"),
-                            amount=np.array([1.0]),
-                        ),
-                        # Vintage improvement scaling
-                        "vintage_improvements": {
-                            2020: 1.0,   # 100% of base (5000 kg)
-                            2030: 0.8,   # 80% of base (4000 kg)
-                        },
-                    },
-                ],
-            },
-        })
-        fg_db.register()
-
-        # Create LCIA method
-        bd.Method(("GWP", "example")).write([
-            (("biosphere3", "CO2"), 1.0),
-        ])
-
-        return fg_db
-
+                    ],
+                },
+            }
+        )
+        fg_db.register()
+
+        # Create LCIA method
+        bd.Method(("GWP", "example")).write(
+            [
+                (("biosphere3", "CO2"), 1.0),
+            ]
+        )
+
+        return fg_db
+
     def test_lca_processor_extracts_vintage_amounts(self, database_with_vintage_params):
         """Test that LCADataProcessor extracts vintage_amounts from exchanges."""
         from datetime import datetime
+
         import bw2data as bd
         import numpy as np
         from bw_temporalis import TemporalDistribution
+
         from optimex import lca_processor
 
         # Create demand
         product_node = bd.get_node(database="foreground", code="vkm")
-        
+
         demand = {
             product_node: TemporalDistribution(
-                date=np.array([datetime(2025, 1, 1).isoformat()], dtype="datetime64[s]"),
+                date=np.array(
+                    [datetime(2025, 1, 1).isoformat()], dtype="datetime64[s]"
+                ),
                 amount=np.array([100]),
             )
         }
@@ -1221,27 +1296,49 @@ def test_lca_processor_extracts_vintage_amounts(self, database_with_vintage_para
         # Verify vintage parameters were extracted
         assert processor.foreground_technosphere_vintages is not None
         assert len(processor.foreground_technosphere_vintages) > 0
-        
+
         # Check specific vintage values
-        assert ("EV", "electricity", 1, 2020) in processor.foreground_technosphere_vintages
-        assert processor.foreground_technosphere_vintages[("EV", "electricity", 1, 2020)] == 30
-        assert ("EV", "electricity", 1, 2030) in processor.foreground_technosphere_vintages
-        assert processor.foreground_technosphere_vintages[("EV", "electricity", 1, 2030)] == 22.5
-
-    def test_lca_processor_extracts_vintage_improvements(self, database_with_vintage_params):
+        assert (
+            "EV",
+            "electricity",
+            1,
+            2020,
+        ) in processor.foreground_technosphere_vintages
+        assert (
+            processor.foreground_technosphere_vintages[("EV", "electricity", 1, 2020)]
+            == 30
+        )
+        assert (
+            "EV",
+            "electricity",
+            1,
+            2030,
+        ) in processor.foreground_technosphere_vintages
+        assert (
+            processor.foreground_technosphere_vintages[("EV", "electricity", 1, 2030)]
+            == 22.5
+        )
+
+    def test_lca_processor_extracts_vintage_improvements(
+        self, database_with_vintage_params
+    ):
         """Test that LCADataProcessor extracts vintage_improvements from exchanges."""
         from datetime import datetime
+
         import bw2data as bd
         import numpy as np
         from bw_temporalis import TemporalDistribution
+
         from optimex import lca_processor
 
         # Create demand
         product_node = bd.get_node(database="foreground", code="vkm")
-        
+
         demand = {
             product_node: TemporalDistribution(
-                date=np.array([datetime(2025, 1, 1).isoformat()], dtype="datetime64[s]"),
+                date=np.array(
+                    [datetime(2025, 1, 1).isoformat()], dtype="datetime64[s]"
+                ),
                 amount=np.array([100]),
             )
         }
@@ -1268,27 +1365,33 @@ def test_lca_processor_extracts_vintage_improvements(self, database_with_vintage
         # Verify vintage improvements were extracted
         assert processor.vintage_improvements is not None
         assert len(processor.vintage_improvements) > 0
-        
+
         # Check specific vintage improvement values
         assert ("EV", "CO2", 2020) in processor.vintage_improvements
         assert processor.vintage_improvements[("EV", "CO2", 2020)] == 1.0
         assert ("EV", "CO2", 2030) in processor.vintage_improvements
         assert processor.vintage_improvements[("EV", "CO2", 2030)] == 0.8
 
-    def test_model_input_manager_uses_database_vintages(self, database_with_vintage_params):
+    def test_model_input_manager_uses_database_vintages(
+        self, database_with_vintage_params
+    ):
         """Test that ModelInputManager includes vintage parameters from database."""
         from datetime import datetime
+
         import bw2data as bd
         import numpy as np
         from bw_temporalis import TemporalDistribution
-        from optimex import lca_processor, converter
+
+        from optimex import converter, lca_processor
 
         # Create demand
         product_node = bd.get_node(database="foreground", code="vkm")
-        
+
         demand = {
             product_node: TemporalDistribution(
-                date=np.array([datetime(2025, 1, 1).isoformat()], dtype="datetime64[s]"),
+                date=np.array(
+                    [datetime(2025, 1, 1).isoformat()], dtype="datetime64[s]"
+                ),
                 amount=np.array([100]),
             )
         }
@@ -1317,8 +1420,13 @@ def test_model_input_manager_uses_database_vintages(self, database_with_vintage_
         # Verify vintage parameters are in model inputs
         assert model_inputs.foreground_technosphere_vintages is not None
         assert len(model_inputs.foreground_technosphere_vintages) > 0
-        assert ("EV", "electricity", 1, 2020) in model_inputs.foreground_technosphere_vintages
-        
+        assert (
+            "EV",
+            "electricity",
+            1,
+            2020,
+        ) in model_inputs.foreground_technosphere_vintages
+
         assert model_inputs.vintage_improvements is not None
         assert len(model_inputs.vintage_improvements) > 0
-        assert ("EV", "CO2", 2020) in model_inputs.vintage_improvements
+        assert ("EV", "CO2", 2020) in model_inputs.vintage_improvements

From fdfbe7a7e8646c506898cf1ad448bb3534362d8f Mon Sep 17 00:00:00 2001
From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com>
Date: Wed, 20 May 2026 23:15:10 +0000
Subject: [PATCH 06/12] Revert "Progress: completed analysis and baseline
 checks"

This reverts commit d8c0d02e398b5f4a32b30c1807dc0f5ad4f90b3b.

Co-authored-by: TimoDiepers <90762029+TimoDiepers@users.noreply.github.com>
---
 .github/workflows/conda-package-deploy.yml    |    8 +-
 .gitignore                                    |    2 +-
 CHANGES.md                                    |    2 +-
 CODE_OF_CONDUCT.md                            |    6 +-
 docs/content/examples/basic_example.md        |   32 +-
 docs/content/examples/data/product_system.svg |    2 +-
 docs/content/examples/methanol_and_iron.md    |   25 +-
 docs/content/installation.md                  |    2 +-
 docs/content/theory_how_it_works.md           |    2 +-
 docs/content/theory_why_time_matters.md       |    2 +-
 docs/convert_notebooks.py                     |    4 +-
 docs/requirements.txt                         |    6 +-
 notebooks/data/model_inputs_2050.json         |    2 +-
 .../data/paper/model_inputs_2050_paper.json   |    2 +-
 notebooks/data/product_system.svg             |    2 +-
 notebooks/plots/paper_figures.py              | 1019 ++++++-----------
 scripts/uv_setup.py                           |   14 +-
 src/optimex/converter.py                      |  238 ++--
 src/optimex/lca_processor.py                  |  141 +--
 src/optimex/optimizer.py                      |   98 +-
 src/optimex/postprocessing.py                 |  519 +++------
 src/optimex/utils.py                          |    3 +-
 ...t_background_process_product_separation.py |  252 ++--
 tests/test_brownfield.py                      |  133 +--
 tests/test_merit_order.py                     |   58 +-
 tests/test_operation_constraints.py           |   15 +-
 tests/test_optimization.py                    |  193 ++--
 tests/test_serialization.py                   |   56 +-
 tests/test_single_route_lca_comparison.py     |  171 ++-
 tests/test_two_level_supply_chain.py          |  348 +++---
 tests/test_vintage_deterministic.py           |  156 ++-
 tests/test_vintage_parameters.py              |  462 +++-----
 32 files changed, 1522 insertions(+), 2453 deletions(-)

diff --git a/.github/workflows/conda-package-deploy.yml b/.github/workflows/conda-package-deploy.yml
index 65cdad2..49d6908 100644
--- a/.github/workflows/conda-package-deploy.yml
+++ b/.github/workflows/conda-package-deploy.yml
@@ -13,7 +13,7 @@ jobs:
   conda-pkg-build:
     name: Build and Publish
     runs-on: ubuntu-latest
-
+    
     steps:
       - name: Checkout Code
         uses: actions/checkout@v4
@@ -38,11 +38,11 @@ jobs:
         run: |
           # 1. Install necessary tools silently
           conda install -y -q conda-build anaconda-client
-
-          # 2. Configure conda to NOT attempt automatic uploads
+          
+          # 2. Configure conda to NOT attempt automatic uploads 
           # (We handle the upload explicitly via the build command)
           conda config --set anaconda_upload no
-
+          
           # 3. Build and Upload in one command using the Token
           conda build conda/ \
             -c conda-forge \
diff --git a/.gitignore b/.gitignore
index 0281659..3e5433a 100644
--- a/.gitignore
+++ b/.gitignore
@@ -146,4 +146,4 @@ notebooks/premise_usage.ipynb
 notebooks/results
 
 # Generated plot files
-notebooks/plots/figs/*.png
+notebooks/plots/figs/*.png
\ No newline at end of file
diff --git a/CHANGES.md b/CHANGES.md
index 469f7ce..5fb32ec 100644
--- a/CHANGES.md
+++ b/CHANGES.md
@@ -27,4 +27,4 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 * Improved user-facing API
 
 ## [0.1.0] - 2025-02-27
-* Initial release.
+* Initial release. 
diff --git a/CODE_OF_CONDUCT.md b/CODE_OF_CONDUCT.md
index 6fa803a..cd5d4ed 100644
--- a/CODE_OF_CONDUCT.md
+++ b/CODE_OF_CONDUCT.md
@@ -56,9 +56,9 @@ further defined and clarified by project maintainers.
 
 Instances of abusive, harassing, or otherwise unacceptable behavior may be
 reported by contacting the project team at [timo.diepers@ltt.rwth-aachen.de]
-(mailto:timo.diepers@ltt.rwth-aachen.de). All complaints will be reviewed and
-investigated and will result in a response that, is deemed necessary and
-appropriate to the circumstances. The project team is obligated to maintain
+(mailto:timo.diepers@ltt.rwth-aachen.de). All complaints will be reviewed and 
+investigated and will result in a response that, is deemed necessary and 
+appropriate to the circumstances. The project team is obligated to maintain 
 confidentiality with regard to the reporter of an incident.
 Further details of specific enforcement policies may be posted separately.
 
diff --git a/docs/content/examples/basic_example.md b/docs/content/examples/basic_example.md
index f02733a..5b815b4 100644
--- a/docs/content/examples/basic_example.md
+++ b/docs/content/examples/basic_example.md
@@ -68,10 +68,10 @@ bd.Database("biosphere3").write(biosphere_data)
 
     100%|██████████| 3/3 [00:00<00:00, 12545.28it/s]
 
-    11:15:06+0100 [info     ] Vacuuming database
-
+    11:15:06+0100 [info     ] Vacuuming database            
 
 
+    
 
 
 ### Characterization Methods
@@ -312,24 +312,24 @@ bg_2050.register()
 
     100%|██████████| 3/3 [00:00<00:00, 30393.51it/s]
 
-    11:15:06+0100 [info     ] Vacuuming database
+    11:15:06+0100 [info     ] Vacuuming database            
     11:15:06+0100 [warning  ] Not able to determine geocollections for all datasets. This database is not ready for regionalization.
 
 
-
+    
     100%|██████████| 3/3 [00:00<00:00, 58798.65it/s]
 
-    11:15:06+0100 [info     ] Vacuuming database
+    11:15:06+0100 [info     ] Vacuuming database            
     11:15:06+0100 [warning  ] Not able to determine geocollections for all datasets. This database is not ready for regionalization.
 
 
-
+    
     100%|██████████| 3/3 [00:00<00:00, 69518.85it/s]
 
-    11:15:06+0100 [info     ] Vacuuming database
-
+    11:15:06+0100 [info     ] Vacuuming database            
 
 
+    
 
 
 ### Foreground Database
@@ -553,10 +553,10 @@ fg.register()
 
     100%|██████████| 6/6 [00:00<00:00, 21041.66it/s]
 
-    11:15:06+0100 [info     ] Vacuuming database
-
+    11:15:06+0100 [info     ] Vacuuming database            
 
 
+    
 
 
 ### Demand
@@ -695,9 +695,9 @@ pp.plot_capacity_balance(detailed=True)
 ```
 
 
-
+    
 ![png](basic_example_files/output_23_0.png)
-
+    
 
 
 
@@ -706,9 +706,9 @@ pp.plot_impacts()
 ```
 
 
-
+    
 ![png](basic_example_files/output_24_0.png)
-
+    
 
 
 
@@ -722,5 +722,7 @@ pp.plot_characterized_dynamic_inventory(
 
 
 
-
+    
 ![png](basic_example_files/output_25_1.png)
+    
+
diff --git a/docs/content/examples/data/product_system.svg b/docs/content/examples/data/product_system.svg
index d384585..37a1170 100644
--- a/docs/content/examples/data/product_system.svg
+++ b/docs/content/examples/data/product_system.svg
@@ -1 +1 @@
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text-decoration="none" transform="matrix(1 0 0 1 1616.85 585)">𝛿</text><text fill="#000000" fill-opacity="1" font-family="Cambria Math,Cambria Math_MSFontService,sans-serif" font-style="normal" font-variant="normal" font-weight="400" font-stretch="normal" font-size="31" text-anchor="start" direction="ltr" writing-mode="lr-tb" unicode-bidi="normal" text-decoration="none" transform="matrix(1 0 0 1 1643.18 585)">=</text><text fill="#000000" fill-opacity="1" font-family="Cambria Math,Cambria Math_MSFontService,sans-serif" font-style="normal" font-variant="normal" font-weight="400" font-stretch="normal" font-size="31" text-anchor="start" direction="ltr" writing-mode="lr-tb" unicode-bidi="normal" text-decoration="none" transform="matrix(1 0 0 1 1674.62 585)">21</text><path d="M498.5 388.372C498.5 380.159 505.158 373.5 513.372 373.5L873.628 373.5C881.842 373.5 888.5 380.159 888.5 388.372L888.5 588.628C888.5 596.842 881.842 603.5 873.628 603.5L513.372 603.5C505.158 603.5 498.5 596.842 498.5 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0 0 1 270.809 548)">=</text><text fill="#000000" fill-opacity="1" font-family="Cambria Math,Cambria Math_MSFontService,sans-serif" font-style="normal" font-variant="normal" font-weight="400" font-stretch="normal" font-size="31" text-anchor="start" direction="ltr" writing-mode="lr-tb" unicode-bidi="normal" text-decoration="none" transform="matrix(1 0 0 1 302.251 548)">0</text><text fill="#000000" fill-opacity="1" font-family="Arial,Arial_MSFontService,sans-serif" font-style="normal" font-variant="normal" font-weight="400" font-stretch="normal" font-size="31" text-anchor="start" direction="ltr" writing-mode="lr-tb" unicode-bidi="normal" text-decoration="none" transform="matrix(1 0 0 1 53.6968 585)">End of Life: </text><text fill="#000000" fill-opacity="1" font-family="Cambria Math,Cambria Math_MSFontService,sans-serif" font-style="normal" font-variant="normal" font-weight="400" font-stretch="normal" font-size="31" text-anchor="start" direction="ltr" writing-mode="lr-tb" 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\ No newline at end of file
diff --git a/docs/content/examples/methanol_and_iron.md b/docs/content/examples/methanol_and_iron.md
index f201794..4b31b2d 100644
--- a/docs/content/examples/methanol_and_iron.md
+++ b/docs/content/examples/methanol_and_iron.md
@@ -1101,9 +1101,9 @@ pp_baseline.plot_capacity_balance(detailed=True)
 ```
 
 
-
+    
 ![png](methanol_and_iron_files/output_48_0.png)
-
+    
 
 
 
@@ -1112,9 +1112,9 @@ pp_baseline.plot_impacts()
 ```
 
 
-
+    
 ![png](methanol_and_iron_files/output_49_0.png)
-
+    
 
 
 
@@ -1216,9 +1216,9 @@ pp_evolution.plot_capacity_balance(detailed=True)
 ```
 
 
-
+    
 ![png](methanol_and_iron_files/output_56_0.png)
-
+    
 
 
 
@@ -1227,9 +1227,9 @@ pp_evolution.plot_impacts()
 ```
 
 
-
+    
 ![png](methanol_and_iron_files/output_57_0.png)
-
+    
 
 
 
@@ -1326,9 +1326,9 @@ pp_constrained.plot_capacity_balance(detailed=True)
 ```
 
 
-
+    
 ![png](methanol_and_iron_files/output_65_0.png)
-
+    
 
 
 
@@ -1337,9 +1337,9 @@ pp_constrained.plot_impacts()
 ```
 
 
-
+    
 ![png](methanol_and_iron_files/output_66_0.png)
-
+    
 
 
 
@@ -1363,3 +1363,4 @@ pp_constrained.df_impacts.to_excel("data/paper/impacts_constrained.xlsx")
 ```
 
     2026-02-10 15:58:56.630 | INFO     | dynamic_characterization.dynamic_characterization:characterize:108 - No custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.
+
diff --git a/docs/content/installation.md b/docs/content/installation.md
index 18501b0..79dec42 100644
--- a/docs/content/installation.md
+++ b/docs/content/installation.md
@@ -208,7 +208,7 @@ Brightway and `optimex` run natively on Apple Silicon. However, the standard `py
         optimex \
         "scikit-umfpack>=0.4.2" \
         "numpy>=2"
-
+    
     conda activate optimex
     ```
 ---
diff --git a/docs/content/theory_how_it_works.md b/docs/content/theory_how_it_works.md
index c137188..a8ddf47 100644
--- a/docs/content/theory_how_it_works.md
+++ b/docs/content/theory_how_it_works.md
@@ -50,4 +50,4 @@ Traditional LCO assumes processes always run at full capacity. `optimex` separat
 
 This separation is important because it enables **vintage-specific dispatch**: when multiple cohorts of the same technology coexist, the optimizer can preferentially utilize cleaner vintages — creating an emissions-aware merit order. It also allows the model to identify strategic overcapacities, where early investment in clean technologies offsets the stranded cost of idled fossil infrastructure.
 
-To make this work, each exchange in a process is classified as either installation-dependent (construction materials, end-of-life treatment) or operation-dependent (production output, fuel consumption, operational emissions). Installation-dependent exchanges are fixed to the installed capacity, while operation-dependent exchanges scale with how much the process is actually operated.
+To make this work, each exchange in a process is classified as either installation-dependent (construction materials, end-of-life treatment) or operation-dependent (production output, fuel consumption, operational emissions). Installation-dependent exchanges are fixed to the installed capacity, while operation-dependent exchanges scale with how much the process is actually operated.
\ No newline at end of file
diff --git a/docs/content/theory_why_time_matters.md b/docs/content/theory_why_time_matters.md
index 1394102..ab404a4 100644
--- a/docs/content/theory_why_time_matters.md
+++ b/docs/content/theory_why_time_matters.md
@@ -18,4 +18,4 @@ These blind spots arise from two interacting temporal dimensions:
 - **Temporal distribution**: Life cycle stages span years or decades. Construction precedes operation, which precedes end-of-life. Each stage has distinct environmental exchanges occurring at different times.
 - **Temporal evolution**: Production systems improve over time. Electricity grids decarbonize, process efficiencies increase, and supply chains shift. The same exchange has a different environmental footprint depending on *when* it occurs.
 
-Distribution determines *when* an exchange occurs; evolution determines its *magnitude* at that moment. Both must be considered jointly. `optimex` addresses this by extending the standard LCA matrices with an explicit time dimension.
+Distribution determines *when* an exchange occurs; evolution determines its *magnitude* at that moment. Both must be considered jointly. `optimex` addresses this by extending the standard LCA matrices with an explicit time dimension.
\ No newline at end of file
diff --git a/docs/convert_notebooks.py b/docs/convert_notebooks.py
index 59ef43b..2161f37 100644
--- a/docs/convert_notebooks.py
+++ b/docs/convert_notebooks.py
@@ -46,7 +46,9 @@ def strip_ansi(text: str) -> str:
     return ANSI_ESCAPE.sub("", text)
 
 
-def convert(notebook_path: Path, output_dir: Path, icon: str, tags: list[str]) -> Path:
+def convert(
+    notebook_path: Path, output_dir: Path, icon: str, tags: list[str]
+) -> Path:
     """Convert *notebook_path* to Markdown in *output_dir*, strip ANSI codes,
     organise images into a <stem>_files/ sub-directory, and inject the Zensical
     icon and tags front-matter.  Returns the output file path."""
diff --git a/docs/requirements.txt b/docs/requirements.txt
index 2d87a18..0f83295 100644
--- a/docs/requirements.txt
+++ b/docs/requirements.txt
@@ -1,5 +1,5 @@
-mkdocs-autorefs
+zensical
 mkdocstrings[python]>=1.0.0
-nbconvert>=7.0
+mkdocs-autorefs
 pymdown-extensions>=10.0
-zensical
+nbconvert>=7.0
diff --git a/notebooks/data/model_inputs_2050.json b/notebooks/data/model_inputs_2050.json
index 8355dba..d9933ca 100644
--- a/notebooks/data/model_inputs_2050.json
+++ b/notebooks/data/model_inputs_2050.json
@@ -74261,4 +74261,4 @@
   "process_operation_limits_min_default": 0.0,
   "cumulative_process_limits_max_default": Infinity,
   "cumulative_process_limits_min_default": 0.0
-}
+}
\ No newline at end of file
diff --git a/notebooks/data/paper/model_inputs_2050_paper.json b/notebooks/data/paper/model_inputs_2050_paper.json
index 9e14827..2d48d80 100644
--- a/notebooks/data/paper/model_inputs_2050_paper.json
+++ b/notebooks/data/paper/model_inputs_2050_paper.json
@@ -76474,4 +76474,4 @@
   "process_operation_limits_min_default": 0.0,
   "cumulative_process_limits_max_default": Infinity,
   "cumulative_process_limits_min_default": 0.0
-}
+}
\ No newline at end of file
diff --git a/notebooks/data/product_system.svg b/notebooks/data/product_system.svg
index d384585..37a1170 100644
--- a/notebooks/data/product_system.svg
+++ b/notebooks/data/product_system.svg
@@ -1 +1 @@
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fill="#000000" fill-opacity="1" font-family="Arial,Arial_MSFontService,sans-serif" font-style="normal" font-variant="normal" font-weight="700" font-stretch="normal" font-size="37" text-anchor="start" direction="ltr" writing-mode="lr-tb" unicode-bidi="normal" text-decoration="none" transform="matrix(1 0 0 1 1639.08 1288)">Pig iron</text><path d="M1430.5 388.372C1430.5 380.159 1437.16 373.5 1445.37 373.5L1805.63 373.5C1813.84 373.5 1820.5 380.159 1820.5 388.372L1820.5 588.628C1820.5 596.842 1813.84 603.5 1805.63 603.5L1445.37 603.5C1437.16 603.5 1430.5 596.842 1430.5 588.628Z" stroke="#000000" stroke-width="4.58333" stroke-linecap="butt" stroke-linejoin="miter" stroke-miterlimit="8" stroke-opacity="1" fill="#FFFFFF" fill-rule="evenodd" fill-opacity="1"/><text fill="#000000" fill-opacity="1" font-family="Arial,Arial_MSFontService,sans-serif" font-style="normal" font-variant="normal" font-weight="700" font-stretch="normal" font-size="37" text-anchor="start" direction="ltr" 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\ No newline at end of file
diff --git a/notebooks/plots/paper_figures.py b/notebooks/plots/paper_figures.py
index 1b8c1e1..9b3a17e 100644
--- a/notebooks/plots/paper_figures.py
+++ b/notebooks/plots/paper_figures.py
@@ -9,23 +9,15 @@
 Demonstrates the capabilities of optimex for transition pathway optimization.
 """
 
-from datetime import datetime
-from pathlib import Path
-
-import matplotlib.dates as mdates
-import matplotlib.pyplot as plt
-import numpy as np
 import pandas as pd
-from matplotlib.lines import Line2D
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.dates as mdates
 from matplotlib.patches import Patch
-from matplotlib.ticker import (
-    AutoMinorLocator,
-    FormatStrFormatter,
-    FuncFormatter,
-    MultipleLocator,
-    NullLocator,
-    ScalarFormatter,
-)
+from matplotlib.lines import Line2D
+from matplotlib.ticker import ScalarFormatter, MultipleLocator, AutoMinorLocator, NullLocator, FuncFormatter, FormatStrFormatter
+from pathlib import Path
+from datetime import datetime
 
 # Configuration
 PLOTS_DIR = Path(__file__).parent / "data"
@@ -81,19 +73,17 @@
 }
 
 # Plot style settings
-plt.rcParams.update(
-    {
-        "font.size": 10,
-        "axes.labelsize": 11,
-        "axes.titlesize": 12,
-        "legend.fontsize": 9,
-        "xtick.labelsize": 9,
-        "ytick.labelsize": 9,
-        "figure.dpi": 150,
-        "savefig.dpi": 300,
-        "savefig.bbox": "tight",
-    }
-)
+plt.rcParams.update({
+    "font.size": 10,
+    "axes.labelsize": 11,
+    "axes.titlesize": 12,
+    "legend.fontsize": 9,
+    "xtick.labelsize": 9,
+    "ytick.labelsize": 9,
+    "figure.dpi": 150,
+    "savefig.dpi": 300,
+    "savefig.bbox": "tight",
+})
 
 bar_width = 0.65
 linewidth_bar_outline = 1
@@ -105,11 +95,7 @@ def load_scenario_data(scenario: str) -> dict:
     for dtype in ["capacity", "production", "demand", "impacts"]:
         filepath = PLOTS_DIR / f"{dtype}_{scenario}.xlsx"
         if filepath.exists():
-            df = pd.read_excel(
-                filepath,
-                index_col=0,
-                header=[0, 1] if dtype in ["production", "impacts"] else 0,
-            )
+            df = pd.read_excel(filepath, index_col=0, header=[0, 1] if dtype in ["production", "impacts"] else 0)
             # Clean up index
             if df.index.name == "Time" or df.index.name == "Category":
                 pass
@@ -140,9 +126,7 @@ def parse_impacts_df(df: pd.DataFrame) -> dict:
     if isinstance(df.columns, pd.MultiIndex):
         categories = df.columns.get_level_values(0).unique()
         for cat in categories:
-            if cat in IMPACT_CATEGORIES or any(
-                ic in str(cat).lower() for ic in IMPACT_CATEGORIES
-            ):
+            if cat in IMPACT_CATEGORIES or any(ic in str(cat).lower() for ic in IMPACT_CATEGORIES):
                 cat_df = df[cat].copy()
                 # Clean up process names
                 if isinstance(cat_df.columns, pd.Index):
@@ -204,7 +188,7 @@ def load_impacts_properly(scenario: str) -> dict:
             result[category] = pd.DataFrame(index=data_df.index)
 
         clean_process = PROCESS_NAMES.get(process, process)
-        col_data = pd.to_numeric(data_df.iloc[:, i - 1], errors="coerce")
+        col_data = pd.to_numeric(data_df.iloc[:, i-1], errors="coerce")
 
         # If process already exists, it's a duplicate - skip
         if clean_process not in result[category].columns:
@@ -234,7 +218,7 @@ def compute_capacity_changes(cap_df: pd.DataFrame, product_col: str) -> tuple:
     retirements = np.zeros(len(capacity))
 
     for i in range(1, len(capacity)):
-        delta = capacity[i] - capacity[i - 1]
+        delta = capacity[i] - capacity[i-1]
         if delta > 0:
             additions[i] = delta
         elif delta < 0:
@@ -302,9 +286,7 @@ def create_combined_results_figure(scenarios_data: dict):
                 cap_mask = (cap_years >= 2025) & (cap_years <= 2050)
                 capacity_values = cap_df[product_col].values[cap_mask] / 1e6
                 row_ylim_max[row] = max(row_ylim_max[row], capacity_values.max())
-                additions, retirements = compute_capacity_changes(
-                    cap_df[cap_mask], product_col
-                )
+                additions, retirements = compute_capacity_changes(cap_df[cap_mask], product_col)
                 row_ylim_max[row] = max(row_ylim_max[row], additions.max() / 1e6)
                 # if retirements.max() > 0:
                 #     row_ylim_min[row] = min(row_ylim_min[row], -retirements.max() / 1e6)
@@ -316,8 +298,8 @@ def create_combined_results_figure(scenarios_data: dict):
     # Common x-axis setup
     years_plot = np.arange(2025, 2051)
     x_positions = np.arange(len(years_plot))
-    cap_positions = x_positions  # - bar_width/2 - 0.02
-    prod_positions = x_positions  # + bar_width/2 + 0.02
+    cap_positions = x_positions # - bar_width/2 - 0.02
+    prod_positions = x_positions # + bar_width/2 + 0.02
 
     xtick_positions = [i for i, y in enumerate(years_plot) if y % 5 == 0]
     xtick_labels = [str(y) for y in years_plot if y % 5 == 0]
@@ -328,7 +310,7 @@ def create_combined_results_figure(scenarios_data: dict):
     # Second pass: create plots
     for row, (product_key, product_label, is_intermediate) in enumerate(products):
         for col, (scenario, scenario_label) in enumerate(SCENARIOS.items()):
-
+            
             # --- Shared Axis logic ---
             # Row 0, Col 0 is the anchor
             if row == 0 and col == 0:
@@ -338,10 +320,8 @@ def create_combined_results_figure(scenarios_data: dict):
                 ax = fig.add_subplot(gs[row, col], sharex=axes_matrix[0, 0])
             else:
                 # Share Y with the first plot in this row, Share X with top row
-                ax = fig.add_subplot(
-                    gs[row, col], sharey=axes_matrix[row, 0], sharex=axes_matrix[0, col]
-                )
-
+                ax = fig.add_subplot(gs[row, col], sharey=axes_matrix[row, 0], sharex=axes_matrix[0, col])
+            
             axes_matrix[row, col] = ax
             data = scenarios_data[scenario]
 
@@ -369,9 +349,7 @@ def create_combined_results_figure(scenarios_data: dict):
                     if product_key.lower() in str(product).lower():
                         clean_process = PROCESS_NAMES.get(process, process)
                         if clean_process not in process_production:
-                            process_production[clean_process] = (
-                                prod_df[c].astype(float).values
-                            )
+                            process_production[clean_process] = prod_df[c].astype(float).values
 
             years = prod_df.index.values
             mask = (years >= 2025) & (years <= 2050)
@@ -383,18 +361,10 @@ def create_combined_results_figure(scenarios_data: dict):
                     values_plot = values[mask] / 1e6
                     if np.any(values_plot > 0.01):
                         color = PROCESS_COLORS.get(process, "gray")
-                        ax.bar(
-                            prod_positions,
-                            values_plot,
-                            bottom=bottom,
-                            label=process,
-                            color=color,
-                            width=bar_width,
-                            edgecolor="white",
-                            linewidth=linewidth_bar_outline,
-                        )
+                        ax.bar(prod_positions, values_plot, bottom=bottom, label=process,
+                               color=color, width=bar_width, edgecolor="white", linewidth=linewidth_bar_outline)
                         bottom += values_plot
-
+            
             # # Compute and plot capacity changes
             # if product_col is not None:
             #     cap_years = cap_df.index.values
@@ -406,11 +376,11 @@ def create_combined_results_figure(scenarios_data: dict):
             #     if np.any(additions > 0.01):
             #         for i, val in enumerate(additions):
             #             if val > 0.01:
-            #                 ax.annotate('',
+            #                 ax.annotate('', 
             #                             xy=(x_positions[i], val),      # The Tip (exactly at val)
             #                             xytext=(x_positions[i], 0),    # The Base
             #                             arrowprops=dict(arrowstyle="-|>",
-            #                                             color="#41811C",
+            #                                             color="#41811C", 
             #                                             lw=1,
             #                                             alpha=1),
             #                             zorder=5)
@@ -419,31 +389,23 @@ def create_combined_results_figure(scenarios_data: dict):
             #     if np.any(retirements > 0.01):
             #         for i, val in enumerate(retirements):
             #             if val > 0.01:
-            #                 ax.annotate('',
+            #                 ax.annotate('', 
             #                             xy=(x_positions[i], -val),     # The Tip (exactly at -val)
             #                             xytext=(x_positions[i], 0),    # The Base
             #                             arrowprops=dict(arrowstyle="-|>",
-            #                                             color="#CC071E",
+            #                                             color="#CC071E", 
             #                                             lw=1.5,
             #                                             alpha=1),
             #                             zorder=5)
-
+                    
             # Plot total capacity line
             if product_col is not None:
                 cap_years = cap_df.index.values
                 cap_mask = (cap_years >= 2025) & (cap_years <= 2050)
                 capacity_values = cap_df[product_col].values[cap_mask] / 1e6
                 if np.any(capacity_values > 0.001):
-                    ax.plot(
-                        x_positions,
-                        capacity_values,
-                        color="#000000",
-                        linestyle="--",
-                        linewidth=1,
-                        marker="",
-                        label="Capacity",
-                        zorder=4,
-                    )
+                    ax.plot(x_positions, capacity_values, color="#000000", linestyle="--",
+                            linewidth=1, marker="", label="Capacity", zorder=4)
 
             # if not is_intermediate:
             #     ax.axhline(y=1, color="red", linestyle="--", linewidth=1)
@@ -455,7 +417,7 @@ def create_combined_results_figure(scenarios_data: dict):
             ax.set_ylim(row_ylim_min[row], row_ylim_max[row])
             ax.grid(True, alpha=0.3, which="both", axis="both", zorder=0)
             ax.set_axisbelow(True)
-
+            
             # X-axis: Only bottom row gets labels
             if row == 3:
                 ax.set_xticks(xtick_positions)
@@ -468,7 +430,7 @@ def create_combined_results_figure(scenarios_data: dict):
             if col == 0:
                 label_suffix = " *" if is_intermediate else ""
                 ax.set_ylabel(f"{product_label}{label_suffix}\n[$10^6$ kg]")
-
+                
                 # Custom tick spacing for Captured CO₂
                 if product_label == "Captured CO₂":
                     ax.yaxis.set_major_locator(MultipleLocator(0.5))
@@ -484,9 +446,7 @@ def create_combined_results_figure(scenarios_data: dict):
     for process, color in PROCESS_COLORS.items():
         all_handles.append(Patch(facecolor=color, edgecolor="white", linewidth=0.5))
         all_labels.append(process)
-    all_handles.append(
-        plt.Line2D([0], [0], color="#000000", linestyle="--", linewidth=1)
-    )
+    all_handles.append(plt.Line2D([0], [0], color="#000000", linestyle="--", linewidth=1))
     all_labels.append("Available capacity")
     # all_handles.append(plt.Line2D([0], [0], color="red", linestyle="--", linewidth=1))
     # all_labels.append("Demand")
@@ -494,67 +454,47 @@ def create_combined_results_figure(scenarios_data: dict):
     # all_labels.append("+ Cap")
     # all_handles.append(Patch(facecolor="#E69679", edgecolor="#CC071E", linewidth=1, hatch="///"))
     # all_labels.append("− Cap")
-
-    labels = [
-        "(a)",
-        "(b)",
-        "(c)",
-        "(d)",
-        "(e)",
-        "(f)",
-        "(g)",
-        "(h)",
-        "(i)",
-        "(j)",
-        "(k)",
-        "(l)",
-    ]
+    
+    labels = ['(a)', '(b)', '(c)', '(d)', '(e)', '(f)', '(g)', '(h)', '(i)', '(j)', '(k)', '(l)']
     label_idx = 0
     for row in range(4):  # Changed from range(2) to range(4)
         for col in range(3):
-            ax = axes_matrix[
-                row, col
-            ]  # Changed from axes[row, col] to axes_matrix[row, col]
+            ax = axes_matrix[row, col]  # Changed from axes[row, col] to axes_matrix[row, col]
             # Position label in top-right corner
-            ax.text(
-                0.02,
-                0.95,
-                labels[label_idx],
-                transform=ax.transAxes,
-                va="top",
-                fontsize=10,
-                bbox=dict(boxstyle="square,pad=0.1", fc="white", ec="none"),
-            )
+            ax.text(0.02, 0.95, labels[label_idx], transform=ax.transAxes,
+                   va='top', fontsize=10,
+                   bbox=dict(boxstyle='square,pad=0.1', fc='white', ec='none'))
             label_idx += 1
 
-    fig.legend(
-        all_handles,
-        all_labels,
-        loc="lower center",
-        bbox_to_anchor=(0.5, 0.02),
-        ncol=4,
-        frameon=False,
-        fontsize=9,
-    )
+    fig.legend(all_handles, all_labels, loc="lower center", bbox_to_anchor=(0.5, 0.02),
+               ncol=4, frameon=False, fontsize=9)
 
     return fig
 
-
+import numpy as np
 import matplotlib.pyplot as plt
+from matplotlib.ticker import ScalarFormatter
+from matplotlib.patches import Patch
+
 import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.ticker import ScalarFormatter
 from matplotlib.patches import Patch
-from matplotlib.ticker import FormatStrFormatter, ScalarFormatter
 
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.ticker import FormatStrFormatter
+from matplotlib.patches import Patch
 
 def create_combined_impacts_figure(scenarios_data: dict):
     """
     Create a combined impacts figure with manual scaling to show 10^x in the Y-axis label.
     Shared Y axes per row, Shared X axes per column.
     """
-
+    
     fig = plt.figure(figsize=(14, 5.5))
     # Reduced wspace and hspace for a tight "scientific paper" look
-    gs = fig.add_gridspec(2, 3, hspace=0.08, wspace=0.06)
+    gs = fig.add_gridspec(2, 3, hspace=0.08, wspace=0.06) 
 
     categories = [
         ("climate_change", "Cumulative Radiative Forcing", "W/m²"),
@@ -569,7 +509,7 @@ def create_combined_impacts_figure(scenarios_data: dict):
     for row, (category, label, unit) in enumerate(categories):
         abs_max = 0
         current_max, current_min = 0, 0
-
+        
         for scenario in scenarios_data.keys():
             impacts = load_impacts_properly(scenario)
             if category in impacts:
@@ -579,14 +519,14 @@ def create_combined_impacts_figure(scenarios_data: dict):
                 neg_sum = imp_df.loc[mask].clip(upper=0).sum(axis=1).min()
                 current_max = max(current_max, pos_sum)
                 current_min = min(current_min, neg_sum)
-
+        
         # Determine the power of 10 (exponent) based on the largest absolute value
         abs_val = max(abs(current_max), abs(current_min))
         if abs_val == 0:
             exponent = 0
         else:
             exponent = int(np.floor(np.log10(abs_val)))
-
+        
         row_powers[row] = exponent
         # Store scaled limits with padding
         row_ylim_max[row] = (current_max / (10**exponent)) * 1.15
@@ -603,7 +543,7 @@ def create_combined_impacts_figure(scenarios_data: dict):
     for row, (category, label_base, unit) in enumerate(categories):
         exponent = row_powers[row]
         scaling_factor = 10**exponent
-
+        
         # Format the Y label to include scientific notation
         # Uses LaTeX for pretty exponents
         y_label = f"{label_base}\n[$10^{{{exponent}}}$ {unit}]"
@@ -611,7 +551,7 @@ def create_combined_impacts_figure(scenarios_data: dict):
         for col, (scenario, scenario_label) in enumerate(SCENARIOS.items()):
             sharex_ax = axes[0, col] if row > 0 else None
             sharey_ax = axes[row, 0] if col > 0 else None
-
+            
             ax = fig.add_subplot(gs[row, col], sharex=sharex_ax, sharey=sharey_ax)
             axes[row, col] = ax
 
@@ -627,31 +567,17 @@ def create_combined_impacts_figure(scenarios_data: dict):
                 for process in data.columns:
                     # MANUALLY SCALE DATA
                     values = data[process].values / scaling_factor
-
+                    
                     if np.any(np.abs(values) > 1e-12):
                         color = PROCESS_COLORS.get(process, "gray")
                         pos_v, neg_v = np.maximum(values, 0), np.minimum(values, 0)
                         if np.any(pos_v > 0):
-                            ax.bar(
-                                x_positions,
-                                pos_v,
-                                bottom=bottom_pos,
-                                color=color,
-                                width=bar_width,
-                                edgecolor="white",
-                                linewidth=linewidth_bar_outline,
-                            )
+                            ax.bar(x_positions, pos_v, bottom=bottom_pos, color=color, 
+                                   width=bar_width, edgecolor="white", linewidth=linewidth_bar_outline)
                             bottom_pos += pos_v
                         if np.any(neg_v < 0):
-                            ax.bar(
-                                x_positions,
-                                neg_v,
-                                bottom=bottom_neg,
-                                color=color,
-                                width=bar_width,
-                                edgecolor="white",
-                                linewidth=linewidth_bar_outline,
-                            )
+                            ax.bar(x_positions, neg_v, bottom=bottom_neg, color=color, 
+                                   width=bar_width, edgecolor="white", linewidth=linewidth_bar_outline)
                             bottom_neg += neg_v
 
             ax.axhline(y=0, color="gray", linewidth=0.5, zorder=0)
@@ -659,32 +585,28 @@ def create_combined_impacts_figure(scenarios_data: dict):
             ax.set_xticklabels(xtick_labels)
             ax.set_xlim(-1, len(years_plot))
             ax.set_ylim(row_ylim_min[row], row_ylim_max[row])
-
+            
             # Custom tick spacing for Cumulative Radiative Forcing
             if category == "climate_change":
-                ax.yaxis.set_major_locator(
-                    MultipleLocator(0.5)
-                )  # Major ticks at whole numbers
+                ax.yaxis.set_major_locator(MultipleLocator(0.5))  # Major ticks at whole numbers
                 ax.set_ylim(-1.6, 3.1)
-
+                
             if category == "water_use":
-                ax.yaxis.set_major_locator(
-                    MultipleLocator(0.5)
-                )  # Major ticks at whole numbers
+                ax.yaxis.set_major_locator(MultipleLocator(0.5))  # Major ticks at whole numbers
                 ax.set_ylim(0, 4.6)
 
             # Simple float formatter for "1.4" style labels
-            ax.yaxis.set_major_formatter(FormatStrFormatter("%.1f"))
+            ax.yaxis.set_major_formatter(FormatStrFormatter('%.1f'))
 
             if col > 0:
                 ax.tick_params(labelleft=False)
-
+            
             if row < 1:
                 ax.tick_params(labelbottom=False)
 
             ax.grid(True, which="both", alpha=0.3, axis="both", zorder=0)
             ax.set_axisbelow(True)
-
+            
             if row == 0:
                 ax.set_title(scenario_label)
             if row == 1:
@@ -701,39 +623,22 @@ def create_combined_impacts_figure(scenarios_data: dict):
             if cat in impacts:
                 for process in impacts[cat].columns:
                     if process not in all_handles:
-                        all_handles[process] = Patch(
-                            facecolor=PROCESS_COLORS.get(process, "gray"),
-                            edgecolor="white",
-                            linewidth=0.5,
-                        )
-
-    labels = ["(a)", "(b)", "(c)", "(d)", "(e)", "(f)"]
+                        all_handles[process] = Patch(facecolor=PROCESS_COLORS.get(process, "gray"), 
+                                                     edgecolor="white", linewidth=0.5)
+                        
+    labels = ['(a)', '(b)', '(c)', '(d)', '(e)', '(f)']
     label_idx = 0
     for row in range(2):
         for col in range(3):
             ax = axes[row, col]
             # Position label in top-right corner
-            ax.text(
-                0.95,
-                0.95,
-                labels[label_idx],
-                transform=ax.transAxes,
-                ha="right",
-                va="top",
-                fontsize=10,
-                bbox=dict(boxstyle="square,pad=0.1", fc="white", ec="none"),
-            )
+            ax.text(0.95, 0.95, labels[label_idx], transform=ax.transAxes,
+                   ha='right', va='top', fontsize=10,
+                   bbox=dict(boxstyle='square,pad=0.1', fc='white', ec='none'))
             label_idx += 1
-
-    fig.legend(
-        all_handles.values(),
-        all_handles.keys(),
-        loc="lower center",
-        bbox_to_anchor=(0.5, -0.02),
-        ncol=min(len(all_handles), 7),
-        frameon=False,
-        fontsize=9,
-    )
+            
+    fig.legend(all_handles.values(), all_handles.keys(), loc="lower center",
+               bbox_to_anchor=(0.5, -0.02), ncol=min(len(all_handles), 7), frameon=False, fontsize=9)
 
     fig.tight_layout()
     fig.subplots_adjust(bottom=0.18, top=0.92, left=0.10)
@@ -744,12 +649,12 @@ def create_combined_impacts_figure(scenarios_data: dict):
 def round_datetime_to_year(date: datetime) -> pd.Timestamp:
     """
     Round a datetime object to the nearest year boundary.
-
+    
     Parameters
     ----------
     date : datetime
         datetime object to be rounded
-
+        
     Returns
     -------
     pd.Timestamp
@@ -766,41 +671,41 @@ def round_datetime_to_year(date: datetime) -> pd.Timestamp:
 def load_characterized_inventory(scenario: str) -> pd.DataFrame:
     """
     Load characterized inventory data for a scenario and aggregate by year and activity.
-
+    
     Args:
         scenario: Scenario name (key from SCENARIOS dict)
-
+        
     Returns:
         DataFrame with yearly aggregated impacts by activity (activity as columns, date as index)
-
+        
     Raises:
         ValueError: If required columns are missing from the data file
     """
     filepath = PLOTS_DIR / f"characterized_inventory_{scenario}.xlsx"
     df = pd.read_excel(filepath)
-
+    
     # Validate required columns exist
-    required_columns = ["date", "activity", "amount"]
+    required_columns = ['date', 'activity', 'amount']
     missing_columns = [col for col in required_columns if col not in df.columns]
     if missing_columns:
         raise ValueError(f"Missing required columns in {filepath}: {missing_columns}")
-
+    
     # Clean up activity names using PROCESS_NAMES mapping
-    df["activity_clean"] = df["activity"].map(lambda x: PROCESS_NAMES.get(x, x))
-
+    df['activity_clean'] = df['activity'].map(lambda x: PROCESS_NAMES.get(x, x))
+    
     # First group by date and activity, sum the amounts
     plot_df = df.groupby(["date", "activity_clean"], as_index=False)["amount"].sum()
-
+    
     # Round dates to nearest year boundary
     plot_df["date"] = plot_df["date"].apply(round_datetime_to_year)
-
+    
     # Group again by rounded date and activity, sum, then pivot
     final_data = (
         plot_df.groupby(["date", "activity_clean"], as_index=False)["amount"]
         .sum()
         .pivot(index="date", columns="activity_clean", values="amount")
     )
-
+    
     return final_data
 
 
@@ -811,28 +716,28 @@ def create_radiative_forcing_figure():
     """
     fig, axes = plt.subplots(2, 3, figsize=(14, 5.5), sharex=True)
     fig.subplots_adjust(hspace=0.08, wspace=0.06)
-
+    
     # Load data for each scenario
     scenario_data = {}
     all_activities = set()
-
+    
     for scenario in SCENARIOS.keys():
         df = load_characterized_inventory(scenario)
         scenario_data[scenario] = df
         all_activities.update(df.columns.tolist())
-
+    
     all_activities = sorted(all_activities)
-
+    
     # Create color mapping
     activity_colors = {}
     for activity in all_activities:
-        activity_colors[activity] = PROCESS_COLORS.get(activity, "gray")
-
+        activity_colors[activity] = PROCESS_COLORS.get(activity, 'gray')
+    
     # Determine power of 10 for each row
     row_powers = {}
     row_ylim_max = {}
     row_ylim_min = {}
-
+    
     # Row 0: Instantaneous
     inst_max = 0
     inst_min = 0
@@ -840,14 +745,14 @@ def create_radiative_forcing_figure():
         if not df.empty:
             inst_max = max(inst_max, np.nanmax(df.values) if df.size > 0 else 0)
             inst_min = min(inst_min, np.nanmin(df.values) if df.size > 0 else 0)
-
+    
     abs_val_inst = max(abs(inst_max), abs(inst_min))
     exponent_inst = int(np.floor(np.log10(abs_val_inst))) if abs_val_inst > 0 else 0
     row_powers[0] = exponent_inst
     scaling_factor_inst = 10**exponent_inst
     row_ylim_max[0] = (inst_max / scaling_factor_inst) * 1.1
     row_ylim_min[0] = (inst_min / scaling_factor_inst) * 1.1 if inst_min < 0 else 0
-
+    
     # Row 1: Cumulative
     cum_max = 0
     cum_min = 0
@@ -855,232 +760,185 @@ def create_radiative_forcing_figure():
         if not df.empty:
             cumsum_df = df.cumsum()
             cumsum_sum = cumsum_df.sum(axis=1)
-            cum_max = max(
-                cum_max, np.nanmax(cumsum_sum.values) if cumsum_sum.size > 0 else 0
-            )
-            cum_min = min(
-                cum_min, np.nanmin(cumsum_sum.values) if cumsum_sum.size > 0 else 0
-            )
-
+            cum_max = max(cum_max, np.nanmax(cumsum_sum.values) if cumsum_sum.size > 0 else 0)
+            cum_min = min(cum_min, np.nanmin(cumsum_sum.values) if cumsum_sum.size > 0 else 0)
+    
     abs_val_cum = max(abs(cum_max), abs(cum_min))
     exponent_cum = int(np.floor(np.log10(abs_val_cum))) if abs_val_cum > 0 else 0
     row_powers[1] = exponent_cum
     scaling_factor_cum = 10**exponent_cum
     row_ylim_max[1] = (cum_max / scaling_factor_cum) * 1.1
     row_ylim_min[1] = (cum_min / scaling_factor_cum) * 1.1 if cum_min < 0 else 0
-
+    
     # Plot each scenario
     for col, (scenario, scenario_label) in enumerate(SCENARIOS.items()):
         df = scenario_data[scenario]
         dates = df.index
-
+        
         # Row 0: Instantaneous radiative forcing (scaled)
         ax_inst = axes[0, col]
         for activity in df.columns:
-            color = activity_colors.get(activity, "gray")
+            color = activity_colors.get(activity, 'gray')
             values_scaled = df[activity].fillna(0).values / scaling_factor_inst
-            ax_inst.plot(
-                dates, values_scaled, linewidth=1.2, color=color, label=activity
-            )
-
+            ax_inst.plot(dates, values_scaled, linewidth=1.2, 
+                        color=color, label=activity)
+            
         # # Add total sum line
         # total_inst = df.sum(axis=1).fillna(0).values / scaling_factor_inst
-        # ax_inst.plot(dates, total_inst, color='black', linestyle='--',
+        # ax_inst.plot(dates, total_inst, color='black', linestyle='--', 
         #             linewidth=1.5, label='Total', zorder=0.6)
-
+        
         ax_inst.set_xlim(datetime(2025, 1, 1), datetime(2051, 1, 1))
         ax_inst.set_ylim(row_ylim_min[0], row_ylim_max[0])
         ax_inst.grid(which="both", alpha=0.3, axis="both", zorder=0)
         ax_inst.set_axisbelow(True)
-
+        
         if col == 0:
-            ax_inst.set_ylabel(
-                f"Instantaneous radiative forcing\n[$10^{{{exponent_inst}}}$ W m$^{{-2}}$]"
-            )
+            ax_inst.set_ylabel(f"Instantaneous radiative forcing\n[$10^{{{exponent_inst}}}$ W m$^{{-2}}$]")
         else:
             ax_inst.tick_params(labelleft=False)
-
+        
         ax_inst.set_title(scenario_label)
         ax_inst.axhline(y=0, color="gray", linewidth=0.5, zorder=0)
-
+        
         # Row 1: Cumulative radiative forcing (scaled)
         ax_cum = axes[1, col]
         cumsum_df = df.cumsum()
-
+        
         activities_in_df = [a for a in all_activities if a in cumsum_df.columns]
-        stack_data = [
-            cumsum_df[a].fillna(0).values / scaling_factor_cum for a in activities_in_df
-        ]
+        stack_data = [cumsum_df[a].fillna(0).values / scaling_factor_cum for a in activities_in_df]
         stack_colors = [activity_colors[a] for a in activities_in_df]
-
+        
         if stack_data:
-            ax_cum.stackplot(
-                dates,
-                *stack_data,
-                labels=activities_in_df,
-                colors=stack_colors,
-                edgecolor="white",
-                linewidth=0.5,
-            )
-
+            ax_cum.stackplot(dates, *stack_data, 
+                            labels=activities_in_df,
+                            colors=stack_colors,
+                            edgecolor="white",
+                            linewidth=0.5)
+        
         ax_cum.set_xlim(datetime(2025, 1, 1), datetime(2051, 1, 1))
         ax_cum.set_ylim(row_ylim_min[1], row_ylim_max[1])
         ax_cum.grid(which="both", alpha=0.3, axis="both", zorder=0)
         ax_cum.set_axisbelow(True)
-
+        
         ax_cum.axhline(y=0, color="gray", linewidth=0.5, zorder=0)
-
+        
         if col == 0:
-            ax_cum.set_ylabel(
-                f"Cumulative radiative forcing\n[$10^{{{exponent_cum}}}$ W m$^{{-2}}$]"
-            )
+            ax_cum.set_ylabel(f"Cumulative radiative forcing\n[$10^{{{exponent_cum}}}$ W m$^{{-2}}$]")
         else:
             ax_cum.tick_params(labelleft=False)
-
+    
     # Add subplot labels
-    labels = ["(a)", "(b)", "(c)", "(d)", "(e)", "(f)"]
+    labels = ['(a)', '(b)', '(c)', '(d)', '(e)', '(f)']
     label_idx = 0
     for row in range(2):
         for col in range(3):
             ax = axes[row, col]
-            ax.text(
-                0.95,
-                0.95,
-                labels[label_idx],
-                transform=ax.transAxes,
-                ha="right",
-                va="top",
-                fontsize=10,
-                bbox=dict(boxstyle="square,pad=0.1", fc="white", ec="none"),
-            )
+            ax.text(0.95, 0.95, labels[label_idx], transform=ax.transAxes,
+                   ha='right', va='top', fontsize=10,
+                   bbox=dict(boxstyle='square,pad=0.1', fc='white', ec='none'))
             label_idx += 1
-
+    
     # Create legend
     unique_handles = []
     unique_labels = []
     for activity in all_activities:
-        color = activity_colors.get(activity, "gray")
+        color = activity_colors.get(activity, 'gray')
         unique_handles.append(Line2D([0], [0], color=color, linewidth=2))
         unique_labels.append(activity)
-
+        
     # # Add total line to legend
     # unique_handles.append(Line2D([0], [0], color='black', linestyle='--', linewidth=1))
     # unique_labels.append('Total')
-
-    fig.legend(
-        unique_handles,
-        unique_labels,
-        loc="lower center",
-        ncol=min(len(unique_labels), 4),
-        bbox_to_anchor=(0.5, 0.02),
-        frameon=False,
-    )
-
+    
+    fig.legend(unique_handles, unique_labels, 
+              loc='lower center', 
+              ncol=min(len(unique_labels), 4),
+              bbox_to_anchor=(0.5, 0.02),
+              frameon=False)
+    
     plt.tight_layout()
     plt.subplots_adjust(bottom=0.15)
-
+    
     return fig
 
-
-from datetime import datetime
-
 import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
+from datetime import datetime
+from matplotlib.ticker import MultipleLocator, FormatStrFormatter
 from matplotlib.patches import Patch
-from matplotlib.ticker import FormatStrFormatter, MultipleLocator
-
 
 def create_combined_forcing_and_impacts_figure(scenarios_data: dict):
     """
-    Fixed combined figure: Ensures all subplot rows (Aggregated RF, Instantaneous RF,
+    Fixed combined figure: Ensures all subplot rows (Aggregated RF, Instantaneous RF, 
     and Water Use) have identical heights.
-
-    The layout uses a single GridSpec with height_ratios to maintain symmetry
+    
+    The layout uses a single GridSpec with height_ratios to maintain symmetry 
     while providing space for grouping titles.
     """
     fig = plt.figure(figsize=(14, 8))
-
+    
     # Define a single GridSpec for 3 rows. Height ratios [1, 1, 1] ensure equal height.
     # hspace=0.4 provides the necessary room for the Section b) title between rows.
     gs = fig.add_gridspec(3, 3, hspace=0.08, wspace=0.06)
-
+    
     # Define the absolute range
     start_year, end_year = 2025, 2050
     full_year_index = np.arange(start_year, end_year + 1)
-
+    
     rf_scenario_data = {}
     all_activities = set()
-
+    
     # Process Radiative Forcing data
     for scenario in SCENARIOS.keys():
         df = load_characterized_inventory(scenario)
-        df_years = df[
-            (df.index.year >= start_year) & (df.index.year <= end_year)
-        ].copy()
+        df_years = df[(df.index.year >= start_year) & (df.index.year <= end_year)].copy()
         df_years.index = df_years.index.year
         df_aligned = df_years.reindex(full_year_index).fillna(0)
         rf_scenario_data[scenario] = df_aligned
         all_activities.update(df_aligned.columns.tolist())
-
+    
     # --- SORTING FOR DAC ---
     all_activities_list = sorted(list(all_activities))
     print(all_activities_list)
-    target_comp = "DAC"
+    target_comp = 'DAC'
     if target_comp in all_activities_list:
         all_activities_list.remove(target_comp)
         all_activities_list.insert(0, target_comp)
-
-    activity_colors = {
-        act: PROCESS_COLORS.get(act, "gray") for act in all_activities_list
-    }
-
+    
+    activity_colors = {act: PROCESS_COLORS.get(act, 'gray') for act in all_activities_list}
+    
     # --- Scaling Pre-calculations ---
     crf_bar_max, crf_bar_min = 0, 0
     for scenario in scenarios_data.keys():
         impacts = load_impacts_properly(scenario)
         if "climate_change" in impacts:
-            data = impacts["climate_change"].loc[
-                (impacts["climate_change"].index >= start_year)
-                & (impacts["climate_change"].index <= end_year)
-            ]
+            data = impacts["climate_change"].loc[(impacts["climate_change"].index >= start_year) & (impacts["climate_change"].index <= end_year)]
             crf_bar_max = max(crf_bar_max, data.clip(lower=0).sum(axis=1).max())
             crf_bar_min = min(crf_bar_min, data.clip(upper=0).sum(axis=1).min())
-    scaling_factor_crf_bar = (
-        10 ** int(np.floor(np.log10(max(abs(crf_bar_max), abs(crf_bar_min)))))
-        if max(abs(crf_bar_max), abs(crf_bar_min)) > 0
-        else 1
-    )
+    scaling_factor_crf_bar = 10**int(np.floor(np.log10(max(abs(crf_bar_max), abs(crf_bar_min))))) if max(abs(crf_bar_max), abs(crf_bar_min)) > 0 else 1
 
     inst_max, inst_min = 0, 0
     for df in rf_scenario_data.values():
         inst_max = max(inst_max, df.clip(lower=0).sum(axis=1).max())
         inst_min = min(inst_min, df.clip(upper=0).sum(axis=1).min())
-    scaling_factor_inst = (
-        10 ** int(np.floor(np.log10(max(abs(inst_max), abs(inst_min)))))
-        if max(abs(inst_max), abs(inst_min)) > 0
-        else 1
-    )
+    scaling_factor_inst = 10**int(np.floor(np.log10(max(abs(inst_max), abs(inst_min))))) if max(abs(inst_max), abs(inst_min)) > 0 else 1
 
     water_max = 0
     for scenario in scenarios_data.keys():
         impacts = load_impacts_properly(scenario)
         if "water_use" in impacts:
-            data = impacts["water_use"].loc[
-                (impacts["water_use"].index >= start_year)
-                & (impacts["water_use"].index <= end_year)
-            ]
+            data = impacts["water_use"].loc[(impacts["water_use"].index >= start_year) & (impacts["water_use"].index <= end_year)]
             water_max = max(water_max, data.clip(lower=0).sum(axis=1).max())
-    scaling_factor_water = (
-        10 ** int(np.floor(np.log10(water_max))) if water_max > 0 else 1
-    )
+    scaling_factor_water = 10**int(np.floor(np.log10(water_max))) if water_max > 0 else 1
 
-    axes = np.empty((3, 3), dtype=object)
+    axes = np.empty((3, 3), dtype=object) 
     xtick_positions = [y for y in full_year_index if y % 5 == 0]
 
     # ===== PLOTTING =====
     for col, (scenario, scenario_label) in enumerate(SCENARIOS.items()):
-
+        
         # ROW 0: AGGREGATED RF (Bars)
         ax0 = fig.add_subplot(gs[0, col], sharey=axes[0, 0] if col > 0 else None)
         axes[0, col] = ax0
@@ -1094,38 +952,20 @@ def create_combined_forcing_and_impacts_figure(scenarios_data: dict):
                     color = activity_colors.get(proc, "gray")
                     pv, nv = np.maximum(vals, 0), np.minimum(vals, 0)
                     if np.any(pv > 0):
-                        ax0.bar(
-                            data.index,
-                            pv,
-                            bottom=bottom_pos,
-                            color=color,
-                            width=0.8,
-                            edgecolor="white",
-                            linewidth=1,
-                        )
+                        ax0.bar(data.index, pv, bottom=bottom_pos, color=color, width=0.8, edgecolor="white", linewidth=1)
                         bottom_pos += pv
                     if np.any(nv < 0):
-                        ax0.bar(
-                            data.index,
-                            nv,
-                            bottom=bottom_neg,
-                            color=color,
-                            width=0.8,
-                            edgecolor="white",
-                            linewidth=1,
-                        )
+                        ax0.bar(data.index, nv, bottom=bottom_neg, color=color, width=0.8, edgecolor="white", linewidth=1)
                         bottom_neg += nv
-
+        
         ax0.set_title(scenario_label, fontsize=11, pad=10)
         ax0.set_xlim(start_year - 1, end_year + 1)
-        ax0.set_ylim(-1.6, 3.1)
+        ax0.set_ylim(-1.6, 3.1) 
         ax0.set_xticks(xtick_positions)
         ax0.set_xticklabels([])
-        if col == 0:
-            ax0.set_ylabel(
-                f"Cumulative Radiative Forcing \n until 2125 [$10^{{{int(np.log10(scaling_factor_crf_bar))}}}$ W m$^{{-2}}$ yr]"
-            )
-        else:
+        if col == 0: 
+            ax0.set_ylabel(f"Cumulative Radiative Forcing \n until 2125 [$10^{{{int(np.log10(scaling_factor_crf_bar))}}}$ W m$^{{-2}}$ yr]")
+        else: 
             ax0.tick_params(labelleft=False)
         ax0.grid(True, alpha=0.3, zorder=0)
         ax0.set_axisbelow(True)
@@ -1135,50 +975,27 @@ def create_combined_forcing_and_impacts_figure(scenarios_data: dict):
         axes[1, col] = ax1
         df_plot = rf_scenario_data[scenario]
         if not df_plot.empty:
-            y_pos, y_neg = np.zeros(len(full_year_index)), np.zeros(
-                len(full_year_index)
-            )
+            y_pos, y_neg = np.zeros(len(full_year_index)), np.zeros(len(full_year_index))
             for act in all_activities_list:
                 if act in df_plot.columns:
                     vals = df_plot[act].values / scaling_factor_inst
                     color = activity_colors.get(act, "gray")
                     vals_pos, vals_neg = np.maximum(vals, 0), np.minimum(vals, 0)
                     if np.any(vals_pos > 0):
-                        ax1.fill_between(
-                            full_year_index,
-                            y_pos,
-                            y_pos + vals_pos,
-                            color=color,
-                            alpha=1,
-                            edgecolor="white",
-                            linewidth=1,
-                        )
+                        ax1.fill_between(full_year_index, y_pos, y_pos + vals_pos, color=color, alpha=1, edgecolor="white", linewidth=1)
                         y_pos += vals_pos
                     if np.any(vals_neg < 0):
-                        ax1.fill_between(
-                            full_year_index,
-                            y_neg,
-                            y_neg + vals_neg,
-                            color=color,
-                            alpha=1,
-                            edgecolor="white",
-                            linewidth=1,
-                        )
+                        ax1.fill_between(full_year_index, y_neg, y_neg + vals_neg, color=color, alpha=1, edgecolor="white", linewidth=1)
                         y_neg += vals_neg
-
-        ax1.axhline(0, color="gray", linewidth=1, zorder=0)
+        
+        ax1.axhline(0, color='gray', linewidth=1, zorder=0)
         ax1.set_xlim(start_year - 1, end_year + 1)
-        ax1.set_ylim(
-            (inst_min / scaling_factor_inst) * 1.1,
-            (inst_max / scaling_factor_inst) * 1.1,
-        )
+        ax1.set_ylim((inst_min/scaling_factor_inst)*1.1, (inst_max/scaling_factor_inst)*1.1)
         ax1.set_xticks(xtick_positions)
         ax1.set_xticklabels([])
-        if col == 0:
-            ax1.set_ylabel(
-                f"Instantaneous Radiative\n Forcing[$10^{{{int(np.log10(scaling_factor_inst))}}}$ W m$^{{-2}}$]"
-            )
-        else:
+        if col == 0: 
+            ax1.set_ylabel(f"Instantaneous Radiative\n Forcing[$10^{{{int(np.log10(scaling_factor_inst))}}}$ W m$^{{-2}}$]")
+        else: 
             ax1.tick_params(labelleft=False)
         ax1.grid(True, alpha=0.3, zorder=0)
         ax1.set_axisbelow(True)
@@ -1194,129 +1011,95 @@ def create_combined_forcing_and_impacts_figure(scenarios_data: dict):
             for proc in data.columns:
                 vals = data[proc].values / scaling_factor_water
                 if np.any(vals > 1e-12):
-                    ax2.bar(
-                        data.index,
-                        vals,
-                        bottom=bottom_pos,
-                        color=PROCESS_COLORS.get(proc, "gray"),
-                        width=0.8,
-                        edgecolor="white",
-                        linewidth=1,
-                    )
+                    ax2.bar(data.index, vals, bottom=bottom_pos, color=PROCESS_COLORS.get(proc, "gray"), width=0.8, edgecolor="white", linewidth=1)
                     bottom_pos += vals
-
+        
         ax2.set_xlim(start_year - 1, end_year + 1)
         ax2.set_xticks(xtick_positions)
         ax2.set_xticklabels([str(y) for y in xtick_positions])
-        ax2.set_ylim(0, (water_max / scaling_factor_water) * 1.15)
+        ax2.set_ylim(0, (water_max/scaling_factor_water)*1.15)
         ax2.set_xlabel("Year")
-        if col == 0:
-            ax2.set_ylabel(
-                f"Water Use\n[$10^{{{int(np.log10(scaling_factor_water))}}}$ m³-eq]"
-            )
-        else:
+        if col == 0: 
+            ax2.set_ylabel(f"Water Use\n[$10^{{{int(np.log10(scaling_factor_water))}}}$ m³-eq]")
+        else: 
             ax2.tick_params(labelleft=False)
         ax2.grid(True, alpha=0.3, zorder=0)
         ax2.set_axisbelow(True)
 
     # ===== TITLES & NUMBERING =====
-    roman = ["(a)", "(b)", "(c)", "(d)", "(e)", "(f)"]
-
+    roman = ['(a)', '(b)', '(c)', '(d)', '(e)', '(f)']
+    
     # "a)" Title is at the top
     # fig.text(0.075, 0.96, "a) Climate change", ha='left', fontsize=14, fontweight='bold')
-
+    
     # "b)" Title is placed in the gap between Row 1 and Row 2
     # The coordinate is determined by the height of the axes
     # fig.text(0.075, 0.37, "b) Water use", ha='left', fontsize=14, fontweight='bold')
     for r in range(2):
         for c in range(3):
-            axes[r, c].text(
-                0.02,
-                0.95,
-                roman[r * 3 + c],
-                transform=axes[r, c].transAxes,
-                va="top",
-                fontsize=10,
-                bbox=dict(boxstyle="square,pad=0.1", fc="white", ec="none"),
-            )
-
+            axes[r, c].text(0.02, 0.95, roman[r*3 + c], transform=axes[r, c].transAxes, 
+                            va='top', fontsize=10, 
+                            bbox=dict(boxstyle='square,pad=0.1', fc='white', ec='none'))
+            
     for c in range(3):
-        axes[2, c].text(
-            0.02,
-            0.95,
-            roman[c],
-            transform=axes[2, c].transAxes,
-            va="top",
-            fontsize=10,
-            bbox=dict(boxstyle="square,pad=0.1", fc="white", ec="none"),
-        )
+        axes[2, c].text(0.02, 0.95, roman[c], transform=axes[2, c].transAxes, 
+                        va='top', fontsize=10, 
+                        bbox=dict(boxstyle='square,pad=0.1', fc='white', ec='none'))
 
     # ===== LEGEND =====
     all_handles = {}
     for act in all_activities_list:
-        all_handles[act] = Patch(
-            facecolor=activity_colors[act], edgecolor="white", linewidth=0.5
-        )
+        all_handles[act] = Patch(facecolor=activity_colors[act], edgecolor="white", linewidth=0.5)
     for scenario in SCENARIOS.keys():
         impacts = load_impacts_properly(scenario)
         for cat in ["climate_change", "water_use"]:
             if cat in impacts:
                 for proc in impacts[cat].columns:
                     if proc not in all_handles:
-                        all_handles[proc] = Patch(
-                            facecolor=PROCESS_COLORS.get(proc, "gray"),
-                            edgecolor="white",
-                            linewidth=0.5,
-                        )
-
-    fig.legend(
-        all_handles.values(),
-        all_handles.keys(),
-        loc="lower center",
-        bbox_to_anchor=(0.5, 0),
-        ncol=min(len(all_handles), 4),
-        frameon=False,
-        fontsize=9,
-    )
-
+                        all_handles[proc] = Patch(facecolor=PROCESS_COLORS.get(proc, "gray"), edgecolor="white", linewidth=0.5)
+    
+    fig.legend(all_handles.values(), all_handles.keys(), loc="lower center", 
+               bbox_to_anchor=(0.5, 0), ncol=min(len(all_handles), 4), 
+               frameon=False, fontsize=9)
+    
     return fig
 
 
 def create_combined_results_and_impacts_figure(scenarios_data: dict):
     """
-    Create a comprehensive combined figure with production mix (results) on top and
+    Create a comprehensive combined figure with production mix (results) on top and 
     impacts (radiative forcing, water use) below.
-
+    
     Layout: 7 rows x 3 columns (scenarios)
     - Rows 0-3: Production results (Methanol, Pig Iron, Captured CO₂, Hydrogen)
     - Rows 4-6: Impacts (Aggregated RF, Instantaneous RF, Water Use)
-
+    
     Axes: Shared Y per row, Shared X per column. Labels only on outer edges.
     """
     fig = plt.figure(figsize=(12, 18))
-
+    
     fig.subplots_adjust(left=0.1, right=0.98, top=0.95, bottom=0.12)
-
+    
     # GridSpec for 7 rows x 3 columns
     gs = fig.add_gridspec(7, 3, hspace=0.08, wspace=0.06)
-
+    
     products = [
         ("methanol", "Methanol", False),
         ("pig iron", "Pig Iron", False),
         ("captured co2", "Captured CO₂", True),
         ("hydrogen", "Hydrogen", True),
     ]
-
+    
     # ===== PART 1: RESULTS (Rows 0-3) =====
-
+    
     # First pass: compute y-axis limits for each product row
     row_ylim_max = {}
     row_ylim_min = {}
-
+    
     for row, (product_key, product_label, is_intermediate) in enumerate(products):
         row_ylim_max[row] = 0.1
         row_ylim_min[row] = 0
-
+        
         for scenario in SCENARIOS.keys():
             data = scenarios_data[scenario]
             filepath = PLOTS_DIR / f"production_{scenario}.xlsx"
@@ -1325,68 +1108,64 @@ def create_combined_results_and_impacts_figure(scenarios_data: dict):
             prod_df = prod_df.iloc[1:]
             prod_df.index = pd.to_numeric(prod_df.index, errors="coerce")
             prod_df = prod_df.dropna()
-
+            
             cap_df = clean_capacity_df(data["capacity"])
             product_col = None
             for c in cap_df.columns:
                 if product_key.lower() in str(c).lower():
                     product_col = c
                     break
-
+            
             years = prod_df.index.values
             mask = (years >= 2025) & (years <= 2050)
-
+            
             prod_total_per_year = np.zeros(mask.sum())
             for c in prod_df.columns:
                 if isinstance(c, tuple):
                     process, product = c
                     if product_key.lower() in str(product).lower():
                         prod_total_per_year += prod_df[c].astype(float).values[mask]
-
+            
             row_ylim_max[row] = max(row_ylim_max[row], prod_total_per_year.max() / 1e6)
-
+            
             if product_col is not None:
                 cap_years = cap_df.index.values
                 cap_mask = (cap_years >= 2025) & (cap_years <= 2050)
                 capacity_values = cap_df[product_col].values[cap_mask] / 1e6
                 row_ylim_max[row] = max(row_ylim_max[row], capacity_values.max())
-                additions, retirements = compute_capacity_changes(
-                    cap_df[cap_mask], product_col
-                )
+                additions, retirements = compute_capacity_changes(cap_df[cap_mask], product_col)
                 row_ylim_max[row] = max(row_ylim_max[row], additions.max() / 1e6)
-
+        
         row_ylim_max[row] *= 1.1
         if row_ylim_min[row] < 0:
             row_ylim_min[row] *= 1.1
-
+    
     # Common x-axis setup for results
     years_plot = np.arange(2025, 2051)
     x_positions = np.arange(len(years_plot))
     prod_positions = x_positions
-
+    
     xtick_positions_results = [i for i, y in enumerate(years_plot) if y % 5 == 0]
     xtick_labels_results = [str(y) for y in years_plot if y % 5 == 0]
-
+    
     # Matrix to store all axes
     axes_matrix = np.empty((7, 3), dtype=object)
-
+    
     # Second pass: create production plots (rows 0-3)
     for row, (product_key, product_label, is_intermediate) in enumerate(products):
         for col, (scenario, scenario_label) in enumerate(SCENARIOS.items()):
-
+            
             # Shared Axis logic
             if row == 0 and col == 0:
                 ax = fig.add_subplot(gs[row, col])
             elif col == 0:
                 ax = fig.add_subplot(gs[row, col], sharex=axes_matrix[0, 0])
             else:
-                ax = fig.add_subplot(
-                    gs[row, col], sharey=axes_matrix[row, 0], sharex=axes_matrix[0, col]
-                )
-
+                ax = fig.add_subplot(gs[row, col], sharey=axes_matrix[row, 0], sharex=axes_matrix[0, col])
+            
             axes_matrix[row, col] = ax
             data = scenarios_data[scenario]
-
+            
             # Load production data
             filepath = PLOTS_DIR / f"production_{scenario}.xlsx"
             prod_df = pd.read_excel(filepath, header=[0, 1])
@@ -1394,7 +1173,7 @@ def create_combined_results_and_impacts_figure(scenarios_data: dict):
             prod_df = prod_df.iloc[1:]
             prod_df.index = pd.to_numeric(prod_df.index, errors="coerce")
             prod_df = prod_df.dropna()
-
+            
             # Get capacity data
             cap_df = clean_capacity_df(data["capacity"])
             product_col = None
@@ -1402,7 +1181,7 @@ def create_combined_results_and_impacts_figure(scenarios_data: dict):
                 if product_key.lower() in str(c).lower():
                     product_col = c
                     break
-
+            
             # Filter for product
             process_production = {}
             for c in prod_df.columns:
@@ -1411,13 +1190,11 @@ def create_combined_results_and_impacts_figure(scenarios_data: dict):
                     if product_key.lower() in str(product).lower():
                         clean_process = PROCESS_NAMES.get(process, process)
                         if clean_process not in process_production:
-                            process_production[clean_process] = (
-                                prod_df[c].astype(float).values
-                            )
-
+                            process_production[clean_process] = prod_df[c].astype(float).values
+            
             years = prod_df.index.values
             mask = (years >= 2025) & (years <= 2050)
-
+            
             # Stack production bars
             if process_production:
                 bottom = np.zeros(len(years_plot))
@@ -1425,135 +1202,99 @@ def create_combined_results_and_impacts_figure(scenarios_data: dict):
                     values_plot = values[mask] / 1e6
                     if np.any(values_plot > 0.01):
                         color = PROCESS_COLORS.get(process, "gray")
-                        ax.bar(
-                            prod_positions,
-                            values_plot,
-                            bottom=bottom,
-                            label=process,
-                            color=color,
-                            width=bar_width,
-                            edgecolor="white",
-                            linewidth=linewidth_bar_outline,
-                        )
+                        ax.bar(prod_positions, values_plot, bottom=bottom, label=process,
+                               color=color, width=bar_width, edgecolor="white", linewidth=linewidth_bar_outline)
                         bottom += values_plot
-
+            
             # Plot total capacity line
             if product_col is not None:
                 cap_years = cap_df.index.values
                 cap_mask = (cap_years >= 2025) & (cap_years <= 2050)
                 capacity_values = cap_df[product_col].values[cap_mask] / 1e6
                 if np.any(capacity_values > 0.001):
-                    ax.plot(
-                        x_positions,
-                        capacity_values,
-                        color="#000000",
-                        linestyle="--",
-                        linewidth=1,
-                        marker="",
-                        label="Capacity",
-                        zorder=4,
-                    )
-
+                    ax.plot(x_positions, capacity_values, color="#000000", linestyle="--",
+                            linewidth=1, marker="", label="Capacity", zorder=4)
+            
             ax.axhline(y=0, color="gray", linewidth=0.5, zorder=0)
-
+            
             # Formatting
             ax.set_xlim(-1, len(years_plot))
             ax.set_ylim(row_ylim_min[row], row_ylim_max[row])
             ax.grid(True, alpha=0.3, which="both", axis="both", zorder=0)
             ax.set_axisbelow(True)
-
+            
             # X-axis: No labels on results rows (impacts will have x labels at bottom)
             ax.tick_params(labelbottom=False)
-
+            
             # Y-axis: Only first column gets labels
             if col == 0:
                 label_suffix = " *" if is_intermediate else ""
                 ax.set_ylabel(f"{product_label}{label_suffix}\n[$10^6$ kg]")
-
+                
                 if product_label == "Captured CO₂":
                     ax.yaxis.set_major_locator(MultipleLocator(0.5))
             else:
                 ax.tick_params(labelleft=False)
-
+            
             # Column titles only on first row
             if row == 0:
                 ax.set_title(scenario_label, fontdict={"fontsize": 12}, pad=10)
-
+    
     # ===== PART 2: IMPACTS (Rows 4-6) =====
-
+    
     start_year, end_year = 2025, 2050
     full_year_index = np.arange(start_year, end_year + 1)
-
+    
     rf_scenario_data = {}
     all_activities = set()
-
+    
     # Process Radiative Forcing data
     for scenario in SCENARIOS.keys():
         df = load_characterized_inventory(scenario)
-        df_years = df[
-            (df.index.year >= start_year) & (df.index.year <= end_year)
-        ].copy()
+        df_years = df[(df.index.year >= start_year) & (df.index.year <= end_year)].copy()
         df_years.index = df_years.index.year
         df_aligned = df_years.reindex(full_year_index).fillna(0)
         rf_scenario_data[scenario] = df_aligned
         all_activities.update(df_aligned.columns.tolist())
-
+    
     # Sorting for DAC
     all_activities_list = sorted(list(all_activities))
-    target_comp = "DAC"
+    target_comp = 'DAC'
     if target_comp in all_activities_list:
         all_activities_list.remove(target_comp)
         all_activities_list.insert(0, target_comp)
-
-    activity_colors = {
-        act: PROCESS_COLORS.get(act, "gray") for act in all_activities_list
-    }
-
+    
+    activity_colors = {act: PROCESS_COLORS.get(act, 'gray') for act in all_activities_list}
+    
     # Scaling Pre-calculations
     crf_bar_max, crf_bar_min = 0, 0
     for scenario in scenarios_data.keys():
         impacts = load_impacts_properly(scenario)
         if "climate_change" in impacts:
-            data = impacts["climate_change"].loc[
-                (impacts["climate_change"].index >= start_year)
-                & (impacts["climate_change"].index <= end_year)
-            ]
+            data = impacts["climate_change"].loc[(impacts["climate_change"].index >= start_year) & (impacts["climate_change"].index <= end_year)]
             crf_bar_max = max(crf_bar_max, data.clip(lower=0).sum(axis=1).max())
             crf_bar_min = min(crf_bar_min, data.clip(upper=0).sum(axis=1).min())
-    scaling_factor_crf_bar = (
-        10 ** int(np.floor(np.log10(max(abs(crf_bar_max), abs(crf_bar_min)))))
-        if max(abs(crf_bar_max), abs(crf_bar_min)) > 0
-        else 1
-    )
-
+    scaling_factor_crf_bar = 10**int(np.floor(np.log10(max(abs(crf_bar_max), abs(crf_bar_min))))) if max(abs(crf_bar_max), abs(crf_bar_min)) > 0 else 1
+    
     inst_max, inst_min = 0, 0
     for df in rf_scenario_data.values():
         inst_max = max(inst_max, df.clip(lower=0).sum(axis=1).max())
         inst_min = min(inst_min, df.clip(upper=0).sum(axis=1).min())
-    scaling_factor_inst = (
-        10 ** int(np.floor(np.log10(max(abs(inst_max), abs(inst_min)))))
-        if max(abs(inst_max), abs(inst_min)) > 0
-        else 1
-    )
-
+    scaling_factor_inst = 10**int(np.floor(np.log10(max(abs(inst_max), abs(inst_min))))) if max(abs(inst_max), abs(inst_min)) > 0 else 1
+    
     water_max = 0
     for scenario in scenarios_data.keys():
         impacts = load_impacts_properly(scenario)
         if "water_use" in impacts:
-            data = impacts["water_use"].loc[
-                (impacts["water_use"].index >= start_year)
-                & (impacts["water_use"].index <= end_year)
-            ]
+            data = impacts["water_use"].loc[(impacts["water_use"].index >= start_year) & (impacts["water_use"].index <= end_year)]
             water_max = max(water_max, data.clip(lower=0).sum(axis=1).max())
-    scaling_factor_water = (
-        10 ** int(np.floor(np.log10(water_max))) if water_max > 0 else 1
-    )
-
+    scaling_factor_water = 10**int(np.floor(np.log10(water_max))) if water_max > 0 else 1
+    
     xtick_positions_impacts = [y for y in full_year_index if y % 5 == 0]
-
+    
     # Plotting impacts rows
     for col, (scenario, scenario_label) in enumerate(SCENARIOS.items()):
-
+        
         # ROW 4: AGGREGATED RF (Bars)
         ax0 = fig.add_subplot(gs[4, col], sharey=axes_matrix[4, 0] if col > 0 else None)
         axes_matrix[4, col] = ax0
@@ -1567,94 +1308,53 @@ def create_combined_results_and_impacts_figure(scenarios_data: dict):
                     color = activity_colors.get(proc, "gray")
                     pv, nv = np.maximum(vals, 0), np.minimum(vals, 0)
                     if np.any(pv > 0):
-                        ax0.bar(
-                            data.index,
-                            pv,
-                            bottom=bottom_pos,
-                            color=color,
-                            width=0.8,
-                            edgecolor="white",
-                            linewidth=1,
-                        )
+                        ax0.bar(data.index, pv, bottom=bottom_pos, color=color, width=0.8, edgecolor="white", linewidth=1)
                         bottom_pos += pv
                     if np.any(nv < 0):
-                        ax0.bar(
-                            data.index,
-                            nv,
-                            bottom=bottom_neg,
-                            color=color,
-                            width=0.8,
-                            edgecolor="white",
-                            linewidth=1,
-                        )
+                        ax0.bar(data.index, nv, bottom=bottom_neg, color=color, width=0.8, edgecolor="white", linewidth=1)
                         bottom_neg += nv
-
+        
         ax0.set_xlim(start_year - 1, end_year + 1)
         ax0.set_ylim(-1.6, 3.1)
         ax0.set_xticks(xtick_positions_impacts)
         ax0.set_xticklabels([])
         if col == 0:
-            ax0.set_ylabel(
-                f"AGWI until 2125 \n [$10^{{{int(np.log10(scaling_factor_crf_bar))}}}$ W m$^{{-2}}$ yr]"
-            )
+            ax0.set_ylabel(f"AGWI until 2125 \n [$10^{{{int(np.log10(scaling_factor_crf_bar))}}}$ W m$^{{-2}}$ yr]")
         else:
             ax0.tick_params(labelleft=False)
         ax0.grid(True, alpha=0.3, zorder=0)
         ax0.set_axisbelow(True)
-
+        
         # ROW 5: INSTANTANEOUS RF (Manual Stacked Area)
         ax1 = fig.add_subplot(gs[5, col], sharey=axes_matrix[5, 0] if col > 0 else None)
         axes_matrix[5, col] = ax1
         df_plot = rf_scenario_data[scenario]
         if not df_plot.empty:
-            y_pos, y_neg = np.zeros(len(full_year_index)), np.zeros(
-                len(full_year_index)
-            )
+            y_pos, y_neg = np.zeros(len(full_year_index)), np.zeros(len(full_year_index))
             for act in all_activities_list:
                 if act in df_plot.columns:
                     vals = df_plot[act].values / scaling_factor_inst
                     color = activity_colors.get(act, "gray")
                     vals_pos, vals_neg = np.maximum(vals, 0), np.minimum(vals, 0)
                     if np.any(vals_pos > 0):
-                        ax1.fill_between(
-                            full_year_index,
-                            y_pos,
-                            y_pos + vals_pos,
-                            color=color,
-                            alpha=1,
-                            edgecolor="white",
-                            linewidth=1,
-                        )
+                        ax1.fill_between(full_year_index, y_pos, y_pos + vals_pos, color=color, alpha=1, edgecolor="white", linewidth=1)
                         y_pos += vals_pos
                     if np.any(vals_neg < 0):
-                        ax1.fill_between(
-                            full_year_index,
-                            y_neg,
-                            y_neg + vals_neg,
-                            color=color,
-                            alpha=1,
-                            edgecolor="white",
-                            linewidth=1,
-                        )
+                        ax1.fill_between(full_year_index, y_neg, y_neg + vals_neg, color=color, alpha=1, edgecolor="white", linewidth=1)
                         y_neg += vals_neg
-
-        ax1.axhline(0, color="gray", linewidth=1, zorder=0)
+        
+        ax1.axhline(0, color='gray', linewidth=1, zorder=0)
         ax1.set_xlim(start_year - 1, end_year + 1)
-        ax1.set_ylim(
-            (inst_min / scaling_factor_inst) * 1.1,
-            (inst_max / scaling_factor_inst) * 1.1,
-        )
+        ax1.set_ylim((inst_min/scaling_factor_inst)*1.1, (inst_max/scaling_factor_inst)*1.1)
         ax1.set_xticks(xtick_positions_impacts)
         ax1.set_xticklabels([])
         if col == 0:
-            ax1.set_ylabel(
-                f"Instantaneous Radiative \n Forcing [$10^{{{int(np.log10(scaling_factor_inst))}}}$ W m$^{{-2}}$]"
-            )
+            ax1.set_ylabel(f"Instantaneous Radiative \n Forcing [$10^{{{int(np.log10(scaling_factor_inst))}}}$ W m$^{{-2}}$]")
         else:
             ax1.tick_params(labelleft=False)
         ax1.grid(True, alpha=0.3, zorder=0)
         ax1.set_axisbelow(True)
-
+    
     # ROW 6: WATER USE
     for col, (scenario, _) in enumerate(SCENARIOS.items()):
         ax2 = fig.add_subplot(gs[6, col], sharey=axes_matrix[6, 0] if col > 0 else None)
@@ -1666,33 +1366,23 @@ def create_combined_results_and_impacts_figure(scenarios_data: dict):
             for proc in data.columns:
                 vals = data[proc].values / scaling_factor_water
                 if np.any(vals > 1e-12):
-                    ax2.bar(
-                        data.index,
-                        vals,
-                        bottom=bottom_pos,
-                        color=PROCESS_COLORS.get(proc, "gray"),
-                        width=0.8,
-                        edgecolor="white",
-                        linewidth=1,
-                    )
+                    ax2.bar(data.index, vals, bottom=bottom_pos, color=PROCESS_COLORS.get(proc, "gray"), width=0.8, edgecolor="white", linewidth=1)
                     bottom_pos += vals
-
+        
         ax2.set_xlim(start_year - 1, end_year + 1)
         ax2.set_xticks(xtick_positions_impacts)
         ax2.set_xticklabels([str(y) for y in xtick_positions_impacts])
-        ax2.set_ylim(0, (water_max / scaling_factor_water) * 1.15)
+        ax2.set_ylim(0, (water_max/scaling_factor_water)*1.15)
         ax2.set_xlabel("Year")
         if col == 0:
-            ax2.set_ylabel(
-                f"Water Use\n[$10^{{{int(np.log10(scaling_factor_water))}}}$ m³-eq]"
-            )
+            ax2.set_ylabel(f"Water Use\n[$10^{{{int(np.log10(scaling_factor_water))}}}$ m³-eq]")
         else:
             ax2.tick_params(labelleft=False)
         ax2.grid(True, alpha=0.3, zorder=0)
         ax2.set_axisbelow(True)
-
+    
     # ===== NEW SECTION: CATEGORY LABELS & BRACKETS =====
-
+    
     def add_row_group_label(start_row, end_row, label_text):
         """
         Draws a vertical label and a bracket next to a range of rows.
@@ -1700,127 +1390,70 @@ def add_row_group_label(start_row, end_row, label_text):
         # Get the bounding boxes of the top-left and bottom-left subplots in the group
         top_pos = axes_matrix[start_row, 0].get_position()
         bottom_pos = axes_matrix[end_row, 0].get_position()
-
+        
         # Calculate vertical center and bounds in figure coordinates
         y_top = top_pos.y1
         y_bottom = bottom_pos.y0
         y_mid = (y_top + y_bottom) / 2
-
+        
         # X positions in figure coordinates (0 to 1 scale)
         # We place the label far left and the bracket between label and Y-axis
         text_x = 0.02
-        bracket_x = 0.032
-
+        bracket_x = 0.032 
+        
         # 1. Add the vertical rotated text
-        fig.text(
-            text_x,
-            y_mid,
-            label_text,
-            rotation="vertical",
-            va="center",
-            ha="center",
-            fontsize=12,
-        )
-
+        fig.text(text_x, y_mid, label_text, 
+                 rotation='vertical', va='center', ha='center',
+                 fontsize=12)
+        
         # 2. Add the bracket (Main vertical line)
-        line = plt.Line2D(
-            [bracket_x, bracket_x],
-            [y_bottom, y_top],
-            transform=fig.transFigure,
-            color="black",
-            linewidth=1,
-        )
+        line = plt.Line2D([bracket_x, bracket_x], [y_bottom, y_top],
+                          transform=fig.transFigure, color='black', linewidth=1)
         fig.lines.append(line)
-
+        
         # 3. Add horizontal ticks at the ends of the bracket
         tick_width = 0.008
-        t_top = plt.Line2D(
-            [bracket_x, bracket_x + tick_width],
-            [y_top, y_top],
-            transform=fig.transFigure,
-            color="black",
-            linewidth=1,
-        )
-        t_bottom = plt.Line2D(
-            [bracket_x, bracket_x + tick_width],
-            [y_bottom, y_bottom],
-            transform=fig.transFigure,
-            color="black",
-            linewidth=1,
-        )
+        t_top = plt.Line2D([bracket_x, bracket_x + tick_width], [y_top, y_top],
+                           transform=fig.transFigure, color='black', linewidth=1)
+        t_bottom = plt.Line2D([bracket_x, bracket_x + tick_width], [y_bottom, y_bottom],
+                              transform=fig.transFigure, color='black', linewidth=1)
         fig.lines.extend([t_top, t_bottom])
 
     # Apply the labels to the specific row ranges
     # Rows 0-3 (Methanol to Hydrogen)
     add_row_group_label(0, 3, "Deployment and Operation")
-
+    
     # Rows 4-6 (RF and Water Use)
     add_row_group_label(4, 6, "Environmental Impacts")
-
+    
     # ===== SUBPLOT LABELS =====
-    labels = [
-        "(a-1)",
-        "(a-2)",
-        "(a-3)",
-        "(b-1)",
-        "(b-2)",
-        "(b-3)",
-        "(c-1)",
-        "(c-2)",
-        "(c-3)",
-        "(d-1)",
-        "(d-2)",
-        "(d-3)",
-        "(e-1)",
-        "(e-2)",
-        "(e-3)",
-        "(f-1)",
-        "(f-2)",
-        "(f-3)",
-        "(g-1)",
-        "(g-2)",
-        "(g-3)",
-    ]
+    labels = ['(a-1)', '(a-2)', '(a-3)', '(b-1)', '(b-2)', '(b-3)', '(c-1)', '(c-2)', '(c-3)', '(d-1)', '(d-2)', '(d-3)',
+              '(e-1)', '(e-2)', '(e-3)', '(f-1)', '(f-2)', '(f-3)', '(g-1)', '(g-2)', '(g-3)']
     label_idx = 0
     for row in range(7):
         for col in range(3):
             ax = axes_matrix[row, col]
-            ax.text(
-                0.02,
-                0.95,
-                labels[label_idx],
-                transform=ax.transAxes,
-                va="top",
-                fontsize=10,
-                bbox=dict(boxstyle="square,pad=0.1", fc="white", ec="none"),
-            )
+            ax.text(0.02, 0.95, labels[label_idx], transform=ax.transAxes,
+                   va='top', fontsize=10,
+                   bbox=dict(boxstyle='square,pad=0.1', fc='white', ec='none'))
             label_idx += 1
-
+    
     # ===== CREATE SHARED LEGEND =====
     all_handles = []
     all_labels_legend = []
-
+    
     # Add process colors from results
     for process, color in PROCESS_COLORS.items():
         all_handles.append(Patch(facecolor=color, edgecolor="white", linewidth=0.5))
         all_labels_legend.append(process)
-
+    
     # Add capacity line
-    all_handles.append(
-        plt.Line2D([0], [0], color="#000000", linestyle="--", linewidth=1)
-    )
+    all_handles.append(plt.Line2D([0], [0], color="#000000", linestyle="--", linewidth=1))
     all_labels_legend.append("Available capacity")
-
-    fig.legend(
-        all_handles,
-        all_labels_legend,
-        loc="lower center",
-        bbox_to_anchor=(0.5, 0.07),
-        ncol=4,
-        frameon=False,
-        fontsize=9,
-    )
-
+    
+    fig.legend(all_handles, all_labels_legend, loc="lower center", bbox_to_anchor=(0.5, 0.07),
+               ncol=4, frameon=False, fontsize=9)
+    
     return fig
 
 
@@ -1853,7 +1486,7 @@ def main():
     fig_combined = create_combined_results_and_impacts_figure(scenarios_data)
     fig_combined.savefig(OUTPUT_DIR / "results_and_impacts.pdf")
     plt.close(fig_combined)
-
+    
     fig_forcing = create_radiative_forcing_figure()
     fig_forcing.savefig(OUTPUT_DIR / "radiative_forcing.pdf")
     plt.close(fig_forcing)
diff --git a/scripts/uv_setup.py b/scripts/uv_setup.py
index c546593..d98e6c2 100644
--- a/scripts/uv_setup.py
+++ b/scripts/uv_setup.py
@@ -1,15 +1,14 @@
-import json
 import os
-import subprocess
 import sys
+import subprocess
 import urllib.request
+import json
 from pathlib import Path
 
 # Configuration
 NB_URL = "https://raw.githubusercontent.com/RWTH-LTT/optimex/refs/heads/main/notebooks/basic_example.ipynb"
 DEPS = ["optimex"]
 
-
 def main():
     # 1. Handle Project Name Input
     # Usage: uv run script.py [project_name]
@@ -19,7 +18,7 @@ def main():
     if project_name != ".":
         print(f"Creating project directory: {project_name}")
         project_path.mkdir(parents=True, exist_ok=True)
-
+    
     # Change working directory to the new project path
     os.chdir(project_path)
 
@@ -40,11 +39,8 @@ def main():
     urllib.request.urlretrieve(NB_URL, "start_here.ipynb")
 
     print(f"\n✅ Done! Project '{project_path.name}' is ready.\n")
-    print(
-        f"Open in Jupyter Lab: cd {project_name} && uv run --with jupyter jupyter lab"
-    )
+    print(f"Open in Jupyter Lab: cd {project_name} && uv run --with jupyter jupyter lab")
     print(f"Open in VS Code: cd {project_name} && code .")
 
-
 if __name__ == "__main__":
-    main()
+    main()
\ No newline at end of file
diff --git a/src/optimex/converter.py b/src/optimex/converter.py
index 602e738..688fc4b 100644
--- a/src/optimex/converter.py
+++ b/src/optimex/converter.py
@@ -9,7 +9,6 @@
     - OptimizationModelInputs: Validated data structure for optimization inputs
     - ModelInputManager: Handles conversion, serialization, and constraint overrides
 """
-
 import copy
 import json
 import pickle
@@ -36,8 +35,7 @@ class OptimizationModelInputs(BaseModel):
         ..., description="Identifiers for all foreground products."
     )
     INTERMEDIATE_FLOW: List[str] = Field(
-        ...,
-        description="Identifiers for background products (from background databases).",
+        ..., description="Identifiers for background products (from background databases)."
     )
     ELEMENTARY_FLOW: List[str] = Field(
         ...,
@@ -87,15 +85,11 @@ class OptimizationModelInputs(BaseModel):
 
     foreground_technosphere: Dict[Tuple[str, str, int], float] = Field(
         ...,
-        description=(
-            "Maps (process, intermediate_flow, process_time) to background flow amount."
-        ),
+        description=("Maps (process, intermediate_flow, process_time) to background flow amount."),
     )
     internal_demand_technosphere: Dict[Tuple[str, str, int], float] = Field(
         ...,
-        description=(
-            "Maps (process, product, process_time) to internal product consumption amount."
-        ),
+        description=("Maps (process, product, process_time) to internal product consumption amount."),
     )
     foreground_biosphere: Dict[Tuple[str, str, int], float] = Field(
         ...,
@@ -106,7 +100,9 @@ class OptimizationModelInputs(BaseModel):
     )
     foreground_production: Dict[Tuple[str, str, int], float] = Field(
         ...,
-        description=("Maps (process, product, process_time) to produced amount."),
+        description=(
+            "Maps (process, product, process_time) to produced amount."
+        ),
     )
 
     # ==================== Vintage-Dependent Parameters ====================
@@ -123,9 +119,7 @@ class OptimizationModelInputs(BaseModel):
     # the same (process, flow), the explicit *_vintages values take precedence.
     # ========================================================================
 
-    foreground_technosphere_vintages: Optional[
-        Dict[Tuple[str, str, int, int], float]
-    ] = Field(
+    foreground_technosphere_vintages: Optional[Dict[Tuple[str, str, int, int], float]] = Field(
         None,
         description=(
             "Vintage-specific technosphere values. Maps (process, intermediate_flow, "
@@ -134,27 +128,23 @@ class OptimizationModelInputs(BaseModel):
             "Takes precedence over vintage_improvements for the same (process, flow)."
         ),
     )
-    foreground_biosphere_vintages: Optional[Dict[Tuple[str, str, int, int], float]] = (
-        Field(
-            None,
-            description=(
-                "Vintage-specific biosphere values. Maps (process, elementary_flow, "
-                "process_time, vintage_year) to amount. Values are linearly interpolated "
-                "for installation years between specified vintages. "
-                "Takes precedence over vintage_improvements for the same (process, flow)."
-            ),
-        )
+    foreground_biosphere_vintages: Optional[Dict[Tuple[str, str, int, int], float]] = Field(
+        None,
+        description=(
+            "Vintage-specific biosphere values. Maps (process, elementary_flow, "
+            "process_time, vintage_year) to amount. Values are linearly interpolated "
+            "for installation years between specified vintages. "
+            "Takes precedence over vintage_improvements for the same (process, flow)."
+        ),
     )
-    foreground_production_vintages: Optional[Dict[Tuple[str, str, int, int], float]] = (
-        Field(
-            None,
-            description=(
-                "Vintage-specific production values. Maps (process, product, process_time, "
-                "vintage_year) to amount. Values are linearly interpolated "
-                "for installation years between specified vintages. "
-                "Takes precedence over vintage_improvements for the same (process, product)."
-            ),
-        )
+    foreground_production_vintages: Optional[Dict[Tuple[str, str, int, int], float]] = Field(
+        None,
+        description=(
+            "Vintage-specific production values. Maps (process, product, process_time, "
+            "vintage_year) to amount. Values are linearly interpolated "
+            "for installation years between specified vintages. "
+            "Takes precedence over vintage_improvements for the same (process, product)."
+        ),
     )
     vintage_improvements: Optional[Dict[Tuple[str, str, int], float]] = Field(
         None,
@@ -170,23 +160,17 @@ class OptimizationModelInputs(BaseModel):
 
     # Internal: Computed sparse overrides (populated by _expand_vintage_parameters)
     # Users should not set these directly - they are derived from the above inputs.
-    foreground_technosphere_vintage_overrides: Optional[
-        Dict[Tuple[str, str, int, int], float]
-    ] = Field(
+    foreground_technosphere_vintage_overrides: Optional[Dict[Tuple[str, str, int, int], float]] = Field(
         None,
         description="[Internal] Computed sparse overrides. Do not set directly.",
         exclude=True,  # Exclude from serialization
     )
-    foreground_biosphere_vintage_overrides: Optional[
-        Dict[Tuple[str, str, int, int], float]
-    ] = Field(
+    foreground_biosphere_vintage_overrides: Optional[Dict[Tuple[str, str, int, int], float]] = Field(
         None,
         description="[Internal] Computed sparse overrides. Do not set directly.",
         exclude=True,
     )
-    foreground_production_vintage_overrides: Optional[
-        Dict[Tuple[str, str, int, int], float]
-    ] = Field(
+    foreground_production_vintage_overrides: Optional[Dict[Tuple[str, str, int, int], float]] = Field(
         None,
         description="[Internal] Computed sparse overrides. Do not set directly.",
         exclude=True,
@@ -324,13 +308,15 @@ class OptimizationModelInputs(BaseModel):
     process_operation_limits_max_default: float = Field(
         default=float("inf"),
         description=(
-            "Default upper bound for process operation if not explicitly " "specified."
+            "Default upper bound for process operation if not explicitly "
+            "specified."
         ),
     )
     process_operation_limits_min_default: float = Field(
         default=0.0,
         description=(
-            "Default lower bound for process operation if not explicitly " "specified."
+            "Default lower bound for process operation if not explicitly "
+            "specified."
         ),
     )
     cumulative_process_limits_max_default: float = Field(
@@ -410,7 +396,9 @@ def validate_keys(keys, valid_set, context):
 
         for key in data.get("internal_demand_technosphere", {}).keys():
             validate_keys([key[0]], processes, "internal_demand_technosphere processes")
-            validate_keys([key[1]], products, "internal_demand_technosphere products")
+            validate_keys(
+                [key[1]], products, "internal_demand_technosphere products"
+            )
             validate_keys(
                 [key[2]], process_times, "internal_demand_technosphere process times"
             )
@@ -424,7 +412,9 @@ def validate_keys(keys, valid_set, context):
 
         for key in data.get("foreground_production", {}).keys():
             validate_keys([key[0]], processes, "foreground_production processes")
-            validate_keys([key[1]], products, "foreground_production products")
+            validate_keys(
+                [key[1]], products, "foreground_production products"
+            )
             validate_keys(
                 [key[2]], process_times, "foreground_production process times"
             )
@@ -454,9 +444,7 @@ def validate_keys(keys, valid_set, context):
         if data.get("category_impact_limits") is not None:
             for key in data["category_impact_limits"].keys():
                 validate_keys([key[0]], categories, "category_impact_limits categories")
-                validate_keys(
-                    [key[1]], system_times, "category_impact_limits system times"
-                )
+                validate_keys([key[1]], system_times, "category_impact_limits system times")
 
         if data.get("cumulative_category_impact_limits") is not None:
             for key in data["cumulative_category_impact_limits"].keys():
@@ -464,39 +452,23 @@ def validate_keys(keys, valid_set, context):
 
         if data.get("process_deployment_limits_max") is not None:
             for key in data["process_deployment_limits_max"].keys():
-                validate_keys(
-                    [key[0]], processes, "process_deployment_limits_max processes"
-                )
-                validate_keys(
-                    [key[1]], system_times, "process_deployment_limits_max system times"
-                )
+                validate_keys([key[0]], processes, "process_deployment_limits_max processes")
+                validate_keys([key[1]], system_times, "process_deployment_limits_max system times")
 
         if data.get("process_deployment_limits_min") is not None:
             for key in data["process_deployment_limits_min"].keys():
-                validate_keys(
-                    [key[0]], processes, "process_deployment_limits_min processes"
-                )
-                validate_keys(
-                    [key[1]], system_times, "process_deployment_limits_min system times"
-                )
+                validate_keys([key[0]], processes, "process_deployment_limits_min processes")
+                validate_keys([key[1]], system_times, "process_deployment_limits_min system times")
 
         if data.get("process_operation_limits_max") is not None:
             for key in data["process_operation_limits_max"].keys():
-                validate_keys(
-                    [key[0]], processes, "process_operation_limits_max processes"
-                )
-                validate_keys(
-                    [key[1]], system_times, "process_operation_limits_max system times"
-                )
+                validate_keys([key[0]], processes, "process_operation_limits_max processes")
+                validate_keys([key[1]], system_times, "process_operation_limits_max system times")
 
         if data.get("process_operation_limits_min") is not None:
             for key in data["process_operation_limits_min"].keys():
-                validate_keys(
-                    [key[0]], processes, "process_operation_limits_min processes"
-                )
-                validate_keys(
-                    [key[1]], system_times, "process_operation_limits_min system times"
-                )
+                validate_keys([key[0]], processes, "process_operation_limits_min processes")
+                validate_keys([key[1]], system_times, "process_operation_limits_min system times")
 
         if data.get("cumulative_process_limits_max") is not None:
             for key in data["cumulative_process_limits_max"].keys():
@@ -588,9 +560,7 @@ def validate_keys(keys, valid_set, context):
                     "foreground_technosphere_vintages intermediate flows",
                 )
                 validate_keys(
-                    [key[2]],
-                    process_times,
-                    "foreground_technosphere_vintages process times",
+                    [key[2]], process_times, "foreground_technosphere_vintages process times"
                 )
 
         # Validate foreground_biosphere_vintages (processes, flows, process_times)
@@ -605,9 +575,7 @@ def validate_keys(keys, valid_set, context):
                     "foreground_biosphere_vintages elementary flows",
                 )
                 validate_keys(
-                    [key[2]],
-                    process_times,
-                    "foreground_biosphere_vintages process times",
+                    [key[2]], process_times, "foreground_biosphere_vintages process times"
                 )
 
         # Validate foreground_production_vintages (processes, products, process_times)
@@ -620,9 +588,7 @@ def validate_keys(keys, valid_set, context):
                     [key[1]], products, "foreground_production_vintages products"
                 )
                 validate_keys(
-                    [key[2]],
-                    process_times,
-                    "foreground_production_vintages process times",
+                    [key[2]], process_times, "foreground_production_vintages process times"
                 )
 
         # Validate vintage_improvements (processes, flows)
@@ -630,7 +596,9 @@ def validate_keys(keys, valid_set, context):
             for key in data["vintage_improvements"].keys():
                 validate_keys([key[0]], processes, "vintage_improvements processes")
                 # Flow can be intermediate, elementary, or product
-                validate_keys([key[1]], all_flows, "vintage_improvements flows")
+                validate_keys(
+                    [key[1]], all_flows, "vintage_improvements flows"
+                )
 
         return data
 
@@ -657,7 +625,6 @@ def validate_constant_operation_flows(self) -> "OptimizationModelInputs":
         ValueError
             If any operational flow has varying values across process time.
         """
-
         def check_constancy(
             flow_data: Dict[Tuple[str, str, int], float], flow_type: str
         ):
@@ -729,7 +696,7 @@ def validate_process_limits_consistency(self) -> "OptimizationModelInputs":
                             f"Cumulative process limit min ({min_val}) > max ({max_val}) "
                             f"for {proc}. Constraints would be infeasible."
                         )
-
+                    
         # Cross-check cumulative vs. per-period limits
         if self.process_deployment_limits_max and self.cumulative_process_limits_min:
             for key in self.cumulative_process_limits_min:
@@ -756,31 +723,23 @@ def validate_process_limits_consistency(self) -> "OptimizationModelInputs":
                         f"process limit max ({max_cum}) for {key}. Constraints would be infeasible."
                     )
 
+
         # Check defaults consistency
-        if (
-            self.process_deployment_limits_min_default
-            > self.process_deployment_limits_max_default
-        ):
+        if self.process_deployment_limits_min_default > self.process_deployment_limits_max_default:
             raise ValueError(
                 f"process_deployment_limits_min_default ({self.process_deployment_limits_min_default}) > "
                 f"process_deployment_limits_max_default ({self.process_deployment_limits_max_default}). "
                 "Constraints would be infeasible."
             )
 
-        if (
-            self.process_operation_limits_min_default
-            > self.process_operation_limits_max_default
-        ):
+        if self.process_operation_limits_min_default > self.process_operation_limits_max_default:
             raise ValueError(
                 f"process_operation_limits_min_default ({self.process_operation_limits_min_default}) > "
                 f"process_operation_limits_max_default ({self.process_operation_limits_max_default}). "
                 "Constraints would be infeasible."
             )
 
-        if (
-            self.cumulative_process_limits_min_default
-            > self.cumulative_process_limits_max_default
-        ):
+        if self.cumulative_process_limits_min_default > self.cumulative_process_limits_max_default:
             raise ValueError(
                 f"cumulative_process_limits_min_default ({self.cumulative_process_limits_min_default}) > "
                 f"cumulative_process_limits_max_default ({self.cumulative_process_limits_max_default}). "
@@ -832,7 +791,8 @@ def validate_process_limits_consistency(self) -> "OptimizationModelInputs":
         if self.flow_limits_min and self.cumulative_flow_limits_max:
             for flow in self.cumulative_flow_limits_max:
                 total_min = sum(
-                    self.flow_limits_min.get((flow, t), 0.0) for t in self.SYSTEM_TIME
+                    self.flow_limits_min.get((flow, t), 0.0)
+                    for t in self.SYSTEM_TIME
                 )
                 max_cum = self.cumulative_flow_limits_max[flow]
                 if total_min > max_cum:
@@ -906,63 +866,51 @@ def _expand_vintage_parameters(self) -> None:
 
         # Handle foreground_technosphere - only create overrides if vintage data exists
         if self.foreground_technosphere_vintages:
-            self.foreground_technosphere_vintage_overrides = (
-                expand_foreground_tensor_with_vintages(
-                    self.foreground_technosphere_vintages,
-                    self.REFERENCE_VINTAGES,
-                    system_times,
-                )
+            self.foreground_technosphere_vintage_overrides = expand_foreground_tensor_with_vintages(
+                self.foreground_technosphere_vintages,
+                self.REFERENCE_VINTAGES,
+                system_times,
             )
         elif self.vintage_improvements and self.foreground_technosphere:
-            self.foreground_technosphere_vintage_overrides = (
-                expand_foreground_tensor_with_evolution(
-                    self.foreground_technosphere,
-                    self.vintage_improvements,
-                    self.REFERENCE_VINTAGES,
-                    system_times,
-                    "INTERMEDIATE_FLOW",
-                )
+            self.foreground_technosphere_vintage_overrides = expand_foreground_tensor_with_evolution(
+                self.foreground_technosphere,
+                self.vintage_improvements,
+                self.REFERENCE_VINTAGES,
+                system_times,
+                "INTERMEDIATE_FLOW",
             )
         # else: no overrides, use base 3D tensor directly
 
         # Handle foreground_biosphere
         if self.foreground_biosphere_vintages:
-            self.foreground_biosphere_vintage_overrides = (
-                expand_foreground_tensor_with_vintages(
-                    self.foreground_biosphere_vintages,
-                    self.REFERENCE_VINTAGES,
-                    system_times,
-                )
+            self.foreground_biosphere_vintage_overrides = expand_foreground_tensor_with_vintages(
+                self.foreground_biosphere_vintages,
+                self.REFERENCE_VINTAGES,
+                system_times,
             )
         elif self.vintage_improvements and self.foreground_biosphere:
-            self.foreground_biosphere_vintage_overrides = (
-                expand_foreground_tensor_with_evolution(
-                    self.foreground_biosphere,
-                    self.vintage_improvements,
-                    self.REFERENCE_VINTAGES,
-                    system_times,
-                    "ELEMENTARY_FLOW",
-                )
+            self.foreground_biosphere_vintage_overrides = expand_foreground_tensor_with_evolution(
+                self.foreground_biosphere,
+                self.vintage_improvements,
+                self.REFERENCE_VINTAGES,
+                system_times,
+                "ELEMENTARY_FLOW",
             )
 
         # Handle foreground_production
         if self.foreground_production_vintages:
-            self.foreground_production_vintage_overrides = (
-                expand_foreground_tensor_with_vintages(
-                    self.foreground_production_vintages,
-                    self.REFERENCE_VINTAGES,
-                    system_times,
-                )
+            self.foreground_production_vintage_overrides = expand_foreground_tensor_with_vintages(
+                self.foreground_production_vintages,
+                self.REFERENCE_VINTAGES,
+                system_times,
             )
         elif self.vintage_improvements and self.foreground_production:
-            self.foreground_production_vintage_overrides = (
-                expand_foreground_tensor_with_evolution(
-                    self.foreground_production,
-                    self.vintage_improvements,
-                    self.REFERENCE_VINTAGES,
-                    system_times,
-                    "PRODUCT",
-                )
+            self.foreground_production_vintage_overrides = expand_foreground_tensor_with_evolution(
+                self.foreground_production,
+                self.vintage_improvements,
+                self.REFERENCE_VINTAGES,
+                system_times,
+                "PRODUCT",
             )
 
     def get_scaled_copy(self) -> Tuple["OptimizationModelInputs", Dict[str, Any]]:
@@ -1360,10 +1308,7 @@ def save_inputs(self, path: str) -> None:
                     data[field] = self._serialize_dict_with_tuple_keys(data[field])
 
             # Special handling for operation_time_limits (values are tuples, not keys)
-            if (
-                "operation_time_limits" in data
-                and data["operation_time_limits"] is not None
-            ):
+            if "operation_time_limits" in data and data["operation_time_limits"] is not None:
                 data["operation_time_limits"] = {
                     k: list(v) for k, v in data["operation_time_limits"].items()
                 }
@@ -1437,10 +1382,7 @@ def load_inputs(self, path: str) -> OptimizationModelInputs:
                     data[field] = self._deserialize_dict_with_tuple_keys(data[field])
 
             # Special handling for operation_time_limits (convert lists back to tuples)
-            if (
-                "operation_time_limits" in data
-                and data["operation_time_limits"] is not None
-            ):
+            if "operation_time_limits" in data and data["operation_time_limits"] is not None:
                 data["operation_time_limits"] = {
                     k: tuple(v) for k, v in data["operation_time_limits"].items()
                 }
diff --git a/src/optimex/lca_processor.py b/src/optimex/lca_processor.py
index 95ef16b..5b7687b 100644
--- a/src/optimex/lca_processor.py
+++ b/src/optimex/lca_processor.py
@@ -10,7 +10,6 @@
     - LCAConfig: Configuration for LCA computations
     - LCADataProcessor: Main class for time-explicit LCA processing
 """
-
 import pickle
 from datetime import datetime
 from enum import Enum
@@ -100,7 +99,7 @@ class TemporalConfig(BaseModel):
         temporal_resolution: Temporal resolution for the model.
             Options: 'year', 'month', 'day'.
         time_horizon: Length of the time horizon (in units of `temporal_resolution`).
-        fixed_time_horizon: If True, the time horizon is calculated from the time of the functional
+        fixed_time_horizon: If True, the time horizon is calculated from the time of the functional 
             unit (FU) instead of the time of emission
         database_dates: Mapping from database names to their respective reference dates.
     """
@@ -240,7 +239,7 @@ def __init__(self, config: LCAConfig) -> None:
         self._characterization = {}
         self._operation_flow = {}
         self._operation_time_limits = {}
-
+        
         # Vintage-dependent parameters extracted from exchange attributes
         self._foreground_technosphere_vintages = {}
         self._foreground_biosphere_vintages = {}
@@ -347,29 +346,17 @@ def internal_demand_technosphere(self) -> dict:
     @property
     def foreground_technosphere_vintages(self) -> Optional[dict]:
         """Read-only access to vintage-specific technosphere values."""
-        return (
-            self._foreground_technosphere_vintages
-            if self._foreground_technosphere_vintages
-            else None
-        )
+        return self._foreground_technosphere_vintages if self._foreground_technosphere_vintages else None
 
     @property
     def foreground_biosphere_vintages(self) -> Optional[dict]:
         """Read-only access to vintage-specific biosphere values."""
-        return (
-            self._foreground_biosphere_vintages
-            if self._foreground_biosphere_vintages
-            else None
-        )
+        return self._foreground_biosphere_vintages if self._foreground_biosphere_vintages else None
 
     @property
     def foreground_production_vintages(self) -> Optional[dict]:
         """Read-only access to vintage-specific production values."""
-        return (
-            self._foreground_production_vintages
-            if self._foreground_production_vintages
-            else None
-        )
+        return self._foreground_production_vintages if self._foreground_production_vintages else None
 
     @property
     def vintage_improvements(self) -> Optional[dict]:
@@ -379,9 +366,7 @@ def vintage_improvements(self) -> Optional[dict]:
     @property
     def reference_vintages(self) -> Optional[list]:
         """Read-only access to reference vintage years."""
-        return (
-            sorted(list(self._reference_vintages)) if self._reference_vintages else None
-        )
+        return sorted(list(self._reference_vintages)) if self._reference_vintages else None
 
     def _parse_demand(self) -> None:
         """
@@ -404,18 +389,18 @@ def _parse_demand(self) -> None:
 
         for product_node, td in raw_demand.items():
             # Validate demand is on product nodes
-            if not hasattr(product_node, "key"):
+            if not hasattr(product_node, 'key'):
                 raise ValueError(
                     f"Demand must be on Brightway Node objects, got {type(product_node)}"
                 )
 
-            if product_node.get("type") != bd.labels.product_node_default:
+            if product_node.get('type') != bd.labels.product_node_default:
                 raise ValueError(
                     f"Demand must be on product nodes. "
                     f"Node {product_node['name']} has type {product_node.get('type')}"
                 )
 
-            product_code = product_node["code"]
+            product_code = product_node['code']
             years = td.date.astype("datetime64[Y]").astype(int) + 1970
             if years[-1] - start_year > longest_demand_interval:
                 longest_demand_interval = years[-1] - start_year
@@ -426,7 +411,7 @@ def _parse_demand(self) -> None:
             )
 
             # Store product information
-            self._products[product_code] = product_node["name"]
+            self._products[product_code] = product_node['name']
 
         self._system_time = range(start_year, start_year + longest_demand_interval + 1)
         logger.info(
@@ -444,11 +429,11 @@ def _construct_foreground_tensors(self) -> None:
         It processes only process nodes (type=process_node_default) and handles
         three types of edges: production edges (to product nodes), consumption edges
         (from background or foreground products), and biosphere edges (emissions).
-
+        
         Additionally, this method extracts vintage-dependent parameters from exchange
         attributes when present:
         - vintage_improvements: Dict mapping vintage years to scaling factors
-        - vintage_amounts: Dict mapping vintage years or (process_time, vintage_year)
+        - vintage_amounts: Dict mapping vintage years or (process_time, vintage_year) 
           tuples to amounts
 
         Side Effects
@@ -471,13 +456,13 @@ def _construct_foreground_tensors(self) -> None:
               indicating if the flow occurs during the operation phase.
             - self._operation_time_limits: dict mapping process codes to their
               operation time limits, if defined.
-            - self._foreground_technosphere_vintages: dict mapping (process_code,
+            - self._foreground_technosphere_vintages: dict mapping (process_code, 
               flow_code, process_time, vintage_year) to vintage-specific amounts.
-            - self._foreground_biosphere_vintages: dict mapping (process_code,
+            - self._foreground_biosphere_vintages: dict mapping (process_code, 
               flow_code, process_time, vintage_year) to vintage-specific amounts.
-            - self._foreground_production_vintages: dict mapping (process_code,
+            - self._foreground_production_vintages: dict mapping (process_code, 
               product_code, process_time, vintage_year) to vintage-specific amounts.
-            - self._vintage_improvements: dict mapping (process_code, flow_code,
+            - self._vintage_improvements: dict mapping (process_code, flow_code, 
               vintage_year) to scaling factors.
             - self._reference_vintages: set of reference vintage years.
         """
@@ -488,7 +473,7 @@ def _construct_foreground_tensors(self) -> None:
 
         for act in self.foreground_db:
             # Only process nodes (not product nodes)
-            if act.get("type") != bd.labels.process_node_default:
+            if act.get('type') != bd.labels.process_node_default:
                 continue
 
             # Store process information
@@ -503,7 +488,7 @@ def _construct_foreground_tensors(self) -> None:
                     TemporalDistribution(
                         date=np.array([0], dtype="timedelta64[Y]"), amount=np.array([1])
                     ),
-                )
+                )                
                 years = temporal_dist.date.astype("timedelta64[Y]").astype(int)
                 # Ensure all years are included in process time
                 self._process_time.update(
@@ -514,15 +499,14 @@ def _construct_foreground_tensors(self) -> None:
                 # Skip if temporal distribution is missing or invalid (empty arrays)
                 if years.size == 0 or temporal_factor.size == 0:
                     logger.debug(
-                        f"Skipping exchange {exc.input} due to missing or invalid temporal distribution."
-                    )
+                        f"Skipping exchange {exc.input} due to missing or invalid temporal distribution.")
                     continue
 
                 edge_type = exc["type"]
                 input_code = exc.input["code"]
                 input_name = exc.input["name"]
                 input_db = exc.input["database"]
-
+                
                 # ========== Extract Vintage Parameters from Exchange Attributes ==========
                 # Vintage parameters allow foreground exchanges to vary based on installation year.
                 # Two attributes are supported on exchanges:
@@ -535,10 +519,10 @@ def _construct_foreground_tensors(self) -> None:
                 #    Format: {vintage_year: amount} OR {(process_time, vintage_year): amount}
                 #    Example: {2020: 60, 2030: 45} or {(1, 2020): 60, (1, 2030): 45}
                 # ==========================================================================
-
+                
                 vintage_amounts = exc.get("vintage_amounts")
                 vintage_improvements = exc.get("vintage_improvements")
-
+                
                 # Process vintage_improvements attribute if present
                 if vintage_improvements is not None:
                     if not isinstance(vintage_improvements, dict):
@@ -547,15 +531,10 @@ def _construct_foreground_tensors(self) -> None:
                             f"got {type(vintage_improvements).__name__}. Skipping."
                         )
                     else:
-                        for (
-                            vintage_year,
-                            scaling_factor,
-                        ) in vintage_improvements.items():
+                        for vintage_year, scaling_factor in vintage_improvements.items():
                             self._reference_vintages.add(vintage_year)
-                            self._vintage_improvements[
-                                (act["code"], input_code, vintage_year)
-                            ] = scaling_factor
-
+                            self._vintage_improvements[(act["code"], input_code, vintage_year)] = scaling_factor
+                
                 # Process vintage_amounts attribute if present
                 if vintage_amounts is not None:
                     if not isinstance(vintage_amounts, dict):
@@ -571,51 +550,41 @@ def _construct_foreground_tensors(self) -> None:
                             elif isinstance(vintage_key, int):
                                 # Just vintage year - apply to all process times from temporal distribution
                                 vintage_year = vintage_key
-                                process_time_vintage = (
-                                    None  # Will be expanded for all years
-                                )
+                                process_time_vintage = None  # Will be expanded for all years
                             else:
                                 logger.warning(
                                     f"Invalid vintage_amounts key {vintage_key} on exchange {exc.input}. "
                                     f"Must be int (vintage year) or tuple (process_time, vintage_year)."
                                 )
                                 continue
-
+                            
                             self._reference_vintages.add(vintage_year)
-
+                            
                             # Determine which process times to apply this vintage value to
                             if process_time_vintage is not None:
                                 process_times_to_update = [process_time_vintage]
                             else:
                                 # Apply to all process times in temporal distribution
                                 process_times_to_update = years
-
+                            
                             for tau in process_times_to_update:
                                 # Store in appropriate vintage dictionary based on edge type
                                 if edge_type == bd.labels.production_edge_default:
-                                    self._foreground_production_vintages[
-                                        (act["code"], input_code, tau, vintage_year)
-                                    ] = vintage_amount
+                                    self._foreground_production_vintages[(act["code"], input_code, tau, vintage_year)] = vintage_amount
                                 elif edge_type == bd.labels.consumption_edge_default:
                                     if input_db != self.foreground_db.name:
                                         # Only for background consumption (technosphere)
-                                        self._foreground_technosphere_vintages[
-                                            (act["code"], input_code, tau, vintage_year)
-                                        ] = vintage_amount
+                                        self._foreground_technosphere_vintages[(act["code"], input_code, tau, vintage_year)] = vintage_amount
                                 elif edge_type == bd.labels.biosphere_edge_default:
-                                    self._foreground_biosphere_vintages[
-                                        (act["code"], input_code, tau, vintage_year)
-                                    ] = vintage_amount
+                                    self._foreground_biosphere_vintages[(act["code"], input_code, tau, vintage_year)] = vintage_amount
 
                 # Handle production edges
                 if edge_type == bd.labels.production_edge_default:
                     product_code = input_code
-                    production_tensor.update(
-                        {
-                            (act["code"], product_code, year): exc["amount"] * factor
-                            for year, factor in zip(years, temporal_factor)
-                        }
-                    )
+                    production_tensor.update({
+                        (act["code"], product_code, year): exc["amount"] * factor
+                        for year, factor in zip(years, temporal_factor)
+                    })
                     if exc.get("operation"):
                         self._operation_flow.update({(act["code"], product_code): True})
                     self._products.setdefault(product_code, input_name)
@@ -624,39 +593,29 @@ def _construct_foreground_tensors(self) -> None:
                 elif edge_type == bd.labels.consumption_edge_default:
                     if input_db == self.foreground_db.name:
                         # Internal demand: foreground product consumed
-                        internal_demand_technosphere.update(
-                            {
-                                (act["code"], input_code, year): exc["amount"] * factor
-                                for year, factor in zip(years, temporal_factor)
-                            }
-                        )
+                        internal_demand_technosphere.update({
+                            (act["code"], input_code, year): exc["amount"] * factor
+                            for year, factor in zip(years, temporal_factor)
+                        })
                         if exc.get("operation"):
-                            self._operation_flow.update(
-                                {(act["code"], input_code): True}
-                            )
+                            self._operation_flow.update({(act["code"], input_code): True})
                         self._products.setdefault(input_code, input_name)
                     else:
                         # External intermediate: background consumption
-                        technosphere_tensor.update(
-                            {
-                                (act["code"], input_code, year): exc["amount"] * factor
-                                for year, factor in zip(years, temporal_factor)
-                            }
-                        )
+                        technosphere_tensor.update({
+                            (act["code"], input_code, year): exc["amount"] * factor
+                            for year, factor in zip(years, temporal_factor)
+                        })
                         if exc.get("operation"):
-                            self._operation_flow.update(
-                                {(act["code"], input_code): True}
-                            )
+                            self._operation_flow.update({(act["code"], input_code): True})
                         self._intermediate_flows.setdefault(input_code, input_name)
 
                 # Handle biosphere edges
                 elif edge_type == bd.labels.biosphere_edge_default:
-                    biosphere_tensor.update(
-                        {
-                            (act["code"], input_code, year): exc["amount"] * factor
-                            for year, factor in zip(years, temporal_factor)
-                        }
-                    )
+                    biosphere_tensor.update({
+                        (act["code"], input_code, year): exc["amount"] * factor
+                        for year, factor in zip(years, temporal_factor)
+                    })
                     if exc.get("operation"):
                         self._operation_flow.update({(act["code"], input_code): True})
                     self._elementary_flows.setdefault(input_code, input_name)
diff --git a/src/optimex/optimizer.py b/src/optimex/optimizer.py
index e945d47..11298a4 100644
--- a/src/optimex/optimizer.py
+++ b/src/optimex/optimizer.py
@@ -61,11 +61,11 @@
 ```
 """
 
+import dill
 import pickle
 from pathlib import Path
 from typing import Any, Dict, Tuple, Union
 
-import dill
 import pyomo.environ as pyo
 from loguru import logger
 from pyomo.contrib.iis import write_iis
@@ -132,7 +132,9 @@ def create_model(
         initialize=scaled_inputs.ELEMENTARY_FLOW,
     )
     model.FLOW = pyo.Set(
-        initialize=lambda m: m.PRODUCT | m.INTERMEDIATE_FLOW | m.ELEMENTARY_FLOW,
+        initialize=lambda m: m.PRODUCT
+        | m.INTERMEDIATE_FLOW
+        | m.ELEMENTARY_FLOW,
         doc="Set of all flows, indexed by f",
     )
     model.CATEGORY = pyo.Set(
@@ -200,15 +202,15 @@ def create_model(
 
     # Store sparse vintage overrides as Python dicts (not Pyomo params)
     # These are looked up at expression construction time
-    model._technosphere_vintage_overrides = (
-        getattr(scaled_inputs, "foreground_technosphere_vintage_overrides", None) or {}
-    )
-    model._biosphere_vintage_overrides = (
-        getattr(scaled_inputs, "foreground_biosphere_vintage_overrides", None) or {}
-    )
-    model._production_vintage_overrides = (
-        getattr(scaled_inputs, "foreground_production_vintage_overrides", None) or {}
-    )
+    model._technosphere_vintage_overrides = getattr(
+        scaled_inputs, 'foreground_technosphere_vintage_overrides', None
+    ) or {}
+    model._biosphere_vintage_overrides = getattr(
+        scaled_inputs, 'foreground_biosphere_vintage_overrides', None
+    ) or {}
+    model._production_vintage_overrides = getattr(
+        scaled_inputs, 'foreground_production_vintage_overrides', None
+    ) or {}
 
     # Precompute sets of (process, flow) pairs that have overrides for O(1) lookup
     model._technosphere_overrides_index = frozenset(
@@ -420,7 +422,7 @@ def _build_active_vintage_index(m):
     model.ACTIVE_VINTAGE_TIME = pyo.Set(
         dimen=3,
         initialize=_build_active_vintage_index,
-        doc="Set of (process, vintage, time) tuples where vintage is in operation phase at time t",
+        doc="Set of (process, vintage, time) tuples where vintage is in operation phase at time t"
     )
 
     # Store category impact limit data for constraint generation
@@ -455,15 +457,13 @@ def _build_active_vintage_index(m):
     model.ProcessDeploymentLimitMax = pyo.Constraint(
         model.PROCESS,
         model.SYSTEM_TIME,
-        rule=lambda m, p, t: m.var_installation[p, t]
-        <= m.process_deployment_limits_max[p, t],
+        rule=lambda m, p, t: m.var_installation[p, t] <= m.process_deployment_limits_max[p, t],
     )
 
     model.ProcessDeploymentLimitMin = pyo.Constraint(
         model.PROCESS,
         model.SYSTEM_TIME,
-        rule=lambda m, p, t: m.var_installation[p, t]
-        >= m.process_deployment_limits_min[p, t],
+        rule=lambda m, p, t: m.var_installation[p, t] >= m.process_deployment_limits_min[p, t],
     )
 
     model.CumulativeProcessLimitMax = pyo.Constraint(
@@ -513,9 +513,7 @@ def process_operation_limit_max_rule(m, p, t):
         ]
         if not active_vintages:
             return pyo.Constraint.Skip
-        total_op = sum(
-            m.var_operation[proc, v, time] for (proc, v, time) in active_vintages
-        )
+        total_op = sum(m.var_operation[proc, v, time] for (proc, v, time) in active_vintages)
         return total_op <= m.process_operation_limits_max[p, t]
 
     model.ProcessOperationLimitMax = pyo.Constraint(
@@ -532,9 +530,7 @@ def process_operation_limit_min_rule(m, p, t):
         ]
         if not active_vintages:
             return pyo.Constraint.Skip
-        total_op = sum(
-            m.var_operation[proc, v, time] for (proc, v, time) in active_vintages
-        )
+        total_op = sum(m.var_operation[proc, v, time] for (proc, v, time) in active_vintages)
         return total_op >= m.process_operation_limits_min[p, t]
 
     model.ProcessOperationLimitMin = pyo.Constraint(
@@ -545,9 +541,7 @@ def process_operation_limit_min_rule(m, p, t):
 
     # Expression builders using sparse vintage override lookup
     # Base tensor is always 3D; overrides are checked first for vintage-specific values
-    def scale_tensor_by_installation(
-        tensor: pyo.Param, flow_set: str, overrides: dict, overrides_index: frozenset
-    ):
+    def scale_tensor_by_installation(tensor: pyo.Param, flow_set: str, overrides: dict, overrides_index: frozenset):
         def expr(m, p, x, t):
             result = 0
             for tau in m.PROCESS_TIME:
@@ -568,16 +562,13 @@ def expr(m, p, x, t):
             model.PROCESS, getattr(model, flow_set), model.SYSTEM_TIME, rule=expr
         )
 
-    def scale_tensor_by_operation(
-        tensor: pyo.Param, flow_set: str, overrides: dict, overrides_index: frozenset
-    ):
+    def scale_tensor_by_operation(tensor: pyo.Param, flow_set: str, overrides: dict, overrides_index: frozenset):
         """
         Scale tensor by operation, summing flows across active vintages.
 
         With 3D var_operation[p, v, t], we sum flow contributions from each
         vintage cohort operating at time t.
         """
-
         def expr(m, p, x, t):
             # Only apply operational scaling to flows marked as operational
             if pyo.value(m.operation_flow[p, x]) == 0:
@@ -588,7 +579,7 @@ def expr(m, p, x, t):
 
             total = 0
             # Sum flows across all active vintages at time t
-            for proc, v, time in m.ACTIVE_VINTAGE_TIME:
+            for (proc, v, time) in m.ACTIVE_VINTAGE_TIME:
                 if proc != p or time != t:
                     continue
 
@@ -617,11 +608,13 @@ def expr(m, p, x, t):
         )
 
     # Create expressions with sparse vintage override lookup
-    model.scaled_technosphere_dependent_on_installation = scale_tensor_by_installation(
-        model.foreground_technosphere,
-        "INTERMEDIATE_FLOW",
-        model._technosphere_vintage_overrides,
-        model._technosphere_overrides_index,
+    model.scaled_technosphere_dependent_on_installation = (
+        scale_tensor_by_installation(
+            model.foreground_technosphere,
+            "INTERMEDIATE_FLOW",
+            model._technosphere_vintage_overrides,
+            model._technosphere_overrides_index,
+        )
     )
     model.scaled_biosphere_dependent_on_installation = scale_tensor_by_installation(
         model.foreground_biosphere,
@@ -648,8 +641,10 @@ def expr(m, p, x, t):
             model.internal_demand_technosphere, "PRODUCT", {}, _empty_index
         )
     )
-    model.scaled_internal_demand_dependent_on_operation = scale_tensor_by_operation(
-        model.internal_demand_technosphere, "PRODUCT", {}, _empty_index
+    model.scaled_internal_demand_dependent_on_operation = (
+        scale_tensor_by_operation(
+            model.internal_demand_technosphere, "PRODUCT", {}, _empty_index
+        )
     )
 
     def scaled_inventory_tensor(model, p, e, t):
@@ -740,7 +735,7 @@ def operation_capacity_constraint_rule(model, p, v, t, r):
     # Build constraint over ACTIVE_VINTAGE_TIME × PRODUCT
     def _build_operation_capacity_constraints(m):
         """Generate constraint indices for per-vintage capacity bounds."""
-        for p, v, t in m.ACTIVE_VINTAGE_TIME:
+        for (p, v, t) in m.ACTIVE_VINTAGE_TIME:
             for r in m.PRODUCT:
                 yield (p, v, t, r)
 
@@ -759,7 +754,7 @@ def product_demand_fulfillment_rule(model, r, t):
         total_production = 0
 
         # Sum production across all active vintages at time t
-        for p, v, time in model.ACTIVE_VINTAGE_TIME:
+        for (p, v, time) in model.ACTIVE_VINTAGE_TIME:
             if time != t:
                 continue
 
@@ -830,10 +825,7 @@ def time_specific_impact_rule(model, c, t):
     # Time-specific category impact limits
     def category_impact_limits_rule(model, c, t):
         if (c, t) in model._category_impact_limits:
-            return (
-                model.time_specific_impact[c, t]
-                <= model._category_impact_limits[(c, t)]
-            )
+            return model.time_specific_impact[c, t] <= model._category_impact_limits[(c, t)]
         return pyo.Constraint.Skip
 
     model.CategoryImpactLimits = pyo.Constraint(
@@ -878,7 +870,7 @@ def total_product_flow_rule(model, r, t):
         Consistent with 3D var_operation[p, v, t].
         """
         total = 0
-        for p, v, time in model.ACTIVE_VINTAGE_TIME:
+        for (p, v, time) in model.ACTIVE_VINTAGE_TIME:
             if time != t:
                 continue
 
@@ -1118,7 +1110,9 @@ def solve_model(
         raise RuntimeError(msg)
 
     model.solutions.load_from(results)
-    logger.info(f"Solver [{solver_name}] termination: {termination}")
+    logger.info(
+        f"Solver [{solver_name}] termination: {termination}"
+    )
 
     # 4) Denormalize objective
     scaled_obj = pyo.value(model.OBJ)
@@ -1151,9 +1145,7 @@ def solve_model(
     return model, true_obj, results
 
 
-def validate_operation_bounds(
-    model: pyo.ConcreteModel, tolerance: float = 1e-6
-) -> Dict[str, Any]:
+def validate_operation_bounds(model: pyo.ConcreteModel, tolerance: float = 1e-6) -> Dict[str, Any]:
     """
     Validate that operation levels respect capacity constraints.
 
@@ -1194,9 +1186,7 @@ def validate_operation_bounds(
 
     # Get sparse overrides for production with precomputed index for O(1) lookup
     production_overrides = getattr(model, "_production_vintage_overrides", {}) or {}
-    production_overrides_index = getattr(
-        model, "_production_overrides_index", frozenset()
-    )
+    production_overrides_index = getattr(model, "_production_overrides_index", frozenset())
     existing_cap_dict = getattr(model, "_existing_capacity_dict", {})
 
     def get_prod_value(p, r, tau, vintage):
@@ -1211,7 +1201,7 @@ def has_prod_overrides(p, r):
         return (p, r) in production_overrides_index
 
     # Validate per-vintage operation bounds
-    for p, v, t in model.ACTIVE_VINTAGE_TIME:
+    for (p, v, t) in model.ACTIVE_VINTAGE_TIME:
         operation_value = pyo.value(model.var_operation[p, v, t])
         op_start = pyo.value(model.process_operation_start[p])
         op_end = pyo.value(model.process_operation_end[p])
@@ -1254,9 +1244,7 @@ def has_prod_overrides(p, r):
             max_violation = max(max_violation, violation)
         elif max_capacity > 0 and operation_value > max_capacity * (1.0 + tolerance):
             violation = operation_value - max_capacity
-            violations.append(
-                (p, v, t, operation_value, max_capacity, "exceeds_capacity")
-            )
+            violations.append((p, v, t, operation_value, max_capacity, "exceeds_capacity"))
             max_violation = max(max_violation, violation)
 
     # Generate summary
diff --git a/src/optimex/postprocessing.py b/src/optimex/postprocessing.py
index 16356d4..71291fb 100644
--- a/src/optimex/postprocessing.py
+++ b/src/optimex/postprocessing.py
@@ -9,7 +9,6 @@
 Key classes:
     - PostProcessor: Extract and visualize optimization results
 """
-
 import math
 
 import bw2data as bd
@@ -219,15 +218,11 @@ def _get_colors_for_dataframe(self, df):
             for col in df.columns:
                 # Use the first element of the tuple for color lookup
                 key = col[0] if isinstance(col, tuple) else col
-                colors.append(
-                    self._color_map.get(key, self._plot_config["colormap"][0])
-                )
+                colors.append(self._color_map.get(key, self._plot_config["colormap"][0]))
         else:
             # Single-level columns
             for col in df.columns:
-                colors.append(
-                    self._color_map.get(col, self._plot_config["colormap"][0])
-                )
+                colors.append(self._color_map.get(col, self._plot_config["colormap"][0]))
 
         return colors
 
@@ -273,7 +268,9 @@ def _create_clean_axes(self, nrows=1, ncols=1, figsize=None):
         Returns fig, flattened list of axes.
         """
         fig_size = figsize or self._plot_config["figsize"]
-        fig, axes = plt.subplots(nrows=nrows, ncols=ncols, figsize=fig_size)
+        fig, axes = plt.subplots(
+            nrows=nrows, ncols=ncols, figsize=fig_size
+        )
         axes = axes.flatten() if isinstance(axes, (np.ndarray, list)) else [axes]
 
         for ax in axes:
@@ -416,7 +413,7 @@ def get_impacts(self) -> pd.DataFrame:
         """
         if hasattr(self, "df_impacts"):
             return self.df_impacts
-
+        
         impacts = {}
         cat_scales = getattr(self.m, "scales", {}).get("characterization", 1.0)
         fg_scale = getattr(self.m, "scales", {}).get("foreground", 1.0)
@@ -439,9 +436,7 @@ def get_impacts(self) -> pd.DataFrame:
         self.df_impacts = df_pivot
         return self.df_impacts
 
-    def get_dynamic_inventory(
-        self, biosphere_database: str = "ecoinvent-3.12-biosphere"
-    ) -> pd.DataFrame:
+    def get_dynamic_inventory(self, biosphere_database: str = "ecoinvent-3.12-biosphere") -> pd.DataFrame:
         """
         Extract the dynamic inventory from the solved model.
 
@@ -472,15 +467,21 @@ def get_dynamic_inventory(
 
         df = pd.DataFrame.from_records(
             [(p, e, t, v) for (p, e, t), v in inventory.items()],
-            columns=["activity", "flow", "date", "amount"],
-        ).astype({"activity": "str", "flow": "str", "amount": "float64"})
+            columns=["activity", "flow", "date", "amount"]
+        ).astype({
+            "activity": "str",
+            "flow": "str",
+            "amount": "float64"
+        })
 
         # Convert year integers to datetime
         df["date"] = pd.to_datetime(df["date"].astype(int), format="%Y")
 
         # Convert flow codes to database IDs
         biosphere_db = bd.Database(biosphere_database)
-        df["flow"] = df["flow"].apply(lambda x: biosphere_db.get(code=x).id)
+        df["flow"] = df["flow"].apply(
+            lambda x: biosphere_db.get(code=x).id
+        )
 
         self.df_dynamic_inventory = df
         return self.df_dynamic_inventory
@@ -520,9 +521,7 @@ def get_characterized_dynamic_inventory(
         from dynamic_characterization import characterize
 
         if df_inventory is None:
-            df_inventory = self.get_dynamic_inventory(
-                biosphere_database=biosphere_database
-            )
+            df_inventory = self.get_dynamic_inventory(biosphere_database=biosphere_database)
 
         df_characterized = characterize(
             df_inventory,
@@ -566,9 +565,7 @@ def plot_characterized_dynamic_inventory(
         """
         if df_characterized is None:
             if base_lcia_method is None:
-                raise ValueError(
-                    "base_lcia_method is required when df_characterized is not provided"
-                )
+                raise ValueError("base_lcia_method is required when df_characterized is not provided")
             df_characterized = self.get_characterized_dynamic_inventory(
                 base_lcia_method=base_lcia_method,
                 metric=metric,
@@ -583,11 +580,8 @@ def plot_characterized_dynamic_inventory(
 
         # Round to nearest year and aggregate
         df_grouped = (
-            df_plot.assign(
-                date_rounded=(df_plot["date"] + pd.offsets.MonthBegin(6))
-                .dt.to_period("Y")
-                .dt.to_timestamp()
-            )
+            df_plot
+            .assign(date_rounded=(df_plot["date"] + pd.offsets.MonthBegin(6)).dt.to_period("Y").dt.to_timestamp())
             .groupby("date_rounded")["amount"]
             .sum()
             .reset_index()
@@ -606,14 +600,8 @@ def plot_characterized_dynamic_inventory(
             label=metric.replace("_", " ").title(),
         )
 
-        ax.set_ylabel(
-            f"{metric.replace('_', ' ').title()}",
-            fontsize=self._plot_config["label_fontsize"],
-        )
-        ax.set_title(
-            f"Dynamic {metric.replace('_', ' ').title()}",
-            fontsize=self._plot_config["title_fontsize"],
-        )
+        ax.set_ylabel(f"{metric.replace('_', ' ').title()}", fontsize=self._plot_config["label_fontsize"])
+        ax.set_title(f"Dynamic {metric.replace('_', ' ').title()}", fontsize=self._plot_config["title_fontsize"])
         ax.set_axisbelow(True)
         ax.grid(
             axis="both",
@@ -683,9 +671,7 @@ def get_operation(self, aggregate_vintages: bool = True) -> pd.DataFrame:
                         if proc == p and time == t
                     )
                     operation_matrix[(t, p)] = total_op
-            df = pd.DataFrame.from_dict(
-                operation_matrix, orient="index", columns=["Value"]
-            )
+            df = pd.DataFrame.from_dict(operation_matrix, orient="index", columns=["Value"])
             df.index = pd.MultiIndex.from_tuples(df.index, names=["Time", "Process"])
             df = df.reset_index()
             df_pivot = df.pivot(index="Time", columns="Process", values="Value")
@@ -697,16 +683,10 @@ def get_operation(self, aggregate_vintages: bool = True) -> pd.DataFrame:
                 (t, p, v): pyo.value(self.m.var_operation[p, v, t])
                 for (p, v, t) in self.m.ACTIVE_VINTAGE_TIME
             }
-            df = pd.DataFrame.from_dict(
-                operation_matrix, orient="index", columns=["Value"]
-            )
-            df.index = pd.MultiIndex.from_tuples(
-                df.index, names=["Time", "Process", "Vintage"]
-            )
+            df = pd.DataFrame.from_dict(operation_matrix, orient="index", columns=["Value"])
+            df.index = pd.MultiIndex.from_tuples(df.index, names=["Time", "Process", "Vintage"])
             df = df.reset_index()
-            df_pivot = df.pivot(
-                index="Time", columns=["Process", "Vintage"], values="Value"
-            )
+            df_pivot = df.pivot(index="Time", columns=["Process", "Vintage"], values="Value")
             return df_pivot
 
     def get_production(self) -> pd.DataFrame:
@@ -724,9 +704,7 @@ def get_production(self) -> pd.DataFrame:
 
         # Get production overrides data from model (if exists)
         production_overrides = getattr(self.m, "_production_vintage_overrides", {})
-        production_overrides_index = getattr(
-            self.m, "_production_overrides_index", frozenset()
-        )
+        production_overrides_index = getattr(self.m, "_production_overrides_index", frozenset())
 
         def get_production_value(p, r, tau, vintage):
             """Get production value, checking sparse overrides first."""
@@ -747,7 +725,7 @@ def has_production_overrides(p, r):
                 for t in self.m.SYSTEM_TIME:
                     # Sum production across all active vintages at time t
                     total_production = 0
-                    for proc, v, time in self.m.ACTIVE_VINTAGE_TIME:
+                    for (proc, v, time) in self.m.ACTIVE_VINTAGE_TIME:
                         if proc != p or time != t:
                             continue
 
@@ -766,9 +744,7 @@ def has_production_overrides(p, r):
                             )
 
                         # Production from this vintage
-                        total_production += production_rate * pyo.value(
-                            self.m.var_operation[p, v, t]
-                        )
+                        total_production += production_rate * pyo.value(self.m.var_operation[p, v, t])
 
                     production_tensor[(p, f, t)] = total_production * fg_scale
 
@@ -798,7 +774,9 @@ def get_demand(self) -> pd.DataFrame:
             for t in self.m.SYSTEM_TIME
         }
         df = pd.DataFrame.from_dict(demand_matrix, orient="index", columns=["Value"])
-        df.index = pd.MultiIndex.from_tuples(df.index, names=["Product", "Time"])
+        df.index = pd.MultiIndex.from_tuples(
+            df.index, names=["Product", "Time"]
+        )
         df = df.reset_index()
         df_pivot = df.pivot(index="Time", columns="Product", values="Value")
         self.df_demand = df_pivot
@@ -837,9 +815,7 @@ def plot_impacts(self, df_impacts=None, annotated=True):
         fig_w = base_w * ncols
         fig_h = base_h * nrows
 
-        fig, axes = self._create_clean_axes(
-            nrows=nrows, ncols=ncols, figsize=(fig_w, fig_h)
-        )
+        fig, axes = self._create_clean_axes(nrows=nrows, ncols=ncols, figsize=(fig_w, fig_h))
 
         all_handles = []
         all_labels = []
@@ -869,9 +845,7 @@ def plot_impacts(self, df_impacts=None, annotated=True):
 
         # Shared legend to the right of figure
         if all_handles:
-            all_handles, all_labels = self._reorder_legend_row_first(
-                all_handles, all_labels, 2
-            )
+            all_handles, all_labels = self._reorder_legend_row_first(all_handles, all_labels, 2)
             fig.legend(
                 handles=all_handles,
                 labels=all_labels,
@@ -910,10 +884,11 @@ def plot_installation(self, df_installation=None, annotated=True):
 
         fig, axes = self._create_clean_axes()
         ax = axes[0]
-        self._apply_bar_styles(df_installation, ax, colors, title="Installed Capacity")
-        ax.set_ylabel(
-            "Installed Capacity", fontsize=self._plot_config["label_fontsize"]
+        self._apply_bar_styles(
+            df_installation, ax, colors, title="Installed Capacity"
         )
+        ax.set_ylabel("Installed Capacity", fontsize=self._plot_config["label_fontsize"])
+
 
         # Legend at bottom
         h, l = ax.get_legend_handles_labels()
@@ -957,9 +932,12 @@ def plot_operation(self, df_operation=None, annotated=True):
 
         fig, axes = self._create_clean_axes()
         ax = axes[0]
-        self._apply_bar_styles(df_operation, ax, colors, title="Operational Level")
+        self._apply_bar_styles(
+            df_operation, ax, colors, title="Operational Level"
+        )
         ax.set_ylabel("Operation Level", fontsize=self._plot_config["label_fontsize"])
 
+
         # Legend at bottom
         h, l = ax.get_legend_handles_labels()
         if h:
@@ -1022,9 +1000,7 @@ def get_existing_capacity(self) -> pd.DataFrame:
             return pd.DataFrame()
 
         df = pd.DataFrame.from_dict(data, orient="index", columns=["Value"])
-        df.index = pd.MultiIndex.from_tuples(
-            df.index, names=["Time", "Process", "Type"]
-        )
+        df.index = pd.MultiIndex.from_tuples(df.index, names=["Time", "Process", "Type"])
         df = df.reset_index()
         df_pivot = df.pivot(index="Time", columns=["Process", "Type"], values="Value")
 
@@ -1053,9 +1029,7 @@ def get_production_capacity(self) -> pd.DataFrame:
 
         # Get production overrides data from model (if exists)
         production_overrides = getattr(self.m, "_production_vintage_overrides", {})
-        production_overrides_index = getattr(
-            self.m, "_production_overrides_index", frozenset()
-        )
+        production_overrides_index = getattr(self.m, "_production_overrides_index", frozenset())
 
         def get_production_value(p, r, tau, vintage):
             """Get production value, checking sparse overrides first."""
@@ -1085,19 +1059,14 @@ def has_production_overrides(p, r):
                         # New installations: sum capacity by vintage
                         for tau in self.m.PROCESS_TIME:
                             vintage = t - tau
-                            if (
-                                vintage in self.m.SYSTEM_TIME
-                                and op_start <= tau <= op_end
-                            ):
+                            if vintage in self.m.SYSTEM_TIME and op_start <= tau <= op_end:
                                 # Production rate for this vintage (sum over all operating taus)
                                 production_per_unit = sum(
                                     get_production_value(p, f, tau_op, vintage)
                                     for tau_op in self.m.PROCESS_TIME
                                     if op_start <= tau_op <= op_end
                                 )
-                                installation = pyo.value(
-                                    self.m.var_installation[p, vintage]
-                                )
+                                installation = pyo.value(self.m.var_installation[p, vintage])
                                 process_capacity += production_per_unit * installation
 
                         # Existing (brownfield) capacity
@@ -1107,9 +1076,7 @@ def has_production_overrides(p, r):
                                 if op_start <= tau_existing <= op_end:
                                     nearest_vintage = min(self.m.SYSTEM_TIME)
                                     production_per_unit = sum(
-                                        get_production_value(
-                                            p, f, tau_op, nearest_vintage
-                                        )
+                                        get_production_value(p, f, tau_op, nearest_vintage)
                                         for tau_op in self.m.PROCESS_TIME
                                         if op_start <= tau_op <= op_end
                                     )
@@ -1141,9 +1108,7 @@ def has_production_overrides(p, r):
                         )
 
                         # Total capacity for this process
-                        total_capacity += (
-                            installations_operating * production_per_installation
-                        )
+                        total_capacity += installations_operating * production_per_installation
 
                 # Store denormalized capacity
                 capacity_tensor[(f, t)] = total_capacity * fg_scale
@@ -1179,18 +1144,10 @@ def _extract_product_data(self, product, prod_df, capacity_df):
             production_cols = [col for col in prod_df.columns if col[1] == product]
             actual_production = prod_df[production_cols].sum(axis=1)
         else:
-            actual_production = (
-                prod_df[product]
-                if product in prod_df.columns
-                else pd.Series(0, index=prod_df.index)
-            )
+            actual_production = prod_df[product] if product in prod_df.columns else pd.Series(0, index=prod_df.index)
 
         # Capacity for this product
-        max_capacity = (
-            capacity_df[product]
-            if product in capacity_df.columns
-            else pd.Series(0, index=capacity_df.index)
-        )
+        max_capacity = capacity_df[product] if product in capacity_df.columns else pd.Series(0, index=capacity_df.index)
 
         # Convert indices to strings for consistent plotting
         actual_production = actual_production.copy()
@@ -1234,9 +1191,7 @@ def _plot_capacity_balance_on_ax(
         show_title : bool, default=True
             If True, show title with product name.
         """
-        actual_production, max_capacity = self._extract_product_data(
-            product, prod_df, capacity_df
-        )
+        actual_production, max_capacity = self._extract_product_data(product, prod_df, capacity_df)
         product_name = self._get_name(product) if annotated else product
 
         x_positions = np.arange(len(actual_production.index))
@@ -1245,24 +1200,24 @@ def _plot_capacity_balance_on_ax(
         ax.plot(
             x_positions,
             actual_production.values,
-            marker="o",
+            marker='o',
             linewidth=self._plot_config["line_width"],
-            label="Production / Demand",
-            color="#00549F",
-            linestyle="-",
-            zorder=3,
+            label='Production / Demand',
+            color='#00549F',
+            linestyle='-',
+            zorder=3
         )
 
         # Plot capacity line
         ax.plot(
             x_positions,
             max_capacity.values,
-            marker="s",
+            marker='s',
             linewidth=self._plot_config["line_width"],
-            label="Max Capacity",
-            color="#000000",
-            linestyle="--",
-            zorder=3,
+            label='Max Capacity',
+            color='#000000',
+            linestyle='--',
+            zorder=3
         )
 
         # Fill area between production and capacity
@@ -1272,9 +1227,9 @@ def _plot_capacity_balance_on_ax(
                 actual_production.values,
                 max_capacity.values,
                 alpha=0.15,
-                color="#00549F",
-                label="Unused Capacity",
-                zorder=2,
+                color='#00549F',
+                label='Unused Capacity',
+                zorder=2
             )
 
         # Set labels and title
@@ -1289,7 +1244,9 @@ def _plot_capacity_balance_on_ax(
 
         if show_title:
             ax.set_title(
-                f"{product_name}", fontsize=self._plot_config["title_fontsize"], pad=10
+                f"{product_name}",
+                fontsize=self._plot_config["title_fontsize"],
+                pad=10
             )
 
         if show_legend:
@@ -1318,9 +1275,7 @@ def _compute_capacity_breakdown(self, product):
 
         # Get production overrides data from model (if exists)
         production_overrides = getattr(self.m, "_production_vintage_overrides", {})
-        production_overrides_index = getattr(
-            self.m, "_production_overrides_index", frozenset()
-        )
+        production_overrides_index = getattr(self.m, "_production_overrides_index", frozenset())
 
         def get_production_value(p, r, tau, vintage):
             """Get production value, checking sparse overrides first."""
@@ -1365,34 +1320,26 @@ def has_production_overrides(p, r):
                     # New capacity entering operation (vintage = t - op_start)
                     t_entering = t - op_start
                     if t_entering in self.m.SYSTEM_TIME:
-                        installation_entering = pyo.value(
-                            self.m.var_installation[p, t_entering]
-                        )
+                        installation_entering = pyo.value(self.m.var_installation[p, t_entering])
                         prod_per_inst_vintage = sum(
                             get_production_value(p, product, tau_op, t_entering)
                             for tau_op in self.m.PROCESS_TIME
                             if op_start <= tau_op <= op_end
                         )
-                        capacity_additions[p][t] = (
-                            installation_entering * prod_per_inst_vintage * fg_scale
-                        )
+                        capacity_additions[p][t] = installation_entering * prod_per_inst_vintage * fg_scale
                     else:
                         capacity_additions[p][t] = 0
 
                     # Capacity exiting operation (vintage = t - op_end - 1)
                     t_exiting = t - op_end - 1
                     if t_exiting in self.m.SYSTEM_TIME:
-                        installation_exiting = pyo.value(
-                            self.m.var_installation[p, t_exiting]
-                        )
+                        installation_exiting = pyo.value(self.m.var_installation[p, t_exiting])
                         prod_per_inst_vintage = sum(
                             get_production_value(p, product, tau_op, t_exiting)
                             for tau_op in self.m.PROCESS_TIME
                             if op_start <= tau_op <= op_end
                         )
-                        capacity_removals[p][t] = (
-                            installation_exiting * prod_per_inst_vintage * fg_scale
-                        )
+                        capacity_removals[p][t] = installation_exiting * prod_per_inst_vintage * fg_scale
                     else:
                         capacity_removals[p][t] = 0
 
@@ -1411,20 +1358,16 @@ def has_production_overrides(p, r):
                             tau_existing_prev = (t - 1) - inst_year
                             if op_start <= tau_existing <= op_end:
                                 if tau_existing_prev < op_start:
-                                    existing_add += (
-                                        capacity * prod_per_inst_existing * fg_scale
-                                    )
+                                    existing_add += capacity * prod_per_inst_existing * fg_scale
                             if tau_existing > op_end:
                                 if op_start <= tau_existing_prev <= op_end:
-                                    existing_rem += (
-                                        capacity * prod_per_inst_existing * fg_scale
-                                    )
+                                    existing_rem += capacity * prod_per_inst_existing * fg_scale
                     existing_additions[p][t] = existing_add
                     existing_removals[p][t] = existing_rem
 
                     # Operation level - sum production across all active vintages
                     total_operation = 0
-                    for proc, v, time in self.m.ACTIVE_VINTAGE_TIME:
+                    for (proc, v, time) in self.m.ACTIVE_VINTAGE_TIME:
                         if proc != p or time != t:
                             continue
                         # Get production rate for this vintage
@@ -1433,9 +1376,7 @@ def has_production_overrides(p, r):
                             for tau_op in self.m.PROCESS_TIME
                             if op_start <= tau_op <= op_end
                         )
-                        total_operation += production_rate * pyo.value(
-                            self.m.var_operation[p, v, t]
-                        )
+                        total_operation += production_rate * pyo.value(self.m.var_operation[p, v, t])
 
                     operation[p][t] = total_operation * fg_scale
                 else:
@@ -1445,24 +1386,16 @@ def has_production_overrides(p, r):
                     # New capacity entering operation
                     t_entering = t - op_start
                     if t_entering in self.m.SYSTEM_TIME:
-                        installation_entering = pyo.value(
-                            self.m.var_installation[p, t_entering]
-                        )
-                        capacity_additions[p][t] = (
-                            installation_entering * prod_per_inst * fg_scale
-                        )
+                        installation_entering = pyo.value(self.m.var_installation[p, t_entering])
+                        capacity_additions[p][t] = installation_entering * prod_per_inst * fg_scale
                     else:
                         capacity_additions[p][t] = 0
 
                     # Capacity exiting operation
                     t_exiting = t - op_end - 1
                     if t_exiting in self.m.SYSTEM_TIME:
-                        installation_exiting = pyo.value(
-                            self.m.var_installation[p, t_exiting]
-                        )
-                        capacity_removals[p][t] = (
-                            installation_exiting * prod_per_inst * fg_scale
-                        )
+                        installation_exiting = pyo.value(self.m.var_installation[p, t_exiting])
+                        capacity_removals[p][t] = installation_exiting * prod_per_inst * fg_scale
                     else:
                         capacity_removals[p][t] = 0
 
@@ -1484,12 +1417,10 @@ def has_production_overrides(p, r):
 
                     # Operation level - sum production across all active vintages
                     total_operation = 0
-                    for proc, v, time in self.m.ACTIVE_VINTAGE_TIME:
+                    for (proc, v, time) in self.m.ACTIVE_VINTAGE_TIME:
                         if proc != p or time != t:
                             continue
-                        total_operation += prod_per_inst * pyo.value(
-                            self.m.var_operation[p, v, t]
-                        )
+                        total_operation += prod_per_inst * pyo.value(self.m.var_operation[p, v, t])
 
                     operation[p][t] = total_operation * fg_scale
 
@@ -1510,13 +1441,11 @@ def has_production_overrides(p, r):
         operation_df.index = operation_df.index.astype(str)
 
         # Filter to only processes with non-zero values
-        has_values = (
-            (capacity_additions_df != 0).any(axis=0)
-            | (capacity_removals_df != 0).any(axis=0)
-            | (existing_additions_df != 0).any(axis=0)
-            | (existing_removals_df != 0).any(axis=0)
-            | (operation_df != 0).any(axis=0)
-        )
+        has_values = ((capacity_additions_df != 0).any(axis=0) |
+                     (capacity_removals_df != 0).any(axis=0) |
+                     (existing_additions_df != 0).any(axis=0) |
+                     (existing_removals_df != 0).any(axis=0) |
+                     (operation_df != 0).any(axis=0))
         capacity_additions_df = capacity_additions_df.loc[:, has_values]
         capacity_removals_df = capacity_removals_df.loc[:, has_values]
         existing_additions_df = existing_additions_df.loc[:, has_values]
@@ -1570,9 +1499,7 @@ def _plot_capacity_balance_detailed_on_ax(
         from matplotlib.lines import Line2D
         from matplotlib.patches import Patch
 
-        actual_production, max_capacity = self._extract_product_data(
-            product, prod_df, capacity_df
-        )
+        actual_production, max_capacity = self._extract_product_data(product, prod_df, capacity_df)
         product_name = self._get_name(product) if annotated else product
 
         # Compute breakdown data
@@ -1587,18 +1514,10 @@ def _plot_capacity_balance_detailed_on_ax(
         process_codes = list(capacity_additions_df.columns)
 
         # Annotate DataFrames
-        capacity_additions_df = self._annotate_dataframe(
-            capacity_additions_df.copy(), annotated
-        )
-        capacity_removals_df = self._annotate_dataframe(
-            capacity_removals_df.copy(), annotated
-        )
-        existing_additions_df = self._annotate_dataframe(
-            existing_additions_df.copy(), annotated
-        )
-        existing_removals_df = self._annotate_dataframe(
-            existing_removals_df.copy(), annotated
-        )
+        capacity_additions_df = self._annotate_dataframe(capacity_additions_df.copy(), annotated)
+        capacity_removals_df = self._annotate_dataframe(capacity_removals_df.copy(), annotated)
+        existing_additions_df = self._annotate_dataframe(existing_additions_df.copy(), annotated)
+        existing_removals_df = self._annotate_dataframe(existing_removals_df.copy(), annotated)
         operation_df = self._annotate_dataframe(operation_df.copy(), annotated)
 
         x_positions = np.arange(len(actual_production.index))
@@ -1616,119 +1535,54 @@ def _plot_capacity_balance_detailed_on_ax(
             bottom_additions = np.zeros(len(x_positions))
             for i, col in enumerate(capacity_additions_df.columns):
                 add_values = capacity_additions_df[col].values
-                clr = self._color_map.get(process_codes[i], "black")
-                ax.bar(
-                    cap_positions,
-                    add_values,
-                    width=bar_width,
-                    bottom=bottom_additions,
-                    color=clr,
-                    hatch="///",
-                    edgecolor="#30A834FF",
-                    linewidth=1.5,
-                    zorder=1,
-                )
+                clr = self._color_map.get(process_codes[i], 'black')
+                ax.bar(cap_positions, add_values, width=bar_width, bottom=bottom_additions,
+                       color=clr, hatch="///", edgecolor="#30A834FF", linewidth=1.5, zorder=1)
                 bottom_additions += add_values
 
             # Plot capacity removals (negative, red border)
             bottom_removals = np.zeros(len(x_positions))
             for i, col in enumerate(capacity_removals_df.columns):
                 rem_values = capacity_removals_df[col].values
-                clr = self._color_map.get(process_codes[i], "black")
-                ax.bar(
-                    cap_positions,
-                    -rem_values,
-                    width=bar_width,
-                    bottom=bottom_removals,
-                    color=clr,
-                    hatch="///",
-                    edgecolor="#CD221FFF",
-                    linewidth=1.5,
-                    zorder=1,
-                )
+                clr = self._color_map.get(process_codes[i], 'black')
+                ax.bar(cap_positions, -rem_values, width=bar_width, bottom=bottom_removals,
+                       color=clr, hatch="///", edgecolor="#CD221FFF", linewidth=1.5, zorder=1)
                 bottom_removals -= rem_values
 
             # Plot operation (solid bars)
             bottom_operation = np.zeros(len(x_positions))
             for i, col in enumerate(operation_df.columns):
                 operation_values = operation_df[col].values
-                clr = self._color_map.get(process_codes[i], "black")
-                ax.bar(
-                    op_positions,
-                    operation_values,
-                    width=bar_width,
-                    bottom=bottom_operation,
-                    alpha=0.9,
-                    color=clr,
-                    edgecolor=self._plot_config["bar_edgecolor"],
-                    linewidth=self._plot_config["bar_linewidth"],
-                    zorder=2,
-                )
+                clr = self._color_map.get(process_codes[i], 'black')
+                ax.bar(op_positions, operation_values, width=bar_width, bottom=bottom_operation,
+                       alpha=0.9, color=clr, edgecolor=self._plot_config["bar_edgecolor"],
+                       linewidth=self._plot_config["bar_linewidth"], zorder=2)
                 bottom_operation += operation_values
 
-            ax.axhline(0, color="gray", linewidth=0.5, zorder=0)
+            ax.axhline(0, color='gray', linewidth=0.5, zorder=0)
 
             # Build legend handles
-            process_legend = [
-                Patch(
-                    facecolor=self._color_map.get(process_codes[i], "black"),
-                    edgecolor="black",
-                    linewidth=0.5,
-                    label=col,
-                )
-                for i, col in enumerate(capacity_additions_df.columns)
-            ]
+            process_legend = [Patch(facecolor=self._color_map.get(process_codes[i], 'black'), edgecolor='black', linewidth=0.5, label=col)
+                            for i, col in enumerate(capacity_additions_df.columns)]
             type_legend = [
-                Patch(
-                    facecolor="white", edgecolor="#30A834", linewidth=2, label="+ Cap"
-                ),
-                Patch(
-                    facecolor="white", edgecolor="#CD221F", linewidth=2, label="− Cap"
-                ),
+                Patch(facecolor="white", edgecolor='#30A834', linewidth=2, label='+ Cap'),
+                Patch(facecolor="white", edgecolor='#CD221F', linewidth=2, label='− Cap'),
             ]
 
         # Plot production and capacity lines
-        ax.plot(
-            x_positions,
-            actual_production.values,
-            marker="o",
-            linewidth=self._plot_config["line_width"],
-            label="Production / Demand",
-            color="#00549F",
-            linestyle="-",
-            zorder=3,
-        )
-        ax.plot(
-            x_positions,
-            max_capacity.values,
-            marker="s",
-            linewidth=self._plot_config["line_width"],
-            label="Max Capacity",
-            color="#000000",
-            linestyle="--",
-            zorder=3,
-        )
+        ax.plot(x_positions, actual_production.values, marker='o',
+                linewidth=self._plot_config["line_width"], label='Production / Demand',
+                color='#00549F', linestyle='-', zorder=3)
+        ax.plot(x_positions, max_capacity.values, marker='s',
+                linewidth=self._plot_config["line_width"], label='Max Capacity',
+                color='#000000', linestyle='--', zorder=3)
 
         # Line legend entries
         line_legend = [
-            Line2D(
-                [0],
-                [0],
-                color="#00549F",
-                marker="o",
-                linestyle="-",
-                linewidth=self._plot_config["line_width"],
-                label="Production / Demand",
-            ),
-            Line2D(
-                [0],
-                [0],
-                color="#000000",
-                marker="s",
-                linestyle="--",
-                linewidth=self._plot_config["line_width"],
-                label="Max Capacity",
-            ),
+            Line2D([0], [0], color='#00549F', marker='o', linestyle='-',
+                   linewidth=self._plot_config["line_width"], label='Production / Demand'),
+            Line2D([0], [0], color='#000000', marker='s', linestyle='--',
+                   linewidth=self._plot_config["line_width"], label='Max Capacity'),
         ]
 
         self._set_smart_xticks(ax, actual_production.index)
@@ -1741,9 +1595,7 @@ def _plot_capacity_balance_detailed_on_ax(
         )
 
         if show_title:
-            ax.set_title(
-                f"{product_name}", fontsize=self._plot_config["title_fontsize"], pad=10
-            )
+            ax.set_title(f"{product_name}", fontsize=self._plot_config["title_fontsize"], pad=10)
 
         if show_legend and process_legend:
             all_handles = process_legend + type_legend + line_legend
@@ -1759,15 +1611,7 @@ def _plot_capacity_balance_detailed_on_ax(
 
         return process_legend, type_legend, line_legend
 
-    def plot_capacity_balance(
-        self,
-        product=None,
-        prod_df=None,
-        capacity_df=None,
-        demand_df=None,
-        annotated=True,
-        detailed=False,
-    ):
+    def plot_capacity_balance(self, product=None, prod_df=None, capacity_df=None, demand_df=None, annotated=True, detailed=False):
         """
         Plot actual production vs maximum available capacity.
 
@@ -1814,28 +1658,18 @@ def plot_capacity_balance(
 
             if detailed:
                 self._plot_capacity_balance_detailed_on_ax(
-                    ax,
-                    product,
-                    prod_df,
-                    capacity_df,
-                    annotated=annotated,
-                    show_legend=True,
-                    show_title=True,
+                    ax, product, prod_df, capacity_df,
+                    annotated=annotated, show_legend=True, show_title=True,
                     legend_position="bottom",
                 )
             else:
                 self._plot_capacity_balance_on_ax(
-                    ax,
-                    product,
-                    prod_df,
-                    capacity_df,
-                    annotated=annotated,
-                    show_legend=True,
-                    show_fill=True,
-                    show_title=True,
+                    ax, product, prod_df, capacity_df,
+                    annotated=annotated, show_legend=True, show_fill=True, show_title=True,
                     legend_position="bottom",
                 )
 
+    
             fig.tight_layout()
             plt.show()
             return
@@ -1870,7 +1704,9 @@ def plot_capacity_balance(
         fig_w = base_w * ncols
         fig_h = base_h * nrows
 
-        fig, axes = plt.subplots(nrows=nrows, ncols=ncols, figsize=(fig_w, fig_h))
+        fig, axes = plt.subplots(
+            nrows=nrows, ncols=ncols, figsize=(fig_w, fig_h)
+        )
 
         if n_products == 1:
             axes = np.array([axes])
@@ -1894,16 +1730,9 @@ def plot_capacity_balance(
         for i, p in enumerate(products):
             ax = axes[i]
             if detailed:
-                proc_legend, type_legend, line_legend = (
-                    self._plot_capacity_balance_detailed_on_ax(
-                        ax,
-                        p,
-                        prod_df,
-                        capacity_df,
-                        annotated=annotated,
-                        show_legend=False,
-                        show_title=True,
-                    )
+                proc_legend, type_legend, line_legend = self._plot_capacity_balance_detailed_on_ax(
+                    ax, p, prod_df, capacity_df,
+                    annotated=annotated, show_legend=False, show_title=True
                 )
                 # Collect process handles from all subplots (deduplicate by label)
                 for handle in proc_legend:
@@ -1916,14 +1745,8 @@ def plot_capacity_balance(
                     line_handles = line_legend
             else:
                 self._plot_capacity_balance_on_ax(
-                    ax,
-                    p,
-                    prod_df,
-                    capacity_df,
-                    annotated=annotated,
-                    show_legend=False,
-                    show_fill=True,
-                    show_title=True,
+                    ax, p, prod_df, capacity_df,
+                    annotated=annotated, show_legend=False, show_fill=True, show_title=True
                 )
                 # Collect handles from all subplots (deduplicate by label)
                 h, l = ax.get_legend_handles_labels()
@@ -1942,9 +1765,7 @@ def plot_capacity_balance(
 
         # Shared legend at bottom of figure
         if all_handles:
-            all_handles, all_labels = self._reorder_legend_row_first(
-                all_handles, all_labels, 2
-            )
+            all_handles, all_labels = self._reorder_legend_row_first(all_handles, all_labels, 2)
             fig.legend(
                 handles=all_handles,
                 labels=all_labels,
@@ -1990,9 +1811,7 @@ def plot_utilization_heatmap(self, product=None, annotated=True, show_values=Tru
 
         # Get production overrides data from model (if exists)
         production_overrides = getattr(self.m, "_production_vintage_overrides", {})
-        production_overrides_index = getattr(
-            self.m, "_production_overrides_index", frozenset()
-        )
+        production_overrides_index = getattr(self.m, "_production_overrides_index", frozenset())
 
         def get_production_value(p, r, tau, vintage):
             """Get production value, checking sparse overrides first."""
@@ -2041,9 +1860,7 @@ def has_production_overrides(p, r):
                                 for tau_op in self.m.PROCESS_TIME
                                 if op_start <= tau_op <= op_end
                             )
-                            installation = pyo.value(
-                                self.m.var_installation[p, vintage]
-                            )
+                            installation = pyo.value(self.m.var_installation[p, vintage])
                             capacity += production_per_unit * installation
 
                     # Add existing (brownfield) capacity
@@ -2063,7 +1880,7 @@ def has_production_overrides(p, r):
 
                     # Operation - sum production across all active vintages
                     operation = 0
-                    for proc, v, time in self.m.ACTIVE_VINTAGE_TIME:
+                    for (proc, v, time) in self.m.ACTIVE_VINTAGE_TIME:
                         if proc != p or time != t:
                             continue
                         # Get production rate for this vintage
@@ -2072,9 +1889,7 @@ def has_production_overrides(p, r):
                             for tau_op in self.m.PROCESS_TIME
                             if op_start <= tau_op <= op_end
                         )
-                        operation += production_rate * pyo.value(
-                            self.m.var_operation[p, v, t]
-                        )
+                        operation += production_rate * pyo.value(self.m.var_operation[p, v, t])
                     operation *= fg_scale
                 else:
                     # 3D calculation: no overrides
@@ -2084,7 +1899,8 @@ def has_production_overrides(p, r):
                     installations_operating = sum(
                         pyo.value(self.m.var_installation[p, t - tau])
                         for tau in self.m.PROCESS_TIME
-                        if (t - tau in self.m.SYSTEM_TIME) and op_start <= tau <= op_end
+                        if (t - tau in self.m.SYSTEM_TIME)
+                        and op_start <= tau <= op_end
                     )
 
                     # Add existing (brownfield) capacity in operation phase
@@ -2098,12 +1914,10 @@ def has_production_overrides(p, r):
 
                     # Calculate operation - sum across all active vintages
                     operation = 0
-                    for proc, v, time in self.m.ACTIVE_VINTAGE_TIME:
+                    for (proc, v, time) in self.m.ACTIVE_VINTAGE_TIME:
                         if proc != p or time != t:
                             continue
-                        operation += prod_per_inst * pyo.value(
-                            self.m.var_operation[p, v, t]
-                        )
+                        operation += prod_per_inst * pyo.value(self.m.var_operation[p, v, t])
                     operation *= fg_scale
 
                 capacity_data[p][t] = capacity
@@ -2113,9 +1927,7 @@ def has_production_overrides(p, r):
                 if capacity > 0.001:
                     utilization_data[p][t] = (operation / capacity) * 100
                 else:
-                    utilization_data[p][
-                        t
-                    ] = np.nan  # No capacity = no utilization possible
+                    utilization_data[p][t] = np.nan  # No capacity = no utilization possible
 
         if not utilization_data:
             raise ValueError(f"No processes produce {product}")
@@ -2126,7 +1938,7 @@ def has_production_overrides(p, r):
         op_df = pd.DataFrame(operation_data).T
 
         # Filter to only times with some capacity
-        has_capacity = cap_df.sum(axis=0) > 0.001
+        has_capacity = (cap_df.sum(axis=0) > 0.001)
         util_df = util_df.loc[:, has_capacity]
         cap_df = cap_df.loc[:, has_capacity]
         op_df = op_df.loc[:, has_capacity]
@@ -2143,14 +1955,12 @@ def has_production_overrides(p, r):
         product_name = self._get_name(product) if annotated else product
 
         # Create figure
-        fig, ax = plt.subplots(
-            figsize=(max(10, len(util_df.columns) * 0.4), max(4, len(util_df) * 0.8))
-        )
+        fig, ax = plt.subplots(figsize=(max(10, len(util_df.columns) * 0.4), max(4, len(util_df) * 0.8)))
 
         # Create heatmap
         # Use a diverging colormap: red (0%) -> yellow (50%) -> green (100%)
         cmap = plt.cm.RdYlGn
-        im = ax.imshow(util_df.values, aspect="auto", cmap=cmap, vmin=0, vmax=100)
+        im = ax.imshow(util_df.values, aspect='auto', cmap=cmap, vmin=0, vmax=100)
 
         # Set ticks
         ax.set_xticks(np.arange(len(util_df.columns)))
@@ -2160,9 +1970,7 @@ def has_production_overrides(p, r):
 
         # Rotate x labels if many years
         if len(util_df.columns) > 15:
-            plt.setp(
-                ax.get_xticklabels(), rotation=45, ha="right", rotation_mode="anchor"
-            )
+            plt.setp(ax.get_xticklabels(), rotation=45, ha='right', rotation_mode='anchor')
 
         # Add value annotations
         if show_values:
@@ -2171,39 +1979,22 @@ def has_production_overrides(p, r):
                     val = util_df.iloc[i, j]
                     if not np.isnan(val):
                         # Choose text color based on background
-                        text_color = "white" if val < 30 or val > 70 else "black"
-                        ax.text(
-                            j,
-                            i,
-                            f"{val:.0f}%",
-                            ha="center",
-                            va="center",
-                            fontsize=self._plot_config["fontsize"] - 3,
-                            color=text_color,
-                        )
+                        text_color = 'white' if val < 30 or val > 70 else 'black'
+                        ax.text(j, i, f'{val:.0f}%', ha='center', va='center',
+                               fontsize=self._plot_config["fontsize"] - 3, color=text_color)
                     else:
-                        ax.text(
-                            j,
-                            i,
-                            "-",
-                            ha="center",
-                            va="center",
-                            fontsize=self._plot_config["fontsize"] - 3,
-                            color="gray",
-                        )
+                        ax.text(j, i, '-', ha='center', va='center',
+                               fontsize=self._plot_config["fontsize"] - 3, color='gray')
 
         # Add colorbar
         cbar = fig.colorbar(im, ax=ax, shrink=0.8)
-        cbar.set_label("Utilization %", fontsize=self._plot_config["fontsize"])
+        cbar.set_label('Utilization %', fontsize=self._plot_config["fontsize"])
         cbar.ax.tick_params(labelsize=self._plot_config["fontsize"] - 1)
 
         # Labels and title
-        ax.set_xlabel("Year", fontsize=self._plot_config["label_fontsize"])
-        ax.set_ylabel("Process", fontsize=self._plot_config["label_fontsize"])
-        ax.set_title(
-            f"Capacity Utilization: {product_name}",
-            fontsize=self._plot_config["title_fontsize"],
-        )
+        ax.set_xlabel('Year', fontsize=self._plot_config["label_fontsize"])
+        ax.set_ylabel('Process', fontsize=self._plot_config["label_fontsize"])
+        ax.set_title(f'Capacity Utilization: {product_name}', fontsize=self._plot_config["title_fontsize"])
 
         fig.tight_layout()
         plt.show()
diff --git a/src/optimex/utils.py b/src/optimex/utils.py
index ca5d06a..ceb0979 100644
--- a/src/optimex/utils.py
+++ b/src/optimex/utils.py
@@ -2,8 +2,9 @@
 Utility functions for optimex.
 """
 
-import bw2data as bd
 import numpy as np
+import bw2data as bd
+
 from bw_temporalis import TemporalDistribution, easy_timedelta_distribution
 
 
diff --git a/tests/test_background_process_product_separation.py b/tests/test_background_process_product_separation.py
index edaec62..662cd80 100644
--- a/tests/test_background_process_product_separation.py
+++ b/tests/test_background_process_product_separation.py
@@ -9,7 +9,6 @@
 This structure mirrors how foreground databases work in optimex and tests
 compatibility with standard Brightway LCA calculations.
 """
-
 from datetime import datetime
 
 import bw2calc as bc
@@ -27,11 +26,11 @@
 def setup_separated_background_system():
     """
     Set up a system where background database has separate process and product nodes.
-
+    
     Background structure:
     - Product node: ("bg_2020", "electricity") - the product
     - Process node: ("bg_2020", "electricity_production") - the process producing it
-
+    
     Foreground structure:
     - Product node: ("foreground", "Widget")
     - Process node: ("foreground", "Widget_Route1") - consumes background electricity product
@@ -40,110 +39,99 @@ def setup_separated_background_system():
 
     # Biosphere
     bio_db = bd.Database("biosphere3")
-    bio_db.write(
-        {
-            ("biosphere3", "CO2"): {
-                "type": "emission",
-                "name": "carbon dioxide",
-            },
-        }
-    )
+    bio_db.write({
+        ("biosphere3", "CO2"): {
+            "type": "emission",
+            "name": "carbon dioxide",
+        },
+    })
     bio_db.register()
 
     # Background database with SEPARATED process and product nodes
     bg_2020 = bd.Database("bg_2020")
-    bg_2020.write(
-        {
-            # Background PRODUCT node
-            ("bg_2020", "electricity"): {
-                "name": "electricity",
-                "unit": "kWh",
-                "type": bd.labels.product_node_default,
-            },
-            # Background PROCESS node that produces the electricity product
-            ("bg_2020", "electricity_production"): {
-                "name": "electricity production process",
-                "location": "GLO",
-                "type": bd.labels.process_node_default,
-                "exchanges": [
-                    {
-                        "amount": 1,
-                        "type": "production",
-                        "input": (
-                            "bg_2020",
-                            "electricity",
-                        ),  # Produces the electricity PRODUCT
-                    },
-                    {
-                        "amount": 0.5,
-                        "type": "biosphere",
-                        "input": ("biosphere3", "CO2"),
-                    },
-                ],
-            },
-        }
-    )
+    bg_2020.write({
+        # Background PRODUCT node
+        ("bg_2020", "electricity"): {
+            "name": "electricity",
+            "unit": "kWh",
+            "type": bd.labels.product_node_default,
+        },
+        # Background PROCESS node that produces the electricity product
+        ("bg_2020", "electricity_production"): {
+            "name": "electricity production process",
+            "location": "GLO",
+            "type": bd.labels.process_node_default,
+            "exchanges": [
+                {
+                    "amount": 1,
+                    "type": "production",
+                    "input": ("bg_2020", "electricity"),  # Produces the electricity PRODUCT
+                },
+                {
+                    "amount": 0.5,
+                    "type": "biosphere",
+                    "input": ("biosphere3", "CO2"),
+                },
+            ],
+        },
+    })
     bg_2020.metadata["representative_time"] = datetime(2020, 1, 1).isoformat()
     bg_2020.register()
 
     # Foreground with single product and single route
     fg = bd.Database("foreground")
-    fg.write(
-        {
-            # Product node
-            ("foreground", "Widget"): {
-                "name": "Widget",
-                "unit": "kg",
-                "type": bd.labels.product_node_default,
-            },
-            # Single production route
-            ("foreground", "Widget_Route1"): {
-                "name": "Widget production, Route 1",
-                "location": "GLO",
-                "type": bd.labels.process_node_default,
-                "operation_time_limits": (1, 2),  # Operation phase at process times 1-2
-                "exchanges": [
-                    {
-                        "amount": 1,  # Produces 1 kg Widget
-                        "type": bd.labels.production_edge_default,
-                        "input": ("foreground", "Widget"),
-                        "temporal_distribution": TemporalDistribution(
-                            date=np.array([0, 1, 2, 3], dtype="timedelta64[Y]"),
-                            amount=np.array([0, 0.5, 0.5, 0]),  # Sums to 1.0
-                        ),
-                        "operation": True,
-                    },
-                    {
-                        "amount": 10,  # Consumes 10 kWh electricity at construction
-                        "type": bd.labels.consumption_edge_default,
-                        "input": ("bg_2020", "electricity"),  # Demands the PRODUCT
-                        "temporal_distribution": TemporalDistribution(
-                            date=np.array([0, 1, 2, 3], dtype="timedelta64[Y]"),
-                            amount=np.array([1, 0, 0, 0]),  # All at construction
-                        ),
-                    },
-                    {
-                        "amount": 5,  # Emits 5 kg CO2 during operation
-                        "type": bd.labels.biosphere_edge_default,
-                        "input": ("biosphere3", "CO2"),
-                        "temporal_distribution": TemporalDistribution(
-                            date=np.array([0, 1, 2, 3], dtype="timedelta64[Y]"),
-                            amount=np.array([0, 0.5, 0.5, 0]),  # During operation
-                        ),
-                        "operation": True,
-                    },
-                ],
-            },
-        }
-    )
+    fg.write({
+        # Product node
+        ("foreground", "Widget"): {
+            "name": "Widget",
+            "unit": "kg",
+            "type": bd.labels.product_node_default,
+        },
+        # Single production route
+        ("foreground", "Widget_Route1"): {
+            "name": "Widget production, Route 1",
+            "location": "GLO",
+            "type": bd.labels.process_node_default,
+            "operation_time_limits": (1, 2),  # Operation phase at process times 1-2
+            "exchanges": [
+                {
+                    "amount": 1,  # Produces 1 kg Widget
+                    "type": bd.labels.production_edge_default,
+                    "input": ("foreground", "Widget"),
+                    "temporal_distribution": TemporalDistribution(
+                        date=np.array([0, 1, 2, 3], dtype="timedelta64[Y]"),
+                        amount=np.array([0, 0.5, 0.5, 0]),  # Sums to 1.0
+                    ),
+                    "operation": True,
+                },
+                {
+                    "amount": 10,  # Consumes 10 kWh electricity at construction
+                    "type": bd.labels.consumption_edge_default,
+                    "input": ("bg_2020", "electricity"),  # Demands the PRODUCT
+                    "temporal_distribution": TemporalDistribution(
+                        date=np.array([0, 1, 2, 3], dtype="timedelta64[Y]"),
+                        amount=np.array([1, 0, 0, 0]),  # All at construction
+                    ),
+                },
+                {
+                    "amount": 5,  # Emits 5 kg CO2 during operation
+                    "type": bd.labels.biosphere_edge_default,
+                    "input": ("biosphere3", "CO2"),
+                    "temporal_distribution": TemporalDistribution(
+                        date=np.array([0, 1, 2, 3], dtype="timedelta64[Y]"),
+                        amount=np.array([0, 0.5, 0.5, 0]),  # During operation
+                    ),
+                    "operation": True,
+                },
+            ],
+        },
+    })
     fg.register()
 
     # Impact method
-    bd.Method(("GWP", "example")).write(
-        [
-            (("biosphere3", "CO2"), 1),
-        ]
-    )
+    bd.Method(("GWP", "example")).write([
+        (("biosphere3", "CO2"), 1),
+    ])
 
 
 def test_separated_background_matches_standard_lca(setup_separated_background_system):
@@ -169,9 +157,7 @@ def test_separated_background_matches_standard_lca(setup_separated_background_sy
     # optimex calculation
     years = range(2020, 2030)
     td_demand = TemporalDistribution(
-        date=np.array(
-            [datetime(year, 1, 1).isoformat() for year in years], dtype="datetime64[s]"
-        ),
+        date=np.array([datetime(year, 1, 1).isoformat() for year in years], dtype='datetime64[s]'),
         amount=np.asarray([0, 0, 100, 0, 0, 0, 0, 0, 0, 0]),  # 100 kg at year 2022
     )
 
@@ -198,6 +184,7 @@ def test_separated_background_matches_standard_lca(setup_separated_background_sy
         optimization_model_inputs,
         name="test_separated_background",
         objective_category="climate_change",
+
     )
 
     _, obj_real, results = optimizer.solve_model(model, solver_name="glpk")
@@ -206,77 +193,72 @@ def test_separated_background_matches_standard_lca(setup_separated_background_sy
     print(f"Difference: {abs(obj_real - expected_gwp)}")
 
     # They should match (within numerical tolerance)
-    assert (
-        pytest.approx(obj_real, rel=1e-3) == expected_gwp
-    ), f"optimex result ({obj_real}) should match standard LCA ({expected_gwp})"
+    assert pytest.approx(obj_real, rel=1e-3) == expected_gwp, (
+        f"optimex result ({obj_real}) should match standard LCA ({expected_gwp})"
+    )
 
     # Additional check: verify postprocessing extracts correct unscaled values
     pp = postprocessing.PostProcessor(model)
     df_impacts = pp.get_impacts()
 
     # Sum all climate_change impacts across all processes and times
-    if "climate_change" in df_impacts.columns.get_level_values(0):
-        climate_change_cols = [
-            col for col in df_impacts.columns if col[0] == "climate_change"
-        ]
+    if 'climate_change' in df_impacts.columns.get_level_values(0):
+        climate_change_cols = [col for col in df_impacts.columns if col[0] == 'climate_change']
         total_cc_from_pp = df_impacts[climate_change_cols].sum().sum()
 
         print(f"\nPostprocessing climate_change total: {total_cc_from_pp}")
         print(f"Expected (from LCA): {expected_gwp}")
 
         # Postprocessing should also match standard LCA
-        assert (
-            pytest.approx(total_cc_from_pp, rel=1e-3) == expected_gwp
-        ), f"Postprocessing climate_change sum ({total_cc_from_pp}) should match standard LCA ({expected_gwp})"
+        assert pytest.approx(total_cc_from_pp, rel=1e-3) == expected_gwp, (
+            f"Postprocessing climate_change sum ({total_cc_from_pp}) should match standard LCA ({expected_gwp})"
+        )
 
 
 def test_separated_background_process_linkage(setup_separated_background_system):
     """
     Verify that background product correctly links to background process.
-
+    
     This test ensures that when the foreground demands a background product,
     the LCA processor correctly identifies and uses the background process
     that produces that product.
     """
-
+    
     # Get the nodes
     electricity_product = bd.get_node(database="bg_2020", name="electricity")
-    electricity_process = bd.get_node(
-        database="bg_2020", name="electricity production process"
-    )
-
+    electricity_process = bd.get_node(database="bg_2020", name="electricity production process")
+    
     # Verify they are correctly typed
-    assert (
-        electricity_product.get("type") == bd.labels.product_node_default
-    ), "Electricity should be a product node"
-    assert (
-        electricity_process.get("type") == bd.labels.process_node_default
-    ), "Electricity production should be a process node"
-
+    assert electricity_product.get('type') == bd.labels.product_node_default, (
+        "Electricity should be a product node"
+    )
+    assert electricity_process.get('type') == bd.labels.process_node_default, (
+        "Electricity production should be a process node"
+    )
+    
     # Verify the process produces the product
     production_exchanges = [
-        exc
-        for exc in electricity_process.exchanges()
-        if exc.get("type") == "production"
+        exc for exc in electricity_process.exchanges()
+        if exc.get('type') == "production"
     ]
-
-    assert (
-        len(production_exchanges) == 1
-    ), "Process should have exactly one production exchange"
-
-    assert (
-        production_exchanges[0]["input"] == electricity_product.key
-    ), "Process should produce the electricity product"
-
+    
+    assert len(production_exchanges) == 1, (
+        "Process should have exactly one production exchange"
+    )
+    
+    assert production_exchanges[0]['input'] == electricity_product.key, (
+        "Process should produce the electricity product"
+    )
+    
     # Verify standard LCA can handle this structure
     widget = bd.get_node(database="foreground", name="Widget")
     lca = bc.LCA({widget: 1}, method=("GWP", "example"))
     lca.lci()
     lca.lcia()
-
+    
     # The LCA should complete successfully with a non-zero result
     assert lca.score > 0, "LCA should produce a positive impact score"
-
+    
     print(f"\nBackground process-product linkage verified!")
     print(f"Product node: {electricity_product}")
     print(f"Process node: {electricity_process}")
diff --git a/tests/test_brownfield.py b/tests/test_brownfield.py
index db0fea3..02310cd 100644
--- a/tests/test_brownfield.py
+++ b/tests/test_brownfield.py
@@ -8,8 +8,8 @@
 - Only contribute operational impacts during their remaining lifetime
 """
 
-import pyomo.environ as pyo
 import pytest
+import pyomo.environ as pyo
 
 from optimex import converter, optimizer
 
@@ -38,11 +38,7 @@ def test_existing_capacity_valid(self):
             "SYSTEM_TIME": [2020, 2021, 2022],
             "CATEGORY": ["climate_change"],
             "operation_time_limits": {"P1": (0, 0)},
-            "demand": {
-                ("product", 2020): 10,
-                ("product", 2021): 10,
-                ("product", 2022): 10,
-            },
+            "demand": {("product", 2020): 10, ("product", 2021): 10, ("product", 2022): 10},
             "foreground_technosphere": {},
             "internal_demand_technosphere": {},
             "foreground_biosphere": {("P1", "CO2", 0): 10},
@@ -187,23 +183,16 @@ def test_existing_capacity_meets_demand(self):
             "internal_demand_technosphere": {},
             # Separate flows for installation vs operation emissions
             "foreground_biosphere": {
-                (
-                    "P1",
-                    "CO2_install",
-                    0,
-                ): 1000,  # HIGH installation emissions (NOT operational)
-                ("P1", "CO2_operate", 1): 1,  # LOW operation emissions
+                ("P1", "CO2_install", 0): 1000,  # HIGH installation emissions (NOT operational)
+                ("P1", "CO2_operate", 1): 1,    # LOW operation emissions
             },
             "foreground_production": {
                 ("P1", "product", 1): 1.0,
             },
             "operation_flow": {
                 ("P1", "product"): True,
-                (
-                    "P1",
-                    "CO2_install",
-                ): False,  # NOT operational (scaled by installation)
-                ("P1", "CO2_operate"): True,  # Operational (scaled by operation)
+                ("P1", "CO2_install"): False,  # NOT operational (scaled by installation)
+                ("P1", "CO2_operate"): True,   # Operational (scaled by operation)
             },
             "background_inventory": {},
             "mapping": {
@@ -223,6 +212,7 @@ def test_existing_capacity_meets_demand(self):
             inputs=model_inputs,
             objective_category="climate_change",
             name="test_brownfield_meets_demand",
+
         )
 
         solved_model, objective, results = optimizer.solve_model(
@@ -236,9 +226,9 @@ def test_existing_capacity_meets_demand(self):
         # Existing capacity of 10 units at tau=1 provides capacity
         # Should be enough to meet demand of 5 with no new installations
         inst_2025 = pyo.value(solved_model.var_installation["P1", 2025])
-        assert (
-            pytest.approx(0, abs=1e-4) == inst_2025
-        ), f"No new installation expected at 2025 (existing capacity should suffice), got {inst_2025}"
+        assert pytest.approx(0, abs=1e-4) == inst_2025, (
+            f"No new installation expected at 2025 (existing capacity should suffice), got {inst_2025}"
+        )
 
     def test_existing_capacity_reduces_emissions(self):
         """
@@ -269,16 +259,8 @@ def test_existing_capacity_reduces_emissions(self):
             "internal_demand_technosphere": {},
             # Separate flows for installation vs operation emissions
             "foreground_biosphere": {
-                (
-                    "P1",
-                    "CO2_install",
-                    0,
-                ): 100,  # Installation emissions (NOT operational)
-                (
-                    "P1",
-                    "CO2_operate",
-                    1,
-                ): 1,  # Operation emissions (same at tau=1 and 2)
+                ("P1", "CO2_install", 0): 100,  # Installation emissions (NOT operational)
+                ("P1", "CO2_operate", 1): 1,    # Operation emissions (same at tau=1 and 2)
                 ("P1", "CO2_operate", 2): 1,
             },
             "foreground_production": {
@@ -287,11 +269,8 @@ def test_existing_capacity_reduces_emissions(self):
             },
             "operation_flow": {
                 ("P1", "product"): True,
-                (
-                    "P1",
-                    "CO2_install",
-                ): False,  # NOT operational (scaled by installation)
-                ("P1", "CO2_operate"): True,  # Operational (scaled by operation)
+                ("P1", "CO2_install"): False,  # NOT operational (scaled by installation)
+                ("P1", "CO2_operate"): True,   # Operational (scaled by operation)
             },
             "background_inventory": {},
             "mapping": {("db_2020", 2022): 1.0, ("db_2020", 2023): 1.0},
@@ -312,6 +291,7 @@ def test_existing_capacity_reduces_emissions(self):
             inputs=greenfield_inputs,
             objective_category="climate_change",
             name="greenfield",
+
         )
         _, greenfield_obj, _ = optimizer.solve_model(
             greenfield_model, solver_name="glpk", tee=False
@@ -320,15 +300,14 @@ def test_existing_capacity_reduces_emissions(self):
         # Brownfield: all capacity already exists
         # Existing capacity at 2021: at 2023, tau = 2023-2021 = 2, in operation!
         brownfield_config = dict(base_config)
-        brownfield_config["existing_capacity"] = {
-            ("P1", 2021): 10.0
-        }  # Plenty of capacity
+        brownfield_config["existing_capacity"] = {("P1", 2021): 10.0}  # Plenty of capacity
 
         brownfield_inputs = converter.OptimizationModelInputs(**brownfield_config)
         brownfield_model = optimizer.create_model(
             inputs=brownfield_inputs,
             objective_category="climate_change",
             name="brownfield",
+
         )
         _, brownfield_obj, _ = optimizer.solve_model(
             brownfield_model, solver_name="glpk", tee=False
@@ -392,7 +371,9 @@ def test_existing_capacity_retirement(self):
                 ("P1", "CO2"): True,
             },
             "background_inventory": {},
-            "mapping": {("db_2020", t): 1.0 for t in [2020, 2021, 2022, 2023]},
+            "mapping": {
+                ("db_2020", t): 1.0 for t in [2020, 2021, 2022, 2023]
+            },
             "characterization": {
                 ("climate_change", "CO2", t): 1.0 for t in [2020, 2021, 2022, 2023]
             },
@@ -405,6 +386,7 @@ def test_existing_capacity_retirement(self):
             inputs=model_inputs,
             objective_category="climate_change",
             name="test_retirement",
+
         )
 
         solved_model, objective, results = optimizer.solve_model(
@@ -420,9 +402,9 @@ def test_existing_capacity_retirement(self):
         )
 
         # Some new installations should be needed to replace retired capacity
-        assert (
-            total_new_installations > 0
-        ), "New installations should be needed after existing capacity retires"
+        assert total_new_installations > 0, (
+            "New installations should be needed after existing capacity retires"
+        )
 
 
 class TestBrownfieldPostprocessing:
@@ -484,9 +466,12 @@ def test_get_existing_capacity(self):
             inputs=model_inputs,
             objective_category="climate_change",
             name="test_postprocessing",
+
         )
 
-        solved_model, _, _ = optimizer.solve_model(model, solver_name="glpk", tee=False)
+        solved_model, _, _ = optimizer.solve_model(
+            model, solver_name="glpk", tee=False
+        )
 
         pp = PostProcessor(solved_model)
         existing_df = pp.get_existing_capacity()
@@ -536,9 +521,7 @@ def test_production_capacity_includes_existing(self):
             # Existing capacity of 10 units installed in 2019
             # At 2020: tau = 1, NOT in operation (0 <= 0 <= 0 means only tau=0)
             # So we need installation year = 2020 to have tau = 0
-            "existing_capacity": {
-                ("P1", 2020 - 0): 10.0
-            },  # This won't work due to validation
+            "existing_capacity": {("P1", 2020 - 0): 10.0},  # This won't work due to validation
         }
 
         # Actually, the validation requires inst_year < min(SYSTEM_TIME)
@@ -563,9 +546,12 @@ def test_production_capacity_includes_existing(self):
             inputs=model_inputs,
             objective_category="climate_change",
             name="test_capacity",
+
         )
 
-        solved_model, _, _ = optimizer.solve_model(model, solver_name="glpk", tee=False)
+        solved_model, _, _ = optimizer.solve_model(
+            model, solver_name="glpk", tee=False
+        )
 
         pp = PostProcessor(solved_model)
         capacity_df = pp.get_production_capacity()
@@ -613,7 +599,9 @@ def test_brownfield_with_vintage_improvements(self):
                 ("Plant", "electricity", 2030): 0.8,  # 20% efficiency improvement
             },
             "internal_demand_technosphere": {},
-            "foreground_biosphere": {("Plant", "CO2", tau): 5 for tau in range(21)},
+            "foreground_biosphere": {
+                ("Plant", "CO2", tau): 5 for tau in range(21)
+            },
             "foreground_production": {
                 ("Plant", "output", tau): 50 if 1 <= tau <= 20 else 0
                 for tau in range(21)
@@ -647,9 +635,7 @@ def test_brownfield_with_vintage_improvements(self):
         # Existing capacity should contribute to meeting demand
         # At 2025, existing capacity from 2015 is at tau = 2025-2015 = 10 (in operation)
         operation_2025 = get_total_operation(solved_model, "Plant", 2025)
-        assert (
-            operation_2025 > 0
-        ), "Operation should be positive when existing capacity exists"
+        assert operation_2025 > 0, "Operation should be positive when existing capacity exists"
 
     def test_brownfield_with_vintage_production_overrides(self):
         """
@@ -676,7 +662,7 @@ def test_brownfield_with_vintage_production_overrides(self):
             # Separate flows for installation vs operation (operational must be constant)
             "foreground_biosphere": {
                 ("Plant", "CO2_install", 0): 100,  # Installation emissions
-                ("Plant", "CO2_operate", 1): 10,  # Operation emissions (constant)
+                ("Plant", "CO2_operate", 1): 10,   # Operation emissions (constant)
                 ("Plant", "CO2_operate", 2): 10,
             },
             # Base production (will be overridden by vintages)
@@ -695,14 +681,15 @@ def test_brownfield_with_vintage_production_overrides(self):
             "operation_flow": {
                 ("Plant", "output"): True,
                 ("Plant", "CO2_install"): False,  # NOT operational
-                ("Plant", "CO2_operate"): True,  # Operational
+                ("Plant", "CO2_operate"): True,   # Operational
             },
             "background_inventory": {},
             "mapping": {("db", t): 1.0 for t in [2025, 2026, 2027]},
             "characterization": {
                 ("GWP", "CO2_install", t): 1.0 for t in [2025, 2026, 2027]
-            }
-            | {("GWP", "CO2_operate", t): 1.0 for t in [2025, 2026, 2027]},
+            } | {
+                ("GWP", "CO2_operate", t): 1.0 for t in [2025, 2026, 2027]
+            },
             # Existing capacity from 2023 (before min SYSTEM_TIME 2025)
             # At 2025: tau = 2025-2023 = 2, in operation (1 <= 2 <= 2)
             # At 2026: tau = 2026-2023 = 3, NOT in operation (3 > 2)
@@ -752,21 +739,24 @@ def test_brownfield_with_mixed_vintage_processes(self):
             "demand": {("output", t): 200 for t in [2025, 2026, 2027]},
             "foreground_technosphere": {
                 ("OldPlant", "electricity", tau): 20 for tau in range(11)
-            }
-            | {("NewPlant", "electricity", tau): 15 for tau in range(11)},
+            } | {
+                ("NewPlant", "electricity", tau): 15 for tau in range(11)
+            },
             # vintage_improvements only for NewPlant
             "vintage_improvements": {
                 ("NewPlant", "electricity", 2025): 1.0,
                 ("NewPlant", "electricity", 2027): 0.8,
             },
             "internal_demand_technosphere": {},
-            "foreground_biosphere": {("OldPlant", "CO2", tau): 10 for tau in range(11)}
-            | {("NewPlant", "CO2", tau): 5 for tau in range(11)},
+            "foreground_biosphere": {
+                ("OldPlant", "CO2", tau): 10 for tau in range(11)
+            } | {
+                ("NewPlant", "CO2", tau): 5 for tau in range(11)
+            },
             "foreground_production": {
                 ("OldPlant", "output", tau): 100 if 1 <= tau <= 10 else 0
                 for tau in range(11)
-            }
-            | {
+            } | {
                 ("NewPlant", "output", tau): 100 if 1 <= tau <= 10 else 0
                 for tau in range(11)
             },
@@ -843,11 +833,7 @@ def test_brownfield_vintage_impact_calculation(self):
             # Separate flows for installation vs operation (operational must be constant)
             # Installation emissions are NOT operational (scaled by installation)
             "foreground_biosphere": {
-                (
-                    "Plant",
-                    "CO2_install",
-                    0,
-                ): 500,  # High installation emissions at construction
+                ("Plant", "CO2_install", 0): 500,  # High installation emissions at construction
             },
             "foreground_production": {
                 ("Plant", "output", 0): 100,
@@ -857,10 +843,7 @@ def test_brownfield_vintage_impact_calculation(self):
             "operation_flow": {
                 ("Plant", "output"): True,
                 ("Plant", "electricity"): True,
-                (
-                    "Plant",
-                    "CO2_install",
-                ): False,  # NOT operational - scales with installation
+                ("Plant", "CO2_install"): False,  # NOT operational - scales with installation
             },
             "background_inventory": {("grid", "electricity", "CO2_operate"): 1.0},
             "mapping": {("grid", 2025): 1.0, ("grid", 2026): 1.0},
@@ -882,10 +865,7 @@ def test_brownfield_vintage_impact_calculation(self):
         _, greenfield_obj, greenfield_results = optimizer.solve_model(
             greenfield_model, solver_name="glpk", tee=False
         )
-        assert (
-            greenfield_results.solver.termination_condition
-            == pyo.TerminationCondition.optimal
-        )
+        assert greenfield_results.solver.termination_condition == pyo.TerminationCondition.optimal
 
         # Brownfield scenario (existing capacity covers demand)
         brownfield_config = dict(base_config)
@@ -900,10 +880,7 @@ def test_brownfield_vintage_impact_calculation(self):
         _, brownfield_obj, brownfield_results = optimizer.solve_model(
             brownfield_model, solver_name="glpk", tee=False
         )
-        assert (
-            brownfield_results.solver.termination_condition
-            == pyo.TerminationCondition.optimal
-        )
+        assert brownfield_results.solver.termination_condition == pyo.TerminationCondition.optimal
 
         # Brownfield should have lower impact (no installation emissions)
         assert brownfield_obj < greenfield_obj, (
diff --git a/tests/test_merit_order.py b/tests/test_merit_order.py
index fcc4bdb..5f53f5b 100644
--- a/tests/test_merit_order.py
+++ b/tests/test_merit_order.py
@@ -13,8 +13,8 @@
 5. Per-vintage capacity: Each vintage bounded by its own installation/existing capacity
 """
 
-import pyomo.environ as pyo
 import pytest
+import pyomo.environ as pyo
 
 from optimex import converter, optimizer
 from optimex.postprocessing import PostProcessor
@@ -103,7 +103,7 @@ def test_var_operation_is_3d(self):
         # Check that var_operation is indexed by ACTIVE_VINTAGE_TIME (3D)
         assert hasattr(model, "var_operation")
         # The variable should be accessible with 3-tuple indices
-        for p, v, t in model.ACTIVE_VINTAGE_TIME:
+        for (p, v, t) in model.ACTIVE_VINTAGE_TIME:
             assert model.var_operation[p, v, t] is not None
 
     def test_brownfield_has_operation_variables(self):
@@ -143,14 +143,15 @@ def test_brownfield_has_operation_variables(self):
 
         # Check that brownfield vintage (2019) appears in ACTIVE_VINTAGE_TIME
         brownfield_entries = [
-            (p, v, t) for (p, v, t) in model.ACTIVE_VINTAGE_TIME if v == 2019
+            (p, v, t) for (p, v, t) in model.ACTIVE_VINTAGE_TIME
+            if v == 2019
         ]
-        assert (
-            len(brownfield_entries) > 0
-        ), "Brownfield vintage 2019 should have entries in ACTIVE_VINTAGE_TIME"
+        assert len(brownfield_entries) > 0, (
+            "Brownfield vintage 2019 should have entries in ACTIVE_VINTAGE_TIME"
+        )
 
         # Check that operation variable exists for brownfield
-        for p, v, t in brownfield_entries:
+        for (p, v, t) in brownfield_entries:
             assert model.var_operation[p, v, t] is not None
 
 
@@ -183,16 +184,15 @@ def test_cleaner_vintage_dispatched_first(self):
                 ("output", 2020): 0,
                 ("output", 2021): 0,
                 ("output", 2022): 0,
-                (
-                    "output",
-                    2023,
-                ): 50,  # Only demand at 2023 when both vintages available
+                ("output", 2023): 50,  # Only demand at 2023 when both vintages available
             },
             # Fuel consumption varies by vintage (technology evolution)
-            "foreground_technosphere": {("Plant", "fuel", tau): 10 for tau in range(4)},
+            "foreground_technosphere": {
+                ("Plant", "fuel", tau): 10 for tau in range(4)
+            },
             "vintage_improvements": {
-                ("Plant", "fuel", 2020): 1.0,  # 2020 vintage: baseline
-                ("Plant", "fuel", 2022): 0.5,  # 2022 vintage: 50% more efficient
+                ("Plant", "fuel", 2020): 1.0,   # 2020 vintage: baseline
+                ("Plant", "fuel", 2022): 0.5,   # 2022 vintage: 50% more efficient
             },
             "internal_demand_technosphere": {},
             "foreground_biosphere": {},  # Emissions come from fuel via background
@@ -206,9 +206,7 @@ def test_cleaner_vintage_dispatched_first(self):
             },
             "background_inventory": {("grid", "fuel", "CO2"): 1.0},
             "mapping": {("grid", t): 1.0 for t in [2020, 2021, 2022, 2023]},
-            "characterization": {
-                ("GWP", "CO2", t): 1.0 for t in [2020, 2021, 2022, 2023]
-            },
+            "characterization": {("GWP", "CO2", t): 1.0 for t in [2020, 2021, 2022, 2023]},
             # Force some installation at both 2020 and 2022
             "process_deployment_limits_min": {
                 ("Plant", 2020): 1.0,  # Force installation in 2020
@@ -263,7 +261,9 @@ def test_brownfield_dispatchable_independently(self):
             "CATEGORY": ["GWP"],
             "operation_time_limits": {"Plant": (1, 5)},
             "demand": {("output", t): 100 for t in [2020, 2021, 2022]},
-            "foreground_technosphere": {("Plant", "fuel", tau): 10 for tau in range(6)},
+            "foreground_technosphere": {
+                ("Plant", "fuel", tau): 10 for tau in range(6)
+            },
             # Technology evolution makes newer vintages more efficient
             "vintage_improvements": {
                 ("Plant", "fuel", 2020): 1.0,
@@ -310,9 +310,7 @@ def test_brownfield_dispatchable_independently(self):
         # Verify brownfield is independently dispatchable (can be < full capacity)
         for v, t, op_val in brownfield_ops:
             # Just verify it's a valid (non-negative) operation value
-            assert (
-                op_val >= -1e-6
-            ), f"Brownfield operation at {t} should be non-negative"
+            assert op_val >= -1e-6, f"Brownfield operation at {t} should be non-negative"
 
 
 class TestOperationLimits:
@@ -375,9 +373,9 @@ def test_operation_limits_apply_to_total(self):
         )
 
         # Total operation should respect the limit
-        assert (
-            total_op_2022 <= 30 + 1e-6
-        ), f"Total operation at 2022 ({total_op_2022:.4f}) should not exceed limit of 30"
+        assert total_op_2022 <= 30 + 1e-6, (
+            f"Total operation at 2022 ({total_op_2022:.4f}) should not exceed limit of 30"
+        )
 
 
 class TestPerVintageCapacity:
@@ -414,9 +412,7 @@ def test_per_vintage_capacity_respected(self):
             "operation_flow": {("P1", "product"): True, ("P1", "CO2"): True},
             "background_inventory": {},
             "mapping": {("db", t): 1.0 for t in [2020, 2021, 2022, 2023]},
-            "characterization": {
-                ("GWP", "CO2", t): 1.0 for t in [2020, 2021, 2022, 2023]
-            },
+            "characterization": {("GWP", "CO2", t): 1.0 for t in [2020, 2021, 2022, 2023]},
         }
 
         model_inputs = converter.OptimizationModelInputs(**model_inputs_dict)
@@ -436,7 +432,7 @@ def test_per_vintage_capacity_respected(self):
         fg_scale = solved_model.scales["foreground"]
         production_per_unit = 50.0 + 50.0 + 50.0  # Sum over tau 0, 1, and 2
 
-        for p, v, t in solved_model.ACTIVE_VINTAGE_TIME:
+        for (p, v, t) in solved_model.ACTIVE_VINTAGE_TIME:
             if v in solved_model.SYSTEM_TIME:
                 # Greenfield: capacity from var_installation
                 installation = pyo.value(solved_model.var_installation[p, v])
@@ -491,9 +487,7 @@ def test_get_operation_aggregate_vintages(self):
 
         # Should have Process columns (not Process, Vintage)
         assert "P1" in op_df.columns
-        assert not isinstance(
-            op_df.columns, type(op_df.columns).__class__
-        )  # Not MultiIndex
+        assert not isinstance(op_df.columns, type(op_df.columns).__class__)  # Not MultiIndex
 
     def test_get_operation_by_vintage(self):
         """Test get_operation with aggregate_vintages=False."""
@@ -537,7 +531,6 @@ def test_get_operation_by_vintage(self):
 
         # Should have MultiIndex columns with (Process, Vintage)
         import pandas as pd
-
         assert isinstance(op_df.columns, pd.MultiIndex), "Columns should be MultiIndex"
         # Columns should be tuples with (Process, Vintage)
         for col in op_df.columns:
@@ -546,7 +539,6 @@ def test_get_operation_by_vintage(self):
     def test_get_operation_by_vintage_column_names(self):
         """Test get_operation(aggregate_vintages=False) returns correct column structure."""
         import pandas as pd
-
         model_inputs_dict = {
             "PROCESS": ["P1"],
             "PRODUCT": ["product"],
diff --git a/tests/test_operation_constraints.py b/tests/test_operation_constraints.py
index 1706f4e..0679678 100644
--- a/tests/test_operation_constraints.py
+++ b/tests/test_operation_constraints.py
@@ -4,7 +4,6 @@
 These tests verify that the OperationLimit constraint correctly bounds
 operation levels by available installed capacity across multiple time periods.
 """
-
 import pyomo.environ as pyo
 import pytest
 
@@ -39,12 +38,7 @@ def test_operation_capacity_single_installation():
         "INTERMEDIATE_FLOW": [],
         "ELEMENTARY_FLOW": ["CO2"],
         "BACKGROUND_ID": ["db_2020"],
-        "PROCESS_TIME": [
-            0,
-            1,
-            2,
-            3,
-        ],  # Construction at 0, operation at 1-2, decommission at 3
+        "PROCESS_TIME": [0, 1, 2, 3],  # Construction at 0, operation at 1-2, decommission at 3
         "SYSTEM_TIME": [2020, 2021, 2022, 2023, 2024],
         "CATEGORY": ["climate_change"],
         "operation_time_limits": {"P1": (1, 2)},
@@ -72,7 +66,8 @@ def test_operation_capacity_single_installation():
             ("db_2020", 2024): 1.0,
         },
         "characterization": {
-            ("climate_change", "CO2", t): 1.0 for t in [2020, 2021, 2022, 2023, 2024]
+            ("climate_change", "CO2", t): 1.0
+            for t in [2020, 2021, 2022, 2023, 2024]
         },
     }
 
@@ -218,8 +213,8 @@ def test_operation_capacity_with_varying_demand():
         "CATEGORY": ["climate_change"],
         "operation_time_limits": {"P1": (1, 2)},
         "demand": {
-            ("product", 2021): 3,  # Low demand
-            ("product", 2022): 8,  # High demand
+            ("product", 2021): 3,   # Low demand
+            ("product", 2022): 8,   # High demand
         },
         "foreground_technosphere": {},
         "internal_demand_technosphere": {},
diff --git a/tests/test_optimization.py b/tests/test_optimization.py
index 6d559ac..63593fd 100644
--- a/tests/test_optimization.py
+++ b/tests/test_optimization.py
@@ -97,12 +97,9 @@ def test_model_solution_is_optimal(solved_system_model):
     [
         # ("fixed", 3.15417e-10),  # Expected value for the fixed model
         ("flex", 1.9172462799736082e-10),  # Expected value for the flexible model
-        (
-            "constrained",
-            1.9197314619763485e-10,
-        ),  # Constrained by limiting P1 to 10 installations (0.13% higher)
+        ("constrained", 1.9197314619763485e-10),  # Constrained by limiting P1 to 10 installations (0.13% higher)
     ],
-    ids=["flex_result", "constrained_process_limit"],  # "fixed_result",
+    ids=["flex_result", "constrained_process_limit"], # "fixed_result",
 )
 def test_system_model(model_type, expected_value, solved_system_model):
     # Get the model from the solved system model fixture
@@ -236,7 +233,9 @@ def test_cumulative_process_limits_respected():
     # Set cumulative limit on low-emission process (30 units total across all years)
     # Total demand is 50, so P2 must provide at least 20
     cumulative_limit = 30.0
-    model_inputs.cumulative_process_limits_max = {"P1_low_emission": cumulative_limit}
+    model_inputs.cumulative_process_limits_max = {
+        "P1_low_emission": cumulative_limit
+    }
 
     # Create and solve model
     model = optimizer.create_model(
@@ -246,7 +245,9 @@ def test_cumulative_process_limits_respected():
     )
 
     solved_model, objective, results = optimizer.solve_model(
-        model, solver_name="glpk", tee=False
+        model,
+        solver_name="glpk",
+        tee=False
     )
 
     # Verify solution is optimal
@@ -279,7 +280,9 @@ def test_cumulative_process_limits_respected():
 
     # Verify P2 is used to meet remaining demand
     total_demand = sum(model_inputs_dict["demand"].values())
-    assert cumulative_p2 > 0, "P2 should be used to meet demand when P1 is limited"
+    assert cumulative_p2 > 0, (
+        "P2 should be used to meet demand when P1 is limited"
+    )
 
     # Verify total capacity roughly equals demand (within 1% tolerance)
     total_capacity = cumulative_p1 + cumulative_p2
@@ -348,17 +351,17 @@ def test_capacity_constraint_with_high_production():
     # - Need var_operation = 10/5 = 2 to produce 10 units
     # - Capacity = 5.0 * installations, so need 5.0 * inst >= 2
     # - Minimum installations = 2/5 = 0.4
-    assert (
-        pytest.approx(0.4, rel=0.01) == inst
-    ), f"With production=5.0, only 0.4 installations needed, got {inst}"
-    assert (
-        pytest.approx(2.0, rel=0.01) == oper
-    ), f"Operation should be 2.0 to produce 10 units, got {oper}"
+    assert pytest.approx(0.4, rel=0.01) == inst, (
+        f"With production=5.0, only 0.4 installations needed, got {inst}"
+    )
+    assert pytest.approx(2.0, rel=0.01) == oper, (
+        f"Operation should be 2.0 to produce 10 units, got {oper}"
+    )
 
     # Verify emissions (capacity-dependent) = 0.4 * 10 = 4 kg CO2
-    assert (
-        pytest.approx(4.0, rel=0.01) == objective
-    ), f"Emissions should be 4.0 kg CO2, got {objective}"
+    assert pytest.approx(4.0, rel=0.01) == objective, (
+        f"Emissions should be 4.0 kg CO2, got {objective}"
+    )
 
 
 def test_operation_limits_respected():
@@ -447,12 +450,12 @@ def test_operation_limits_respected():
         get_total_operation(solved_baseline, "P2_high_emission", t)
         for t in solved_baseline.SYSTEM_TIME
     )
-    assert (
-        pytest.approx(300, rel=0.01) == p1_operation_baseline
-    ), f"Without limits, P1 should handle all demand (300), got {p1_operation_baseline}"
-    assert (
-        pytest.approx(0, abs=0.01) == p2_operation_baseline
-    ), f"Without limits, P2 should not be used, got {p2_operation_baseline}"
+    assert pytest.approx(300, rel=0.01) == p1_operation_baseline, (
+        f"Without limits, P1 should handle all demand (300), got {p1_operation_baseline}"
+    )
+    assert pytest.approx(0, abs=0.01) == p2_operation_baseline, (
+        f"Without limits, P2 should not be used, got {p2_operation_baseline}"
+    )
 
     # Now solve WITH operation limits on P1
     # Limit P1 operation to 50 per year (150 total), forcing P2 to handle the rest
@@ -483,9 +486,9 @@ def test_operation_limits_respected():
         p2_op = get_total_operation(solved_limited, "P2_high_emission", t)
 
         # P1 should be at the limit (binding constraint)
-        assert (
-            p1_op <= operation_limit * 1.001
-        ), f"P1 operation at {t} ({p1_op:.2f}) exceeds limit ({operation_limit})"
+        assert p1_op <= operation_limit * 1.001, (
+            f"P1 operation at {t} ({p1_op:.2f}) exceeds limit ({operation_limit})"
+        )
         assert p1_op >= operation_limit * 0.999, (
             f"P1 operation at {t} ({p1_op:.2f}) should be at limit ({operation_limit}), "
             "constraint may not be binding"
@@ -493,19 +496,19 @@ def test_operation_limits_respected():
 
         # P2 should handle the remaining demand (100 - 50 = 50)
         expected_p2 = 100 - operation_limit
-        assert (
-            pytest.approx(expected_p2, rel=0.01) == p2_op
-        ), f"P2 operation at {t} should be {expected_p2}, got {p2_op}"
+        assert pytest.approx(expected_p2, rel=0.01) == p2_op, (
+            f"P2 operation at {t} should be {expected_p2}, got {p2_op}"
+        )
 
     # Verify that the limited solution has higher emissions
     # Baseline: 300 * 1 = 300 kg CO2
     # Limited: 150 * 1 + 150 * 10 = 150 + 1500 = 1650 kg CO2
-    assert (
-        obj_limited > obj_baseline
-    ), f"Limited objective ({obj_limited}) should be higher than baseline ({obj_baseline})"
-    assert (
-        pytest.approx(1650, rel=0.01) == obj_limited
-    ), f"Limited objective should be 1650 kg CO2, got {obj_limited}"
+    assert obj_limited > obj_baseline, (
+        f"Limited objective ({obj_limited}) should be higher than baseline ({obj_baseline})"
+    )
+    assert pytest.approx(1650, rel=0.01) == obj_limited, (
+        f"Limited objective should be 1650 kg CO2, got {obj_limited}"
+    )
 
 
 def test_cumulative_category_impact_limit_respected():
@@ -610,12 +613,12 @@ def test_cumulative_category_impact_limit_respected():
         get_total_operation(solved_baseline, "P1_low_co2", t)
         for t in solved_baseline.SYSTEM_TIME
     )
-    assert (
-        pytest.approx(50, rel=0.01) == p1_op_baseline
-    ), f"Without limits, P1 should handle all demand (50), got {p1_op_baseline}"
-    assert (
-        pytest.approx(50, rel=0.01) == obj_baseline
-    ), f"Baseline climate impact should be 50, got {obj_baseline}"
+    assert pytest.approx(50, rel=0.01) == p1_op_baseline, (
+        f"Without limits, P1 should handle all demand (50), got {p1_op_baseline}"
+    )
+    assert pytest.approx(50, rel=0.01) == obj_baseline, (
+        f"Baseline climate impact should be 50, got {obj_baseline}"
+    )
 
     # Calculate baseline land_use impact
     baseline_land_use = (
@@ -623,9 +626,9 @@ def test_cumulative_category_impact_limit_respected():
         * solved_baseline.scales["foreground"]
         * solved_baseline.scales["characterization"]["land_use"]
     )
-    assert (
-        pytest.approx(500, rel=0.01) == baseline_land_use
-    ), f"Baseline land_use should be 500, got {baseline_land_use}"
+    assert pytest.approx(500, rel=0.01) == baseline_land_use, (
+        f"Baseline land_use should be 500, got {baseline_land_use}"
+    )
 
     # Now solve WITH category impact limit on land_use
     # Limit land_use to 200 (requires 30 units from P2)
@@ -637,9 +640,7 @@ def test_cumulative_category_impact_limit_respected():
     land_use_limit = 200.0
 
     model_inputs_limited = converter.OptimizationModelInputs(**model_inputs_dict)
-    model_inputs_limited.cumulative_category_impact_limits = {
-        "land_use": land_use_limit
-    }
+    model_inputs_limited.cumulative_category_impact_limits = {"land_use": land_use_limit}
 
     model_limited = optimizer.create_model(
         inputs=model_inputs_limited,
@@ -662,9 +663,9 @@ def test_cumulative_category_impact_limit_respected():
     )
 
     # Verify land_use respects the limit (within 1% tolerance)
-    assert (
-        actual_land_use <= land_use_limit * 1.01
-    ), f"Actual land_use ({actual_land_use:.2f}) exceeds limit ({land_use_limit})"
+    assert actual_land_use <= land_use_limit * 1.01, (
+        f"Actual land_use ({actual_land_use:.2f}) exceeds limit ({land_use_limit})"
+    )
 
     # Verify land_use is at or near the limit (constraint should be binding)
     assert actual_land_use >= land_use_limit * 0.99, (
@@ -673,17 +674,17 @@ def test_cumulative_category_impact_limit_respected():
     )
 
     # Verify the limited solution has higher climate impact (forced to use P2)
-    assert (
-        obj_limited > obj_baseline
-    ), f"Limited objective ({obj_limited}) should be higher than baseline ({obj_baseline})"
+    assert obj_limited > obj_baseline, (
+        f"Limited objective ({obj_limited}) should be higher than baseline ({obj_baseline})"
+    )
 
     # Expected: P1 uses ~16.67, P2 uses ~33.33
     # With land_use constraint: 10*x + 1*(50-x) <= 200  =>  x <= 150/9 = 16.67
     # Climate impact = 1*x + 5*(50-x) = 250 - 4x = 250 - 4*(150/9) = 183.33
     expected_climate = 250 - 4 * (150 / 9)  # ~183.33
-    assert (
-        pytest.approx(expected_climate, rel=0.01) == obj_limited
-    ), f"Limited climate impact should be ~{expected_climate:.1f}, got {obj_limited}"
+    assert pytest.approx(expected_climate, rel=0.01) == obj_limited, (
+        f"Limited climate impact should be ~{expected_climate:.1f}, got {obj_limited}"
+    )
 
 
 def test_time_specific_category_impact_limit_respected():
@@ -794,14 +795,14 @@ def test_time_specific_category_impact_limit_respected():
     )
 
     # Verify land_use in 2021 respects the limit
-    assert (
-        actual_land_use_2021 <= land_use_limit_2021 * 1.01
-    ), f"Land_use in 2021 ({actual_land_use_2021:.2f}) exceeds limit ({land_use_limit_2021})"
+    assert actual_land_use_2021 <= land_use_limit_2021 * 1.01, (
+        f"Land_use in 2021 ({actual_land_use_2021:.2f}) exceeds limit ({land_use_limit_2021})"
+    )
 
     # Verify constraint is binding (at the limit)
-    assert (
-        actual_land_use_2021 >= land_use_limit_2021 * 0.99
-    ), f"Land_use in 2021 ({actual_land_use_2021:.2f}) is below limit, constraint not binding"
+    assert actual_land_use_2021 >= land_use_limit_2021 * 0.99, (
+        f"Land_use in 2021 ({actual_land_use_2021:.2f}) is below limit, constraint not binding"
+    )
 
     # Verify years without limits are not constrained (should be at 100)
     actual_land_use_2020 = (
@@ -809,18 +810,18 @@ def test_time_specific_category_impact_limit_respected():
         * fg_scale
         * cat_scale
     )
-    assert (
-        pytest.approx(100, rel=0.01) == actual_land_use_2020
-    ), f"Land_use in 2020 should be 100 (all P1), got {actual_land_use_2020}"
+    assert pytest.approx(100, rel=0.01) == actual_land_use_2020, (
+        f"Land_use in 2020 should be 100 (all P1), got {actual_land_use_2020}"
+    )
 
     actual_land_use_2022 = (
         pyo.value(solved_limited.time_specific_impact["land_use", 2022])
         * fg_scale
         * cat_scale
     )
-    assert (
-        pytest.approx(100, rel=0.01) == actual_land_use_2022
-    ), f"Land_use in 2022 should be 100 (all P1), got {actual_land_use_2022}"
+    assert pytest.approx(100, rel=0.01) == actual_land_use_2022, (
+        f"Land_use in 2022 should be 100 (all P1), got {actual_land_use_2022}"
+    )
 
 
 def test_time_specific_flow_limits_respected():
@@ -890,9 +891,9 @@ def test_time_specific_flow_limits_respected():
 
     # Without limits, P1 should handle all demand (lowest emissions)
     # Total CO2 = 30 * 1 = 30 kg
-    assert (
-        pytest.approx(30, rel=0.01) == obj_baseline
-    ), f"Baseline emissions should be 30, got {obj_baseline}"
+    assert pytest.approx(30, rel=0.01) == obj_baseline, (
+        f"Baseline emissions should be 30, got {obj_baseline}"
+    )
 
     # Test 1: MINIMUM flow limit - force higher emissions in 2022
     # Require CO2 >= 20 in 2022 (higher than optimal of 10)
@@ -923,14 +924,12 @@ def test_time_specific_flow_limits_respected():
 
     # Calculate actual CO2 emissions in 2022 (denormalize)
     fg_scale = solved_min.scales["foreground"]
-    actual_co2_2022 = (
-        pyo.value(solved_min.total_elementary_flow["CO2", 2022]) * fg_scale
-    )
+    actual_co2_2022 = pyo.value(solved_min.total_elementary_flow["CO2", 2022]) * fg_scale
 
     # Verify CO2 meets the minimum limit
-    assert (
-        actual_co2_2022 >= co2_min_2022 * 0.99
-    ), f"Actual CO2 in 2022 ({actual_co2_2022:.2f}) is below min limit ({co2_min_2022})"
+    assert actual_co2_2022 >= co2_min_2022 * 0.99, (
+        f"Actual CO2 in 2022 ({actual_co2_2022:.2f}) is below min limit ({co2_min_2022})"
+    )
 
     # Verify constraint is binding (optimizer should hit exactly the min)
     assert actual_co2_2022 <= co2_min_2022 * 1.01, (
@@ -941,9 +940,9 @@ def test_time_specific_flow_limits_respected():
     # Verify objective is higher than baseline (forced suboptimal in one year)
     # Expected: 2 years at 10 kg + 1 year at 20 kg = 40 kg
     expected_obj = 2 * 10 + 20  # 40 kg
-    assert (
-        pytest.approx(expected_obj, rel=0.01) == obj_min
-    ), f"Limited objective should be {expected_obj}, got {obj_min}"
+    assert pytest.approx(expected_obj, rel=0.01) == obj_min, (
+        f"Limited objective should be {expected_obj}, got {obj_min}"
+    )
 
     # Test 2: MAX flow limit below achievable minimum should be infeasible
     # Optimal CO2 per year is 10 kg (all P1). Limit to 8 kg should be infeasible.
@@ -1071,9 +1070,9 @@ def test_cumulative_flow_limits_respected():
     )
 
     # Verify total CO2 meets the minimum limit
-    assert (
-        total_co2 >= cumulative_min * 0.99
-    ), f"Total CO2 ({total_co2:.2f}) is below min limit ({cumulative_min})"
+    assert total_co2 >= cumulative_min * 0.99, (
+        f"Total CO2 ({total_co2:.2f}) is below min limit ({cumulative_min})"
+    )
 
     # Verify constraint is binding
     assert total_co2 <= cumulative_min * 1.01, (
@@ -1082,9 +1081,9 @@ def test_cumulative_flow_limits_respected():
     )
 
     # Verify objective matches expected (50 kg)
-    assert (
-        pytest.approx(cumulative_min, rel=0.01) == obj_min
-    ), f"Limited objective should be {cumulative_min}, got {obj_min}"
+    assert pytest.approx(cumulative_min, rel=0.01) == obj_min, (
+        f"Limited objective should be {cumulative_min}, got {obj_min}"
+    )
 
     # Test 2: MAX cumulative flow limit below achievable should be infeasible
     # Optimal total CO2 is 30 kg. Limit to 25 kg should be infeasible.
@@ -1167,9 +1166,7 @@ def test_product_flow_limits_respected():
     results = solver.solve(model_limited, tee=False)
 
     # Model should be infeasible since demand (10) > production limit (5)
-    assert (
-        results.solver.termination_condition == pyo.TerminationCondition.infeasible
-    ), (
+    assert results.solver.termination_condition == pyo.TerminationCondition.infeasible, (
         f"Model should be infeasible when product limit < demand, "
         f"got {results.solver.termination_condition}"
     )
@@ -1188,9 +1185,7 @@ def test_product_flow_limits_respected():
     results_cum = solver.solve(model_cumulative, tee=False)
 
     # Model should be infeasible since total demand (30) > cumulative limit (25)
-    assert (
-        results_cum.solver.termination_condition == pyo.TerminationCondition.infeasible
-    ), (
+    assert results_cum.solver.termination_condition == pyo.TerminationCondition.infeasible, (
         f"Model should be infeasible when cumulative product limit < total demand, "
         f"got {results_cum.solver.termination_condition}"
     )
@@ -1277,9 +1272,9 @@ def test_cumulative_flow_limits_background_inventory():
         pyo.value(solved_baseline.scaled_inventory["P1", "rare_element", t]) * fg_scale
         for t in solved_baseline.SYSTEM_TIME
     )
-    assert (
-        pytest.approx(15, rel=0.01) == total_inventory
-    ), f"Baseline total inventory should be 15, got {total_inventory}"
+    assert pytest.approx(15, rel=0.01) == total_inventory, (
+        f"Baseline total inventory should be 15, got {total_inventory}"
+    )
 
     # Now test with cumulative limit of 10 (less than the 15 needed)
     # This should make the model infeasible since rare_element comes from background
@@ -1303,9 +1298,7 @@ def test_cumulative_flow_limits_background_inventory():
     # - This generates 15 kg of rare_element from background (30 * 0.5)
     # - But cumulative limit is only 10 kg
     # Therefore, the constraint cannot be satisfied.
-    assert (
-        results.solver.termination_condition == pyo.TerminationCondition.infeasible
-    ), (
+    assert results.solver.termination_condition == pyo.TerminationCondition.infeasible, (
         f"Model should be infeasible when cumulative rare_element limit (10) < "
         f"required background flows (15), got {results.solver.termination_condition}. "
         "This indicates the cumulative flow constraint is not considering background inventory flows."
@@ -1385,9 +1378,7 @@ def test_time_specific_flow_limits_background_inventory():
     solver = pyo.SolverFactory("glpk")
     results = solver.solve(model_limited, tee=False)
 
-    assert (
-        results.solver.termination_condition == pyo.TerminationCondition.infeasible
-    ), (
+    assert results.solver.termination_condition == pyo.TerminationCondition.infeasible, (
         f"Model should be infeasible when time-specific rare_element limit (3) < "
         f"required background flows (5) in 2020, got {results.solver.termination_condition}. "
         "This indicates the time-specific flow constraint is not considering background inventory flows."
diff --git a/tests/test_serialization.py b/tests/test_serialization.py
index 64ea956..5792b5c 100644
--- a/tests/test_serialization.py
+++ b/tests/test_serialization.py
@@ -2,7 +2,6 @@
 Test serialization and deserialization of OptimizationModelInputs with tuple keys,
 and saving/loading of solved Pyomo models.
 """
-
 import json
 import tempfile
 from pathlib import Path
@@ -64,25 +63,22 @@ def test_json_serialization_round_trip(abstract_system_model_inputs):
 
             # Check that all keys are tuples in loaded data
             for key in loaded_dict.keys():
-                assert isinstance(
-                    key, tuple
-                ), f"Key in {field} should be tuple, got {type(key)}"
+                assert isinstance(key, tuple), (
+                    f"Key in {field} should be tuple, got {type(key)}"
+                )
 
             # Check that the dictionaries are equal
-            assert (
-                loaded_dict == original_dict
-            ), f"Field {field} not preserved after JSON round-trip"
+            assert loaded_dict == original_dict, (
+                f"Field {field} not preserved after JSON round-trip"
+            )
 
         # Verify operation_time_limits (values are tuples)
         if original_inputs.operation_time_limits is not None:
             for key, value in loaded_inputs.operation_time_limits.items():
-                assert isinstance(
-                    value, tuple
-                ), f"Value in operation_time_limits should be tuple, got {type(value)}"
-            assert (
-                loaded_inputs.operation_time_limits
-                == original_inputs.operation_time_limits
-            )
+                assert isinstance(value, tuple), (
+                    f"Value in operation_time_limits should be tuple, got {type(value)}"
+                )
+            assert loaded_inputs.operation_time_limits == original_inputs.operation_time_limits
 
         # Verify other fields
         assert loaded_inputs.PROCESS == original_inputs.PROCESS
@@ -155,22 +151,10 @@ def test_json_with_optional_fields():
         loaded_inputs = manager_loaded.load(str(json_path))
 
         # Verify optional fields with tuple keys are preserved
-        assert (
-            loaded_inputs.process_deployment_limits_max
-            == original_inputs.process_deployment_limits_max
-        )
-        assert (
-            loaded_inputs.process_deployment_limits_min
-            == original_inputs.process_deployment_limits_min
-        )
-        assert (
-            loaded_inputs.process_operation_limits_max
-            == original_inputs.process_operation_limits_max
-        )
-        assert (
-            loaded_inputs.process_operation_limits_min
-            == original_inputs.process_operation_limits_min
-        )
+        assert loaded_inputs.process_deployment_limits_max == original_inputs.process_deployment_limits_max
+        assert loaded_inputs.process_deployment_limits_min == original_inputs.process_deployment_limits_min
+        assert loaded_inputs.process_operation_limits_max == original_inputs.process_operation_limits_max
+        assert loaded_inputs.process_operation_limits_min == original_inputs.process_operation_limits_min
         assert loaded_inputs.process_coupling == original_inputs.process_coupling
 
 
@@ -241,15 +225,15 @@ def test_solved_model_save_and_load(solved_system_model):
             for t in model.SYSTEM_TIME:
                 original_install = pyo.value(model.var_installation[p, t])
                 loaded_install = pyo.value(loaded_model.var_installation[p, t])
-                assert (
-                    abs(original_install - loaded_install) < 1e-9
-                ), f"Installation value mismatch for ({p}, {t})"
+                assert abs(original_install - loaded_install) < 1e-9, (
+                    f"Installation value mismatch for ({p}, {t})"
+                )
 
                 original_op = get_total_operation(model, p, t)
                 loaded_op = get_total_operation(loaded_model, p, t)
-                assert (
-                    abs(original_op - loaded_op) < 1e-9
-                ), f"Operation value mismatch for ({p}, {t})"
+                assert abs(original_op - loaded_op) < 1e-9, (
+                    f"Operation value mismatch for ({p}, {t})"
+                )
 
         # Verify the loaded model works with PostProcessor
         pp = PostProcessor(loaded_model)
diff --git a/tests/test_single_route_lca_comparison.py b/tests/test_single_route_lca_comparison.py
index 287b0fe..32eff90 100644
--- a/tests/test_single_route_lca_comparison.py
+++ b/tests/test_single_route_lca_comparison.py
@@ -4,7 +4,6 @@
 This is the simplest possible test: one product, one production route, no optimization.
 Verifies that optimex produces the same results as bw2calc.LCA for a trivial case.
 """
-
 from datetime import datetime
 
 import bw2calc as bc
@@ -25,100 +24,84 @@ def setup_single_route_system():
 
     # Biosphere
     bio_db = bd.Database("biosphere3")
-    bio_db.write(
-        {
-            ("biosphere3", "CO2"): {
-                "type": "emission",
-                "name": "carbon dioxide",
-            },
-        }
-    )
+    bio_db.write({
+        ("biosphere3", "CO2"): {
+            "type": "emission",
+            "name": "carbon dioxide",
+        },
+    })
     bio_db.register()
 
     # Background database
     bg_2020 = bd.Database("db_2020")
-    bg_2020.write(
-        {
-            ("db_2020", "electricity"): {
-                "name": "electricity production",
-                "location": "GLO",
-                "reference product": "electricity",
-                "exchanges": [
-                    {
-                        "amount": 1,
-                        "type": "production",
-                        "input": ("db_2020", "electricity"),
-                    },
-                    {
-                        "amount": 0.5,
-                        "type": "biosphere",
-                        "input": ("biosphere3", "CO2"),
-                    },
-                ],
-            },
-        }
-    )
+    bg_2020.write({
+        ("db_2020", "electricity"): {
+            "name": "electricity production",
+            "location": "GLO",
+            "reference product": "electricity",
+            "exchanges": [
+                {"amount": 1, "type": "production", "input": ("db_2020", "electricity")},
+                {"amount": 0.5, "type": "biosphere", "input": ("biosphere3", "CO2")},
+            ],
+        },
+    })
     bg_2020.metadata["representative_time"] = datetime(2020, 1, 1).isoformat()
     bg_2020.register()
 
     # Foreground with single product and single route
     fg = bd.Database("foreground")
-    fg.write(
-        {
-            # Product node
-            ("foreground", "Widget"): {
-                "name": "Widget",
-                "unit": "kg",
-                "type": bd.labels.product_node_default,
-            },
-            # Single production route
-            ("foreground", "Widget_Route1"): {
-                "name": "Widget production, Route 1",
-                "location": "GLO",
-                "type": bd.labels.process_node_default,
-                "operation_time_limits": (1, 2),  # Operation phase at process times 1-2
-                "exchanges": [
-                    {
-                        "amount": 1,  # Produces 1 kg Widget
-                        "type": bd.labels.production_edge_default,
-                        "input": ("foreground", "Widget"),
-                        "temporal_distribution": TemporalDistribution(
-                            date=np.array([0, 1, 2, 3], dtype="timedelta64[Y]"),
-                            amount=np.array([0, 0.5, 0.5, 0]),  # Sums to 1.0
-                        ),
-                        "operation": True,
-                    },
-                    {
-                        "amount": 10,  # Consumes 10 kWh electricity at construction
-                        "type": bd.labels.consumption_edge_default,
-                        "input": ("db_2020", "electricity"),
-                        "temporal_distribution": TemporalDistribution(
-                            date=np.array([0, 1, 2, 3], dtype="timedelta64[Y]"),
-                            amount=np.array([1, 0, 0, 0]),  # All at construction
-                        ),
-                    },
-                    {
-                        "amount": 5,  # Emits 5 kg CO2 during operation
-                        "type": bd.labels.biosphere_edge_default,
-                        "input": ("biosphere3", "CO2"),
-                        "temporal_distribution": TemporalDistribution(
-                            date=np.array([0, 1, 2, 3], dtype="timedelta64[Y]"),
-                            amount=np.array([0, 0.5, 0.5, 0]),  # During operation
-                        ),
-                        "operation": True,
-                    },
-                ],
-            },
-        }
-    )
+    fg.write({
+        # Product node
+        ("foreground", "Widget"): {
+            "name": "Widget",
+            "unit": "kg",
+            "type": bd.labels.product_node_default,
+        },
+        # Single production route
+        ("foreground", "Widget_Route1"): {
+            "name": "Widget production, Route 1",
+            "location": "GLO",
+            "type": bd.labels.process_node_default,
+            "operation_time_limits": (1, 2),  # Operation phase at process times 1-2
+            "exchanges": [
+                {
+                    "amount": 1,  # Produces 1 kg Widget
+                    "type": bd.labels.production_edge_default,
+                    "input": ("foreground", "Widget"),
+                    "temporal_distribution": TemporalDistribution(
+                        date=np.array([0, 1, 2, 3], dtype="timedelta64[Y]"),
+                        amount=np.array([0, 0.5, 0.5, 0]),  # Sums to 1.0
+                    ),
+                    "operation": True,
+                },
+                {
+                    "amount": 10,  # Consumes 10 kWh electricity at construction
+                    "type": bd.labels.consumption_edge_default,
+                    "input": ("db_2020", "electricity"),
+                    "temporal_distribution": TemporalDistribution(
+                        date=np.array([0, 1, 2, 3], dtype="timedelta64[Y]"),
+                        amount=np.array([1, 0, 0, 0]),  # All at construction
+                    ),
+                },
+                {
+                    "amount": 5,  # Emits 5 kg CO2 during operation
+                    "type": bd.labels.biosphere_edge_default,
+                    "input": ("biosphere3", "CO2"),
+                    "temporal_distribution": TemporalDistribution(
+                        date=np.array([0, 1, 2, 3], dtype="timedelta64[Y]"),
+                        amount=np.array([0, 0.5, 0.5, 0]),  # During operation
+                    ),
+                    "operation": True,
+                },
+            ],
+        },
+    })
     fg.register()
 
     # Impact method
-    bd.Method(("GWP", "example")).write(
-        [
-            (("biosphere3", "CO2"), 1),
-        ]
-    )
+    bd.Method(("GWP", "example")).write([
+        (("biosphere3", "CO2"), 1),
+    ])
 
 
 def test_single_route_matches_standard_lca(setup_single_route_system):
@@ -143,9 +126,7 @@ def test_single_route_matches_standard_lca(setup_single_route_system):
     # optimex calculation
     years = range(2020, 2030)
     td_demand = TemporalDistribution(
-        date=np.array(
-            [datetime(year, 1, 1).isoformat() for year in years], dtype="datetime64[s]"
-        ),
+        date=np.array([datetime(year, 1, 1).isoformat() for year in years], dtype='datetime64[s]'),
         amount=np.asarray([0, 0, 100, 0, 0, 0, 0, 0, 0, 0]),  # 100 kg at year 2022
     )
 
@@ -180,25 +161,23 @@ def test_single_route_matches_standard_lca(setup_single_route_system):
     print(f"Difference: {abs(obj_real - expected_gwp)}")
 
     # They should match (within numerical tolerance)
-    assert (
-        pytest.approx(obj_real, rel=1e-3) == expected_gwp
-    ), f"optimex result ({obj_real}) should match standard LCA ({expected_gwp})"
+    assert pytest.approx(obj_real, rel=1e-3) == expected_gwp, (
+        f"optimex result ({obj_real}) should match standard LCA ({expected_gwp})"
+    )
 
     # Additional check: verify postprocessing extracts correct unscaled values
     pp = postprocessing.PostProcessor(model)
     df_impacts = pp.get_impacts()
 
     # Sum all climate_change impacts across all processes and times
-    if "climate_change" in df_impacts.columns.get_level_values(0):
-        climate_change_cols = [
-            col for col in df_impacts.columns if col[0] == "climate_change"
-        ]
+    if 'climate_change' in df_impacts.columns.get_level_values(0):
+        climate_change_cols = [col for col in df_impacts.columns if col[0] == 'climate_change']
         total_cc_from_pp = df_impacts[climate_change_cols].sum().sum()
 
         print(f"\nPostprocessing climate_change total: {total_cc_from_pp}")
         print(f"Expected (from LCA): {expected_gwp}")
 
         # Postprocessing should also match standard LCA
-        assert (
-            pytest.approx(total_cc_from_pp, rel=1e-3) == expected_gwp
-        ), f"Postprocessing climate_change sum ({total_cc_from_pp}) should match standard LCA ({expected_gwp})"
+        assert pytest.approx(total_cc_from_pp, rel=1e-3) == expected_gwp, (
+            f"Postprocessing climate_change sum ({total_cc_from_pp}) should match standard LCA ({expected_gwp})"
+        )
diff --git a/tests/test_two_level_supply_chain.py b/tests/test_two_level_supply_chain.py
index f3bccf0..e100828 100644
--- a/tests/test_two_level_supply_chain.py
+++ b/tests/test_two_level_supply_chain.py
@@ -5,7 +5,6 @@
 This test verifies that optimex produces the same results as a standard LCA
 when there are no optimization choices (single route).
 """
-
 from datetime import datetime
 
 import bw2calc as bc
@@ -26,144 +25,137 @@ def setup_two_level_system():
 
     # Biosphere
     bio_db = bd.Database("biosphere3")
-    bio_db.write(
-        {
-            ("biosphere3", "CO2"): {
-                "type": "emission",
-                "name": "carbon dioxide",
-            },
-        }
-    )
+    bio_db.write({
+        ("biosphere3", "CO2"): {
+            "type": "emission",
+            "name": "carbon dioxide",
+        },
+    })
     bio_db.register()
 
     # Background database
     bg_2020 = bd.Database("db_2020")
-    bg_2020.write(
-        {
-            ("db_2020", "I1"): {
-                "name": "node I1",
-                "location": "somewhere",
-                "reference product": "I1",
-                "exchanges": [
-                    {"amount": 1, "type": "production", "input": ("db_2020", "I1")},
-                    {"amount": 1, "type": "biosphere", "input": ("biosphere3", "CO2")},
-                ],
-            },
-        }
-    )
+    bg_2020.write({
+        ("db_2020", "I1"): {
+            "name": "node I1",
+            "location": "somewhere",
+            "reference product": "I1",
+            "exchanges": [
+                {"amount": 1, "type": "production", "input": ("db_2020", "I1")},
+                {"amount": 1, "type": "biosphere", "input": ("biosphere3", "CO2")},
+            ],
+        },
+    })
     bg_2020.metadata["representative_time"] = datetime(2020, 1, 1).isoformat()
     bg_2020.register()
 
     # Foreground with two-level supply chain
     fg = bd.Database("foreground")
-    fg.write(
-        {
-            # Product 1 (intermediate product)
-            ("foreground", "Product 1"): {
-                "name": "Product 1",
-                "unit": "kg",
-                "type": bd.labels.product_node_default,
-            },
-            ("foreground", "P1R1"): {
-                "name": "Product 1 production, Route 1",
-                "location": "somewhere",
-                "type": bd.labels.process_node_default,
-                "operation_time_limits": (1, 2),
-                "exchanges": [
-                    {
-                        "amount": 1,
-                        "type": bd.labels.production_edge_default,
-                        "input": ("foreground", "Product 1"),
-                        "temporal_distribution": TemporalDistribution(
-                            date=np.array(range(4), dtype="timedelta64[Y]"),
-                            amount=np.array([0, 0.5, 0.5, 0]),
-                        ),
-                        "operation": True,
-                    },
-                    {
-                        "amount": 27.5,
-                        "type": bd.labels.consumption_edge_default,
-                        "input": ("db_2020", "I1"),
-                        "temporal_distribution": TemporalDistribution(
-                            date=np.array(range(4), dtype="timedelta64[Y]"),
-                            amount=np.array([1, 0, 0, 0]),
-                        ),
-                    },
-                    {
-                        "amount": 20,
-                        "type": bd.labels.biosphere_edge_default,
-                        "input": ("biosphere3", "CO2"),
-                        "temporal_distribution": TemporalDistribution(
-                            date=np.array(range(4), dtype="timedelta64[Y]"),
-                            amount=np.array([0, 0.5, 0.5, 0]),
-                        ),
-                        "operation": True,
-                    },
-                ],
-            },
-            # Product 2 (final product, consumes Product 1)
-            ("foreground", "Product 2"): {
-                "name": "Product 2",
-                "unit": "kg",
-                "type": bd.labels.product_node_default,
-            },
-            ("foreground", "P2R1"): {
-                "name": "Product 2 production, Route 1",
-                "location": "somewhere",
-                "type": bd.labels.process_node_default,
-                "operation_time_limits": (1, 2),
-                "exchanges": [
-                    {
-                        "amount": 1,
-                        "type": bd.labels.production_edge_default,
-                        "input": ("foreground", "Product 2"),
-                        "temporal_distribution": TemporalDistribution(
-                            date=np.array(range(4), dtype="timedelta64[Y]"),
-                            amount=np.array([0, 0.5, 0.5, 0]),
-                        ),
-                        "operation": True,
-                    },
-                    {
-                        "amount": 1,
-                        "type": bd.labels.consumption_edge_default,
-                        "input": ("foreground", "Product 1"),
-                        "temporal_distribution": TemporalDistribution(
-                            date=np.array(range(4), dtype="timedelta64[Y]"),
-                            amount=np.array([0, 0.5, 0.5, 0]),
-                        ),
-                        "operation": True,
-                    },
-                    {
-                        "amount": 15.5,
-                        "type": bd.labels.consumption_edge_default,
-                        "input": ("db_2020", "I1"),
-                        "temporal_distribution": TemporalDistribution(
-                            date=np.array(range(4), dtype="timedelta64[Y]"),
-                            amount=np.array([1, 0, 0, 0]),
-                        ),
-                    },
-                    {
-                        "amount": 20,
-                        "type": bd.labels.biosphere_edge_default,
-                        "input": ("biosphere3", "CO2"),
-                        "temporal_distribution": TemporalDistribution(
-                            date=np.array(range(4), dtype="timedelta64[Y]"),
-                            amount=np.array([0, 0.5, 0.5, 0]),
-                        ),
-                        "operation": True,
-                    },
-                ],
-            },
-        }
-    )
+    fg.write({
+        # Product 1 (intermediate product)
+        ("foreground", "Product 1"): {
+            "name": "Product 1",
+            "unit": "kg",
+            "type": bd.labels.product_node_default,
+        },
+        ("foreground", "P1R1"): {
+            "name": "Product 1 production, Route 1",
+            "location": "somewhere",
+            "type": bd.labels.process_node_default,
+            "operation_time_limits": (1, 2),
+            "exchanges": [
+                {
+                    "amount": 1,
+                    "type": bd.labels.production_edge_default,
+                    "input": ("foreground", "Product 1"),
+                    "temporal_distribution": TemporalDistribution(
+                        date=np.array(range(4), dtype="timedelta64[Y]"),
+                        amount=np.array([0, 0.5, 0.5, 0]),
+                    ),
+                    "operation": True,
+                },
+                {
+                    "amount": 27.5,
+                    "type": bd.labels.consumption_edge_default,
+                    "input": ("db_2020", "I1"),
+                    "temporal_distribution": TemporalDistribution(
+                        date=np.array(range(4), dtype="timedelta64[Y]"),
+                        amount=np.array([1, 0, 0, 0]),
+                    ),
+                },
+                {
+                    "amount": 20,
+                    "type": bd.labels.biosphere_edge_default,
+                    "input": ("biosphere3", "CO2"),
+                    "temporal_distribution": TemporalDistribution(
+                        date=np.array(range(4), dtype="timedelta64[Y]"),
+                        amount=np.array([0, 0.5, 0.5, 0]),
+                    ),
+                    "operation": True,
+                },
+            ],
+        },
+
+        # Product 2 (final product, consumes Product 1)
+        ("foreground", "Product 2"): {
+            "name": "Product 2",
+            "unit": "kg",
+            "type": bd.labels.product_node_default,
+        },
+        ("foreground", "P2R1"): {
+            "name": "Product 2 production, Route 1",
+            "location": "somewhere",
+            "type": bd.labels.process_node_default,
+            "operation_time_limits": (1, 2),
+            "exchanges": [
+                {
+                    "amount": 1,
+                    "type": bd.labels.production_edge_default,
+                    "input": ("foreground", "Product 2"),
+                    "temporal_distribution": TemporalDistribution(
+                        date=np.array(range(4), dtype="timedelta64[Y]"),
+                        amount=np.array([0, 0.5, 0.5, 0]),
+                    ),
+                    "operation": True,
+                },
+                {
+                    "amount": 1,
+                    "type": bd.labels.consumption_edge_default,
+                    "input": ("foreground", "Product 1"),
+                    "temporal_distribution": TemporalDistribution(
+                        date=np.array(range(4), dtype="timedelta64[Y]"),
+                        amount=np.array([0, 0.5, 0.5, 0]),
+                    ),
+                    "operation": True,
+                },
+                {
+                    "amount": 15.5,
+                    "type": bd.labels.consumption_edge_default,
+                    "input": ("db_2020", "I1"),
+                    "temporal_distribution": TemporalDistribution(
+                        date=np.array(range(4), dtype="timedelta64[Y]"),
+                        amount=np.array([1, 0, 0, 0]),
+                    ),
+                },
+                {
+                    "amount": 20,
+                    "type": bd.labels.biosphere_edge_default,
+                    "input": ("biosphere3", "CO2"),
+                    "temporal_distribution": TemporalDistribution(
+                        date=np.array(range(4), dtype="timedelta64[Y]"),
+                        amount=np.array([0, 0.5, 0.5, 0]),
+                    ),
+                    "operation": True,
+                },
+            ],
+        },
+    })
     fg.register()
 
     # Impact method
-    bd.Method(("GWP", "example")).write(
-        [
-            (("biosphere3", "CO2"), 1),
-        ]
-    )
+    bd.Method(("GWP", "example")).write([
+        (("biosphere3", "CO2"), 1),
+    ])
 
 
 def test_two_level_supply_chain_matches_lca(setup_two_level_system):
@@ -181,9 +173,7 @@ def test_two_level_supply_chain_matches_lca(setup_two_level_system):
     # optimex calculation
     years = range(2020, 2030)
     td_demand = TemporalDistribution(
-        date=np.array(
-            [datetime(year, 1, 1).isoformat() for year in years], dtype="datetime64[s]"
-        ),
+        date=np.array([datetime(year, 1, 1).isoformat() for year in years], dtype='datetime64[s]'),
         amount=np.asarray([0, 0, 10, 0, 0, 0, 0, 0, 0, 0]),
     )
 
@@ -218,28 +208,26 @@ def test_two_level_supply_chain_matches_lca(setup_two_level_system):
     print(f"Difference: {abs(obj_real - expected_gwp)}")
 
     # They should match (within numerical tolerance)
-    assert (
-        pytest.approx(obj_real, rel=1e-3) == expected_gwp
-    ), f"optimex result ({obj_real}) should match standard LCA ({expected_gwp})"
+    assert pytest.approx(obj_real, rel=1e-3) == expected_gwp, (
+        f"optimex result ({obj_real}) should match standard LCA ({expected_gwp})"
+    )
 
     # Additional check: verify postprocessing extracts correct unscaled values
     pp = postprocessing.PostProcessor(model)
     df_impacts = pp.get_impacts()
 
     # Sum all climate_change impacts across all processes and times
-    if "climate_change" in df_impacts.columns.get_level_values(0):
-        climate_change_cols = [
-            col for col in df_impacts.columns if col[0] == "climate_change"
-        ]
+    if 'climate_change' in df_impacts.columns.get_level_values(0):
+        climate_change_cols = [col for col in df_impacts.columns if col[0] == 'climate_change']
         total_cc_from_pp = df_impacts[climate_change_cols].sum().sum()
 
         print(f"\nPostprocessing climate_change total: {total_cc_from_pp}")
         print(f"Expected (from LCA): {expected_gwp}")
 
         # Postprocessing should also match standard LCA
-        assert (
-            pytest.approx(total_cc_from_pp, rel=1e-3) == expected_gwp
-        ), f"Postprocessing climate_change sum ({total_cc_from_pp}) should match standard LCA ({expected_gwp})"
+        assert pytest.approx(total_cc_from_pp, rel=1e-3) == expected_gwp, (
+            f"Postprocessing climate_change sum ({total_cc_from_pp}) should match standard LCA ({expected_gwp})"
+        )
 
 
 def test_two_level_supply_chain_multi_temporal_demand(setup_two_level_system):
@@ -263,9 +251,9 @@ def test_two_level_supply_chain_multi_temporal_demand(setup_two_level_system):
     }
 
     # Calculate expected total impact from standard LCA
-    print("\n" + "=" * 80)
+    print("\n" + "="*80)
     print("STANDARD LCA CALCULATIONS")
-    print("=" * 80)
+    print("="*80)
     expected_gwp_total = 0
     for year, amount in demand_schedule.items():
         lca = bc.LCA({product_2: amount}, method=("GWP", "example"))
@@ -281,9 +269,7 @@ def test_two_level_supply_chain_multi_temporal_demand(setup_two_level_system):
     demand_amounts = [demand_schedule.get(year, 0) for year in years]
 
     td_demand = TemporalDistribution(
-        date=np.array(
-            [datetime(year, 1, 1).isoformat() for year in years], dtype="datetime64[s]"
-        ),
+        date=np.array([datetime(year, 1, 1).isoformat() for year in years], dtype='datetime64[s]'),
         amount=np.asarray(demand_amounts),
     )
 
@@ -314,17 +300,17 @@ def test_two_level_supply_chain_multi_temporal_demand(setup_two_level_system):
 
     _, obj_real, results = optimizer.solve_model(model, solver_name="glpk")
 
-    print("\n" + "=" * 80)
+    print("\n" + "="*80)
     print("OPTIMEX RESULTS")
-    print("=" * 80)
+    print("="*80)
     print(f"optimex GWP: {obj_real:.2f} kg CO2")
     print(f"Expected GWP: {expected_gwp_total:.2f} kg CO2")
     print(f"Difference: {abs(obj_real - expected_gwp_total):.4f}")
 
     # Verify total impact matches
-    assert (
-        pytest.approx(obj_real, rel=1e-3) == expected_gwp_total
-    ), f"optimex result ({obj_real}) should match sum of standard LCA ({expected_gwp_total})"
+    assert pytest.approx(obj_real, rel=1e-3) == expected_gwp_total, (
+        f"optimex result ({obj_real}) should match sum of standard LCA ({expected_gwp_total})"
+    )
 
     # Extract and verify temporal distribution of results
     pp = postprocessing.PostProcessor(model)
@@ -332,14 +318,10 @@ def test_two_level_supply_chain_multi_temporal_demand(setup_two_level_system):
     df_operation = pp.get_operation()
     df_impacts = pp.get_impacts()
 
-    print("\n" + "=" * 80)
+    print("\n" + "="*80)
     print("INSTALLATION (Product 2 production)")
-    print("=" * 80)
-    p2_process_id = [
-        p
-        for p in model.PROCESS
-        if model.process_names[p] == "Product 2 production, Route 1"
-    ][0]
+    print("="*80)
+    p2_process_id = [p for p in model.PROCESS if model.process_names[p] == "Product 2 production, Route 1"][0]
     if p2_process_id in df_installation.columns:
         print(df_installation[[p2_process_id]])
 
@@ -349,16 +331,12 @@ def test_two_level_supply_chain_multi_temporal_demand(setup_two_level_system):
             # Check that there's installation in years before demand
             years_before = [y for y in df_installation.index if y < year]
             if years_before:
-                total_installation_before = df_installation.loc[
-                    years_before, p2_process_id
-                ].sum()
-                print(
-                    f"\nInstallation before {year} (demand={amount}): {total_installation_before:.2f}"
-                )
+                total_installation_before = df_installation.loc[years_before, p2_process_id].sum()
+                print(f"\nInstallation before {year} (demand={amount}): {total_installation_before:.2f}")
 
-    print("\n" + "=" * 80)
+    print("\n" + "="*80)
     print("OPERATION (Product 2 production)")
-    print("=" * 80)
+    print("="*80)
     if p2_process_id in df_operation.columns:
         print(df_operation[[p2_process_id]])
 
@@ -368,18 +346,14 @@ def test_two_level_supply_chain_multi_temporal_demand(setup_two_level_system):
                 operation = df_operation.loc[year, p2_process_id]
                 print(f"\nYear {year}: Operation={operation:.2f}, Demand={amount}")
                 # Operation should equal demand for single-route system
-                assert (
-                    pytest.approx(operation, rel=1e-2) == amount
-                ), f"Operation at {year} ({operation}) should match demand ({amount})"
+                assert pytest.approx(operation, rel=1e-2) == amount, (
+                    f"Operation at {year} ({operation}) should match demand ({amount})"
+                )
 
-    print("\n" + "=" * 80)
+    print("\n" + "="*80)
     print("INTERNAL DEMAND VERIFICATION (Product 1)")
-    print("=" * 80)
-    p1_process_id = [
-        p
-        for p in model.PROCESS
-        if model.process_names[p] == "Product 1 production, Route 1"
-    ][0]
+    print("="*80)
+    p1_process_id = [p for p in model.PROCESS if model.process_names[p] == "Product 1 production, Route 1"][0]
 
     # Product 2 consumes 1 unit of Product 1 per unit produced
     # So Product 1 operation should match Product 2 operation
@@ -393,29 +367,25 @@ def test_two_level_supply_chain_multi_temporal_demand(setup_two_level_system):
         # They should match (Product 2 needs 1 unit of Product 1 per unit)
         for year in demand_schedule.keys():
             if year in df_operation.index:
-                assert (
-                    pytest.approx(p1_operation[year], rel=1e-2) == p2_operation[year]
-                ), (
+                assert pytest.approx(p1_operation[year], rel=1e-2) == p2_operation[year], (
                     f"Product 1 operation ({p1_operation[year]}) should match "
                     f"Product 2 operation ({p2_operation[year]}) at year {year}"
                 )
 
     # Verify postprocessing impact totals
-    if "climate_change" in df_impacts.columns.get_level_values(0):
-        climate_change_cols = [
-            col for col in df_impacts.columns if col[0] == "climate_change"
-        ]
+    if 'climate_change' in df_impacts.columns.get_level_values(0):
+        climate_change_cols = [col for col in df_impacts.columns if col[0] == 'climate_change']
         total_cc_from_pp = df_impacts[climate_change_cols].sum().sum()
 
-        print("\n" + "=" * 80)
+        print("\n" + "="*80)
         print("POSTPROCESSING VERIFICATION")
-        print("=" * 80)
+        print("="*80)
         print(f"Total impact from PostProcessor: {total_cc_from_pp:.2f}")
         print(f"Expected (from LCA): {expected_gwp_total:.2f}")
 
-        assert (
-            pytest.approx(total_cc_from_pp, rel=1e-3) == expected_gwp_total
-        ), f"Postprocessing total ({total_cc_from_pp}) should match expected ({expected_gwp_total})"
+        assert pytest.approx(total_cc_from_pp, rel=1e-3) == expected_gwp_total, (
+            f"Postprocessing total ({total_cc_from_pp}) should match expected ({expected_gwp_total})"
+        )
 
         # Verify impacts are distributed across correct years
         print("\nImpact distribution by year:")
@@ -424,6 +394,6 @@ def test_two_level_supply_chain_multi_temporal_demand(setup_two_level_system):
             if yearly_impacts[year] > 0.1:  # Only show non-negligible impacts
                 print(f"  {year}: {yearly_impacts[year]:.2f} kg CO2")
 
-    print("\n" + "=" * 80)
+    print("\n" + "="*80)
     print("TEST PASSED: Multi-temporal two-level supply chain works correctly!")
-    print("=" * 80)
+    print("="*80)
diff --git a/tests/test_vintage_deterministic.py b/tests/test_vintage_deterministic.py
index ef86824..5764a6f 100644
--- a/tests/test_vintage_deterministic.py
+++ b/tests/test_vintage_deterministic.py
@@ -18,9 +18,8 @@
 4. Sum up total impacts and compare with model results
 """
 
-import pyomo.environ as pyo
 import pytest
-
+import pyomo.environ as pyo
 from optimex import converter, optimizer
 
 
@@ -119,9 +118,9 @@ def test_single_process_production_equals_demand(self):
         for t in [2025, 2026, 2027]:
             var_op = get_total_operation(solved_model, "Plant", t)
             expected_operation = 100 / 10  # demand / production_rate
-            assert var_op == pytest.approx(
-                expected_operation, rel=0.001
-            ), f"At {t}: var_operation = {var_op}, expected {expected_operation}"
+            assert var_op == pytest.approx(expected_operation, rel=0.001), (
+                f"At {t}: var_operation = {var_op}, expected {expected_operation}"
+            )
 
     def test_vintage_evolution_impacts_with_manual_calculation(self):
         """
@@ -217,16 +216,14 @@ def test_vintage_evolution_impacts_with_manual_calculation(self):
 
         # Check var_operation
         var_op_2026 = get_total_operation(solved_model, "Plant", 2026)
-        assert var_op_2026 == pytest.approx(
-            1.0, rel=0.001
-        ), f"var_operation at 2026 = {var_op_2026}"
+        assert var_op_2026 == pytest.approx(1.0, rel=0.001), f"var_operation at 2026 = {var_op_2026}"
 
         # Check objective (total CO2)
         # At 2026: 9 kWh * 0.5 kg/kWh = 4.5 kg
         expected_impact = 4.5
-        assert objective == pytest.approx(
-            expected_impact, rel=0.01
-        ), f"Objective = {objective}, expected {expected_impact}"
+        assert objective == pytest.approx(expected_impact, rel=0.01), (
+            f"Objective = {objective}, expected {expected_impact}"
+        )
 
     def test_multi_year_with_background_evolution(self):
         """
@@ -312,9 +309,9 @@ def test_multi_year_with_background_evolution(self):
         # Verify var_operation = 1 at each year (demand/production_rate = 100/100)
         for t in [2025, 2026, 2027]:
             var_op = get_total_operation(solved_model, "Plant", t)
-            assert var_op == pytest.approx(
-                1.0, rel=0.001
-            ), f"var_operation at {t} = {var_op}, expected 1.0"
+            assert var_op == pytest.approx(1.0, rel=0.001), (
+                f"var_operation at {t} = {var_op}, expected 1.0"
+            )
 
         # Manual calculation:
         # 2025: vintage=2025, evolution=1.0, elec=10*1=10, CO2/kWh=0.5, impact=5
@@ -322,9 +319,9 @@ def test_multi_year_with_background_evolution(self):
         # 2027: vintage=2027, evolution=0.8, elec=10*0.8=8, CO2/kWh=0.3, impact=2.4
         # Total = 11
         expected_impact = 5 + 3.6 + 2.4
-        assert objective == pytest.approx(
-            expected_impact, rel=0.01
-        ), f"Objective = {objective}, expected {expected_impact}"
+        assert objective == pytest.approx(expected_impact, rel=0.01), (
+            f"Objective = {objective}, expected {expected_impact}"
+        )
 
     def test_simple_no_vintage_baseline(self):
         """
@@ -392,9 +389,9 @@ def test_simple_no_vintage_baseline(self):
 
         # Impact = electricity * CO2_per_kWh = 10 * 0.5 = 5
         expected_impact = 5.0
-        assert objective == pytest.approx(
-            expected_impact, rel=0.001
-        ), f"Objective = {objective}, expected {expected_impact}"
+        assert objective == pytest.approx(expected_impact, rel=0.001), (
+            f"Objective = {objective}, expected {expected_impact}"
+        )
 
     def test_vintage_improvements_reduces_impact(self):
         """
@@ -465,9 +462,7 @@ def test_vintage_improvements_reduces_impact(self):
             ("Plant", "electricity", 2025): 1.0,
             ("Plant", "electricity", 2027): 0.5,  # 50% less electricity
         }
-        with_evolution_inputs = converter.OptimizationModelInputs(
-            **with_evolution_config
-        )
+        with_evolution_inputs = converter.OptimizationModelInputs(**with_evolution_config)
         with_evolution_model = optimizer.create_model(
             inputs=with_evolution_inputs,
             objective_category="GWP",
@@ -479,9 +474,9 @@ def test_vintage_improvements_reduces_impact(self):
 
         # No evolution: 3 years * 10 kWh * 0.5 = 15 kg CO2
         expected_no_evolution = 15.0
-        assert no_evolution_obj == pytest.approx(
-            expected_no_evolution, rel=0.01
-        ), f"No evolution impact = {no_evolution_obj}, expected {expected_no_evolution}"
+        assert no_evolution_obj == pytest.approx(expected_no_evolution, rel=0.01), (
+            f"No evolution impact = {no_evolution_obj}, expected {expected_no_evolution}"
+        )
 
         # With evolution:
         # 2025: 10 * 1.0 * 0.5 = 5
@@ -489,9 +484,9 @@ def test_vintage_improvements_reduces_impact(self):
         # 2027: 10 * 0.5 * 0.5 = 2.5
         # Total = 11.25
         expected_with_evolution = 5 + 3.75 + 2.5
-        assert with_evolution_obj == pytest.approx(
-            expected_with_evolution, rel=0.01
-        ), f"With evolution impact = {with_evolution_obj}, expected {expected_with_evolution}"
+        assert with_evolution_obj == pytest.approx(expected_with_evolution, rel=0.01), (
+            f"With evolution impact = {with_evolution_obj}, expected {expected_with_evolution}"
+        )
 
         # With evolution should be lower
         assert with_evolution_obj < no_evolution_obj, (
@@ -587,9 +582,9 @@ def test_postprocessor_production_matches_demand(self):
             print(f"t={t}: production={production}, demand={demand}")
 
             # This assertion will PASS now that postprocessor uses 4D calculation
-            assert production == pytest.approx(
-                demand, rel=0.01
-            ), f"Production {production} should match demand {demand} at t={t}"
+            assert production == pytest.approx(demand, rel=0.01), (
+                f"Production {production} should match demand {demand} at t={t}"
+            )
 
     def test_vintage_improvements_only_affects_specified_flows(self):
         """
@@ -700,9 +695,9 @@ def test_vintage_improvements_only_affects_specified_flows(self):
             demand = 600
             print(f"t={t}: total_production={total_production}, demand={demand}")
 
-            assert total_production == pytest.approx(
-                demand, rel=0.01
-            ), f"Total production {total_production} should match demand {demand} at t={t}"
+            assert total_production == pytest.approx(demand, rel=0.01), (
+                f"Total production {total_production} should match demand {demand} at t={t}"
+            )
 
         # Also verify var_operation values are reasonable
         # With production_rate = 300 per installation for 3D path:
@@ -807,11 +802,11 @@ def test_single_foreground_process_simple_vintage(self):
 
         model_inputs = converter.OptimizationModelInputs(**inputs)
         model = optimizer.create_model(
-            inputs=model_inputs, objective_category="GWP", name="simple_vintage_test"
-        )
-        solved, obj, results = optimizer.solve_model(
-            model, solver_name="glpk", tee=False
+            inputs=model_inputs,
+            objective_category="GWP",
+            name="simple_vintage_test"
         )
+        solved, obj, results = optimizer.solve_model(model, solver_name="glpk", tee=False)
 
         assert results.solver.termination_condition == pyo.TerminationCondition.optimal
 
@@ -820,15 +815,13 @@ def test_single_foreground_process_simple_vintage(self):
         production_rate = 1.0
         actual_production = production_rate * operation_2022
 
-        assert actual_production == pytest.approx(
-            100.0, rel=1e-6
-        ), f"Production ({actual_production}) must exactly equal demand (100)"
+        assert actual_production == pytest.approx(100.0, rel=1e-6), \
+            f"Production ({actual_production}) must exactly equal demand (100)"
 
         # Verify total impact: 100 × 1.5 × 0.5 = 75 kg CO2
         expected_impact = 100 * 1.5 * 0.5
-        assert obj == pytest.approx(
-            expected_impact, rel=1e-5
-        ), f"Impact ({obj}) should match manual calculation ({expected_impact})"
+        assert obj == pytest.approx(expected_impact, rel=1e-5), \
+            f"Impact ({obj}) should match manual calculation ({expected_impact})"
 
     def test_single_process_with_background_vintage(self):
         """
@@ -887,18 +880,18 @@ def test_single_process_with_background_vintage(self):
                 ("grid_2022", 2021): 0.5,
                 ("grid_2022", 2022): 1.0,
             },
-            "characterization": {("GWP", "CO2", t): 1.0 for t in [2020, 2021, 2022]},
+            "characterization": {
+                ("GWP", "CO2", t): 1.0 for t in [2020, 2021, 2022]
+            },
         }
 
         model_inputs = converter.OptimizationModelInputs(**inputs)
         model = optimizer.create_model(
             inputs=model_inputs,
             objective_category="GWP",
-            name="background_vintage_test",
-        )
-        solved, obj, results = optimizer.solve_model(
-            model, solver_name="glpk", tee=False
+            name="background_vintage_test"
         )
+        solved, obj, results = optimizer.solve_model(model, solver_name="glpk", tee=False)
 
         assert results.solver.termination_condition == pyo.TerminationCondition.optimal
 
@@ -907,15 +900,13 @@ def test_single_process_with_background_vintage(self):
         production_rate = 1.0
         actual_production = production_rate * operation_2021
 
-        assert actual_production == pytest.approx(
-            50.0, rel=1e-6
-        ), f"Production ({actual_production}) must exactly equal demand (50)"
+        assert actual_production == pytest.approx(50.0, rel=1e-6), \
+            f"Production ({actual_production}) must exactly equal demand (50)"
 
         # Manual: 50 × 10 × 0.75 = 375 kg CO2
         expected_impact = 50 * 10 * 0.75
-        assert obj == pytest.approx(
-            expected_impact, rel=1e-5
-        ), f"Impact ({obj}) should match manual calculation ({expected_impact})"
+        assert obj == pytest.approx(expected_impact, rel=1e-5), \
+            f"Impact ({obj}) should match manual calculation ({expected_impact})"
 
     def test_combined_foreground_and_background_vintage(self):
         """
@@ -974,16 +965,18 @@ def test_combined_foreground_and_background_vintage(self):
                 ("grid_2022", 2021): 0.5,
                 ("grid_2022", 2022): 1.0,
             },
-            "characterization": {("GWP", "CO2", t): 1.0 for t in [2020, 2021, 2022]},
+            "characterization": {
+                ("GWP", "CO2", t): 1.0 for t in [2020, 2021, 2022]
+            },
         }
 
         model_inputs = converter.OptimizationModelInputs(**inputs)
         model = optimizer.create_model(
-            inputs=model_inputs, objective_category="GWP", name="combined_vintage_test"
-        )
-        solved, obj, results = optimizer.solve_model(
-            model, solver_name="glpk", tee=False
+            inputs=model_inputs,
+            objective_category="GWP",
+            name="combined_vintage_test"
         )
+        solved, obj, results = optimizer.solve_model(model, solver_name="glpk", tee=False)
 
         assert results.solver.termination_condition == pyo.TerminationCondition.optimal
 
@@ -991,15 +984,13 @@ def test_combined_foreground_and_background_vintage(self):
         operation_2022 = get_total_operation(solved, "Plant", 2022)
         actual_production = operation_2022
 
-        assert actual_production == pytest.approx(
-            100.0, rel=1e-6
-        ), f"Production ({actual_production}) must exactly equal demand (100)"
+        assert actual_production == pytest.approx(100.0, rel=1e-6), \
+            f"Production ({actual_production}) must exactly equal demand (100)"
 
         # Manual: 100 × 1.5 × 0.5 = 75 kg CO2
         expected_impact = 100 * 1.5 * 0.5
-        assert obj == pytest.approx(
-            expected_impact, rel=1e-5
-        ), f"Impact ({obj}) should match manual calculation ({expected_impact})"
+        assert obj == pytest.approx(expected_impact, rel=1e-5), \
+            f"Impact ({obj}) should match manual calculation ({expected_impact})"
 
     def test_multiple_installations_different_vintages(self):
         """
@@ -1059,17 +1050,21 @@ def test_multiple_installations_different_vintages(self):
             "background_inventory": {
                 ("grid", "electricity", "CO2"): 0.5,
             },
-            "mapping": {("grid", t): 1.0 for t in [2020, 2021, 2022]},
-            "characterization": {("GWP", "CO2", t): 1.0 for t in [2020, 2021, 2022]},
+            "mapping": {
+                ("grid", t): 1.0 for t in [2020, 2021, 2022]
+            },
+            "characterization": {
+                ("GWP", "CO2", t): 1.0 for t in [2020, 2021, 2022]
+            },
         }
 
         model_inputs = converter.OptimizationModelInputs(**inputs)
         model = optimizer.create_model(
-            inputs=model_inputs, objective_category="GWP", name="multiple_vintages_test"
-        )
-        solved, obj, results = optimizer.solve_model(
-            model, solver_name="glpk", tee=False
+            inputs=model_inputs,
+            objective_category="GWP",
+            name="multiple_vintages_test"
         )
+        solved, obj, results = optimizer.solve_model(model, solver_name="glpk", tee=False)
 
         assert results.solver.termination_condition == pyo.TerminationCondition.optimal
 
@@ -1077,14 +1072,11 @@ def test_multiple_installations_different_vintages(self):
         operation_2021 = get_total_operation(solved, "Plant", 2021)
         operation_2022 = get_total_operation(solved, "Plant", 2022)
 
-        assert operation_2021 == pytest.approx(
-            100.0, rel=1e-6
-        ), f"Production at t=2021 must equal demand (100)"
-        assert operation_2022 == pytest.approx(
-            150.0, rel=1e-6
-        ), f"Production at t=2022 must equal demand (150)"
+        assert operation_2021 == pytest.approx(100.0, rel=1e-6), \
+            f"Production at t=2021 must equal demand (100)"
+        assert operation_2022 == pytest.approx(150.0, rel=1e-6), \
+            f"Production at t=2022 must equal demand (150)"
 
         # Impact should be in reasonable range based on vintage improvements
-        assert (
-            200 <= obj <= 260
-        ), f"Impact ({obj}) should be in reasonable range based on vintage improvements"
+        assert 200 <= obj <= 260, \
+            f"Impact ({obj}) should be in reasonable range based on vintage improvements"
diff --git a/tests/test_vintage_parameters.py b/tests/test_vintage_parameters.py
index 54c2ea8..7a76653 100644
--- a/tests/test_vintage_parameters.py
+++ b/tests/test_vintage_parameters.py
@@ -28,12 +28,7 @@ def base_model_inputs(self):
             "SYSTEM_TIME": [2020, 2021, 2022, 2023, 2024, 2025],
             "CATEGORY": ["climate_change"],
             "operation_time_limits": {"EV": (1, 2)},
-            "demand": {
-                ("vkm", 2022): 100,
-                ("vkm", 2023): 100,
-                ("vkm", 2024): 100,
-                ("vkm", 2025): 100,
-            },
+            "demand": {("vkm", 2022): 100, ("vkm", 2023): 100, ("vkm", 2024): 100, ("vkm", 2025): 100},
             "foreground_technosphere": {
                 ("EV", "electricity", 0): 0,
                 ("EV", "electricity", 1): 50,
@@ -115,12 +110,8 @@ def test_model_accepts_foreground_technosphere_vintages(self, base_model_inputs)
         }
         model = converter.OptimizationModelInputs(**base_model_inputs)
         assert ("EV", "electricity", 1, 2020) in model.foreground_technosphere_vintages
-        assert (
-            model.foreground_technosphere_vintages[("EV", "electricity", 1, 2020)] == 60
-        )
-        assert (
-            model.foreground_technosphere_vintages[("EV", "electricity", 1, 2025)] == 40
-        )
+        assert model.foreground_technosphere_vintages[("EV", "electricity", 1, 2020)] == 60
+        assert model.foreground_technosphere_vintages[("EV", "electricity", 1, 2025)] == 40
         assert model.REFERENCE_VINTAGES == [2020, 2025]
 
     def test_model_accepts_foreground_biosphere_vintages(self, base_model_inputs):
@@ -182,9 +173,7 @@ def test_validation_flow_in_vintage_tensors_must_exist(self, base_model_inputs):
         with pytest.raises(ValueError, match="Invalid keys.*nonexistent_flow"):
             converter.OptimizationModelInputs(**base_model_inputs)
 
-    def test_validation_process_time_in_vintage_tensors_must_exist(
-        self, base_model_inputs
-    ):
+    def test_validation_process_time_in_vintage_tensors_must_exist(self, base_model_inputs):
         """Test that process times in vintage tensors must exist in PROCESS_TIME."""
         base_model_inputs["foreground_technosphere_vintages"] = {
             ("EV", "electricity", 99, 2020): 60,  # 99 not in PROCESS_TIME
@@ -307,9 +296,7 @@ def vintage_model_inputs(self):
             },
             "operation_flow": {("EV", "vkm"): True, ("EV", "electricity"): True},
             "background_inventory": {("db_2020", "electricity", "CO2"): 0.5},
-            "mapping": {
-                ("db_2020", t): 1.0 for t in [2020, 2021, 2022, 2023, 2024, 2025]
-            },
+            "mapping": {("db_2020", t): 1.0 for t in [2020, 2021, 2022, 2023, 2024, 2025]},
             "characterization": {
                 ("climate_change", "CO2", t): 1.0
                 for t in [2020, 2021, 2022, 2023, 2024, 2025]
@@ -333,9 +320,7 @@ def test_expand_foreground_technosphere_exact_vintage(self, vintage_model_inputs
         assert effective[("EV", "electricity", 1, 2025)] == 40
         assert effective[("EV", "electricity", 2, 2025)] == 40
 
-    def test_expand_foreground_technosphere_interpolated_vintage(
-        self, vintage_model_inputs
-    ):
+    def test_expand_foreground_technosphere_interpolated_vintage(self, vintage_model_inputs):
         """Test that effective tensor interpolates between reference vintages."""
         model = converter.OptimizationModelInputs(**vintage_model_inputs)
         effective = converter.expand_foreground_tensor_with_vintages(
@@ -434,9 +419,7 @@ def vintage_optimization_inputs(self):
             },
             "operation_flow": {("EV", "vkm"): True, ("EV", "electricity"): True},
             "background_inventory": {("db_2020", "electricity", "CO2"): 0.5},
-            "mapping": {
-                ("db_2020", t): 1.0 for t in [2020, 2021, 2022, 2023, 2024, 2025]
-            },
+            "mapping": {("db_2020", t): 1.0 for t in [2020, 2021, 2022, 2023, 2024, 2025]},
             "characterization": {
                 ("climate_change", "CO2", t): 1.0
                 for t in [2020, 2021, 2022, 2023, 2024, 2025]
@@ -465,9 +448,7 @@ def test_model_solves_with_vintages(self, vintage_optimization_inputs):
             objective_category="climate_change",
             name="test_vintage_model",
         )
-        solved_model, obj, results = optimizer.solve_model(
-            model, solver_name="glpk", tee=False
-        )
+        solved_model, obj, results = optimizer.solve_model(model, solver_name="glpk", tee=False)
         assert results.solver.termination_condition.name == "optimal"
 
     def test_later_vintage_has_lower_impact(self, vintage_optimization_inputs):
@@ -480,9 +461,8 @@ def test_later_vintage_has_lower_impact(self, vintage_optimization_inputs):
 
         So the optimizer should prefer later installations if given the choice.
         """
-        import pyomo.environ as pyo
-
         from optimex import optimizer
+        import pyomo.environ as pyo
 
         model_inputs = converter.OptimizationModelInputs(**vintage_optimization_inputs)
         model = optimizer.create_model(
@@ -490,9 +470,7 @@ def test_later_vintage_has_lower_impact(self, vintage_optimization_inputs):
             objective_category="climate_change",
             name="test_vintage_model",
         )
-        solved_model, obj, results = optimizer.solve_model(
-            model, solver_name="glpk", tee=False
-        )
+        solved_model, obj, results = optimizer.solve_model(model, solver_name="glpk", tee=False)
 
         # Get total installations at different years
         # Earlier installations (2020-2022) should have higher per-unit impact
@@ -501,10 +479,12 @@ def test_later_vintage_has_lower_impact(self, vintage_optimization_inputs):
         # Check that the model accounts for vintage differences
         # The exact behavior depends on demand timing and process lifetimes
         early_installations = sum(
-            pyo.value(solved_model.var_installation["EV", t]) for t in [2020, 2021]
+            pyo.value(solved_model.var_installation["EV", t])
+            for t in [2020, 2021]
         )
         late_installations = sum(
-            pyo.value(solved_model.var_installation["EV", t]) for t in [2023, 2024]
+            pyo.value(solved_model.var_installation["EV", t])
+            for t in [2023, 2024]
         )
 
         # Both should be non-negative (basic sanity check)
@@ -522,9 +502,8 @@ def test_optimizer_prefers_efficient_vintage(self):
         Optimizer should prefer installing in 2024 (gets 2025-interpolated efficiency)
         over installing earlier (gets worse efficiency).
         """
-        import pyomo.environ as pyo
-
         from optimex import optimizer
+        import pyomo.environ as pyo
 
         inputs = {
             "PROCESS": ["Plant"],
@@ -542,7 +521,7 @@ def test_optimizer_prefers_efficient_vintage(self):
             "foreground_technosphere": {},
             "foreground_technosphere_vintages": {
                 ("Plant", "electricity", 1, 2020): 100,  # Inefficient
-                ("Plant", "electricity", 1, 2025): 50,  # Efficient
+                ("Plant", "electricity", 1, 2025): 50,   # Efficient
             },
             "internal_demand_technosphere": {},
             "foreground_biosphere": {("Plant", "CO2", 0): 0},
@@ -551,10 +530,7 @@ def test_optimizer_prefers_efficient_vintage(self):
                 ("Plant", "output", 1, 2020): 100,
                 ("Plant", "output", 1, 2025): 100,
             },
-            "operation_flow": {
-                ("Plant", "output"): True,
-                ("Plant", "electricity"): True,
-            },
+            "operation_flow": {("Plant", "output"): True, ("Plant", "electricity"): True},
             "background_inventory": {("grid", "electricity", "CO2"): 1.0},
             "mapping": {("grid", t): 1.0 for t in range(2020, 2026)},
             "characterization": {("GWP", "CO2", t): 1.0 for t in range(2020, 2026)},
@@ -564,9 +540,7 @@ def test_optimizer_prefers_efficient_vintage(self):
         model = optimizer.create_model(
             inputs=model_inputs, objective_category="GWP", name="efficiency_test"
         )
-        solved, obj, results = optimizer.solve_model(
-            model, solver_name="glpk", tee=False
-        )
+        solved, obj, results = optimizer.solve_model(model, solver_name="glpk", tee=False)
         assert results.solver.termination_condition.name == "optimal"
 
         # Check installation pattern - should prefer later years (more efficient)
@@ -578,12 +552,9 @@ def test_optimizer_prefers_efficient_vintage(self):
         # 2024 vintage interpolates to 90% of way to 2025 efficiency: 100 - 0.8*(100-50) = 60
         # Earlier vintages are progressively worse
         # Optimizer should install as late as possible (2024)
-        assert (
-            installations[2024] > 0
-        ), "Optimizer should install in 2024 (most efficient vintage)"
-        assert sum(installations[t] for t in [2020, 2021, 2022]) == pytest.approx(
-            0, abs=1e-6
-        ), "Optimizer should avoid early installations (inefficient vintages)"
+        assert installations[2024] > 0, "Optimizer should install in 2024 (most efficient vintage)"
+        assert sum(installations[t] for t in [2020, 2021, 2022]) == pytest.approx(0, abs=1e-6), \
+            "Optimizer should avoid early installations (inefficient vintages)"
 
 
 class TestTechnologyEvolutionIntegration:
@@ -595,9 +566,8 @@ def test_vintage_improvements_affects_optimization(self):
 
         Same scenario as above but using evolution factors instead of explicit vintages.
         """
-        import pyomo.environ as pyo
-
         from optimex import optimizer
+        import pyomo.environ as pyo
 
         inputs = {
             "PROCESS": ["Plant"],
@@ -627,10 +597,7 @@ def test_vintage_improvements_affects_optimization(self):
                 ("Plant", "output", 0): 0,
                 ("Plant", "output", 1): 100,
             },
-            "operation_flow": {
-                ("Plant", "output"): True,
-                ("Plant", "electricity"): True,
-            },
+            "operation_flow": {("Plant", "output"): True, ("Plant", "electricity"): True},
             "background_inventory": {("grid", "electricity", "CO2"): 1.0},
             "mapping": {("grid", t): 1.0 for t in range(2020, 2026)},
             "characterization": {("GWP", "CO2", t): 1.0 for t in range(2020, 2026)},
@@ -640,9 +607,7 @@ def test_vintage_improvements_affects_optimization(self):
         model = optimizer.create_model(
             inputs=model_inputs, objective_category="GWP", name="evolution_test"
         )
-        solved, obj, results = optimizer.solve_model(
-            model, solver_name="glpk", tee=False
-        )
+        solved, obj, results = optimizer.solve_model(model, solver_name="glpk", tee=False)
         assert results.solver.termination_condition.name == "optimal"
 
         # Should prefer later installation (lower electricity consumption via evolution factor)
@@ -650,9 +615,7 @@ def test_vintage_improvements_affects_optimization(self):
         early_install = sum(
             pyo.value(solved.var_installation["Plant", t]) for t in [2020, 2021, 2022]
         )
-        assert (
-            late_install > early_install
-        ), "Optimizer should prefer later vintages with evolution factors"
+        assert late_install > early_install, "Optimizer should prefer later vintages with evolution factors"
 
 
 class TestMixedVintageScenarios:
@@ -667,9 +630,8 @@ def test_mixed_processes_some_with_overrides(self):
 
         Both should work correctly in the same model.
         """
-        import pyomo.environ as pyo
-
         from optimex import optimizer
+        import pyomo.environ as pyo
 
         inputs = {
             "PROCESS": ["ProcessA", "ProcessB"],
@@ -693,26 +655,20 @@ def test_mixed_processes_some_with_overrides(self):
             # ProcessA: vintage-dependent (4D sparse override)
             "foreground_technosphere_vintages": {
                 ("ProcessA", "electricity", 1, 2020): 100,  # Inefficient
-                ("ProcessA", "electricity", 1, 2025): 50,  # Efficient
+                ("ProcessA", "electricity", 1, 2025): 50,   # Efficient
             },
             "internal_demand_technosphere": {},
             "foreground_biosphere": {
-                ("ProcessA", "CO2", 0): 0,
-                ("ProcessA", "CO2", 1): 0,
-                ("ProcessB", "CO2", 0): 0,
-                ("ProcessB", "CO2", 1): 0,
+                ("ProcessA", "CO2", 0): 0, ("ProcessA", "CO2", 1): 0,
+                ("ProcessB", "CO2", 0): 0, ("ProcessB", "CO2", 1): 0,
             },
             "foreground_production": {
-                ("ProcessA", "output", 0): 0,
-                ("ProcessA", "output", 1): 100,
-                ("ProcessB", "output", 0): 0,
-                ("ProcessB", "output", 1): 100,
+                ("ProcessA", "output", 0): 0, ("ProcessA", "output", 1): 100,
+                ("ProcessB", "output", 0): 0, ("ProcessB", "output", 1): 100,
             },
             "operation_flow": {
-                ("ProcessA", "output"): True,
-                ("ProcessA", "electricity"): True,
-                ("ProcessB", "output"): True,
-                ("ProcessB", "electricity"): True,
+                ("ProcessA", "output"): True, ("ProcessA", "electricity"): True,
+                ("ProcessB", "output"): True, ("ProcessB", "electricity"): True,
             },
             "background_inventory": {("grid", "electricity", "CO2"): 1.0},
             "mapping": {("grid", t): 1.0 for t in range(2020, 2026)},
@@ -723,9 +679,7 @@ def test_mixed_processes_some_with_overrides(self):
         model = optimizer.create_model(
             inputs=model_inputs, objective_category="GWP", name="mixed_test"
         )
-        solved, obj, results = optimizer.solve_model(
-            model, solver_name="glpk", tee=False
-        )
+        solved, obj, results = optimizer.solve_model(model, solver_name="glpk", tee=False)
         assert results.solver.termination_condition.name == "optimal"
 
         # Model should solve and make sensible choices
@@ -818,16 +772,13 @@ def test_baseline_without_vintage_parameters(self, simple_system_base):
         model = optimizer.create_model(
             inputs=model_inputs, objective_category="GWP", name="baseline"
         )
-        solved, obj, results = optimizer.solve_model(
-            model, solver_name="glpk", tee=False
-        )
+        solved, obj, results = optimizer.solve_model(model, solver_name="glpk", tee=False)
 
         assert results.solver.termination_condition.name == "optimal"
 
         # Check objective (total impact)
-        assert obj == pytest.approx(
-            100.0, rel=1e-5
-        ), "Baseline impact should be 100 kg CO2 (100 elec * 1 kg/elec * 1.0 CF)"
+        assert obj == pytest.approx(100.0, rel=1e-5), \
+            "Baseline impact should be 100 kg CO2 (100 elec * 1 kg/elec * 1.0 CF)"
 
     def test_vintage_50_percent_improvement_halves_impact(self, simple_system_base):
         """
@@ -855,16 +806,13 @@ def test_vintage_50_percent_improvement_halves_impact(self, simple_system_base):
         model = optimizer.create_model(
             inputs=model_inputs, objective_category="GWP", name="with_evolution"
         )
-        solved, obj, results = optimizer.solve_model(
-            model, solver_name="glpk", tee=False
-        )
+        solved, obj, results = optimizer.solve_model(model, solver_name="glpk", tee=False)
 
         assert results.solver.termination_condition.name == "optimal"
 
         # Check objective is halved (50% efficiency = 50% impact)
-        assert obj == pytest.approx(
-            50.0, rel=1e-5
-        ), "Impact should be 50 kg CO2 (50% of baseline due to efficiency improvement)"
+        assert obj == pytest.approx(50.0, rel=1e-5), \
+            "Impact should be 50 kg CO2 (50% of baseline due to efficiency improvement)"
 
     def test_vintage_explicit_values_affect_optimization(self):
         """
@@ -917,18 +865,15 @@ def test_vintage_explicit_values_affect_optimization(self):
         model = optimizer.create_model(
             inputs=model_inputs, objective_category="GWP", name="explicit_vintages"
         )
-        solved, obj, results = optimizer.solve_model(
-            model, solver_name="glpk", tee=False
-        )
+        solved, obj, results = optimizer.solve_model(model, solver_name="glpk", tee=False)
 
         assert results.solver.termination_condition.name == "optimal"
 
         # Installing in 2021 (for operation in 2022) with interpolated 2021 vintage:
         # Interpolated electricity = (100 + 50) / 2 = 75
         # 75 electricity * 1 kg CO2/elec = 75 kg CO2
-        assert obj == pytest.approx(
-            75.0, rel=1e-5
-        ), "Impact should be 75 kg CO2 (interpolated 2021 vintage efficiency)"
+        assert obj == pytest.approx(75.0, rel=1e-5), \
+            "Impact should be 75 kg CO2 (interpolated 2021 vintage efficiency)"
 
     def test_compare_baseline_vs_improved_full_workflow(self, simple_system_base):
         """
@@ -966,9 +911,8 @@ def test_compare_baseline_vs_improved_full_workflow(self, simple_system_base):
         improvement_ratio = improved_obj / baseline_obj
         expected_ratio = 0.7  # 30% improvement means 70% of baseline
 
-        assert improvement_ratio == pytest.approx(
-            expected_ratio, rel=1e-5
-        ), f"Impact ratio should be {expected_ratio}, got {improvement_ratio}"
+        assert improvement_ratio == pytest.approx(expected_ratio, rel=1e-5), \
+            f"Impact ratio should be {expected_ratio}, got {improvement_ratio}"
 
     def test_biosphere_vintage_affects_direct_emissions(self):
         """
@@ -1018,9 +962,7 @@ def test_biosphere_vintage_affects_direct_emissions(self):
         model_2020 = optimizer.create_model(
             inputs=model_inputs_2020, objective_category="GWP", name="biosphere_2020"
         )
-        _, obj_2020, results_2020 = optimizer.solve_model(
-            model_2020, solver_name="glpk", tee=False
-        )
+        _, obj_2020, results_2020 = optimizer.solve_model(model_2020, solver_name="glpk", tee=False)
         assert results_2020.solver.termination_condition.name == "optimal"
 
         # Test 2022 vintage: demand in 2022 allows 2021 installation (closer to 2022)
@@ -1031,22 +973,20 @@ def test_biosphere_vintage_affects_direct_emissions(self):
         model_2022 = optimizer.create_model(
             inputs=model_inputs_2022, objective_category="GWP", name="biosphere_2022"
         )
-        _, obj_2022, results_2022 = optimizer.solve_model(
-            model_2022, solver_name="glpk", tee=False
-        )
+        _, obj_2022, results_2022 = optimizer.solve_model(model_2022, solver_name="glpk", tee=False)
         assert results_2022.solver.termination_condition.name == "optimal"
 
         # 2020 vintage should have higher emissions than 2021 vintage (interpolated toward 2022)
         # 2020 vintage: 200 kg CO2 per unit
         # 2021 vintage (interpolated): (200 + 100) / 2 = 150 kg CO2 per unit
         # Expected ratio: 200 / 150 = 1.33
-        assert obj_2022 < obj_2020, "Later vintage should have lower emissions"
+        assert obj_2022 < obj_2020, \
+            "Later vintage should have lower emissions"
 
         expected_ratio = 200 / 150  # 2020 emissions / 2021 interpolated emissions
         actual_ratio = obj_2020 / obj_2022
-        assert actual_ratio == pytest.approx(
-            expected_ratio, rel=1e-2
-        ), f"Emission ratio should be ~{expected_ratio:.2f}, got {actual_ratio:.2f}"
+        assert actual_ratio == pytest.approx(expected_ratio, rel=1e-2), \
+            f"Emission ratio should be ~{expected_ratio:.2f}, got {actual_ratio:.2f}"
 
     def test_production_vintage_affects_capacity(self):
         """
@@ -1101,9 +1041,7 @@ def test_production_vintage_affects_capacity(self):
         model = optimizer.create_model(
             inputs=model_inputs, objective_category="GWP", name="production_vintage"
         )
-        solved, obj, results = optimizer.solve_model(
-            model, solver_name="glpk", tee=False
-        )
+        solved, obj, results = optimizer.solve_model(model, solver_name="glpk", tee=False)
 
         assert results.solver.termination_condition.name == "optimal"
 
@@ -1111,11 +1049,11 @@ def test_production_vintage_affects_capacity(self):
         # Need 200/150 = 1.333 units to meet demand
         # Fuel: 1.333 * 50 = 66.67
         # Impact: 66.67 * 2 = 133.33 kg CO2
-        assert obj == pytest.approx(
-            133.33, rel=1e-3
-        ), "Impact should be ~133.33 kg CO2 (interpolated production capacity)"
-
+        assert obj == pytest.approx(133.33, rel=1e-3), \
+            "Impact should be ~133.33 kg CO2 (interpolated production capacity)"
 
+
+
 class TestDatabaseVintageParameterExtraction:
     """Tests for extracting vintage parameters from Brightway database."""
 
@@ -1123,7 +1061,6 @@ class TestDatabaseVintageParameterExtraction:
     def database_with_vintage_params(self, request):
         """Create a test database with vintage parameter attributes."""
         from datetime import datetime
-
         import bw2data as bd
         import numpy as np
         from bw2data.tests import bw2test
@@ -1132,144 +1069,132 @@ def database_with_vintage_params(self, request):
         # Set up test project
         project_name = "__test_vintage_db__"
         bd.projects.set_current(project_name)
-
+        
         # Register cleanup to delete project after test
         def cleanup():
             try:
                 bd.projects.delete_project(project_name, delete_dir=True)
             except:
                 pass  # Ignore cleanup errors
-
-        request.addfinalizer(cleanup)
-
-        # Create biosphere database
-        bio_db = bd.Database("biosphere3")
-        bio_db.write(
-            {
-                ("biosphere3", "CO2"): {
-                    "type": "emission",
-                    "name": "carbon dioxide",
-                },
-            }
-        )
-        bio_db.register()
-
-        # Create background database
-        bg_db = bd.Database("db_2020")
-        bg_db.write(
-            {
-                ("db_2020", "electricity"): {
-                    "name": "electricity",
-                    "location": "GLO",
-                    "reference product": "electricity",
-                    "exchanges": [
-                        {
-                            "amount": 1,
-                            "type": "production",
-                            "input": ("db_2020", "electricity"),
-                        },
-                        {
-                            "amount": 0.5,
-                            "type": "biosphere",
-                            "input": ("biosphere3", "CO2"),
-                        },
-                    ],
-                },
-            }
-        )
-        bg_db.metadata["representative_time"] = datetime(2020, 1, 1).isoformat()
-        bg_db.register()
+        
+        request.addfinalizer(cleanup)
+        
+        # Create biosphere database
+        bio_db = bd.Database("biosphere3")
+        bio_db.write({
+            ("biosphere3", "CO2"): {
+                "type": "emission",
+                "name": "carbon dioxide",
+            },
+        })
+        bio_db.register()
+
+        # Create background database
+        bg_db = bd.Database("db_2020")
+        bg_db.write({
+            ("db_2020", "electricity"): {
+                "name": "electricity",
+                "location": "GLO",
+                "reference product": "electricity",
+                "exchanges": [
+                    {
+                        "amount": 1,
+                        "type": "production",
+                        "input": ("db_2020", "electricity"),
+                    },
+                    {
+                        "amount": 0.5,
+                        "type": "biosphere",
+                        "input": ("biosphere3", "CO2"),
+                    },
+                ],
+            },
+        })
+        bg_db.metadata["representative_time"] = datetime(2020, 1, 1).isoformat()
+        bg_db.register()
 
         # Create foreground database with vintage parameters
         fg_db = bd.Database("foreground")
-        fg_db.write(
-            {
-                # Product node
-                ("foreground", "vkm"): {
-                    "name": "vehicle-km",
-                    "type": bd.labels.product_node_default,
-                    "unit": "km",
-                },
-                # Process node with vintage-dependent exchange
-                ("foreground", "EV"): {
-                    "name": "Electric Vehicle",
-                    "type": bd.labels.process_node_default,
-                    "operation_time_limits": (1, 2),
-                    "exchanges": [
-                        {
-                            "amount": 1,
-                            "type": bd.labels.production_edge_default,
-                            "input": ("foreground", "vkm"),
-                            "temporal_distribution": TemporalDistribution(
-                                date=np.array([0, 1, 2, 3], dtype="timedelta64[Y]"),
-                                amount=np.array([0, 0.5, 0.5, 0]),
-                            ),
-                            "operation": True,
-                        },
-                        {
-                            "amount": 60,  # Base amount (will be overridden by vintage_amounts)
-                            "type": bd.labels.consumption_edge_default,
-                            "input": ("db_2020", "electricity"),
-                            "temporal_distribution": TemporalDistribution(
-                                date=np.array([1, 2], dtype="timedelta64[Y]"),
-                                amount=np.array([0.5, 0.5]),
-                            ),
-                            "operation": True,
-                            # NEW: Vintage-specific values
-                            "vintage_amounts": {
-                                (1, 2020): 30,  # τ=1, 2020 vintage: 30 MJ/vkm
-                                (2, 2020): 30,  # τ=2, 2020 vintage: 30 MJ/vkm
-                                (1, 2030): 22.5,  # τ=1, 2030 vintage: 22.5 MJ/vkm
-                                (2, 2030): 22.5,  # τ=2, 2030 vintage: 22.5 MJ/vkm
-                            },
-                        },
-                        {
-                            "amount": 5000,  # Manufacturing emissions
-                            "type": bd.labels.biosphere_edge_default,
-                            "input": ("biosphere3", "CO2"),
-                            "temporal_distribution": TemporalDistribution(
-                                date=np.array([0], dtype="timedelta64[Y]"),
-                                amount=np.array([1.0]),
-                            ),
-                            # Vintage improvement scaling
-                            "vintage_improvements": {
-                                2020: 1.0,  # 100% of base (5000 kg)
-                                2030: 0.8,  # 80% of base (4000 kg)
-                            },
+        fg_db.write({
+            # Product node
+            ("foreground", "vkm"): {
+                "name": "vehicle-km",
+                "type": bd.labels.product_node_default,
+                "unit": "km",
+            },
+            # Process node with vintage-dependent exchange
+            ("foreground", "EV"): {
+                "name": "Electric Vehicle",
+                "type": bd.labels.process_node_default,
+                "operation_time_limits": (1, 2),
+                "exchanges": [
+                    {
+                        "amount": 1,
+                        "type": bd.labels.production_edge_default,
+                        "input": ("foreground", "vkm"),
+                        "temporal_distribution": TemporalDistribution(
+                            date=np.array([0, 1, 2, 3], dtype="timedelta64[Y]"),
+                            amount=np.array([0, 0.5, 0.5, 0]),
+                        ),
+                        "operation": True,
+                    },
+                    {
+                        "amount": 60,  # Base amount (will be overridden by vintage_amounts)
+                        "type": bd.labels.consumption_edge_default,
+                        "input": ("db_2020", "electricity"),
+                        "temporal_distribution": TemporalDistribution(
+                            date=np.array([1, 2], dtype="timedelta64[Y]"),
+                            amount=np.array([0.5, 0.5]),
+                        ),
+                        "operation": True,
+                        # NEW: Vintage-specific values
+                        "vintage_amounts": {
+                            (1, 2020): 30,  # τ=1, 2020 vintage: 30 MJ/vkm
+                            (2, 2020): 30,  # τ=2, 2020 vintage: 30 MJ/vkm
+                            (1, 2030): 22.5,  # τ=1, 2030 vintage: 22.5 MJ/vkm
+                            (2, 2030): 22.5,  # τ=2, 2030 vintage: 22.5 MJ/vkm
                         },
-                    ],
-                },
-            }
-        )
-        fg_db.register()
-
-        # Create LCIA method
-        bd.Method(("GWP", "example")).write(
-            [
-                (("biosphere3", "CO2"), 1.0),
-            ]
-        )
-
-        return fg_db
-
+                    },
+                    {
+                        "amount": 5000,  # Manufacturing emissions
+                        "type": bd.labels.biosphere_edge_default,
+                        "input": ("biosphere3", "CO2"),
+                        "temporal_distribution": TemporalDistribution(
+                            date=np.array([0], dtype="timedelta64[Y]"),
+                            amount=np.array([1.0]),
+                        ),
+                        # Vintage improvement scaling
+                        "vintage_improvements": {
+                            2020: 1.0,   # 100% of base (5000 kg)
+                            2030: 0.8,   # 80% of base (4000 kg)
+                        },
+                    },
+                ],
+            },
+        })
+        fg_db.register()
+
+        # Create LCIA method
+        bd.Method(("GWP", "example")).write([
+            (("biosphere3", "CO2"), 1.0),
+        ])
+
+        return fg_db
+
     def test_lca_processor_extracts_vintage_amounts(self, database_with_vintage_params):
         """Test that LCADataProcessor extracts vintage_amounts from exchanges."""
         from datetime import datetime
-
         import bw2data as bd
         import numpy as np
         from bw_temporalis import TemporalDistribution
-
         from optimex import lca_processor
 
         # Create demand
         product_node = bd.get_node(database="foreground", code="vkm")
-
+        
         demand = {
             product_node: TemporalDistribution(
-                date=np.array(
-                    [datetime(2025, 1, 1).isoformat()], dtype="datetime64[s]"
-                ),
+                date=np.array([datetime(2025, 1, 1).isoformat()], dtype="datetime64[s]"),
                 amount=np.array([100]),
             )
         }
@@ -1296,49 +1221,27 @@ def test_lca_processor_extracts_vintage_amounts(self, database_with_vintage_para
         # Verify vintage parameters were extracted
         assert processor.foreground_technosphere_vintages is not None
         assert len(processor.foreground_technosphere_vintages) > 0
-
+        
         # Check specific vintage values
-        assert (
-            "EV",
-            "electricity",
-            1,
-            2020,
-        ) in processor.foreground_technosphere_vintages
-        assert (
-            processor.foreground_technosphere_vintages[("EV", "electricity", 1, 2020)]
-            == 30
-        )
-        assert (
-            "EV",
-            "electricity",
-            1,
-            2030,
-        ) in processor.foreground_technosphere_vintages
-        assert (
-            processor.foreground_technosphere_vintages[("EV", "electricity", 1, 2030)]
-            == 22.5
-        )
-
-    def test_lca_processor_extracts_vintage_improvements(
-        self, database_with_vintage_params
-    ):
+        assert ("EV", "electricity", 1, 2020) in processor.foreground_technosphere_vintages
+        assert processor.foreground_technosphere_vintages[("EV", "electricity", 1, 2020)] == 30
+        assert ("EV", "electricity", 1, 2030) in processor.foreground_technosphere_vintages
+        assert processor.foreground_technosphere_vintages[("EV", "electricity", 1, 2030)] == 22.5
+
+    def test_lca_processor_extracts_vintage_improvements(self, database_with_vintage_params):
         """Test that LCADataProcessor extracts vintage_improvements from exchanges."""
         from datetime import datetime
-
         import bw2data as bd
         import numpy as np
         from bw_temporalis import TemporalDistribution
-
         from optimex import lca_processor
 
         # Create demand
         product_node = bd.get_node(database="foreground", code="vkm")
-
+        
         demand = {
             product_node: TemporalDistribution(
-                date=np.array(
-                    [datetime(2025, 1, 1).isoformat()], dtype="datetime64[s]"
-                ),
+                date=np.array([datetime(2025, 1, 1).isoformat()], dtype="datetime64[s]"),
                 amount=np.array([100]),
             )
         }
@@ -1365,33 +1268,27 @@ def test_lca_processor_extracts_vintage_improvements(
         # Verify vintage improvements were extracted
         assert processor.vintage_improvements is not None
         assert len(processor.vintage_improvements) > 0
-
+        
         # Check specific vintage improvement values
         assert ("EV", "CO2", 2020) in processor.vintage_improvements
         assert processor.vintage_improvements[("EV", "CO2", 2020)] == 1.0
         assert ("EV", "CO2", 2030) in processor.vintage_improvements
         assert processor.vintage_improvements[("EV", "CO2", 2030)] == 0.8
 
-    def test_model_input_manager_uses_database_vintages(
-        self, database_with_vintage_params
-    ):
+    def test_model_input_manager_uses_database_vintages(self, database_with_vintage_params):
         """Test that ModelInputManager includes vintage parameters from database."""
         from datetime import datetime
-
         import bw2data as bd
         import numpy as np
         from bw_temporalis import TemporalDistribution
-
-        from optimex import converter, lca_processor
+        from optimex import lca_processor, converter
 
         # Create demand
         product_node = bd.get_node(database="foreground", code="vkm")
-
+        
         demand = {
             product_node: TemporalDistribution(
-                date=np.array(
-                    [datetime(2025, 1, 1).isoformat()], dtype="datetime64[s]"
-                ),
+                date=np.array([datetime(2025, 1, 1).isoformat()], dtype="datetime64[s]"),
                 amount=np.array([100]),
             )
         }
@@ -1420,13 +1317,8 @@ def test_model_input_manager_uses_database_vintages(
         # Verify vintage parameters are in model inputs
         assert model_inputs.foreground_technosphere_vintages is not None
         assert len(model_inputs.foreground_technosphere_vintages) > 0
-        assert (
-            "EV",
-            "electricity",
-            1,
-            2020,
-        ) in model_inputs.foreground_technosphere_vintages
-
+        assert ("EV", "electricity", 1, 2020) in model_inputs.foreground_technosphere_vintages
+        
         assert model_inputs.vintage_improvements is not None
         assert len(model_inputs.vintage_improvements) > 0
-        assert ("EV", "CO2", 2020) in model_inputs.vintage_improvements
+        assert ("EV", "CO2", 2020) in model_inputs.vintage_improvements

From 6661c8300ae5f6319accbb5b96610a548088b22f Mon Sep 17 00:00:00 2001
From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com>
Date: Wed, 20 May 2026 23:17:30 +0000
Subject: [PATCH 07/12] Add optional foreground DB name support to
 LCADataProcessor

Agent-Logs-Url: https://github.com/RWTH-LTT/optimex/sessions/98d5a08c-042c-46e8-93f3-9271a9c3e3bd

Co-authored-by: TimoDiepers <90762029+TimoDiepers@users.noreply.github.com>
---
 src/optimex/lca_processor.py                  | 19 ++++-
 .../test_lca_processor_foreground_db_name.py  | 80 +++++++++++++++++++
 2 files changed, 95 insertions(+), 4 deletions(-)
 create mode 100644 tests/test_lca_processor_foreground_db_name.py

diff --git a/src/optimex/lca_processor.py b/src/optimex/lca_processor.py
index 5b7687b..686c794 100644
--- a/src/optimex/lca_processor.py
+++ b/src/optimex/lca_processor.py
@@ -166,6 +166,7 @@ class LCAConfig(BaseModel):
         temporal: Temporal configuration for model time behavior.
         characterization_methods: List of characterization method configurations.
         background_inventory: Configuration for background inventory data calculation.
+        foreground_db_name: Name of the foreground Brightway database.
     """
 
     demand: Dict[bd.backends.proxies.Activity, TemporalDistribution]
@@ -174,6 +175,10 @@ class LCAConfig(BaseModel):
     background_inventory: Optional[BackgroundInventoryConfig] = Field(
         default_factory=BackgroundInventoryConfig
     )
+    foreground_db_name: str = Field(
+        "foreground",
+        description="Name of the foreground Brightway database.",
+    )
 
     class Config:
         arbitrary_types_allowed = True
@@ -190,7 +195,9 @@ class LCADataProcessor:
     calculations and retrieve LCA results.
     """
 
-    def __init__(self, config: LCAConfig) -> None:
+    def __init__(
+        self, config: LCAConfig, foreground_db_name: str = "foreground"
+    ) -> None:
         """
         Initialize the LCADataProcessor with the LCA configuration.
 
@@ -199,11 +206,15 @@ def __init__(self, config: LCAConfig) -> None:
         config : LCAConfig
             The configuration object containing all settings for demand,
             temporal parameters, characterization methods, and background inventory.
+        foreground_db_name : str, optional
+            The name of the foreground Brightway database, by default "foreground".
         """
         self.config = config
-        if "foreground" not in bd.databases:
-            raise ValueError("Foreground database 'foreground' is not defined.")
-        self.foreground_db = bd.Database("foreground")
+        if foreground_db_name not in bd.databases:
+            raise ValueError(
+                f"Foreground database '{foreground_db_name}' is not defined."
+            )
+        self.foreground_db = bd.Database(foreground_db_name)
         self.background_dbs = {}
         if config.temporal.database_dates is not None:
             self.background_dbs = {
diff --git a/tests/test_lca_processor_foreground_db_name.py b/tests/test_lca_processor_foreground_db_name.py
new file mode 100644
index 0000000..360ede6
--- /dev/null
+++ b/tests/test_lca_processor_foreground_db_name.py
@@ -0,0 +1,80 @@
+from datetime import datetime
+from types import SimpleNamespace
+from unittest.mock import MagicMock
+
+import pytest
+
+from optimex import lca_processor
+
+
+def _minimal_config() -> lca_processor.LCAConfig:
+    temporal = lca_processor.TemporalConfig(
+        start_date=datetime(2025, 1, 1),
+        database_dates={},
+    )
+    return lca_processor.LCAConfig.model_construct(
+        demand={},
+        temporal=temporal,
+        characterization_methods=[],
+        background_inventory=lca_processor.BackgroundInventoryConfig(),
+        foreground_db_name="foreground",
+    )
+
+
+def _patch_processor_methods(monkeypatch: pytest.MonkeyPatch) -> None:
+    monkeypatch.setattr(
+        lca_processor.LCADataProcessor,
+        "_parse_demand",
+        lambda _: None,
+    )
+    monkeypatch.setattr(
+        lca_processor.LCADataProcessor, "_construct_foreground_tensors", lambda _: None
+    )
+    monkeypatch.setattr(
+        lca_processor.LCADataProcessor, "_prepare_background_inventory", lambda _: None
+    )
+    monkeypatch.setattr(
+        lca_processor.LCADataProcessor,
+        "_construct_characterization_tensor",
+        lambda _: None,
+    )
+    monkeypatch.setattr(
+        lca_processor.LCADataProcessor,
+        "_construct_mapping_matrix",
+        lambda _: None,
+    )
+
+
+def test_custom_foreground_db_name_is_used(monkeypatch: pytest.MonkeyPatch) -> None:
+    _patch_processor_methods(monkeypatch)
+    config = _minimal_config()
+
+    monkeypatch.setattr(lca_processor.bd, "databases", {"custom_foreground": {}})
+    db_factory = MagicMock(
+        side_effect=lambda name: SimpleNamespace(name=name, metadata={})
+    )
+    monkeypatch.setattr(lca_processor.bd, "Database", db_factory)
+    monkeypatch.setattr(
+        lca_processor.bd, "config", SimpleNamespace(biosphere="biosphere3")
+    )
+
+    processor = lca_processor.LCADataProcessor(
+        config, foreground_db_name="custom_foreground"
+    )
+
+    assert processor.foreground_db.name == "custom_foreground"
+    assert any(
+        call.args == ("custom_foreground",) for call in db_factory.call_args_list
+    )
+
+
+def test_missing_custom_foreground_db_raises(monkeypatch: pytest.MonkeyPatch) -> None:
+    _patch_processor_methods(monkeypatch)
+    config = _minimal_config()
+
+    monkeypatch.setattr(lca_processor.bd, "databases", {"foreground": {}})
+
+    with pytest.raises(
+        ValueError, match="Foreground database 'missing_db' is not defined."
+    ):
+        lca_processor.LCADataProcessor(config, foreground_db_name="missing_db")

From 9a026aac6220f68cf5e9bae984eb19a431c6fee4 Mon Sep 17 00:00:00 2001
From: TimoDiepers <timo.diepers@gmail.com>
Date: Thu, 21 May 2026 12:32:30 +0200
Subject: [PATCH 08/12] update changes.md

---
 CHANGES.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/CHANGES.md b/CHANGES.md
index 5fb32ec..3879a96 100644
--- a/CHANGES.md
+++ b/CHANGES.md
@@ -6,6 +6,7 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
 and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
 ## [Unreleased]
+* Added foreground_db_name argument to LCAConfig
 
 ## [0.4.2] - 2026-04-27
 * Fix conda publish workflow

From 465d01594d800434e7aee48d5341a7c2439005f9 Mon Sep 17 00:00:00 2001
From: TimoDiepers <timo.diepers@gmail.com>
Date: Fri, 5 Jun 2026 07:34:47 +0200
Subject: [PATCH 09/12] identify flows by tuples now code

---
 src/optimex/lca_processor.py | 44 ++++++++++++++++++++++++++++++------
 1 file changed, 37 insertions(+), 7 deletions(-)

diff --git a/src/optimex/lca_processor.py b/src/optimex/lca_processor.py
index 686c794..d765554 100644
--- a/src/optimex/lca_processor.py
+++ b/src/optimex/lca_processor.py
@@ -619,7 +619,18 @@ def _construct_foreground_tensors(self) -> None:
                         })
                         if exc.get("operation"):
                             self._operation_flow.update({(act["code"], input_code): True})
-                        self._intermediate_flows.setdefault(input_code, input_name)
+                        # Store identity attributes, not just the code: premise assigns a
+                        # different code to the same activity in each scenario database, so
+                        # background activities are resolved across databases by
+                        # (name, reference product, location), not by code.
+                        self._intermediate_flows.setdefault(
+                            input_code,
+                            {
+                                "name": input_name,
+                                "reference product": exc.input.get("reference product"),
+                                "location": exc.input.get("location"),
+                            },
+                        )
 
                 # Handle biosphere edges
                 elif edge_type == bd.labels.biosphere_edge_default:
@@ -669,7 +680,9 @@ def _calculate_inventory_of_db(
         db_name : str
             Name of the background database to analyze.
         intermediate_flows : dict
-            Dictionary mapping intermediate flow codes to flow names.
+            Dictionary mapping intermediate flow codes (foreground reference codes)
+            to identity metadata dicts with keys "name", "reference product", and
+            "location", used to resolve the activity in each background database.
         methods : List[tuple]
             A List of LCIA methods represented by a tuple (e.g.,
             `("EF v3.1", "climate change", "global warming potential (GWP100)")`).
@@ -692,12 +705,29 @@ def _calculate_inventory_of_db(
         elementary_flows = {}
         activity_cache = {}
 
-        # Cache activity objects by looking up intermediate flows in the database
-        for key in intermediate_flows.keys():
+        # Resolve each intermediate flow in this database by identity
+        # (name, reference product, location) rather than code. premise assigns a
+        # different code to the same activity in each scenario database, so a
+        # code-based lookup silently drops activities from prospective databases.
+        # The tensor stays keyed by the foreground reference code (`key`) for
+        # consistency across databases.
+        for key, meta in intermediate_flows.items():
             try:
-                activity_cache[key] = db.get(code=key)
-            except Exception as e:  # Catch exceptions (e.g., if key is not valid)
-                logger.warning(f"Failed to get activity for key '{key}': {e}")
+                if isinstance(meta, dict):
+                    kwargs = {"database": db_name, "name": meta["name"]}
+                    if meta.get("reference product") is not None:
+                        kwargs["product"] = meta["reference product"]
+                    if meta.get("location") is not None:
+                        kwargs["location"] = meta["location"]
+                    activity_cache[key] = bd.get_node(**kwargs)
+                else:
+                    # Backward compatibility (e.g. legacy pickled inputs): code lookup.
+                    activity_cache[key] = db.get(code=key)
+            except Exception as e:  # Catch exceptions (e.g., if activity not found)
+                logger.warning(
+                    f"Failed to resolve intermediate flow {meta!r} (code '{key}') "
+                    f"in '{db_name}': {e}"
+                )
         function_unit_dict = {activity: 1 for activity in activity_cache.values()}
 
         lca = bc.LCA(function_unit_dict, next(iter(methods)))

From d310db3c97197a8a488e7937153ea2829d0cb7e0 Mon Sep 17 00:00:00 2001
From: TimoDiepers <timo.diepers@gmail.com>
Date: Fri, 5 Jun 2026 07:55:52 +0200
Subject: [PATCH 10/12] add baseline design in evolution scenario analysis

---
 .../data/2026-05-28_model_inputs_2050.json    | 76287 ++++++++++++++++
 notebooks/data/paper/baseline_design.json     |     1 +
 .../characterized_inventory_evolution.xlsx    |   Bin 7415594 -> 7357299 bytes
 ...rison_baseline_design_under_evolution.xlsx |   Bin 0 -> 5591 bytes
 .../paper/dynamic_inventory_evolution.xlsx    |   Bin 4525359 -> 4522202 bytes
 notebooks/data/paper/evolution_results.json   |     1 +
 notebooks/methanol_and_iron.ipynb             |  2521 +-
 .../impacts_baseline_under_evolution.xlsx     |   Bin 0 -> 10907 bytes
 .../plots/figs/baselineunderevo_annual_rf.svg |  1506 +
 notebooks/plots/figs/radiative_forcing.pdf    |   Bin 42982 -> 42850 bytes
 notebooks/plots/figs/results_and_impacts.pdf  |   Bin 57944 -> 57944 bytes
 notebooks/plots/paper_figures.py              |    62 +
 12 files changed, 80307 insertions(+), 71 deletions(-)
 create mode 100644 notebooks/data/2026-05-28_model_inputs_2050.json
 create mode 100644 notebooks/data/paper/baseline_design.json
 create mode 100644 notebooks/data/paper/comparison_baseline_design_under_evolution.xlsx
 create mode 100644 notebooks/data/paper/evolution_results.json
 create mode 100644 notebooks/plots/data/impacts_baseline_under_evolution.xlsx
 create mode 100644 notebooks/plots/figs/baselineunderevo_annual_rf.svg

diff --git a/notebooks/data/2026-05-28_model_inputs_2050.json b/notebooks/data/2026-05-28_model_inputs_2050.json
new file mode 100644
index 0000000..f7a11f9
--- /dev/null
+++ b/notebooks/data/2026-05-28_model_inputs_2050.json
@@ -0,0 +1,76287 @@
+{
+  "PROCESS": [
+    "Carbon dioxide hydrogenation to methanol",
+    "Natural gas reforming",
+    "Blast furnace with carbon capture",
+    "PEM Electrolysis",
+    "Blast furnace",
+    "Direct reduction of iron",
+    "direct air carbon capture"
+  ],
+  "PRODUCT": [
+    "methanol",
+    "pig iron",
+    "hydrogen",
+    "captured CO2"
+  ],
+  "INTERMEDIATE_FLOW": [
+    "39dc63fb53b84294886127100bca8c3d",
+    "09370bb4d7f34b18bed6356e828de523",
+    "d5245d02adb3426daccdbe909ff124da",
+    "19a88bd5ac5a4a7cbb73dc5e92af338f",
+    "c08de56cae4f4e97a62085043c1e8de4",
+    "68316f3f624c4d9db909fde0e91afe84",
+    "8ecb5e81423c426784b449b9b967977d",
+    "549b3549b77845d094b24fa744a5e58c",
+    "fabc322317eb43d18d9c629b0aa415a2",
+    "c70ce0e0cec540a592a4ea4e1588bdd2",
+    "d8f5f90eaba143c18d124aefe72bb8ba",
+    "dd6f8998dcc74deab015e00da68153c7",
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+    "PEM Electrolysis": "PEM Electrolysis",
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+    "Direct reduction of iron": "Direct reduction of iron",
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+}
\ No newline at end of file
diff --git a/notebooks/data/paper/baseline_design.json b/notebooks/data/paper/baseline_design.json
new file mode 100644
index 0000000..09350eb
--- /dev/null
+++ b/notebooks/data/paper/baseline_design.json
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iron\t2021\t2036": 0.0, "Direct reduction of iron\t2021\t2037": 0.0, "Direct reduction of iron\t2021\t2038": 0.0, "Direct reduction of iron\t2021\t2039": 0.0, "Direct reduction of iron\t2021\t2040": 0.0, "Direct reduction of iron\t2021\t2041": 0.0, "Direct reduction of iron\t2021\t2042": 0.0, "Direct reduction of iron\t2021\t2043": 0.0, "Direct reduction of iron\t2021\t2044": 0.0, "Direct reduction of iron\t2021\t2045": 0.0, "Direct reduction of iron\t2021\t2046": 0.0, "Direct reduction of iron\t2022\t2022": 0.0, "Direct reduction of iron\t2022\t2023": 0.0, "Direct reduction of iron\t2022\t2024": 0.0, "Direct reduction of iron\t2022\t2025": 0.0, "Direct reduction of iron\t2022\t2026": 0.0, "Direct reduction of iron\t2022\t2027": 0.0, "Direct reduction of iron\t2022\t2028": 0.0, "Direct reduction of iron\t2022\t2029": 0.0, "Direct reduction of iron\t2022\t2030": 0.0, "Direct reduction of iron\t2022\t2031": 0.0, "Direct reduction of iron\t2022\t2032": 0.0, "Direct reduction of 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iron\t2042\t2043": 0.0, "Direct reduction of iron\t2042\t2044": 0.0, "Direct reduction of iron\t2042\t2045": 0.0, "Direct reduction of iron\t2042\t2046": 0.0, "Direct reduction of iron\t2042\t2047": 0.0, "Direct reduction of iron\t2042\t2048": 0.0, "Direct reduction of iron\t2042\t2049": 0.0, "Direct reduction of iron\t2042\t2050": 0.0, "Direct reduction of iron\t2043\t2043": 0.0, "Direct reduction of iron\t2043\t2044": 0.0, "Direct reduction of iron\t2043\t2045": 0.0, "Direct reduction of iron\t2043\t2046": 0.0, "Direct reduction of iron\t2043\t2047": 0.0, "Direct reduction of iron\t2043\t2048": 0.0, "Direct reduction of iron\t2043\t2049": 0.0, "Direct reduction of iron\t2043\t2050": 0.0, "Direct reduction of iron\t2044\t2044": 0.0, "Direct reduction of iron\t2044\t2045": 0.0, "Direct reduction of iron\t2044\t2046": 0.0, "Direct reduction of iron\t2044\t2047": 0.0, "Direct reduction of iron\t2044\t2048": 0.0, "Direct reduction of iron\t2044\t2049": 0.0, "Direct reduction of iron\t2044\t2050": 0.0, "Direct reduction of iron\t2045\t2045": 0.0, "Direct reduction of iron\t2045\t2046": 0.0, "Direct reduction of iron\t2045\t2047": 0.0, "Direct reduction of iron\t2045\t2048": 0.0, "Direct reduction of iron\t2045\t2049": 0.0, "Direct reduction of iron\t2045\t2050": 0.0, "Direct reduction of iron\t2046\t2046": 0.0, "Direct reduction of iron\t2046\t2047": 0.0, "Direct reduction of iron\t2046\t2048": 0.0, "Direct reduction of iron\t2046\t2049": 0.0, "Direct reduction of iron\t2046\t2050": 0.0, "Direct reduction of iron\t2047\t2047": 0.0, "Direct reduction of iron\t2047\t2048": 0.0, "Direct reduction of iron\t2047\t2049": 0.0, "Direct reduction of iron\t2047\t2050": 0.0, "Direct reduction of iron\t2048\t2048": 0.0, "Direct reduction of iron\t2048\t2049": 0.0, "Direct reduction of iron\t2048\t2050": 0.0, "Direct reduction of iron\t2049\t2049": 0.0, "Direct reduction of iron\t2049\t2050": 0.0, "Direct reduction of iron\t2050\t2050": 0.0, "Carbon dioxide hydrogenation to methanol\t2020\t2020": 0.0, "Carbon dioxide hydrogenation to methanol\t2020\t2021": 0.0, "Carbon dioxide hydrogenation to methanol\t2020\t2022": 0.0, "Carbon dioxide hydrogenation to methanol\t2020\t2023": 0.0, "Carbon dioxide hydrogenation to methanol\t2020\t2024": 0.0, "Carbon dioxide hydrogenation to methanol\t2020\t2025": 0.0, "Carbon dioxide hydrogenation to methanol\t2020\t2026": 0.0, "Carbon dioxide hydrogenation to methanol\t2020\t2027": 0.0, "Carbon dioxide hydrogenation to methanol\t2020\t2028": 0.0, "Carbon dioxide hydrogenation to methanol\t2020\t2029": 0.0, "Carbon dioxide hydrogenation to methanol\t2020\t2030": 0.0, "Carbon dioxide hydrogenation to methanol\t2020\t2031": 0.0, "Carbon dioxide hydrogenation to methanol\t2020\t2032": 0.0, "Carbon dioxide hydrogenation to methanol\t2020\t2033": 0.0, "Carbon dioxide hydrogenation to methanol\t2020\t2034": 0.0, "Carbon dioxide hydrogenation to methanol\t2020\t2035": 0.0, "Carbon dioxide hydrogenation to methanol\t2021\t2021": 0.0, "Carbon dioxide hydrogenation to methanol\t2021\t2022": 0.0, "Carbon dioxide hydrogenation to methanol\t2021\t2023": 0.0, "Carbon dioxide hydrogenation to methanol\t2021\t2024": 0.0, "Carbon dioxide hydrogenation to methanol\t2021\t2025": 0.0, "Carbon dioxide hydrogenation to methanol\t2021\t2026": 0.0, "Carbon dioxide hydrogenation to methanol\t2021\t2027": 0.0, "Carbon dioxide hydrogenation to methanol\t2021\t2028": 0.0, "Carbon dioxide hydrogenation to methanol\t2021\t2029": 0.0, "Carbon dioxide hydrogenation to methanol\t2021\t2030": 0.0, "Carbon dioxide hydrogenation to methanol\t2021\t2031": 0.0, "Carbon dioxide hydrogenation to methanol\t2021\t2032": 0.0, "Carbon dioxide hydrogenation to methanol\t2021\t2033": 0.0, "Carbon dioxide hydrogenation to methanol\t2021\t2034": 0.0, "Carbon dioxide hydrogenation to methanol\t2021\t2035": 0.0, "Carbon dioxide hydrogenation to methanol\t2021\t2036": 0.0, "Carbon dioxide hydrogenation to methanol\t2022\t2022": 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methanol\t2024\t2025": 0.0, "Carbon dioxide hydrogenation to methanol\t2024\t2026": 0.0, "Carbon dioxide hydrogenation to methanol\t2024\t2027": 0.0, "Carbon dioxide hydrogenation to methanol\t2024\t2028": 0.0, "Carbon dioxide hydrogenation to methanol\t2024\t2029": 0.0, "Carbon dioxide hydrogenation to methanol\t2024\t2030": 0.0, "Carbon dioxide hydrogenation to methanol\t2024\t2031": 0.0, "Carbon dioxide hydrogenation to methanol\t2024\t2032": 0.0, "Carbon dioxide hydrogenation to methanol\t2024\t2033": 0.0, "Carbon dioxide hydrogenation to methanol\t2024\t2034": 0.0, "Carbon dioxide hydrogenation to methanol\t2024\t2035": 0.0, "Carbon dioxide hydrogenation to methanol\t2024\t2036": 0.0, "Carbon dioxide hydrogenation to methanol\t2024\t2037": 0.0, "Carbon dioxide hydrogenation to methanol\t2024\t2038": 0.0, "Carbon dioxide hydrogenation to methanol\t2024\t2039": 0.0, "Carbon dioxide hydrogenation to methanol\t2025\t2025": 0.0, "Carbon dioxide hydrogenation to methanol\t2025\t2026": 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hydrogenation to methanol\t2029\t2032": 0.0, "Carbon dioxide hydrogenation to methanol\t2029\t2033": 0.0, "Carbon dioxide hydrogenation to methanol\t2029\t2034": 0.0, "Carbon dioxide hydrogenation to methanol\t2029\t2035": 0.0, "Carbon dioxide hydrogenation to methanol\t2029\t2036": 0.0, "Carbon dioxide hydrogenation to methanol\t2029\t2037": 0.0, "Carbon dioxide hydrogenation to methanol\t2029\t2038": 0.0, "Carbon dioxide hydrogenation to methanol\t2029\t2039": 0.0, "Carbon dioxide hydrogenation to methanol\t2029\t2040": 0.0, "Carbon dioxide hydrogenation to methanol\t2029\t2041": 0.0, "Carbon dioxide hydrogenation to methanol\t2029\t2042": 0.0, "Carbon dioxide hydrogenation to methanol\t2029\t2043": 0.0, "Carbon dioxide hydrogenation to methanol\t2029\t2044": 0.0, "Carbon dioxide hydrogenation to methanol\t2030\t2030": 0.0, "Carbon dioxide hydrogenation to methanol\t2030\t2031": 0.0, "Carbon dioxide hydrogenation to methanol\t2030\t2032": 0.0, "Carbon dioxide hydrogenation to 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diff --git a/notebooks/data/paper/evolution_results.json b/notebooks/data/paper/evolution_results.json
new file mode 100644
index 0000000..1595227
--- /dev/null
+++ b/notebooks/data/paper/evolution_results.json
@@ -0,0 +1 @@
+{"obj_evolution": 1.8174601463918366e-06, "climate_per_year": {"2020": 0.0, "2021": 0.0, "2022": 0.0, "2023": 0.0, "2024": 0.0, "2025": 1.9373475432753717e-07, "2026": 1.9161853870933812e-07, "2027": 1.8950880569191958e-07, "2028": 1.7964941648052274e-07, "2029": 1.6444800397808597e-07, "2030": 1.5118373648857862e-07, "2031": 1.553014442819613e-07, "2032": 1.597362857345075e-07, "2033": 1.553194523762663e-07, "2034": 1.2802992807413454e-07, "2035": 1.0722305731111617e-07, "2036": 8.71609696633349e-08, "2037": 6.707900010484075e-08, "2038": 4.602587870281385e-08, "2039": 2.7412137909256174e-08, "2040": 8.29472526569706e-09, "2041": 1.9942170743679617e-09, "2042": -8.396647137755386e-10, "2043": -8.09517111735208e-09, "2044": -1.2973750942355845e-08, "2045": -1.699811381316972e-08, "2046": -2.2644060113871463e-08, "2047": -2.719060276750607e-08, "2048": -3.162386474786534e-08, "2049": -3.572172378182036e-08, "2050": -4.0173253784725643e-08}}
\ No newline at end of file
diff --git a/notebooks/methanol_and_iron.ipynb b/notebooks/methanol_and_iron.ipynb
index 88e7da5..e6270bc 100644
--- a/notebooks/methanol_and_iron.ipynb
+++ b/notebooks/methanol_and_iron.ipynb
@@ -39,6 +39,16 @@
     "bd.projects.set_current(\"optimex_remind\")"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b2a6349c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bd.databases"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "357d08f8",
@@ -211,7 +221,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "8f462215",
    "metadata": {},
    "outputs": [],
@@ -282,7 +292,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "bddca945",
    "metadata": {},
    "outputs": [],
@@ -306,7 +316,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "id": "c77cdfca",
    "metadata": {},
    "outputs": [],
@@ -323,7 +333,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "id": "4fcc3280",
    "metadata": {},
    "outputs": [],
@@ -398,7 +408,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "id": "c48024d8",
    "metadata": {},
    "outputs": [],
@@ -488,7 +498,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "id": "fe43d5df",
    "metadata": {},
    "outputs": [],
@@ -580,7 +590,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "id": "cc78a175",
    "metadata": {},
    "outputs": [],
@@ -597,7 +607,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "id": "9fc9b701",
    "metadata": {},
    "outputs": [],
@@ -729,7 +739,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "id": "ead19895",
    "metadata": {},
    "outputs": [],
@@ -746,7 +756,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "id": "45d16a6c",
    "metadata": {},
    "outputs": [],
@@ -861,7 +871,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "id": "0a57239d",
    "metadata": {},
    "outputs": [],
@@ -878,7 +888,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "id": "0603d260",
    "metadata": {},
    "outputs": [],
@@ -958,7 +968,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "id": "6b84fb28",
    "metadata": {},
    "outputs": [],
@@ -1081,7 +1091,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
    "id": "5b86072c",
    "metadata": {},
    "outputs": [],
@@ -1125,7 +1135,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 23,
    "id": "183bd009",
    "metadata": {},
    "outputs": [],
@@ -1156,7 +1166,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 24,
    "id": "debee4d1",
    "metadata": {},
    "outputs": [],
@@ -1194,10 +1204,2142 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 25,
    "id": "b590bffc",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[32m2026-05-28 09:49:02.478\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36moptimex.lca_processor\u001b[0m:\u001b[36m_parse_demand\u001b[0m:\u001b[36m428\u001b[0m - \u001b[1mIdentified demand in system time range of %s for products %s\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:49:02.491\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36moptimex.lca_processor\u001b[0m:\u001b[36m_construct_foreground_tensors\u001b[0m:\u001b[36m661\u001b[0m - \u001b[1mConstructed foreground tensors.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:49:02.492\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36moptimex.lca_processor\u001b[0m:\u001b[36mlog_tensor_dimensions\u001b[0m:\u001b[36m656\u001b[0m - \u001b[1mTechnosphere (external) shape: (7 processes, 20 flows, 26 years) with 577 total entries.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:49:02.492\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36moptimex.lca_processor\u001b[0m:\u001b[36mlog_tensor_dimensions\u001b[0m:\u001b[36m656\u001b[0m - \u001b[1mInternal demand shape: (2 processes, 2 flows, 26 years) with 58 total entries.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:49:02.492\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36moptimex.lca_processor\u001b[0m:\u001b[36mlog_tensor_dimensions\u001b[0m:\u001b[36m656\u001b[0m - \u001b[1mBiosphere shape: (6 processes, 4 flows, 26 years) with 292 total entries.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:49:02.493\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36moptimex.lca_processor\u001b[0m:\u001b[36mlog_tensor_dimensions\u001b[0m:\u001b[36m656\u001b[0m - \u001b[1mProduction shape: (7 processes, 4 flows, 26 years) with 171 total entries.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:49:02.493\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36moptimex.lca_processor\u001b[0m:\u001b[36m_calculate_inventory_of_db\u001b[0m:\u001b[36m702\u001b[0m - \u001b[1mCalculating inventory for database: ei312_REMIND-EU_SSP2_NDC_2020\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:49:23.255\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36moptimex.lca_processor\u001b[0m:\u001b[36m_calculate_inventory_of_db\u001b[0m:\u001b[36m735\u001b[0m - \u001b[1mFactorized LCI for database: ei312_REMIND-EU_SSP2_NDC_2020\u001b[0m\n",
+      "100%|██████████| 20/20 [00:40<00:00,  2.01s/it]\n",
+      "\u001b[32m2026-05-28 09:50:03.414\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36moptimex.lca_processor\u001b[0m:\u001b[36m_calculate_inventory_of_db\u001b[0m:\u001b[36m775\u001b[0m - \u001b[1mFinished calculating inventory for database: ei312_REMIND-EU_SSP2_NDC_2020\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:50:03.420\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36moptimex.lca_processor\u001b[0m:\u001b[36m_calculate_inventory_of_db\u001b[0m:\u001b[36m702\u001b[0m - \u001b[1mCalculating inventory for database: ei312_REMIND-EU_SSP2_NDC_2030\u001b[0m\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 2.80e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "\u001b[32m2026-05-28 09:50:24.820\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36moptimex.lca_processor\u001b[0m:\u001b[36m_calculate_inventory_of_db\u001b[0m:\u001b[36m735\u001b[0m - \u001b[1mFactorized LCI for database: ei312_REMIND-EU_SSP2_NDC_2030\u001b[0m\n",
+      "  0%|          | 0/20 [00:00<?, ?it/s]/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 2.80e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 2.80e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 2.80e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "  5%|▌         | 1/20 [00:02<00:41,  2.20s/it]/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 2.80e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 2.80e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 2.80e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 2.80e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      " 10%|█         | 2/20 [00:04<00:37,  2.09s/it]/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 2.80e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 2.80e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 2.80e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 2.80e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      " 15%|█▌        | 3/20 [00:06<00:35,  2.07s/it]/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 2.80e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 2.80e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 2.80e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 2.80e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      " 20%|██        | 4/20 [00:08<00:33,  2.12s/it]/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 2.80e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 2.80e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 2.80e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 2.80e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      " 25%|██▌       | 5/20 [00:10<00:31,  2.10s/it]/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 2.80e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 2.80e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 2.80e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 2.80e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      " 30%|███       | 6/20 [00:12<00:29,  2.08s/it]/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 2.80e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 2.80e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 2.80e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 2.80e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      " 35%|███▌      | 7/20 [00:14<00:26,  2.07s/it]/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 2.80e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 2.80e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 2.80e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 2.80e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      " 40%|████      | 8/20 [00:16<00:24,  2.06s/it]/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 2.80e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 2.80e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 2.80e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 2.80e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      " 45%|████▌     | 9/20 [00:18<00:22,  2.05s/it]/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 2.80e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 2.80e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 2.80e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 2.80e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      " 50%|█████     | 10/20 [00:20<00:20,  2.08s/it]/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 2.80e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 2.80e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 2.80e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 2.80e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      " 55%|█████▌    | 11/20 [00:23<00:19,  2.16s/it]/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 2.80e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 2.80e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 2.80e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 2.80e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      " 60%|██████    | 12/20 [00:25<00:17,  2.19s/it]/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 2.80e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 2.80e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 2.80e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 2.80e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      " 65%|██████▌   | 13/20 [00:27<00:15,  2.18s/it]/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 2.80e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 2.80e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 2.80e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 2.80e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      " 70%|███████   | 14/20 [00:29<00:13,  2.20s/it]/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 2.80e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 2.80e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 2.80e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 2.80e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      " 75%|███████▌  | 15/20 [00:31<00:10,  2.19s/it]/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 2.80e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 2.80e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 2.80e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 2.80e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      " 80%|████████  | 16/20 [00:34<00:08,  2.21s/it]/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 2.80e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 2.80e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 2.80e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 2.80e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      " 85%|████████▌ | 17/20 [00:36<00:06,  2.19s/it]/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 2.80e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 2.80e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 2.80e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 2.80e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      " 90%|█████████ | 18/20 [00:38<00:04,  2.18s/it]/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 2.80e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 2.80e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 2.80e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 2.80e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      " 95%|█████████▌| 19/20 [00:40<00:02,  2.19s/it]/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 2.80e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 2.80e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 2.80e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 2.80e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "100%|██████████| 20/20 [00:43<00:00,  2.15s/it]\n",
+      "\u001b[32m2026-05-28 09:51:07.904\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36moptimex.lca_processor\u001b[0m:\u001b[36m_calculate_inventory_of_db\u001b[0m:\u001b[36m775\u001b[0m - \u001b[1mFinished calculating inventory for database: ei312_REMIND-EU_SSP2_NDC_2030\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:51:07.914\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36moptimex.lca_processor\u001b[0m:\u001b[36m_calculate_inventory_of_db\u001b[0m:\u001b[36m702\u001b[0m - \u001b[1mCalculating inventory for database: ei312_REMIND-EU_SSP2_NDC_2040\u001b[0m\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.54e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "\u001b[32m2026-05-28 09:51:30.565\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36moptimex.lca_processor\u001b[0m:\u001b[36m_calculate_inventory_of_db\u001b[0m:\u001b[36m735\u001b[0m - \u001b[1mFactorized LCI for database: ei312_REMIND-EU_SSP2_NDC_2040\u001b[0m\n",
+      "  0%|          | 0/20 [00:00<?, ?it/s]/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.54e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.54e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.54e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "  5%|▌         | 1/20 [00:02<00:42,  2.22s/it]/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.54e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.54e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.54e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.54e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      " 10%|█         | 2/20 [00:04<00:38,  2.14s/it]/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.54e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.54e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.54e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.54e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      " 15%|█▌        | 3/20 [00:06<00:35,  2.08s/it]/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.54e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.54e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.54e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.54e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      " 20%|██        | 4/20 [00:08<00:33,  2.06s/it]/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.54e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.54e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.54e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.54e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      " 25%|██▌       | 5/20 [00:10<00:30,  2.04s/it]/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.54e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.54e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.54e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.54e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      " 30%|███       | 6/20 [00:12<00:29,  2.08s/it]/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.54e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.54e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.54e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.54e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      " 35%|███▌      | 7/20 [00:14<00:26,  2.06s/it]/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.54e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.54e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.54e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.54e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      " 40%|████      | 8/20 [00:16<00:24,  2.07s/it]/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.54e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.54e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.54e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.54e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      " 45%|████▌     | 9/20 [00:18<00:23,  2.11s/it]/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.54e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.54e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.54e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.54e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      " 50%|█████     | 10/20 [00:20<00:20,  2.08s/it]/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.54e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.54e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.54e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.54e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      " 55%|█████▌    | 11/20 [00:22<00:18,  2.09s/it]/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.54e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.54e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.54e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.54e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      " 60%|██████    | 12/20 [00:25<00:16,  2.09s/it]/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.54e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.54e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.54e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.54e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      " 65%|██████▌   | 13/20 [00:27<00:14,  2.12s/it]/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.54e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.54e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.54e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.54e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      " 70%|███████   | 14/20 [00:29<00:12,  2.14s/it]/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.54e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.54e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.54e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.54e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      " 75%|███████▌  | 15/20 [00:31<00:10,  2.14s/it]/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.54e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.54e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.54e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.54e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      " 80%|████████  | 16/20 [00:33<00:08,  2.12s/it]/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.54e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.54e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.54e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.54e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      " 85%|████████▌ | 17/20 [00:35<00:06,  2.11s/it]/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.54e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.54e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.54e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.54e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      " 90%|█████████ | 18/20 [00:37<00:04,  2.09s/it]/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.54e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.54e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.54e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.54e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      " 95%|█████████▌| 19/20 [00:39<00:02,  2.07s/it]/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.54e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.54e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.54e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.54e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "100%|██████████| 20/20 [00:42<00:00,  2.10s/it]\n",
+      "\u001b[32m2026-05-28 09:52:12.582\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36moptimex.lca_processor\u001b[0m:\u001b[36m_calculate_inventory_of_db\u001b[0m:\u001b[36m775\u001b[0m - \u001b[1mFinished calculating inventory for database: ei312_REMIND-EU_SSP2_NDC_2040\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:52:12.594\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36moptimex.lca_processor\u001b[0m:\u001b[36m_calculate_inventory_of_db\u001b[0m:\u001b[36m702\u001b[0m - \u001b[1mCalculating inventory for database: ei312_REMIND-EU_SSP2_NDC_2050\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:52:34.133\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36moptimex.lca_processor\u001b[0m:\u001b[36m_calculate_inventory_of_db\u001b[0m:\u001b[36m735\u001b[0m - \u001b[1mFactorized LCI for database: ei312_REMIND-EU_SSP2_NDC_2050\u001b[0m\n",
+      "100%|██████████| 20/20 [00:42<00:00,  2.12s/it]\n",
+      "\u001b[32m2026-05-28 09:53:16.463\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36moptimex.lca_processor\u001b[0m:\u001b[36m_calculate_inventory_of_db\u001b[0m:\u001b[36m775\u001b[0m - \u001b[1mFinished calculating inventory for database: ei312_REMIND-EU_SSP2_NDC_2050\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:53:16.473\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36moptimex.lca_processor\u001b[0m:\u001b[36m_calculate_inventory_of_db\u001b[0m:\u001b[36m702\u001b[0m - \u001b[1mCalculating inventory for database: ei312_REMIND-EU_SSP2_NDC_2075\u001b[0m\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 3.44e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "\u001b[32m2026-05-28 09:53:38.415\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36moptimex.lca_processor\u001b[0m:\u001b[36m_calculate_inventory_of_db\u001b[0m:\u001b[36m735\u001b[0m - \u001b[1mFactorized LCI for database: ei312_REMIND-EU_SSP2_NDC_2075\u001b[0m\n",
+      "  0%|          | 0/20 [00:00<?, ?it/s]/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 3.44e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 3.44e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 3.44e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "  5%|▌         | 1/20 [00:02<00:43,  2.27s/it]/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 3.44e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 3.44e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 3.44e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 3.44e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      " 10%|█         | 2/20 [00:04<00:38,  2.12s/it]/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 3.44e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 3.44e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 3.44e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 3.44e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      " 15%|█▌        | 3/20 [00:06<00:35,  2.11s/it]/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 3.44e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 3.44e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 3.44e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 3.44e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      " 20%|██        | 4/20 [00:08<00:33,  2.12s/it]/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 3.44e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 3.44e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 3.44e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 3.44e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      " 25%|██▌       | 5/20 [00:10<00:31,  2.12s/it]/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 3.44e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 3.44e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 3.44e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 3.44e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      " 30%|███       | 6/20 [00:12<00:29,  2.10s/it]/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 3.44e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 3.44e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 3.44e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 3.44e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      " 35%|███▌      | 7/20 [00:14<00:26,  2.06s/it]/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 3.44e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 3.44e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 3.44e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 3.44e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      " 40%|████      | 8/20 [00:16<00:25,  2.10s/it]/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 3.44e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 3.44e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 3.44e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 3.44e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      " 45%|████▌     | 9/20 [00:19<00:23,  2.15s/it]/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 3.44e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 3.44e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 3.44e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 3.44e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      " 50%|█████     | 10/20 [00:23<00:26,  2.69s/it]/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 3.44e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 3.44e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 3.44e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 3.44e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      " 55%|█████▌    | 11/20 [00:26<00:25,  2.87s/it]/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 3.44e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 3.44e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 3.44e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 3.44e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      " 60%|██████    | 12/20 [00:28<00:22,  2.81s/it]/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 3.44e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 3.44e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 3.44e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 3.44e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      " 65%|██████▌   | 13/20 [00:31<00:18,  2.70s/it]/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 3.44e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 3.44e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 3.44e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 3.44e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      " 70%|███████   | 14/20 [00:33<00:15,  2.55s/it]/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 3.44e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 3.44e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 3.44e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 3.44e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      " 75%|███████▌  | 15/20 [00:36<00:12,  2.58s/it]/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 3.44e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 3.44e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 3.44e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 3.44e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      " 80%|████████  | 16/20 [00:38<00:10,  2.50s/it]/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 3.44e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 3.44e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 3.44e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 3.44e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      " 85%|████████▌ | 17/20 [00:41<00:07,  2.55s/it]/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 3.44e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 3.44e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 3.44e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 3.44e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      " 90%|█████████ | 18/20 [00:43<00:05,  2.53s/it]/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 3.44e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 3.44e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 3.44e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 3.44e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      " 95%|█████████▌| 19/20 [00:45<00:02,  2.43s/it]/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 3.44e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 3.44e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 3.44e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 3.44e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "100%|██████████| 20/20 [00:48<00:00,  2.42s/it]\n",
+      "\u001b[32m2026-05-28 09:54:26.779\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36moptimex.lca_processor\u001b[0m:\u001b[36m_calculate_inventory_of_db\u001b[0m:\u001b[36m775\u001b[0m - \u001b[1mFinished calculating inventory for database: ei312_REMIND-EU_SSP2_NDC_2075\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:54:26.790\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36moptimex.lca_processor\u001b[0m:\u001b[36m_calculate_inventory_of_db\u001b[0m:\u001b[36m702\u001b[0m - \u001b[1mCalculating inventory for database: ei312_REMIND-EU_SSP2_NDC_2100\u001b[0m\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.64e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "\u001b[32m2026-05-28 09:54:49.189\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36moptimex.lca_processor\u001b[0m:\u001b[36m_calculate_inventory_of_db\u001b[0m:\u001b[36m735\u001b[0m - \u001b[1mFactorized LCI for database: ei312_REMIND-EU_SSP2_NDC_2100\u001b[0m\n",
+      "  0%|          | 0/20 [00:00<?, ?it/s]/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.64e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.64e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.64e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "  5%|▌         | 1/20 [00:02<00:40,  2.15s/it]/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.64e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.64e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.64e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.64e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      " 10%|█         | 2/20 [00:04<00:37,  2.07s/it]/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.64e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.64e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.64e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.64e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      " 15%|█▌        | 3/20 [00:06<00:34,  2.03s/it]/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.64e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.64e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.64e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.64e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      " 20%|██        | 4/20 [00:08<00:32,  2.04s/it]/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.64e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.64e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.64e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.64e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      " 25%|██▌       | 5/20 [00:10<00:30,  2.03s/it]/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.64e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.64e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.64e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.64e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      " 30%|███       | 6/20 [00:12<00:30,  2.19s/it]/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.64e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.64e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.64e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.64e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      " 35%|███▌      | 7/20 [00:14<00:28,  2.16s/it]/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.64e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.64e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.64e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.64e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      " 40%|████      | 8/20 [00:17<00:28,  2.39s/it]/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.64e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.64e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.64e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.64e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      " 45%|████▌     | 9/20 [00:20<00:27,  2.46s/it]/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.64e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.64e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.64e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.64e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      " 50%|█████     | 10/20 [00:22<00:23,  2.38s/it]/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.64e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.64e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.64e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.64e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      " 55%|█████▌    | 11/20 [00:24<00:20,  2.28s/it]/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.64e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.64e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.64e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.64e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      " 60%|██████    | 12/20 [00:26<00:17,  2.20s/it]/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.64e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.64e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.64e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.64e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      " 65%|██████▌   | 13/20 [00:28<00:15,  2.15s/it]/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.64e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.64e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.64e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.64e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      " 70%|███████   | 14/20 [00:31<00:13,  2.25s/it]/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.64e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.64e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.64e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.64e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      " 75%|███████▌  | 15/20 [00:33<00:11,  2.25s/it]/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.64e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.64e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.64e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.64e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      " 80%|████████  | 16/20 [00:35<00:09,  2.26s/it]/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.64e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.64e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.64e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.64e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      " 85%|████████▌ | 17/20 [00:37<00:06,  2.25s/it]/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.64e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.64e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.64e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.64e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      " 90%|█████████ | 18/20 [00:39<00:04,  2.21s/it]/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.64e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.64e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.64e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.64e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      " 95%|█████████▌| 19/20 [00:42<00:02,  2.44s/it]/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.64e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.64e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.64e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "/Users/timodiepers/Documents/Coding/optimex/.venv/lib/python3.13/site-packages/scikits/umfpack/umfpack.py:737: UmfpackWarning: (almost) singular matrix! (estimated cond. number: 1.64e+12)\n",
+      "  warnings.warn(msg, UmfpackWarning)\n",
+      "100%|██████████| 20/20 [00:45<00:00,  2.29s/it]\n",
+      "\u001b[32m2026-05-28 09:55:35.012\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36moptimex.lca_processor\u001b[0m:\u001b[36m_calculate_inventory_of_db\u001b[0m:\u001b[36m775\u001b[0m - \u001b[1mFinished calculating inventory for database: ei312_REMIND-EU_SSP2_NDC_2100\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:35.028\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36moptimex.lca_processor\u001b[0m:\u001b[36m_prepare_background_inventory\u001b[0m:\u001b[36m882\u001b[0m - \u001b[1mComputed background inventory using method: sequential\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:35.135\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:35.313\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:35.432\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:35.433\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:35.548\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:35.550\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:35.667\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:35.668\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:35.789\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:35.790\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:35.906\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:35.908\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:36.025\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:36.026\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:36.143\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:36.144\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:36.262\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:36.263\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:36.379\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:36.381\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:36.500\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:36.501\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:36.618\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:36.619\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:36.734\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:36.736\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:36.855\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:36.856\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:36.985\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:36.986\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:37.106\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:37.107\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:37.222\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:37.223\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:37.339\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:37.340\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:37.457\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:37.458\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:37.575\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:37.577\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:37.691\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:37.692\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:37.809\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:37.810\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:37.927\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:37.928\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:38.045\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:38.047\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:38.161\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:38.163\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:38.283\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:38.399\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:38.401\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:38.520\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:38.521\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:38.636\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:38.637\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:38.764\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:38.765\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:38.882\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:38.883\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:39.000\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:39.001\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:39.117\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:39.118\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:39.235\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:39.236\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:39.353\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:39.355\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:39.473\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:39.474\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:39.590\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:39.592\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:39.734\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:39.735\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:39.852\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:39.853\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:39.976\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:39.977\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:40.123\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:40.124\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:40.241\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:40.243\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:40.365\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:40.366\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:40.484\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:40.486\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:40.603\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:40.604\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:40.719\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:40.720\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:40.840\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:40.841\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:40.956\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:40.957\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:41.075\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:41.076\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:41.191\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:41.192\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:41.309\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:41.310\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:41.423\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:41.424\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:41.541\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:41.542\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:41.657\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:41.658\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:41.783\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:41.784\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:41.899\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:41.900\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:42.018\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:42.019\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:42.134\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:42.135\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:42.252\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:42.253\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:42.370\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:42.371\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:42.489\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:42.491\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:42.607\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:42.608\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:42.726\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:42.727\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:42.842\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:42.843\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:42.960\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:42.961\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:43.084\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:43.085\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:43.249\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:43.251\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:43.367\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:43.368\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:43.485\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:43.486\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:43.602\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:43.603\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:43.722\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:43.723\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:43.840\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:43.841\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:43.959\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:43.960\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:44.075\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:44.076\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:44.194\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:44.195\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:44.310\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:44.311\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:44.435\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:44.436\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:44.550\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:44.552\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:44.668\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:44.669\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:44.784\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:44.785\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:44.901\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:44.902\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:45.017\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:45.018\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:45.135\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:45.136\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:45.251\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:45.252\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:45.370\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:45.372\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:45.487\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:45.488\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:45.614\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:45.615\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:45.729\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:45.730\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:45.851\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:45.966\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:45.967\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:46.083\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:46.084\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:46.201\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:46.202\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:46.319\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:46.321\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:46.435\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:46.436\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:46.554\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:46.555\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:46.670\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:46.671\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:46.796\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:46.797\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:46.912\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:46.913\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:47.031\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:47.032\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:47.148\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:47.149\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:47.270\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:47.272\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:47.390\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:47.391\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:47.513\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:47.514\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:47.653\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:47.654\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:47.779\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:47.781\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:47.909\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:47.911\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:48.030\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:48.032\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:48.149\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:48.151\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:48.272\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:48.273\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:48.390\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:48.392\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:48.538\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:48.539\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:48.717\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:48.718\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:48.862\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:48.868\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:48.988\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:48.990\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:49.116\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:49.117\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:49.272\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:49.395\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:49.397\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:49.542\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:49.543\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:49.717\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:49.718\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:49.837\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:49.838\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:49.992\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:49.993\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:50.115\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:50.116\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:50.240\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:50.241\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:50.368\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:50.369\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:50.490\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:50.492\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:50.613\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:50.615\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:50.735\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:50.736\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:50.856\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:50.857\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:50.990\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:50.992\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:51.120\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:51.122\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:51.243\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:51.244\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:51.372\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:51.373\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:51.494\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:51.496\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:51.616\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:51.617\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:51.733\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:51.734\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:51.855\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:51.856\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:51.978\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:51.979\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:52.100\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:52.101\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:52.218\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:52.219\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:52.351\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:52.353\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:52.470\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:52.471\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:52.592\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:52.593\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:52.711\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:52.712\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:52.833\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:52.835\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:52.951\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:52.952\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:53.074\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:53.075\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:53.238\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:53.240\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:53.370\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:53.372\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:53.490\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:53.491\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:53.610\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:53.612\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:53.731\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:53.732\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:53.850\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:53.851\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:53.966\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:53.967\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:54.089\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:54.209\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:54.211\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:54.355\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:54.357\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:54.477\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:54.478\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:54.602\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:54.603\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:54.722\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:54.723\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:54.842\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:54.843\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:54.981\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:54.982\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:55.102\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:55.103\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:55.229\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:55.230\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:55.348\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:55.350\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:55.468\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:55.469\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:55.592\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:55.734\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:55.740\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:55.894\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:55.896\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:56.044\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:56.046\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:56.184\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:56.186\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:56.318\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:56.319\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:56.476\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:56.478\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:56.630\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:56.632\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:56.782\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:56.784\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:56.920\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:56.921\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:57.049\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:57.050\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:57.184\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:57.185\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:57.309\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:57.311\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:57.437\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:57.438\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:57.567\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:57.569\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:57.693\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:57.695\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:57.826\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:57.828\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:57.978\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:57.988\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:58.150\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:58.279\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:58.408\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:58.409\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:58.534\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:58.535\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:58.661\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:58.662\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:58.787\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:58.788\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:58.979\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:58.980\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:59.120\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:59.121\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:59.288\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:59.407\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:59.408\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:59.547\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:59.549\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:59.668\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:59.669\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:59.790\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:59.791\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:55:59.912\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:00.043\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:00.044\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:00.163\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:00.164\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:00.285\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:00.287\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:00.412\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:00.413\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:00.532\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:00.533\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:00.648\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:00.650\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:00.772\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:00.905\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:00.906\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:01.029\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:01.030\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:01.156\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:01.157\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:01.283\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:01.284\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:01.409\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:01.410\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:01.541\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:01.543\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:01.665\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:01.666\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:01.854\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:01.855\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:02.003\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:02.005\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:02.137\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:02.138\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:02.262\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:02.263\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:02.394\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:02.396\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:02.523\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:02.524\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:02.645\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:02.646\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:02.763\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:02.765\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:02.890\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:02.891\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:03.008\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:03.010\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:03.146\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:03.147\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:03.349\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:03.351\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:03.499\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:03.501\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:03.633\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:03.634\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:03.760\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:03.761\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:03.885\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:03.886\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:04.011\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:04.012\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:04.139\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:04.141\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:04.261\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:04.263\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:04.381\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:04.382\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:04.503\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:04.505\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:04.626\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:04.627\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:04.747\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:04.748\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:04.874\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:04.875\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:04.995\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:04.996\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:05.148\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:05.149\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:05.268\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:05.269\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:05.385\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:05.386\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:05.504\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:05.505\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:05.631\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:05.633\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:05.750\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:05.751\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:05.870\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:05.871\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:05.990\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:05.991\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:06.110\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:06.112\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:06.235\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:06.236\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:06.367\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:06.368\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:06.487\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:06.488\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:06.606\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:06.608\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:06.734\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:06.735\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:06.874\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:06.877\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:07.041\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:07.044\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:07.190\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:07.192\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:07.326\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:07.328\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:07.458\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:07.460\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:07.595\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:07.598\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:07.724\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:07.725\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:07.873\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:07.875\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:08.054\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:08.055\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:08.186\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:08.187\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:08.312\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:08.313\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:08.440\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:08.442\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:08.573\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:08.574\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:08.697\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:08.699\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:08.824\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:08.825\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:08.944\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:08.945\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:09.063\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:09.064\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:09.184\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:09.185\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:09.309\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:09.310\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:09.439\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:09.441\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:09.571\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:09.572\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:09.704\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:09.822\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:09.824\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:09.948\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:09.950\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:10.069\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:10.070\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:10.199\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:10.201\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:10.321\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:10.322\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:10.464\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:10.588\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:10.589\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:10.711\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:10.712\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:10.831\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:10.832\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:11.015\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:11.017\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:11.159\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:11.160\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:11.307\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:11.309\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:11.446\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:11.447\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:11.635\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:11.639\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:11.965\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:11.966\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:12.095\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:12.097\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:12.219\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:12.221\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:12.354\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:12.356\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:12.490\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:12.491\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:12.630\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:12.632\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:12.758\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:12.759\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:12.987\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:12.988\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:13.113\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:13.114\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:13.237\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:13.238\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:13.379\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:13.382\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:13.537\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:13.539\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:13.665\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:13.787\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:13.788\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:13.906\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:13.908\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:14.041\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:14.043\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:14.161\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:14.162\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:14.387\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:14.388\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:14.507\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:14.508\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:14.628\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:14.629\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:14.760\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:14.762\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:14.886\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:14.887\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:15.015\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:15.017\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:15.142\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:15.143\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:15.268\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:15.270\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:15.401\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:15.402\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:15.519\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:15.521\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:15.640\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:15.641\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:15.804\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:15.932\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:15.933\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:16.060\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:16.062\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:16.183\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:16.184\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:16.311\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:16.312\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:16.436\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:16.437\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:16.592\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:16.593\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:16.724\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:16.725\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:16.842\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:16.843\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:16.964\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:16.965\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:17.084\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:17.085\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:17.205\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:17.206\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:17.335\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:17.336\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:17.454\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:17.455\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:17.577\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:17.578\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:17.696\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:17.697\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:17.821\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:17.943\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:17.945\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:18.065\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:18.066\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:18.185\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:18.186\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:18.314\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:18.456\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:18.457\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:18.585\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:18.586\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:18.732\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:18.852\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:18.853\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:18.970\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:18.971\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:19.093\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:19.094\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:19.234\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:19.235\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:19.377\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:19.378\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:19.499\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:19.500\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:19.621\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:19.622\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:19.745\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:19.747\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:19.881\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:19.883\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:20.003\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:20.004\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:20.141\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:20.143\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:20.276\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:20.277\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:20.432\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:20.433\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:20.566\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:20.567\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:20.689\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:20.691\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:20.808\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:20.809\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:20.949\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:20.953\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:21.090\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:21.096\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:21.235\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:21.237\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:21.353\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:21.354\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:21.473\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:21.475\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:21.591\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:21.592\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:21.712\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:21.714\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:21.842\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:21.843\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:21.977\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:21.979\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:22.146\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:22.149\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:22.287\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:22.288\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:22.404\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:22.406\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:22.533\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:22.534\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:22.651\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:22.653\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:22.777\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:22.895\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:22.897\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:23.017\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:23.018\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:23.153\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:23.155\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:23.273\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:23.274\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:23.394\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:23.396\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:23.518\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:23.519\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:23.640\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:23.641\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:23.781\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:23.782\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:23.902\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:23.904\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:24.024\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:24.025\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:24.145\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:24.147\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:24.269\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:24.270\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:24.434\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:24.435\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:24.619\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:24.621\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:24.757\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:24.758\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:24.884\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:24.885\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:25.023\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:25.024\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:25.151\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:25.451\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:25.455\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:25.603\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:25.604\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:25.765\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:25.766\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:26.097\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:26.100\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:26.242\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:26.243\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:26.513\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:26.572\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:26.712\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:26.713\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:26.834\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:26.836\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:26.956\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:26.957\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:27.081\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:27.082\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:27.209\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:27.210\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:27.331\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:27.333\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:27.676\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:27.976\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:27.979\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:28.114\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:28.115\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:28.240\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:28.242\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:28.363\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:28.364\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:28.494\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:28.495\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:28.640\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:28.641\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:28.762\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:28.764\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:28.886\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:28.888\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:29.076\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:29.210\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:29.211\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:29.335\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:29.336\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:29.464\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:29.465\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:29.600\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:29.603\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:29.749\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:29.754\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:29.907\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:29.908\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:30.077\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:30.080\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:30.358\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:30.614\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:30.619\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:30.745\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:30.746\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:30.891\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:30.893\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:31.022\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:31.024\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:31.185\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:31.187\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:31.466\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:31.468\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:31.862\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:31.864\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:32.037\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:32.040\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:32.169\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:32.170\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:32.295\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:32.297\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:32.441\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:32.444\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:32.617\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:32.618\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:32.739\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:32.740\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:32.945\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:33.079\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:33.214\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:33.215\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:33.336\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:33.337\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:33.483\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:33.486\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:33.617\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:33.620\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:33.763\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:33.764\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:33.883\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:33.884\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:34.009\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:34.011\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:34.133\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:34.135\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:34.317\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:34.318\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:34.440\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:34.442\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:34.567\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:34.568\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:34.692\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:34.824\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:34.826\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:34.989\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:34.991\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:35.113\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:35.114\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:35.231\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:35.232\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:35.363\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:35.365\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:35.481\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:35.483\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:35.603\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:35.604\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:35.721\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:35.722\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:35.851\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:35.852\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:35.969\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:35.971\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:36.092\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:36.094\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:36.211\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:36.213\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:36.344\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:36.345\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:36.462\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:36.463\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:36.586\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:36.588\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:36.707\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:36.708\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:36.843\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:36.844\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:36.967\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:36.968\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:37.095\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:37.096\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:37.215\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:37.216\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:37.348\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:37.349\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:37.467\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:37.468\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:37.587\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:37.588\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:37.706\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:37.707\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:37.839\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:37.841\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:37.957\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:37.958\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:38.076\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:38.077\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:38.195\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:38.196\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:38.361\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:38.364\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:38.483\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:38.485\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:38.608\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:38.609\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:38.727\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:38.728\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:38.860\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:38.861\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:38.979\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:38.980\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:39.103\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:39.105\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:39.269\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:39.271\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:39.407\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:39.409\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:39.526\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:39.527\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:39.661\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:39.663\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:39.786\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:39.787\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:39.921\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:39.922\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:40.042\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:40.043\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:40.163\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:40.165\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:40.288\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:40.289\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:40.419\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:40.421\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:40.541\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:40.543\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:40.666\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:40.668\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:40.792\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:40.793\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:40.987\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:40.988\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:41.114\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:41.116\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:41.241\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:41.243\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:41.364\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:41.365\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:41.497\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:41.499\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:41.654\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:41.655\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:41.779\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:41.780\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:41.899\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:41.900\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:42.031\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:42.032\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:42.150\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:42.151\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:42.272\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:42.273\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:42.390\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:42.392\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:42.525\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:42.526\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:42.642\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:42.644\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:42.767\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:42.884\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:42.885\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:43.013\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:43.014\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:43.134\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:43.135\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:43.260\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:43.389\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:43.391\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:43.521\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:43.522\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:43.640\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:43.642\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:43.760\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:43.762\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:43.878\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:43.879\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:44.008\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:44.009\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:44.128\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:44.129\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:44.247\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:44.248\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:44.366\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:44.367\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:44.499\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:44.501\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:44.618\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:44.620\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:44.750\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:44.752\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:44.869\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:44.870\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:45.003\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:45.004\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:45.121\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:45.122\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:45.246\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:45.247\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:45.369\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:45.370\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:45.500\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:45.501\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:45.621\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:45.623\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:45.742\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:45.743\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:45.862\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:45.863\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:45.989\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:45.991\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:46.108\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:46.109\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:46.263\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:46.265\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:46.385\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:46.387\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:46.603\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:46.604\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:46.732\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:46.733\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:46.853\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:46.854\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:46.974\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:46.976\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:47.106\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:47.108\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:47.227\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:47.228\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:47.365\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:47.368\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:47.503\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:47.505\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:47.652\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:47.654\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:47.778\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:47.779\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:47.902\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:47.903\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:48.035\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:48.037\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:48.170\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:48.171\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:48.306\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:48.423\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:48.425\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:48.546\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:48.547\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:48.677\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:48.678\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:48.803\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:48.804\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:48.930\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:48.931\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:49.055\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:49.057\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:49.185\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:49.186\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:49.314\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:49.316\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:49.434\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:49.435\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:49.568\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:49.569\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:49.685\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:49.686\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:49.805\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:49.806\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:49.937\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:49.939\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:50.113\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:50.115\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:50.233\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:50.234\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:50.364\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:50.365\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:50.481\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:50.483\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:50.603\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:50.604\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:50.730\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:50.731\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:50.853\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:50.854\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:50.971\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:50.972\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:51.101\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:51.102\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:51.220\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:51.221\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:51.340\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:51.341\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:51.469\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:51.470\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:51.589\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:51.591\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:51.708\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:51.709\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:51.840\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:51.841\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:51.958\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:51.960\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:52.079\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:52.080\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:52.205\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:52.206\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:52.327\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:52.329\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:52.450\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:52.451\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:52.579\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:52.581\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:52.698\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:52.699\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:52.871\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:52.873\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:52.998\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:52.999\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:53.119\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:53.120\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:53.260\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:53.262\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:53.453\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:53.455\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:53.582\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:53.584\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:53.707\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:53.708\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:53.840\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:53.841\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:53.961\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:53.962\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:54.080\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:54.081\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:54.217\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:54.219\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:54.339\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:54.341\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:54.482\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:54.485\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:54.619\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:54.620\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:54.750\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:54.752\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:54.876\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:54.877\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:55.016\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:55.018\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:55.134\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:55.135\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:55.256\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:55.257\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:55.384\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:55.385\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:55.506\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:55.508\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:55.626\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:55.627\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:55.758\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:55.759\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:55.954\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:55.955\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:56.076\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:56.077\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:56.202\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:56.204\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:56.323\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:56.324\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:56.440\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:56.442\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:56.579\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:56.695\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:56.697\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:56.817\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:56.818\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:56.945\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:56.947\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:57.067\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:57.069\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:57.187\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:57.188\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:57.320\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:57.321\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:57.440\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:57.441\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:57.562\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:57.564\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:57.698\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:57.699\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:57.819\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:57.820\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:57.940\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:57.941\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:58.074\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:58.075\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:58.193\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:58.195\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:58.315\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:58.317\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:58.448\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:58.449\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:58.570\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:58.571\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:58.720\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:58.721\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:58.851\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:58.852\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:58.971\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:58.973\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:59.093\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:59.094\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:59.219\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:59.220\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:59.339\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:59.340\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:59.459\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:59.460\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:59.595\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:59.713\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:59.715\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:59.835\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:59.836\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:59.963\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:56:59.964\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:00.119\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:00.120\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:00.236\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:00.237\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:00.434\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:00.436\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:00.556\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:00.676\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:00.677\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:00.803\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:00.804\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:00.980\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:00.985\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:01.196\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:01.198\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:01.363\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:01.366\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:01.589\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:01.590\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:01.744\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:01.746\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:01.914\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:01.916\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:02.044\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:02.045\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:02.203\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:02.206\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:02.369\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:02.372\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:02.508\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:02.509\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:02.634\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:02.635\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:02.767\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:02.769\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:02.989\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:03.068\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:03.318\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:03.320\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:03.508\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:03.509\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:03.681\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:03.685\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:03.866\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:03.867\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:03.988\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:03.990\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:04.112\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:04.113\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:04.242\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:04.245\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:04.368\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:04.372\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:04.499\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:04.501\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:04.644\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:04.645\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:04.762\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:04.763\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:04.891\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:04.893\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:05.036\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:05.038\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:05.224\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:05.225\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:05.420\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:05.421\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:05.573\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:05.574\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:05.697\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:05.698\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:05.823\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:05.825\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:05.964\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:05.965\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:06.092\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:06.093\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:06.213\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:06.214\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:06.350\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:06.354\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:06.475\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:06.478\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:06.606\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:06.608\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:06.755\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:06.758\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:06.884\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:06.886\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:07.010\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:07.014\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:07.155\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:07.158\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:07.323\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:07.325\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:07.834\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:07.836\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:07.992\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:08.202\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:08.203\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:08.324\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:08.325\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:08.461\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:08.462\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:08.581\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:08.582\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:08.704\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:08.705\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:08.836\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:08.838\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:08.960\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:08.962\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:09.083\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:09.221\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:09.347\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:09.471\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:09.472\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:09.663\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:09.664\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:09.862\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:09.863\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:09.996\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:09.997\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:10.168\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:10.169\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:10.325\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:10.327\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:10.471\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:10.473\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:10.622\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:10.623\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:10.782\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:10.784\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:10.936\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:11.083\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:11.084\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:11.217\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:11.218\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:11.357\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:11.360\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:11.503\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:11.505\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:11.704\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:11.706\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:11.828\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:11.829\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:11.965\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:11.966\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:12.113\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:12.238\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:12.239\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:12.375\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:12.377\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:12.502\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:12.503\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:12.624\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:12.625\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:12.758\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:12.759\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:12.883\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:12.885\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:13.057\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:13.058\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:13.191\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:13.192\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:13.315\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:13.316\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:13.443\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:13.444\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:13.601\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:13.602\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:13.733\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:13.735\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:13.857\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:13.858\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:13.993\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:13.994\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:14.113\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:14.115\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:14.241\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:14.243\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:14.416\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:14.417\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:14.550\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:14.673\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:14.674\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:14.807\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:14.809\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:14.940\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:14.941\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:15.077\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:15.079\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:15.216\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:15.218\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:15.342\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:15.343\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m82\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:15.475\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m133\u001b[0m - \u001b[33m\u001b[1mThere are no flows to characterize. Please make sure your time horizon matches the timing of emissions and make sure there are characterization functions for the flows in the dynamic inventories.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:15.476\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36moptimex.lca_processor\u001b[0m:\u001b[36m_construct_characterization_tensor\u001b[0m:\u001b[36m1046\u001b[0m - \u001b[1mDynamic CRF characterization for climate_change completed.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:15.495\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36moptimex.lca_processor\u001b[0m:\u001b[36m_construct_characterization_tensor\u001b[0m:\u001b[36m993\u001b[0m - \u001b[1mStatic characterization for method particulate_matter completed.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:15.523\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36moptimex.lca_processor\u001b[0m:\u001b[36m_construct_characterization_tensor\u001b[0m:\u001b[36m993\u001b[0m - \u001b[1mStatic characterization for method land_use completed.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:15.534\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36moptimex.lca_processor\u001b[0m:\u001b[36m_construct_characterization_tensor\u001b[0m:\u001b[36m993\u001b[0m - \u001b[1mStatic characterization for method water_use completed.\u001b[0m\n",
+      "\u001b[32m2026-05-28 09:57:15.537\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36moptimex.lca_processor\u001b[0m:\u001b[36m_construct_mapping_matrix\u001b[0m:\u001b[36m936\u001b[0m - \u001b[1mConstructed mapping matrix for background databases based on linear interpolation.\u001b[0m\n"
+     ]
+    }
+   ],
    "source": [
     "from optimex import converter\n",
     "\n",
@@ -1208,13 +3350,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 27,
    "id": "5ee94233",
    "metadata": {},
    "outputs": [],
    "source": [
     "manager.save(\n",
-    "    \"data/model_inputs_2050.json\"\n",
+    "    \"data/2026-05-28_model_inputs_2050.json\"\n",
     ")  # if you want to save the model inputs to a file"
    ]
   },
@@ -1232,7 +3374,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "id": "432e9d02",
    "metadata": {},
    "outputs": [],
@@ -1258,7 +3400,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 31,
    "id": "27fb129c",
    "metadata": {},
    "outputs": [],
@@ -1283,7 +3425,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 5,
    "id": "c17653c6",
    "metadata": {},
    "outputs": [
@@ -1291,9 +3433,9 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\u001b[32m2026-02-11 11:19:16.373\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36moptimex.optimizer\u001b[0m:\u001b[36mcreate_model\u001b[0m:\u001b[36m116\u001b[0m - \u001b[1mCreating sets\u001b[0m\n",
-      "\u001b[32m2026-02-11 11:19:16.374\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36moptimex.optimizer\u001b[0m:\u001b[36mcreate_model\u001b[0m:\u001b[36m158\u001b[0m - \u001b[1mCreating parameters\u001b[0m\n",
-      "\u001b[32m2026-02-11 11:19:16.519\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36moptimex.optimizer\u001b[0m:\u001b[36mcreate_model\u001b[0m:\u001b[36m448\u001b[0m - \u001b[1mCreating variables\u001b[0m\n"
+      "\u001b[32m2026-05-28 08:17:16.390\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36moptimex.optimizer\u001b[0m:\u001b[36mcreate_model\u001b[0m:\u001b[36m116\u001b[0m - \u001b[1mCreating sets\u001b[0m\n",
+      "\u001b[32m2026-05-28 08:17:16.392\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36moptimex.optimizer\u001b[0m:\u001b[36mcreate_model\u001b[0m:\u001b[36m158\u001b[0m - \u001b[1mCreating parameters\u001b[0m\n",
+      "\u001b[32m2026-05-28 08:17:16.585\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36moptimex.optimizer\u001b[0m:\u001b[36mcreate_model\u001b[0m:\u001b[36m448\u001b[0m - \u001b[1mCreating variables\u001b[0m\n"
      ]
     }
    ],
@@ -1309,7 +3451,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 6,
    "id": "7b57537d",
    "metadata": {},
    "outputs": [
@@ -1317,9 +3459,9 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\u001b[32m2026-02-11 11:21:21.815\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36moptimex.optimizer\u001b[0m:\u001b[36msolve_model\u001b[0m:\u001b[36m1113\u001b[0m - \u001b[1mSolver [gurobi] termination: optimal\u001b[0m\n",
-      "\u001b[32m2026-02-11 11:22:08.049\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36moptimex.optimizer\u001b[0m:\u001b[36msolve_model\u001b[0m:\u001b[36m1127\u001b[0m - \u001b[1mObjective (scaled): 2778\u001b[0m\n",
-      "\u001b[32m2026-02-11 11:22:08.052\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36moptimex.optimizer\u001b[0m:\u001b[36msolve_model\u001b[0m:\u001b[36m1128\u001b[0m - \u001b[1mObjective (real):   4.62873e-06\u001b[0m\n"
+      "\u001b[32m2026-05-28 08:19:17.517\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36moptimex.optimizer\u001b[0m:\u001b[36msolve_model\u001b[0m:\u001b[36m1113\u001b[0m - \u001b[1mSolver [gurobi] termination: optimal\u001b[0m\n",
+      "\u001b[32m2026-05-28 08:20:04.327\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36moptimex.optimizer\u001b[0m:\u001b[36msolve_model\u001b[0m:\u001b[36m1127\u001b[0m - \u001b[1mObjective (scaled): 2778\u001b[0m\n",
+      "\u001b[32m2026-05-28 08:20:04.333\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36moptimex.optimizer\u001b[0m:\u001b[36msolve_model\u001b[0m:\u001b[36m1128\u001b[0m - \u001b[1mObjective (real):   4.62873e-06\u001b[0m\n"
      ]
     }
    ],
@@ -1408,6 +3550,34 @@
     "pp_baseline.df_impacts.to_excel(\"data/paper/impacts_no_evolution.xlsx\")"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0da5e14f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Persist the baseline DESIGN (real-unit decision variables) + objective so the\n",
+    "# Scenario can run WITHOUT keeping m_baseline in RAM.\n",
+    "import json as _json\n",
+    "import pyomo.environ as _pyo\n",
+    "\n",
+    "baseline_design = {\n",
+    "    \"obj_baseline\": float(obj_baseline),\n",
+    "    \"installation\": {\n",
+    "        f\"{p}\\t{t}\": float(_pyo.value(m_baseline.var_installation[p, t]))\n",
+    "        for p in m_baseline.PROCESS\n",
+    "        for t in m_baseline.SYSTEM_TIME\n",
+    "    },\n",
+    "    \"operation\": {\n",
+    "        f\"{p}\\t{v}\\t{t}\": float(_pyo.value(m_baseline.var_operation[p, v, t]))\n",
+    "        for (p, v, t) in m_baseline.ACTIVE_VINTAGE_TIME\n",
+    "    },\n",
+    "}\n",
+    "with open(\"data/paper/baseline_design.json\", \"w\") as _f:\n",
+    "    _json.dump(baseline_design, _f)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "7c0cffcd",
@@ -1420,7 +3590,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 32,
    "id": "b542334a",
    "metadata": {},
    "outputs": [],
@@ -1458,7 +3628,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 15,
    "id": "0720dd37",
    "metadata": {},
    "outputs": [
@@ -1466,9 +3636,9 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\u001b[32m2026-02-11 11:25:24.863\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36moptimex.optimizer\u001b[0m:\u001b[36mcreate_model\u001b[0m:\u001b[36m116\u001b[0m - \u001b[1mCreating sets\u001b[0m\n",
-      "\u001b[32m2026-02-11 11:25:24.865\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36moptimex.optimizer\u001b[0m:\u001b[36mcreate_model\u001b[0m:\u001b[36m158\u001b[0m - \u001b[1mCreating parameters\u001b[0m\n",
-      "\u001b[32m2026-02-11 11:25:25.014\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36moptimex.optimizer\u001b[0m:\u001b[36mcreate_model\u001b[0m:\u001b[36m448\u001b[0m - \u001b[1mCreating variables\u001b[0m\n"
+      "\u001b[32m2026-05-28 08:24:50.660\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36moptimex.optimizer\u001b[0m:\u001b[36mcreate_model\u001b[0m:\u001b[36m116\u001b[0m - \u001b[1mCreating sets\u001b[0m\n",
+      "\u001b[32m2026-05-28 08:24:50.662\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36moptimex.optimizer\u001b[0m:\u001b[36mcreate_model\u001b[0m:\u001b[36m158\u001b[0m - \u001b[1mCreating parameters\u001b[0m\n",
+      "\u001b[32m2026-05-28 08:24:50.807\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36moptimex.optimizer\u001b[0m:\u001b[36mcreate_model\u001b[0m:\u001b[36m448\u001b[0m - \u001b[1mCreating variables\u001b[0m\n"
      ]
     }
    ],
@@ -1484,7 +3654,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 16,
    "id": "e6299b28",
    "metadata": {},
    "outputs": [
@@ -1492,9 +3662,9 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\u001b[32m2026-02-11 11:27:35.494\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36moptimex.optimizer\u001b[0m:\u001b[36msolve_model\u001b[0m:\u001b[36m1113\u001b[0m - \u001b[1mSolver [gurobi] termination: optimal\u001b[0m\n",
-      "\u001b[32m2026-02-11 11:28:20.604\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36moptimex.optimizer\u001b[0m:\u001b[36msolve_model\u001b[0m:\u001b[36m1127\u001b[0m - \u001b[1mObjective (scaled): 1090.77\u001b[0m\n",
-      "\u001b[32m2026-02-11 11:28:20.607\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36moptimex.optimizer\u001b[0m:\u001b[36msolve_model\u001b[0m:\u001b[36m1128\u001b[0m - \u001b[1mObjective (real):   1.81746e-06\u001b[0m\n"
+      "\u001b[32m2026-05-28 08:27:07.716\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36moptimex.optimizer\u001b[0m:\u001b[36msolve_model\u001b[0m:\u001b[36m1113\u001b[0m - \u001b[1mSolver [gurobi] termination: optimal\u001b[0m\n",
+      "\u001b[32m2026-05-28 08:27:54.922\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36moptimex.optimizer\u001b[0m:\u001b[36msolve_model\u001b[0m:\u001b[36m1127\u001b[0m - \u001b[1mObjective (scaled): 1090.77\u001b[0m\n",
+      "\u001b[32m2026-05-28 08:27:54.924\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36moptimex.optimizer\u001b[0m:\u001b[36msolve_model\u001b[0m:\u001b[36m1128\u001b[0m - \u001b[1mObjective (real):   1.81746e-06\u001b[0m\n"
      ]
     }
    ],
@@ -1506,7 +3676,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 18,
    "id": "eafc6bb6",
    "metadata": {},
    "outputs": [],
@@ -1560,31 +3730,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": null,
    "id": "fb9cf162",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\u001b[32m2026-02-10 15:44:25.663\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdynamic_characterization.dynamic_characterization\u001b[0m:\u001b[36mcharacterize\u001b[0m:\u001b[36m108\u001b[0m - \u001b[1mNo custom dynamic characterization functions provided. Using default dynamic             characterization functions. The flows that are characterized are based on the selection                of the initially chosen impact category.\u001b[0m\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "pp_evolution.get_characterized_dynamic_inventory(base_lcia_method=method_climate_change)\n",
+    "# Persist evolution objective scalars for the Scenario 4 summary\n",
+    "# so it can run without keeping m_evolution / pp_evolution in RAM.\n",
+    "import json as _json\n",
     "\n",
-    "pp_evolution.df_dynamic_inventory.to_excel(\n",
-    "    \"data/paper/dynamic_inventory_evolution.xlsx\"\n",
-    ")\n",
-    "pp_evolution.df_characterized_inventory.to_excel(\n",
-    "    \"data/paper/characterized_inventory_evolution.xlsx\"\n",
-    ")\n",
-    "pp_evolution.df_production.to_excel(\"data/paper/production_evolution.xlsx\")\n",
-    "pp_evolution.df_demand.to_excel(\"data/paper/demand_evolution.xlsx\")\n",
-    "pp_evolution.get_production_capacity().to_excel(\"data/paper/capacity_evolution.xlsx\")\n",
-    "pp_evolution.df_impacts.to_excel(\"data/paper/impacts_evolution.xlsx\")"
+    "_s_evol = pp_evolution.get_impacts()[\"climate_change\"].sum(axis=1)\n",
+    "evolution_results = {\n",
+    "    \"obj_evolution\": float(obj_evolution),\n",
+    "    \"climate_per_year\": {int(y): float(v) for y, v in _s_evol.items()},\n",
+    "}\n",
+    "with open(\"data/paper/evolution_results.json\", \"w\") as _f:\n",
+    "    _json.dump(evolution_results, _f)\n"
    ]
   },
   {
@@ -1599,7 +3760,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "id": "326d873d",
    "metadata": {},
    "outputs": [],
@@ -1609,7 +3770,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
    "id": "d94d4b4b",
    "metadata": {},
    "outputs": [],
@@ -1638,7 +3799,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": null,
    "id": "4206abe8",
    "metadata": {},
    "outputs": [
@@ -1646,9 +3807,9 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\u001b[32m2026-02-11 11:31:25.100\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36moptimex.optimizer\u001b[0m:\u001b[36mcreate_model\u001b[0m:\u001b[36m116\u001b[0m - \u001b[1mCreating sets\u001b[0m\n",
-      "\u001b[32m2026-02-11 11:31:25.102\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36moptimex.optimizer\u001b[0m:\u001b[36mcreate_model\u001b[0m:\u001b[36m158\u001b[0m - \u001b[1mCreating parameters\u001b[0m\n",
-      "\u001b[32m2026-02-11 11:31:25.250\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36moptimex.optimizer\u001b[0m:\u001b[36mcreate_model\u001b[0m:\u001b[36m448\u001b[0m - \u001b[1mCreating variables\u001b[0m\n"
+      "\u001b[32m2026-05-28 07:55:28.421\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36moptimex.optimizer\u001b[0m:\u001b[36mcreate_model\u001b[0m:\u001b[36m116\u001b[0m - \u001b[1mCreating sets\u001b[0m\n",
+      "\u001b[32m2026-05-28 07:55:28.422\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36moptimex.optimizer\u001b[0m:\u001b[36mcreate_model\u001b[0m:\u001b[36m158\u001b[0m - \u001b[1mCreating parameters\u001b[0m\n",
+      "\u001b[32m2026-05-28 07:55:28.570\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36moptimex.optimizer\u001b[0m:\u001b[36mcreate_model\u001b[0m:\u001b[36m448\u001b[0m - \u001b[1mCreating variables\u001b[0m\n"
      ]
     }
    ],
@@ -1664,7 +3825,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": null,
    "id": "59bb4b45",
    "metadata": {},
    "outputs": [
@@ -1672,9 +3833,9 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\u001b[32m2026-02-11 11:33:24.088\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36moptimex.optimizer\u001b[0m:\u001b[36msolve_model\u001b[0m:\u001b[36m1113\u001b[0m - \u001b[1mSolver [gurobi] termination: optimal\u001b[0m\n",
-      "\u001b[32m2026-02-11 11:34:08.907\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36moptimex.optimizer\u001b[0m:\u001b[36msolve_model\u001b[0m:\u001b[36m1127\u001b[0m - \u001b[1mObjective (scaled): 1765.32\u001b[0m\n",
-      "\u001b[32m2026-02-11 11:34:08.908\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36moptimex.optimizer\u001b[0m:\u001b[36msolve_model\u001b[0m:\u001b[36m1128\u001b[0m - \u001b[1mObjective (real):   2.9414e-06\u001b[0m\n"
+      "\u001b[32m2026-05-28 07:57:43.773\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36moptimex.optimizer\u001b[0m:\u001b[36msolve_model\u001b[0m:\u001b[36m1113\u001b[0m - \u001b[1mSolver [gurobi] termination: optimal\u001b[0m\n",
+      "\u001b[32m2026-05-28 07:58:29.584\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36moptimex.optimizer\u001b[0m:\u001b[36msolve_model\u001b[0m:\u001b[36m1127\u001b[0m - \u001b[1mObjective (scaled): 1765.32\u001b[0m\n",
+      "\u001b[32m2026-05-28 07:58:29.586\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36moptimex.optimizer\u001b[0m:\u001b[36msolve_model\u001b[0m:\u001b[36m1128\u001b[0m - \u001b[1mObjective (real):   2.9414e-06\u001b[0m\n"
      ]
     }
    ],
@@ -1770,6 +3931,224 @@
     ")\n",
     "pp_constrained.df_impacts.to_excel(\"data/paper/impacts_constrained.xlsx\")"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cfcab747",
+   "metadata": {},
+   "source": [
+    "## Scenario 4: Baseline Design Under Evolution\n",
+    "\n",
+    "How much worse is a portfolio optimized for a **static** background once the world actually **evolves**? We take the optimal design from Scenario 1 (baseline / no evolution — installation and operation decided under a frozen 2020 background) and evaluate its climate impact under the Scenario 2 evolution conditions (evolving REMIND-EU SSP2-NDC background + vintage improvements).\n",
+    "\n",
+    "The design is fully fixed: every `var_installation` and `var_operation` is locked to the baseline optimum, so there are no degrees of freedom left and no re-optimization is needed — we rebuild the evolution-structured model and simply evaluate its objective expression. `var_installation` and `var_operation` are already in real units (see `PostProcessor`), so the variables need no rescaling; only the objective is denormalized (`scaled_obj * fg_scale * cat_scale`), exactly as `solve_model` does.\n",
+    "\n",
+    "Comparing this fixed baseline design against the freely-optimized evolution design (Scenario 2) — both under the same evolution conditions — quantifies the penalty of having designed for the wrong background."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f06552cc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[32m2026-05-28 10:02:59.505\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36moptimex.optimizer\u001b[0m:\u001b[36mcreate_model\u001b[0m:\u001b[36m116\u001b[0m - \u001b[1mCreating sets\u001b[0m\n",
+      "\u001b[32m2026-05-28 10:02:59.508\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36moptimex.optimizer\u001b[0m:\u001b[36mcreate_model\u001b[0m:\u001b[36m158\u001b[0m - \u001b[1mCreating parameters\u001b[0m\n",
+      "\u001b[32m2026-05-28 10:02:59.657\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36moptimex.optimizer\u001b[0m:\u001b[36mcreate_model\u001b[0m:\u001b[36m448\u001b[0m - \u001b[1mCreating variables\u001b[0m\n"
+     ]
+    },
+    {
+     "ename": "NameError",
+     "evalue": "name 'postprocessing' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mNameError\u001b[39m                                 Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[34]\u001b[39m\u001b[32m, line 36\u001b[39m\n\u001b[32m     32\u001b[39m fg_scale = model_fixed.scales[\u001b[33m\"foreground\"\u001b[39m]\n\u001b[32m     33\u001b[39m cat_scale = model_fixed.scales[\u001b[33m\"characterization\"\u001b[39m][\u001b[33m\"climate_change\"\u001b[39m]\n\u001b[32m     34\u001b[39m obj_fixed = scaled_obj * fg_scale * cat_scale\n\u001b[32m     35\u001b[39m \n\u001b[32m---> \u001b[39m\u001b[32m36\u001b[39m pp_fixed = postprocessing.PostProcessor(model_fixed, plot_config={\u001b[33m\"figsize\"\u001b[39m: (\u001b[32m8\u001b[39m, \u001b[32m4\u001b[39m)})\n\u001b[32m     37\u001b[39m \n\u001b[32m     38\u001b[39m \u001b[38;5;66;03m# Load evolution results from disk (no need for m_evolution / pp_evolution in RAM).\u001b[39;00m\n\u001b[32m     39\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m open(\u001b[33m\"data/paper/evolution_results.json\"\u001b[39m) \u001b[38;5;28;01mas\u001b[39;00m f:\n",
+      "\u001b[31mNameError\u001b[39m: name 'postprocessing' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "import json\n",
+    "import pyomo.environ as pyo\n",
+    "from optimex import optimizer\n",
+    "\n",
+    "# Load the baseline design from disk (no need for m_baseline in RAM).\n",
+    "with open(\"data/paper/baseline_design.json\") as f:\n",
+    "    baseline_design = json.load(f)\n",
+    "obj_baseline = baseline_design[\"obj_baseline\"]\n",
+    "\n",
+    "# Rebuild the evolution-structured model (same as Scenario 2). This is the ONLY\n",
+    "# optimization model that needs to be in RAM for Scenario 4.\n",
+    "manager.load_inputs(\"data/2026-05-28_model_inputs_2050.json\")\n",
+    "optimization_model_inputs_fixed = manager.override(\n",
+    "    existing_capacity=existing_capacities,\n",
+    "    vintage_improvements=vintage_improvements,\n",
+    ")\n",
+    "model_fixed = optimizer.create_model(\n",
+    "    optimization_model_inputs_fixed,\n",
+    "    name=\"baseline_design_under_evolution\",\n",
+    "    objective_category=\"climate_change\",\n",
+    ")\n",
+    "\n",
+    "# Fix the design to the baseline values (already in real units, see PostProcessor).\n",
+    "for p in model_fixed.PROCESS:\n",
+    "    for t in model_fixed.SYSTEM_TIME:\n",
+    "        model_fixed.var_installation[p, t].fix(baseline_design[\"installation\"][f\"{p}\\t{t}\"])\n",
+    "for (p, v, t) in model_fixed.ACTIVE_VINTAGE_TIME:\n",
+    "    model_fixed.var_operation[p, v, t].fix(baseline_design[\"operation\"][f\"{p}\\t{v}\\t{t}\"])\n",
+    "\n",
+    "# Denormalize the objective exactly as solve_model does: scaled_obj * fg_scale * cat_scale\n",
+    "scaled_obj = pyo.value(model_fixed.OBJ)\n",
+    "fg_scale = model_fixed.scales[\"foreground\"]\n",
+    "cat_scale = model_fixed.scales[\"characterization\"][\"climate_change\"]\n",
+    "obj_fixed = scaled_obj * fg_scale * cat_scale"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "1caaa841",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Baseline design @ baseline background (Scenario 1): 4.62873e-06\n",
+      "Baseline design @ evolution background (fixed):     4.40604e-06\n",
+      "Evolution-optimized design (Scenario 2):            1.81746e-06\n"
+     ]
+    }
+   ],
+   "source": [
+    "from optimex import postprocessing\n",
+    "\n",
+    "pp_fixed = postprocessing.PostProcessor(model_fixed, plot_config={\"figsize\": (8, 4)})\n",
+    "\n",
+    "# Load evolution results from disk (no need for m_evolution / pp_evolution in RAM).\n",
+    "with open(\"data/paper/evolution_results.json\") as f:\n",
+    "    evolution_results = json.load(f)\n",
+    "obj_evolution = evolution_results[\"obj_evolution\"]\n",
+    "\n",
+    "print(f\"Baseline design @ baseline background (Scenario 1): {obj_baseline:.6g}\")\n",
+    "print(f\"Baseline design @ evolution background (fixed):     {obj_fixed:.6g}\")\n",
+    "print(f\"Evolution-optimized design (Scenario 2):            {obj_evolution:.6g}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "19e55a53",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                                 objective (climate_change)   background               design\n",
+      "baseline                                           0.000005  frozen 2020   baseline-optimized\n",
+      "baseline_design_under_evolution                    0.000004    evolution   baseline-optimized\n",
+      "evolution_optimum                                  0.000002    evolution  evolution-optimized\n",
+      "\n",
+      "Under evolution conditions, the baseline design causes +142.4% climate impact vs the evolution-optimized design (absolute: 2.5886e-06).\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "abs_gap = obj_fixed - obj_evolution\n",
+    "rel_gap = abs_gap / obj_evolution\n",
+    "\n",
+    "summary = pd.DataFrame(\n",
+    "    {\n",
+    "        \"objective (climate_change)\": [obj_baseline, obj_fixed, obj_evolution],\n",
+    "        \"background\": [\"frozen 2020\", \"evolution\", \"evolution\"],\n",
+    "        \"design\": [\"baseline-optimized\", \"baseline-optimized\", \"evolution-optimized\"],\n",
+    "    },\n",
+    "    index=[\"baseline\", \"baseline_design_under_evolution\", \"evolution_optimum\"],\n",
+    ")\n",
+    "print(summary.to_string())\n",
+    "print(\n",
+    "    f\"\\nUnder evolution conditions, the baseline design causes +{rel_gap * 100:.1f}% \"\n",
+    "    f\"climate impact vs the evolution-optimized design (absolute: {abs_gap:.4e}).\"\n",
+    ")\n",
+    "summary.to_excel(\"data/paper/comparison_baseline_design_under_evolution.xlsx\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "d74b8636",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(6, 4))\n",
+    "labels = [\"baseline design\\nunder evolution\", \"evolution-optimized\\ndesign\"]\n",
+    "values = [obj_fixed, obj_evolution]\n",
+    "bars = ax.bar(labels, values, color=[\"#d96459\", \"#588c7e\"])\n",
+    "ax.set_ylabel(\"Climate change objective (CRF)\")\n",
+    "ax.set_title(f\"Designing for a static background costs +{rel_gap * 100:.0f}%\")\n",
+    "for b, val in zip(bars, values):\n",
+    "    ax.text(b.get_x() + b.get_width() / 2, val, f\"{val:.3e}\", ha=\"center\", va=\"bottom\")\n",
+    "ax.margins(y=0.15)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7d5a387d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Export the baseline design under evolution as a STANDARD scenario,\n",
+    "# so plots/paper_figures.py loads its annual RF via load_impacts_properly, exactly like\n",
+    "# the other scenarios. Reference (impacts_no_evolution.xlsx) and evolution-optimized\n",
+    "# (impacts_fg_bg_evolution.xlsx) are already on disk.\n",
+    "imp_fixed = pp_fixed.get_impacts()\n",
+    "pp_fixed.df_impacts.to_excel(\"plots/data/impacts_baseline_under_evolution.xlsx\")\n",
+    "\n",
+    "# Quick preview: annual RF (CRF per emission-year).\n",
+    "s_ref = pp_baseline.get_impacts()[\"climate_change\"].sum(axis=1)   # baseline design, baseline background\n",
+    "s_fixed = imp_fixed[\"climate_change\"].sum(axis=1)                 # baseline design, evolution background\n",
+    "s_evol = pd.Series(\n",
+    "    {int(y): v for y, v in evolution_results[\"climate_per_year\"].items()}\n",
+    ").sort_index()                                                    # evolution-optimized\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(8, 4))\n",
+    "ax.fill_between(s_fixed.index, s_evol.reindex(s_fixed.index).values, s_fixed.values,\n",
+    "                where=(s_fixed.values >= s_evol.reindex(s_fixed.index).values),\n",
+    "                color=\"#CC071E\", alpha=0.15, label=\"excess impact\")\n",
+    "ax.plot(s_ref.index, s_ref.values, color=\"#646567\", linestyle=\"--\",\n",
+    "        label=\"baseline design, baseline background\")\n",
+    "ax.plot(s_fixed.index, s_fixed.values, color=\"#CC071E\",\n",
+    "        label=\"baseline design, evolution background\")\n",
+    "ax.plot(s_evol.index, s_evol.values, color=\"#57AB27\", label=\"evolution-optimized\")\n",
+    "ax.set_xlabel(\"Year\"); ax.set_ylabel(\"Annual RF (CRF per emission-year)\")\n",
+    "ax.legend(); ax.grid(alpha=0.3)\n",
+    "plt.tight_layout(); plt.show()\n"
+   ]
   }
  ],
  "metadata": {
diff --git a/notebooks/plots/data/impacts_baseline_under_evolution.xlsx b/notebooks/plots/data/impacts_baseline_under_evolution.xlsx
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diff --git a/notebooks/plots/figs/results_and_impacts.pdf b/notebooks/plots/figs/results_and_impacts.pdf
index aaddf41331b3770bddbcb10372d8c34e529ddb5c..600516882a730bc7b0c03f41c1337c1e6a135894 100644
GIT binary patch
delta 24
gcmca{g!#r1<_!mKbD9~L8kied7+Y>WdApwt0E>AFR{#J2

delta 24
gcmca{g!#r1<_!mKbDA2M8X6mz8klW9dApwt0E+zzNB{r;

diff --git a/notebooks/plots/paper_figures.py b/notebooks/plots/paper_figures.py
index 9b3a17e..8ba53d6 100644
--- a/notebooks/plots/paper_figures.py
+++ b/notebooks/plots/paper_figures.py
@@ -1457,6 +1457,63 @@ def add_row_group_label(start_row, end_row, label_text):
     return fig
 
 
+def create_baselineunderevo_annual_rf_figure(baselineunderevo="baseline_under_evolution",
+                                           optimized="fg_bg_evolution",
+                                           reference="no_evolution"):
+    """
+    Annual radiative forcing (cumulative RF until 2125, attributed per emission year):
+      - reference:      baseline design under the baseline (frozen 2020) background
+      - baselineunderevo: baseline design under the evolution background
+      - optimized:      evolution-optimized design under the evolution background
+
+    The excess of the baselineunderevo over the optimized design (both under evolution)
+    is shaded. Reuses the standard per-scenario `impacts_<scenario>.xlsx` outputs via
+    `load_impacts_properly`.
+    """
+    start_year, end_year = 2025, 2050
+    years = np.arange(start_year, end_year + 1)
+
+    def agg_rf(scenario):
+        impacts = load_impacts_properly(scenario)
+        if "climate_change" not in impacts:
+            return pd.Series(0.0, index=years)
+        return impacts["climate_change"].reindex(years).fillna(0).sum(axis=1)
+
+    cf = agg_rf(baselineunderevo)
+    opt = agg_rf(optimized)
+    ref = agg_rf(reference)
+
+    m = max(float(np.nanmax(np.abs(s.values))) for s in (cf, opt, ref))
+    exponent = int(np.floor(np.log10(m))) if m > 0 else 0
+    sf = 10 ** exponent
+
+    c_base, c_opt, c_ref = "#CC071E", "#57AB27", "#646567"  # RWTH red / green / gray
+
+    fig, ax = plt.subplots(figsize=(7, 4.5))
+    ax.fill_between(years, opt.values / sf, cf.values / sf,
+                    where=(cf.values >= opt.values), color=c_base, alpha=0.15,
+                    linewidth=0, label="Excess impact")
+    ax.plot(years, ref.values / sf, color=c_ref, linewidth=1.5, linestyle="--",
+            label="Baseline design, baseline background")
+    ax.plot(years, cf.values / sf, color=c_base, linewidth=1.5,
+            label="Baseline design, evolution background")
+    ax.plot(years, opt.values / sf, color=c_opt, linewidth=1.5,
+            label="Evolution-optimized design")
+
+    ax.axhline(0, color="gray", linewidth=0.5, zorder=0)
+    ax.set_xlim(start_year - 1, end_year + 1)
+    ax.set_xticks([y for y in years if y % 5 == 0])
+    ax.set_xlabel("Year")
+    ax.set_ylabel(f"Cumulative Radiative Forcing\nuntil 2125 [$10^{{{exponent}}}$ W m$^{{-2}}$ yr]")
+    ax.grid(True, alpha=0.3, which="both", axis="both", zorder=0)
+    ax.set_axisbelow(True)
+    ax.legend(frameon=False, fontsize=9, loc="upper left",
+              bbox_to_anchor=(1.02, 1.0), borderaxespad=0)
+
+    fig.tight_layout()
+    return fig
+
+
 def main():
     """Generate all paper figures."""
     print("Loading scenario data...")
@@ -1491,6 +1548,11 @@ def main():
     fig_forcing.savefig(OUTPUT_DIR / "radiative_forcing.pdf")
     plt.close(fig_forcing)
 
+    print("Creating baselineunderevo annual RF figure...")
+    fig_cf = create_baselineunderevo_annual_rf_figure()
+    fig_cf.savefig(OUTPUT_DIR / "baselineunderevo_annual_rf.svg")
+    plt.close(fig_cf)
+
     # print("Creating capacity balance figure...")
     # fig_capacity = create_capacity_balance_figure(scenarios_data)
     # fig_capacity.savefig(OUTPUT_DIR / "capacity_balance.pdf")

From d4f33455eacf7cb2dc6064350aa666a2279667a0 Mon Sep 17 00:00:00 2001
From: TimoDiepers <timo.diepers@gmail.com>
Date: Fri, 31 Jul 2026 13:55:21 +0200
Subject: [PATCH 11/12] fix: amortize installation impacts over a unit's
 lifetime output

An installed unit's capacity was treated as available in every year of its
operation window, so one unit could deliver its whole lifetime production each
year. Installation-dependent flows (construction, end-of-life) were therefore
spread over too much output, under-counting them by the number of operating
years a vintage was dispatched.

Production and operation-dependent flows are now taken at the vintage's current
lifecycle stage tau = t - v, and the capacity bound compares unit counts:
var_operation[p, v, t] <= var_installation[p, v].

Postprocessing capacity outputs report annual capacity, so they stay comparable
with production; get_installation returns unit counts, which are a lifetime
quantity. Docstrings, docs and changelog state the convention explicitly.
---
 CHANGES.md                                    |   2 +
 docs/content/constraints.md                   |  11 +-
 docs/content/foreground_modeling.md           |  30 +-
 docs/content/postprocessing_guide.md          |  57 +-
 docs/content/quickstart.md                    |   5 +-
 docs/content/theory_how_it_works.md           |   6 +-
 notebooks/plots/paper_figures.py              |  18 +-
 src/optimex/converter.py                      |  30 +-
 src/optimex/optimizer.py                      | 230 +++-----
 src/optimex/postprocessing.py                 | 512 ++++++++----------
 ...t_background_process_product_separation.py |  16 +-
 tests/test_brownfield.py                      |  22 +-
 tests/test_merit_order.py                     |  26 +-
 tests/test_operation_constraints.py           | 110 ++--
 tests/test_optimization.py                    |  47 +-
 tests/test_single_route_lca_comparison.py     | 193 ++++++-
 tests/test_two_level_supply_chain.py          |  43 +-
 17 files changed, 764 insertions(+), 594 deletions(-)

diff --git a/CHANGES.md b/CHANGES.md
index 3879a96..8ec8a24 100644
--- a/CHANGES.md
+++ b/CHANGES.md
@@ -7,6 +7,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ## [Unreleased]
 * Added foreground_db_name argument to LCAConfig
+* Fixed under-counting of installation impacts: one installed unit now delivers its production over its whole lifetime, not in every operating year (changes results for processes with multi-year operation windows)
+* Postprocessing capacity outputs (`get_production_capacity`, `plot_capacity_balance`, `plot_utilization_heatmap`) now report annual capacity, comparable with production
 
 ## [0.4.2] - 2026-04-27
 * Fix conda publish workflow
diff --git a/docs/content/constraints.md b/docs/content/constraints.md
index f3fec71..7c64f5d 100644
--- a/docs/content/constraints.md
+++ b/docs/content/constraints.md
@@ -35,7 +35,10 @@ solved_model, objective, results = optimizer.solve_model(model)
 
 ## Process Deployment Limits
 
-Control how much capacity can be installed for each process.
+Control how many units of a process can be installed.
+
+!!! note "Limits are in process units"
+    Deployment limits bound `var_installation`, which counts process **units**. One unit delivers the production of its production exchange over its whole lifetime, so a limit of 50 allows a lifetime output of 50 reference-flow units, not an annual output of 50. See [Foreground Modeling](foreground_modeling.md#what-one-installed-unit-means).
 
 ### Time-Specific Deployment Limits
 
@@ -100,6 +103,8 @@ Control how much a process can operate in each time period.
 !!! note "Operation vs Installation"
     Operation limits constrain how much of the installed capacity is actually used in each period. A process can only operate up to its installed capacity, but operation limits can further restrict this.
 
+    Both variables are counted in process units: `var_operation[p, v, t]` is how many units of vintage `v` run in year `t`, bounded by `var_installation[p, v]`. An operation limit therefore caps the units running in that year, summed over all vintages — multiply by the process's output per unit and year to express it as a production limit (or use [flow limits](#flow-limits) to bound production directly).
+
 **Example: Limit coal plant operation during phase-out**
 ```python
 model_inputs.process_operation_limits_max = {
@@ -266,7 +271,9 @@ Model systems with pre-existing infrastructure that was installed before the opt
 
 | Field | Type | Description |
 |-------|------|-------------|
-| `existing_capacity` | `Dict[Tuple[str, int], float]` | Capacity at `(process, installation_year)` |
+| `existing_capacity` | `Dict[Tuple[str, int], float]` | Units at `(process, installation_year)` |
+
+Existing capacity is given in the same process units as `var_installation`, and the entries behave like vintages of that installation year: they can run while their lifecycle stage falls inside the operation window, and they carry no installation impacts.
 
 !!! info "Brownfield vs Greenfield"
     - **Greenfield**: Optimization starts from scratch with no existing capacity
diff --git a/docs/content/foreground_modeling.md b/docs/content/foreground_modeling.md
index d16fbe7..b6a4730 100644
--- a/docs/content/foreground_modeling.md
+++ b/docs/content/foreground_modeling.md
@@ -132,9 +132,37 @@ TemporalDistribution(
 
 ---
 
+## What One Installed Unit Means
+
+This convention decides how installation impacts are amortized, so it is worth stating explicitly:
+
+**One unit of a process delivers the production stated by its production exchange — over its entire lifetime, not per year.**
+
+For the example above (`amount=1`, spread as `[0, 0.5, 0.5, 0]` over a two-year operation window):
+
+| Quantity | Value |
+|----------|-------|
+| Lifetime output of one unit | 1 kg (the sum of the temporal distribution) |
+| Output of one running unit per year | 0.5 kg (the per-τ entry) |
+| Units needed to deliver 10 kg in a single year | 20 |
+| Units needed to deliver 10 kg in each of two consecutive years | 20 (one cohort, fully used) |
+
+Consequences for the optimization:
+
+- `var_installation[p, v]` counts **units**, and installation-dependent exchanges (construction, end-of-life) are incurred once per unit.
+- `var_operation[p, v, t]` counts how many of those units **run in year t**, bounded by the units installed: `var_operation[p, v, t] ≤ var_installation[p, v]`.
+- Annual output is `production[τ = t - v] × var_operation[p, v, t]`.
+
+This is what makes optimex agree with a standard LCA of the delivered amount whenever every unit is fully utilized. If the demand profile does not allow full utilization — for instance a single demand year for a process with a multi-year operation window — the model builds capacity that is partly idle, and the resulting impact is legitimately *higher* than the standard LCA score of the delivered amount.
+
+!!! tip "Reporting an annual capacity"
+    Because installed units cover a whole lifetime, `PostProcessor.get_installation()` is not comparable with per-year production. Use `PostProcessor.get_production_capacity()`, which multiplies unit counts by the output per unit and year.
+
+---
+
 ## The Operation Flag
 
-Exchanges marked with `"operation": True` **scale with the operational level** $\mathbf{o}_{t,v}$. This enables flexible operation where a process can run below its installed capacity $\mathbf{s}_v$ (see [Theory: Flexible Operation](theory.md#step-5-flexible-operation)).
+Exchanges marked with `"operation": True` **scale with the operational level** $\mathbf{o}_{t,v}$, the number of units running. This enables flexible operation where a process can run below its installed capacity $\mathbf{s}_v$ (see [Theory: Flexible Operation](theory.md#step-5-flexible-operation)).
 
 ```python
 "exchanges": [
diff --git a/docs/content/postprocessing_guide.md b/docs/content/postprocessing_guide.md
index 56cbd56..77f202b 100644
--- a/docs/content/postprocessing_guide.md
+++ b/docs/content/postprocessing_guide.md
@@ -75,7 +75,10 @@ df_installation = pp.get_installation()
 Returns a DataFrame with:
 - **Index**: System time (years)
 - **Columns**: Process IDs
-- **Values**: Capacity installed at that time
+- **Values**: Number of process **units** installed at that time
+
+!!! warning "Installed units are a lifetime quantity"
+    One installed unit delivers the production of its production exchange over its **whole lifetime** (the sum of its temporal distribution), spread across its operation window. These numbers are therefore not an annual capacity and must not be compared directly with production or demand. For the per-year quantity, use [`get_production_capacity()`](#production-capacity).
 
 ```
 Process     ProcessA    ProcessB
@@ -88,10 +91,10 @@ Time
 
 **Common analyses:**
 ```python
-# Total capacity installed per process
-total_capacity = df_installation.sum()
+# Total units installed per process
+total_units = df_installation.sum()
 
-# Cumulative capacity over time
+# Cumulative units installed over time
 cumulative = df_installation.cumsum()
 
 # When each process was first deployed
@@ -102,7 +105,7 @@ first_deployment = df_installation[df_installation > 0].idxmax()
 
 ### Operation Levels
 
-Get how much each process operates per time period:
+Get how many units of each process run per time period:
 
 ```python
 df_operation = pp.get_operation()
@@ -111,7 +114,7 @@ df_operation = pp.get_operation()
 Returns a DataFrame with:
 - **Index**: System time (years)
 - **Columns**: Process IDs
-- **Values**: Operation level (units operating)
+- **Values**: Number of units running in that year
 
 ```
 Process     ProcessA    ProcessB
@@ -124,11 +127,31 @@ Time
 
 **Capacity utilization:**
 ```python
-# Calculate utilization rate
-cumulative_capacity = df_installation.cumsum()
-utilization = df_operation / cumulative_capacity
+# Units running vs. units available: compare production against annual capacity
+df_capacity = pp.get_production_capacity()   # per year
+df_production = pp.get_production()          # per year
+utilization = df_production.groupby(level="Product", axis=1).sum() / df_capacity
 ```
 
+Do not divide `df_operation` by the cumulative sum of `df_installation`: units only run within their operation window, so a cumulative sum counts units that were never or are no longer available. `plot_utilization_heatmap()` does this correctly.
+
+---
+
+### Production Capacity
+
+Get the maximum **annual** production available in each year:
+
+```python
+df_capacity = pp.get_production_capacity()
+```
+
+Returns a DataFrame with:
+- **Index**: System time (years)
+- **Columns**: Products
+- **Values**: Output the installed and existing units could deliver in that year
+
+This multiplies the units of every vintage that is in its operation phase by the output that vintage yields per unit and year, so it is directly comparable with `get_production()` and `get_demand()`.
+
 ---
 
 ### Production
@@ -203,7 +226,7 @@ fig = pp.plot_installation()
 
 ### Operation Plot
 
-Shows operation levels over time:
+Shows how many units run over time:
 
 ```python
 fig = pp.plot_operation()
@@ -213,7 +236,7 @@ fig = pp.plot_operation()
 
 ### Capacity Balance Plot
 
-Compares actual production with maximum available capacity:
+Compares actual production with the maximum annual capacity available:
 
 ```python
 fig = pp.plot_capacity_balance()
@@ -232,12 +255,16 @@ For custom analysis, access the solved model directly:
 ```python
 import pyomo.environ as pyo
 
-# Get decision variable values
+# Get decision variable values: units installed per vintage, and units of each
+# vintage running in a given year
 for p in solved_model.PROCESS:
     for t in solved_model.SYSTEM_TIME:
         installation = pyo.value(solved_model.var_installation[p, t])
-        operation = pyo.value(solved_model.var_operation[p, t])
-        print(f"{p}, {t}: install={installation:.2f}, operate={operation:.2f}")
+        print(f"{p}, installed in {t}: {installation:.2f} units")
+
+for (p, v, t) in solved_model.ACTIVE_VINTAGE_TIME:
+    operation = pyo.value(solved_model.var_operation[p, v, t])
+    print(f"{p}, vintage {v}, year {t}: {operation:.2f} units running")
 
 # Access expressions
 for c in solved_model.CATEGORY:
@@ -315,7 +342,7 @@ for cat in df_impacts.columns.get_level_values(0).unique():
     total = df_impacts[cat].sum().sum()
     print(f"  {cat}: {total:.2f}")
 
-print(f"\nTotal capacity installed by process:")
+print(f"\nTotal units installed by process:")
 for proc in df_installation.columns:
     total = df_installation[proc].sum()
     print(f"  {proc}: {total:.2f}")
diff --git a/docs/content/quickstart.md b/docs/content/quickstart.md
index f88467e..f97d8ee 100644
--- a/docs/content/quickstart.md
+++ b/docs/content/quickstart.md
@@ -190,8 +190,9 @@ model_inputs.existing_capacity = {("old_plant", 2010): 500.0}
 | Method | Returns | Description |
 |--------|---------|-------------|
 | `get_impacts()` | DataFrame | Impact by process, category, time |
-| `get_installation()` | DataFrame | Capacity installed per time |
-| `get_operation()` | DataFrame | Operation level per time |
+| `get_installation()` | DataFrame | Process units installed per time (a lifetime quantity, not an annual capacity) |
+| `get_production_capacity()` | DataFrame | Maximum annual production available per time |
+| `get_operation()` | DataFrame | Units running per time |
 | `get_production()` | DataFrame | Production by process and product |
 | `get_demand()` | DataFrame | Demand fulfillment over time |
 | `plot_impacts()` | Figure | Stacked area plot of impacts |
diff --git a/docs/content/theory_how_it_works.md b/docs/content/theory_how_it_works.md
index a8ddf47..d8a89ca 100644
--- a/docs/content/theory_how_it_works.md
+++ b/docs/content/theory_how_it_works.md
@@ -45,8 +45,10 @@ Both the foreground and background systems can evolve over time:
 
 Traditional LCO assumes processes always run at full capacity. `optimex` separates the decision into two components:
 
-- **Capacity installation**: How much capacity is built at a given time (vintage)
-- **Operational level**: How much of that capacity is actually used at each point in time
+- **Capacity installation**: How many process units are built at a given time (vintage)
+- **Operational level**: How many of those units are actually running at each point in time
+
+One installed unit corresponds to one reference-flow unit of the underlying LCA process: it delivers the production stated by its production exchange over its **whole lifetime**, so the per-time-step entry of that exchange is what one running unit yields **per year**. Installation-dependent exchanges are charged once per unit, which means the construction burden of a unit is amortized over its lifetime output — no more and no less. A demand profile that leaves part of a unit's lifetime unused therefore shows up as idle capacity and a correspondingly higher impact.
 
 This separation is important because it enables **vintage-specific dispatch**: when multiple cohorts of the same technology coexist, the optimizer can preferentially utilize cleaner vintages — creating an emissions-aware merit order. It also allows the model to identify strategic overcapacities, where early investment in clean technologies offsets the stranded cost of idled fossil infrastructure.
 
diff --git a/notebooks/plots/paper_figures.py b/notebooks/plots/paper_figures.py
index 8ba53d6..00f9571 100644
--- a/notebooks/plots/paper_figures.py
+++ b/notebooks/plots/paper_figures.py
@@ -109,7 +109,15 @@ def load_scenario_data(scenario: str) -> dict:
 
 
 def clean_capacity_df(df: pd.DataFrame) -> pd.DataFrame:
-    """Clean capacity DataFrame and rename columns."""
+    """
+    Clean capacity DataFrame and rename columns.
+
+    The capacity files are exported from `PostProcessor.get_production_capacity()`,
+    which reports ANNUAL production capacity (installed units times the output per
+    unit and year). That is the quantity to plot against annual production - the raw
+    installation numbers from `get_installation()` are unit counts covering a unit's
+    whole lifetime and are not comparable with per-year production.
+    """
     df = df.copy()
     df.index = df.index.astype(int)
     df.columns = [PRODUCT_NAMES.get(c, c) for c in df.columns]
@@ -405,7 +413,7 @@ def create_combined_results_figure(scenarios_data: dict):
                 capacity_values = cap_df[product_col].values[cap_mask] / 1e6
                 if np.any(capacity_values > 0.001):
                     ax.plot(x_positions, capacity_values, color="#000000", linestyle="--",
-                            linewidth=1, marker="", label="Capacity", zorder=4)
+                            linewidth=1, marker="", label="Annual capacity", zorder=4)
 
             # if not is_intermediate:
             #     ax.axhline(y=1, color="red", linestyle="--", linewidth=1)
@@ -447,7 +455,7 @@ def create_combined_results_figure(scenarios_data: dict):
         all_handles.append(Patch(facecolor=color, edgecolor="white", linewidth=0.5))
         all_labels.append(process)
     all_handles.append(plt.Line2D([0], [0], color="#000000", linestyle="--", linewidth=1))
-    all_labels.append("Available capacity")
+    all_labels.append("Available annual capacity")
     # all_handles.append(plt.Line2D([0], [0], color="red", linestyle="--", linewidth=1))
     # all_labels.append("Demand")
     # all_handles.append(Patch(facecolor="#BDCD00", edgecolor="#41811C", linewidth=1, hatch="///"))
@@ -1213,7 +1221,7 @@ def create_combined_results_and_impacts_figure(scenarios_data: dict):
                 capacity_values = cap_df[product_col].values[cap_mask] / 1e6
                 if np.any(capacity_values > 0.001):
                     ax.plot(x_positions, capacity_values, color="#000000", linestyle="--",
-                            linewidth=1, marker="", label="Capacity", zorder=4)
+                            linewidth=1, marker="", label="Annual capacity", zorder=4)
             
             ax.axhline(y=0, color="gray", linewidth=0.5, zorder=0)
             
@@ -1449,7 +1457,7 @@ def add_row_group_label(start_row, end_row, label_text):
     
     # Add capacity line
     all_handles.append(plt.Line2D([0], [0], color="#000000", linestyle="--", linewidth=1))
-    all_labels_legend.append("Available capacity")
+    all_labels_legend.append("Available annual capacity")
     
     fig.legend(all_handles, all_labels_legend, loc="lower center", bbox_to_anchor=(0.5, 0.07),
                ncol=4, frameon=False, fontsize=9)
diff --git a/src/optimex/converter.py b/src/optimex/converter.py
index 688fc4b..82e1e95 100644
--- a/src/optimex/converter.py
+++ b/src/optimex/converter.py
@@ -101,7 +101,9 @@ class OptimizationModelInputs(BaseModel):
     foreground_production: Dict[Tuple[str, str, int], float] = Field(
         ...,
         description=(
-            "Maps (process, product, process_time) to produced amount."
+            "Maps (process, product, process_time) to produced amount. The entry is "
+            "the output of one running unit in that year of its lifecycle; the sum "
+            "over the operation window is the lifetime output of one installed unit."
         ),
     )
 
@@ -219,16 +221,31 @@ class OptimizationModelInputs(BaseModel):
         ),
     )
     process_deployment_limits_max: Optional[Dict[Tuple[str, int], float]] = Field(
-        None, description="Upper bounds on (process, system_time) deployment."
+        None,
+        description=(
+            "Upper bounds on (process, system_time) deployment, in process units. One "
+            "unit delivers its production temporal distribution over its whole lifetime."
+        ),
     )
     process_deployment_limits_min: Optional[Dict[Tuple[str, int], float]] = Field(
-        None, description="Lower bounds on (process, system_time) deployment."
+        None,
+        description=(
+            "Lower bounds on (process, system_time) deployment, in process units."
+        ),
     )
     process_operation_limits_max: Optional[Dict[Tuple[str, int], float]] = Field(
-        None, description="Upper bounds on (process, system_time) operation."
+        None,
+        description=(
+            "Upper bounds on (process, system_time) operation, i.e. on the number of "
+            "units running in that year, summed over all vintages."
+        ),
     )
     process_operation_limits_min: Optional[Dict[Tuple[str, int], float]] = Field(
-        None, description="Lower bounds on (process, system_time) operation."
+        None,
+        description=(
+            "Lower bounds on (process, system_time) operation, i.e. on the number of "
+            "units running in that year, summed over all vintages."
+        ),
     )
     cumulative_process_limits_max: Optional[Dict[str, float]] = Field(
         None, description=("Global upper bound on cumulative deployment for a process.")
@@ -248,7 +265,8 @@ class OptimizationModelInputs(BaseModel):
         None,
         description=(
             "Existing (brownfield) capacity installed before the optimization horizon. "
-            "Maps (process, installation_year) to capacity amount. Installation years "
+            "Maps (process, installation_year) to a number of process units, the same "
+            "unit as var_installation. Installation years "
             "must be before min(SYSTEM_TIME). These capacities contribute to operation "
             "and production but their installation impacts are excluded (sunk costs)."
         ),
diff --git a/src/optimex/optimizer.py b/src/optimex/optimizer.py
index 11298a4..5775465 100644
--- a/src/optimex/optimizer.py
+++ b/src/optimex/optimizer.py
@@ -4,13 +4,35 @@
 This module creates and solves Pyomo optimization models that minimize environmental
 impacts over time while meeting demand constraints and respecting process limits.
 
+## Unit Convention
+
+One "unit" of a process is one reference-flow unit of the underlying LCA process:
+installing 1 unit delivers exactly the production stated by its production temporal
+distribution, i.e. `sum_tau foreground_production[p, r, tau]`, spread over the whole
+operation window. The per-tau entry is therefore the unit's output *per year of
+operation*, and the sum over the window is its *lifetime* output.
+
+Consequences:
+- `var_installation[p, v]` counts units of vintage v; its installation-dependent
+  flows (construction, end-of-life) are incurred once per unit.
+- `var_operation[p, v, t]` counts how many of those units are running in year t,
+  so the capacity bound is `var_operation[p, v, t] <= var_installation[p, v]`.
+- Annual output at time t of vintage v is
+  `foreground_production[p, r, t - v] * var_operation[p, v, t]`, never the sum over
+  the operation window (that would let one unit deliver its lifetime output every
+  year and under-count installation impacts by the number of operating years).
+- To convert installed units into an annual production capacity for reporting or
+  plotting, multiply by the same per-tau rate — see
+  `PostProcessor.get_production_capacity()`.
+
 ## Scaling Convention
 
 The optimization uses a two-tier scaling system for numerical stability:
 
 ### Decision Variables (REAL UNITS)
 - `var_installation[p, t]`: Number of process units installed (dimensionless)
-- `var_operation[p, t]`: Operation level (dimensionless, 0 to capacity)
+- `var_operation[p, v, t]`: Units of vintage v running at time t (dimensionless,
+  0 to the units available from that vintage)
 
 Both decision variables remain in REAL (unscaled) units to:
 1. Maintain physical interpretability
@@ -52,12 +74,12 @@
 Example constraint dimensional analysis:
 ```
 ProductDemandFulfillment:
-    production [kg SCALED/operation] × var_operation [#] = demand [kg SCALED] ✓
+    production[p, r, t-v] [kg SCALED/(unit·year)] × var_operation [# units running]
+        = demand [kg SCALED] ✓
 
-OperationLimit (LHS):
-    var_operation [#] × production [kg SCALED/operation] × fg_scale = [kg REAL]
-OperationLimit (RHS):
-    production [kg SCALED/process] × fg_scale × var_installation [#] = [kg REAL]
+OperationCapacity:
+    var_operation [# units running] <= var_installation [# units installed] ✓
+    (both REAL unit counts, so no scaling factor appears)
 ```
 """
 
@@ -567,37 +589,31 @@ def scale_tensor_by_operation(tensor: pyo.Param, flow_set: str, overrides: dict,
         Scale tensor by operation, summing flows across active vintages.
 
         With 3D var_operation[p, v, t], we sum flow contributions from each
-        vintage cohort operating at time t.
+        vintage cohort operating at time t. The flow taken from the tensor is the
+        one at the vintage's current lifecycle stage tau = t - v, i.e. the flow
+        *per operating unit and year* — not the sum over the whole operation
+        window, which is the unit's lifetime total.
         """
         def expr(m, p, x, t):
             # Only apply operational scaling to flows marked as operational
             if pyo.value(m.operation_flow[p, x]) == 0:
                 return 0
 
-            op_start = pyo.value(m.process_operation_start[p])
-            op_end = pyo.value(m.process_operation_end[p])
-
             total = 0
             # Sum flows across all active vintages at time t
             for (proc, v, time) in m.ACTIVE_VINTAGE_TIME:
                 if proc != p or time != t:
                     continue
 
-                # Get vintage-specific flow rate
+                tau = t - v  # lifecycle stage of this vintage in year t
+                if tau not in m.PROCESS_TIME:
+                    continue
+
+                # Get vintage-specific flow rate for this lifecycle stage
                 if (p, x) in overrides_index:
-                    # Vintage-aware: sum flow rates across all operating taus for this vintage
-                    flow_rate = sum(
-                        overrides.get((p, x, tau, v), tensor[p, x, tau])
-                        for tau in m.PROCESS_TIME
-                        if op_start <= tau <= op_end
-                    )
+                    flow_rate = overrides.get((p, x, tau, v), tensor[p, x, tau])
                 else:
-                    # No overrides: all vintages have same flow rate
-                    flow_rate = sum(
-                        tensor[p, x, tau]
-                        for tau in m.PROCESS_TIME
-                        if op_start <= tau <= op_end
-                    )
+                    flow_rate = tensor[p, x, tau]
 
                 total += flow_rate * m.var_operation[p, v, t]
 
@@ -688,60 +704,32 @@ def has_production_overrides(p, r):
         """Check if any vintage overrides exist for this process/product."""
         return (p, r) in model._production_overrides_index
 
-    def operation_capacity_constraint_rule(model, p, v, t, r):
+    def operation_capacity_constraint_rule(model, p, v, t):
         """
-        Per-vintage capacity constraint: var_operation[p, v, t] ≤ capacity_for_vintage(p, v, t)
+        Per-vintage capacity constraint: var_operation[p, v, t] ≤ units of vintage v.
 
-        This constraint ensures each vintage's operation level cannot exceed its
-        own production capacity.
+        Both variables count process UNITS: var_installation[p, v] is how many units
+        of vintage v exist, var_operation[p, v, t] is how many of them are running in
+        year t. A unit's production temporal distribution states what one unit yields
+        over its whole lifetime, so the annual output of a running unit is the
+        per-tau entry, and the bound here is a plain unit count comparison.
 
-        For greenfield (v in SYSTEM_TIME): capacity = production_rate * var_installation[p, v]
-        For brownfield (v not in SYSTEM_TIME): capacity = production_rate * existing_capacity[p, v]
+        For greenfield (v in SYSTEM_TIME): bound is var_installation[p, v]
+        For brownfield (v not in SYSTEM_TIME): bound is existing_capacity[p, v]
         """
-        fg_scale = model.scales["foreground"]
-        op_start = pyo.value(model.process_operation_start[p])
-        op_end = pyo.value(model.process_operation_end[p])
-
-        # Calculate production rate for this vintage
-        if has_production_overrides(p, r):
-            production_per_unit = sum(
-                pyo.value(get_production_value(p, r, tau_op, v))
-                for tau_op in model.PROCESS_TIME
-                if op_start <= tau_op <= op_end
-            )
-        else:
-            production_per_unit = sum(
-                model.foreground_production[p, r, tau_op]
-                for tau_op in model.PROCESS_TIME
-                if op_start <= tau_op <= op_end
-            )
-
-        if pyo.value(production_per_unit) == 0:
-            return pyo.Constraint.Skip
-
-        # Determine capacity based on vintage type
         if v in model.SYSTEM_TIME:
-            # Greenfield: capacity from var_installation
-            capacity = production_per_unit * model.var_installation[p, v] * fg_scale
-        else:
-            # Brownfield: capacity from existing_capacity dict
-            existing_cap = model._existing_capacity_dict.get((p, v), 0)
-            if existing_cap == 0:
-                return pyo.Constraint.Skip
-            capacity = pyo.value(production_per_unit) * existing_cap * fg_scale
-
-        return model.var_operation[p, v, t] <= capacity
+            # Greenfield: units available come from var_installation
+            return model.var_operation[p, v, t] <= model.var_installation[p, v]
 
-    # Build constraint over ACTIVE_VINTAGE_TIME × PRODUCT
-    def _build_operation_capacity_constraints(m):
-        """Generate constraint indices for per-vintage capacity bounds."""
-        for (p, v, t) in m.ACTIVE_VINTAGE_TIME:
-            for r in m.PRODUCT:
-                yield (p, v, t, r)
+        # Brownfield: units available come from the existing_capacity dict
+        existing_cap = model._existing_capacity_dict.get((p, v), 0)
+        if existing_cap == 0:
+            return pyo.Constraint.Skip
+        return model.var_operation[p, v, t] <= existing_cap
 
     model.OperationCapacity = pyo.Constraint(
-        _build_operation_capacity_constraints(model),
-        rule=lambda m, p, v, t, r: operation_capacity_constraint_rule(m, p, v, t, r),
+        model.ACTIVE_VINTAGE_TIME,
+        rule=operation_capacity_constraint_rule,
     )
 
     def product_demand_fulfillment_rule(model, r, t):
@@ -749,7 +737,9 @@ def product_demand_fulfillment_rule(model, r, t):
         Demand constraint: total_production == external_demand + internal_consumption
 
         With 3D var_operation[p, v, t], sum production across all active vintages.
-        Each vintage may have different production rates (if overrides exist).
+        The rate used is the vintage's annual output at its current lifecycle stage
+        tau = t - v; the sum over the whole operation window is the unit's lifetime
+        output, not its annual output.
         """
         total_production = 0
 
@@ -758,22 +748,15 @@ def product_demand_fulfillment_rule(model, r, t):
             if time != t:
                 continue
 
-            op_start = pyo.value(model.process_operation_start[p])
-            op_end = pyo.value(model.process_operation_end[p])
+            tau = t - v  # lifecycle stage of this vintage in year t
+            if tau not in model.PROCESS_TIME:
+                continue
 
-            # Get production rate for this vintage
+            # Get annual production rate for this vintage at this lifecycle stage
             if has_production_overrides(p, r):
-                production_rate = sum(
-                    pyo.value(get_production_value(p, r, tau_op, v))
-                    for tau_op in model.PROCESS_TIME
-                    if op_start <= tau_op <= op_end
-                )
+                production_rate = get_production_value(p, r, tau, v)
             else:
-                production_rate = sum(
-                    model.foreground_production[p, r, tau_op]
-                    for tau_op in model.PROCESS_TIME
-                    if op_start <= tau_op <= op_end
-                )
+                production_rate = model.foreground_production[p, r, tau]
 
             total_production += production_rate * model.var_operation[p, v, t]
 
@@ -867,29 +850,22 @@ def cumulative_category_impact_limit_rule(model, c):
     def total_product_flow_rule(model, r, t):
         """
         Calculate total product output at time t, summing across all active vintages.
-        Consistent with 3D var_operation[p, v, t].
+        Uses each vintage's annual output at its lifecycle stage tau = t - v.
         """
         total = 0
         for (p, v, time) in model.ACTIVE_VINTAGE_TIME:
             if time != t:
                 continue
 
-            op_start = pyo.value(model.process_operation_start[p])
-            op_end = pyo.value(model.process_operation_end[p])
+            tau = t - v  # lifecycle stage of this vintage in year t
+            if tau not in model.PROCESS_TIME:
+                continue
 
-            # Get production rate for this vintage
+            # Get annual production rate for this vintage at this lifecycle stage
             if has_production_overrides(p, r):
-                production_rate = sum(
-                    pyo.value(get_production_value(p, r, tau_op, v))
-                    for tau_op in model.PROCESS_TIME
-                    if op_start <= tau_op <= op_end
-                )
+                production_rate = get_production_value(p, r, tau, v)
             else:
-                production_rate = sum(
-                    model.foreground_production[p, r, tau_op]
-                    for tau_op in model.PROCESS_TIME
-                    if op_start <= tau_op <= op_end
-                )
+                production_rate = model.foreground_production[p, r, tau]
 
             total += production_rate * model.var_operation[p, v, t]
         return total
@@ -1150,11 +1126,12 @@ def validate_operation_bounds(model: pyo.ConcreteModel, tolerance: float = 1e-6)
     Validate that operation levels respect capacity constraints.
 
     This function performs post-solve validation to ensure that var_operation[p, v, t]
-    does not exceed the production capacity for each vintage.
+    does not exceed the units available from that vintage. Both variables count
+    process units, so the comparison needs no production rate.
 
     With 3D operation variables:
-    - Greenfield: var_operation[p, v, t] <= production_rate * var_installation[p, v] * fg_scale
-    - Brownfield: var_operation[p, v, t] <= production_rate * existing_capacity[p, v] * fg_scale
+    - Greenfield: var_operation[p, v, t] <= var_installation[p, v]
+    - Brownfield: var_operation[p, v, t] <= existing_capacity[p, v]
 
     Parameters
     ----------
@@ -1182,60 +1159,19 @@ def validate_operation_bounds(model: pyo.ConcreteModel, tolerance: float = 1e-6)
 
     violations = []
     max_violation = 0.0
-    fg_scale = model.scales["foreground"]
 
-    # Get sparse overrides for production with precomputed index for O(1) lookup
-    production_overrides = getattr(model, "_production_vintage_overrides", {}) or {}
-    production_overrides_index = getattr(model, "_production_overrides_index", frozenset())
     existing_cap_dict = getattr(model, "_existing_capacity_dict", {})
 
-    def get_prod_value(p, r, tau, vintage):
-        """Get production value, checking sparse overrides first."""
-        key = (p, r, tau, vintage)
-        if key in production_overrides:
-            return production_overrides[key]
-        return pyo.value(model.foreground_production[p, r, tau])
-
-    def has_prod_overrides(p, r):
-        """O(1) check if any vintage overrides exist for this process/product."""
-        return (p, r) in production_overrides_index
-
     # Validate per-vintage operation bounds
     for (p, v, t) in model.ACTIVE_VINTAGE_TIME:
         operation_value = pyo.value(model.var_operation[p, v, t])
-        op_start = pyo.value(model.process_operation_start[p])
-        op_end = pyo.value(model.process_operation_end[p])
-
-        max_capacity = 0.0
-        for r in model.PRODUCT:
-            # Get production rate for this vintage
-            if has_prod_overrides(p, r):
-                production_rate = sum(
-                    get_prod_value(p, r, tau_op, v)
-                    for tau_op in model.PROCESS_TIME
-                    if op_start <= tau_op <= op_end
-                )
-            else:
-                production_rate = sum(
-                    pyo.value(model.foreground_production[p, r, tau_op])
-                    for tau_op in model.PROCESS_TIME
-                    if op_start <= tau_op <= op_end
-                )
-
-            if production_rate == 0:
-                continue
 
-            # Calculate capacity for this vintage
-            if v in model.SYSTEM_TIME:
-                # Greenfield: capacity from var_installation
-                installed = pyo.value(model.var_installation[p, v])
-                capacity = production_rate * installed * fg_scale
-            else:
-                # Brownfield: capacity from existing_capacity dict
-                existing_cap = existing_cap_dict.get((p, v), 0)
-                capacity = production_rate * existing_cap * fg_scale
-
-            max_capacity = max(max_capacity, capacity)
+        # Units available from this vintage (operation and installation are both
+        # unit counts, so no production rate enters here)
+        if v in model.SYSTEM_TIME:
+            max_capacity = pyo.value(model.var_installation[p, v])
+        else:
+            max_capacity = existing_cap_dict.get((p, v), 0)
 
         # Check if operation exceeds capacity
         if operation_value < -tolerance:
diff --git a/src/optimex/postprocessing.py b/src/optimex/postprocessing.py
index 71291fb..5dc6ac2 100644
--- a/src/optimex/postprocessing.py
+++ b/src/optimex/postprocessing.py
@@ -617,8 +617,25 @@ def plot_characterized_dynamic_inventory(
     def get_installation(self) -> pd.DataFrame:
         """
         Extracts the installation data from the model and returns it as a DataFrame.
-        The DataFrame will have a MultiIndex with 'Time' and 'Process'.
-        The values are the installed capacities for each process at each time step.
+
+        Values are the decision variable `var_installation[p, v]` itself, unchanged:
+        the number of process UNITS built in vintage year v. One unit delivers its full
+        lifetime production (the sum of its production temporal distribution) spread
+        over its operation window.
+
+        Two things follow, and both matter when reading plots:
+
+        1. The index is the year of INSTALLATION, not a year of production. A unit
+           installed in 2030 with an operation window of tau 1-20 produces in 2031-2050.
+        2. The values are a lifetime quantity, so they are not an annual capacity and
+           must not be compared with the per-year values from `get_production()` or
+           `get_demand()`. Use `get_production_capacity()` for the annual capacity that
+           installed units and existing stock make available in each year.
+
+        Returns
+        -------
+        pd.DataFrame
+            Time (vintage year) as index, Process as columns, units as values.
         """
         # var_installation is already in real units, no scaling needed
         installation_matrix = {
@@ -639,8 +656,15 @@ def get_operation(self, aggregate_vintages: bool = True) -> pd.DataFrame:
         """
         Extracts the operation data from the model and returns it as a DataFrame.
 
-        With 3D var_operation[p, v, t], this method can either aggregate across
-        vintages (backward compatible) or return per-vintage data.
+        Values are the decision variable `var_operation[p, v, t]` itself: the number of
+        UNITS of vintage v that run in year t, summed over vintages by default. Unlike
+        `get_installation()`, the index is the year of OPERATION.
+
+        Units running are not a production volume: multiply by the output per unit and
+        year (the production entry at the vintage's lifecycle stage) to get production,
+        or simply use `get_production()`. Units running can be compared directly with
+        `get_installation()` only per vintage, since operation of a vintage is bounded
+        by the units installed in that vintage.
 
         Parameters
         ----------
@@ -655,6 +679,7 @@ def get_operation(self, aggregate_vintages: bool = True) -> pd.DataFrame:
             If aggregate_vintages=True: DataFrame with Time as index, Process as columns.
             If aggregate_vintages=False: DataFrame with Time as index,
                 (Process, Vintage) MultiIndex columns.
+            Values are counts of running units in both cases.
 
         Note: var_operation is not scaled because when both demand and
         foreground_production are scaled by the same factor, the scaling
@@ -719,9 +744,6 @@ def has_production_overrides(p, r):
 
         for p in self.m.PROCESS:
             for f in self.m.PRODUCT:
-                op_start = pyo.value(self.m.process_operation_start[p])
-                op_end = pyo.value(self.m.process_operation_end[p])
-
                 for t in self.m.SYSTEM_TIME:
                     # Sum production across all active vintages at time t
                     total_production = 0
@@ -729,18 +751,16 @@ def has_production_overrides(p, r):
                         if proc != p or time != t:
                             continue
 
-                        # Get production rate for this vintage
+                        tau = t - v  # lifecycle stage of this vintage in year t
+                        if tau not in self.m.PROCESS_TIME:
+                            continue
+
+                        # Annual output per running unit at this lifecycle stage
                         if has_production_overrides(p, f):
-                            production_rate = sum(
-                                pyo.value(get_production_value(p, f, tau_op, v))
-                                for tau_op in self.m.PROCESS_TIME
-                                if op_start <= tau_op <= op_end
-                            )
+                            production_rate = pyo.value(get_production_value(p, f, tau, v))
                         else:
-                            production_rate = sum(
-                                pyo.value(self.m.foreground_production[p, f, tau_op])
-                                for tau_op in self.m.PROCESS_TIME
-                                if op_start <= tau_op <= op_end
+                            production_rate = pyo.value(
+                                self.m.foreground_production[p, f, tau]
                             )
 
                         # Production from this vintage
@@ -863,6 +883,12 @@ def plot_installation(self, df_installation=None, annotated=True):
         """
         Plot a stacked bar chart for installation data.
 
+        Bars show `var_installation`: the number of process units built in each vintage
+        year, a LIFETIME quantity plotted at the year of installation. This is not an
+        annual capacity and does not line up with production in the same year - use
+        `plot_capacity_balance()` to compare production against the annual capacity
+        those units make available.
+
         Parameters
         ----------
         df_installation : DataFrame, optional
@@ -885,9 +911,11 @@ def plot_installation(self, df_installation=None, annotated=True):
         fig, axes = self._create_clean_axes()
         ax = axes[0]
         self._apply_bar_styles(
-            df_installation, ax, colors, title="Installed Capacity"
+            df_installation, ax, colors, title="Installed Units by Vintage"
+        )
+        ax.set_ylabel(
+            "Installed units (lifetime)", fontsize=self._plot_config["label_fontsize"]
         )
-        ax.set_ylabel("Installed Capacity", fontsize=self._plot_config["label_fontsize"])
 
 
         # Legend at bottom
@@ -911,6 +939,10 @@ def plot_operation(self, df_operation=None, annotated=True):
         """
         Plot a stacked bar chart for operation data.
 
+        Bars show `var_operation` summed over vintages: how many units run in each year.
+        Note the different meaning of the x-axis compared to `plot_installation()`,
+        which is indexed by the year of installation.
+
         Parameters
         ----------
         df_operation : DataFrame, optional
@@ -933,9 +965,9 @@ def plot_operation(self, df_operation=None, annotated=True):
         fig, axes = self._create_clean_axes()
         ax = axes[0]
         self._apply_bar_styles(
-            df_operation, ax, colors, title="Operational Level"
+            df_operation, ax, colors, title="Units Running"
         )
-        ax.set_ylabel("Operation Level", fontsize=self._plot_config["label_fontsize"])
+        ax.set_ylabel("Units running", fontsize=self._plot_config["label_fontsize"])
 
 
         # Legend at bottom
@@ -1008,20 +1040,23 @@ def get_existing_capacity(self) -> pd.DataFrame:
 
     def get_production_capacity(self) -> pd.DataFrame:
         """
-        Calculate maximum available production capacity for each product at each time step.
-
-        Capacity is determined by counting installations in their operation phase and
-        multiplying by their production coefficients. This includes both new installations
-        (from var_installation) and existing (brownfield) capacity.
-
-        Note: Uses vintage-aware 4D calculation when production overrides exist,
-        matching the optimizer's capacity constraint calculation.
+        Calculate maximum available ANNUAL production capacity for each product at
+        each time step.
+
+        Installed units (`get_installation()`) are counted in process units, and one
+        unit yields its full lifetime production over the whole operation window. This
+        method converts those units into the output they can deliver *in a given year*:
+        for every vintage active at time t, its unit count is multiplied by the
+        production coefficient at its current lifecycle stage tau = t - v. This is the
+        quantity to compare against actual production (`get_production()`), which is
+        also per year. Includes both new installations (from var_installation) and
+        existing (brownfield) capacity.
 
         Returns
         -------
         pd.DataFrame
             DataFrame with Time as index and Products as columns.
-            Values represent maximum production capacity (not actual production).
+            Values represent maximum annual production capacity (not actual production).
         """
         capacity_tensor = {}
         fg_scale = getattr(self.m, "scales", {}).get("foreground", 1.0)
@@ -1042,73 +1077,33 @@ def has_production_overrides(p, r):
             """Check if any vintage overrides exist for this process/product."""
             return (p, r) in production_overrides_index
 
+        def units_available(p, v):
+            """Units of vintage v that can run: greenfield installs or brownfield stock."""
+            if v in self.m.SYSTEM_TIME:
+                return pyo.value(self.m.var_installation[p, v])
+            return existing_cap_dict.get((p, v), 0)
+
         for f in self.m.PRODUCT:
             for t in self.m.SYSTEM_TIME:
-                # Calculate total capacity across all processes
+                # Sum annual capacity over all vintages active at time t
                 total_capacity = 0
 
-                for p in self.m.PROCESS:
-                    op_start = pyo.value(self.m.process_operation_start[p])
-                    op_end = pyo.value(self.m.process_operation_end[p])
+                for (p, v, time) in self.m.ACTIVE_VINTAGE_TIME:
+                    if time != t:
+                        continue
+
+                    tau = t - v  # lifecycle stage of this vintage in year t
+                    if tau not in self.m.PROCESS_TIME:
+                        continue
 
                     if has_production_overrides(p, f):
-                        # 4D vintage-aware capacity calculation
-                        # Each vintage may have different production rates
-                        process_capacity = 0
-
-                        # New installations: sum capacity by vintage
-                        for tau in self.m.PROCESS_TIME:
-                            vintage = t - tau
-                            if vintage in self.m.SYSTEM_TIME and op_start <= tau <= op_end:
-                                # Production rate for this vintage (sum over all operating taus)
-                                production_per_unit = sum(
-                                    get_production_value(p, f, tau_op, vintage)
-                                    for tau_op in self.m.PROCESS_TIME
-                                    if op_start <= tau_op <= op_end
-                                )
-                                installation = pyo.value(self.m.var_installation[p, vintage])
-                                process_capacity += production_per_unit * installation
-
-                        # Existing (brownfield) capacity
-                        for (proc, inst_year), capacity in existing_cap_dict.items():
-                            if proc == p:
-                                tau_existing = t - inst_year
-                                if op_start <= tau_existing <= op_end:
-                                    nearest_vintage = min(self.m.SYSTEM_TIME)
-                                    production_per_unit = sum(
-                                        get_production_value(p, f, tau_op, nearest_vintage)
-                                        for tau_op in self.m.PROCESS_TIME
-                                        if op_start <= tau_op <= op_end
-                                    )
-                                    process_capacity += production_per_unit * capacity
-
-                        total_capacity += process_capacity
+                        annual_production_per_unit = get_production_value(p, f, tau, v)
                     else:
-                        # 3D calculation: no overrides, all vintages have same production rate
-                        # Count new installations in operation phase at time t
-                        installations_operating = sum(
-                            pyo.value(self.m.var_installation[p, t - tau])
-                            for tau in self.m.PROCESS_TIME
-                            if (t - tau in self.m.SYSTEM_TIME)
-                            and op_start <= tau <= op_end
+                        annual_production_per_unit = pyo.value(
+                            self.m.foreground_production[p, f, tau]
                         )
 
-                        # Add existing (brownfield) capacity in operation phase
-                        for (proc, inst_year), capacity in existing_cap_dict.items():
-                            if proc == p:
-                                tau_existing = t - inst_year
-                                if op_start <= tau_existing <= op_end:
-                                    installations_operating += capacity
-
-                        # Production capacity per installation (sum over operation phase)
-                        production_per_installation = sum(
-                            pyo.value(self.m.foreground_production[p, f, tau])
-                            for tau in self.m.PROCESS_TIME
-                            if op_start <= tau <= op_end
-                        )
-
-                        # Total capacity for this process
-                        total_capacity += installations_operating * production_per_installation
+                    total_capacity += annual_production_per_unit * units_available(p, v)
 
                 # Store denormalized capacity
                 capacity_tensor[(f, t)] = total_capacity * fg_scale
@@ -1172,6 +1167,9 @@ def _plot_capacity_balance_on_ax(
         """
         Plot production vs capacity lines on a given axis.
 
+        Both series are per year and in product units; the capacity line is the annual
+        capacity from `get_production_capacity()`, not the installed unit counts.
+
         Parameters
         ----------
         ax : matplotlib.axes.Axes
@@ -1181,7 +1179,7 @@ def _plot_capacity_balance_on_ax(
         prod_df : pd.DataFrame
             Production DataFrame from get_production().
         capacity_df : pd.DataFrame
-            Capacity DataFrame from get_production_capacity().
+            Annual capacity DataFrame from get_production_capacity().
         annotated : bool, default=True
             If True, show human-readable names instead of codes.
         show_legend : bool, default=True
@@ -1214,7 +1212,7 @@ def _plot_capacity_balance_on_ax(
             max_capacity.values,
             marker='s',
             linewidth=self._plot_config["line_width"],
-            label='Max Capacity',
+            label='Max annual capacity',
             color='#000000',
             linestyle='--',
             zorder=3
@@ -1234,7 +1232,7 @@ def _plot_capacity_balance_on_ax(
 
         # Set labels and title
         self._set_smart_xticks(ax, actual_production.index)
-        ax.set_ylabel("Quantity", fontsize=self._plot_config["label_fontsize"])
+        ax.set_ylabel("Quantity per year", fontsize=self._plot_config["label_fontsize"])
         ax.set_axisbelow(True)
         ax.grid(
             axis="both",
@@ -1268,7 +1266,10 @@ def _compute_capacity_breakdown(self, product):
             existing_additions_df, existing_removals_df, operation_df.
             All DataFrames have process columns and time index.
 
-        Note: Uses vintage-aware 4D calculation when production overrides exist.
+        All capacity figures are ANNUAL production capacity: unit counts multiplied by
+        the production coefficient at the relevant lifecycle stage, so they are
+        directly comparable with per-year production. Uses vintage-specific rates when
+        production overrides exist.
         """
         fg_scale = getattr(self.m, "scales", {}).get("foreground", 1.0)
         existing_cap_dict = getattr(self.m, "_existing_capacity_dict", {})
@@ -1288,6 +1289,14 @@ def has_production_overrides(p, r):
             """Check if any vintage overrides exist for this process/product."""
             return (p, r) in production_overrides_index
 
+        def annual_rate(p, r, tau, vintage):
+            """Annual output per running unit of `vintage` at lifecycle stage `tau`."""
+            if tau not in self.m.PROCESS_TIME:
+                return 0.0
+            if has_production_overrides(p, r):
+                return pyo.value(get_production_value(p, r, tau, vintage))
+            return pyo.value(self.m.foreground_production[p, r, tau])
+
         capacity_additions = {p: {} for p in self.m.PROCESS}
         capacity_removals = {p: {} for p in self.m.PROCESS}
         existing_additions = {p: {} for p in self.m.PROCESS}
@@ -1299,14 +1308,12 @@ def has_production_overrides(p, r):
                 op_start = pyo.value(self.m.process_operation_start[p])
                 op_end = pyo.value(self.m.process_operation_end[p])
 
-                # Base production per installation (3D, for processes without overrides)
-                prod_per_inst_3d = sum(
-                    pyo.value(self.m.foreground_production[p, product, tau])
+                # Skip processes that never produce this product
+                if all(
+                    annual_rate(p, product, tau, min(self.m.SYSTEM_TIME)) == 0
                     for tau in self.m.PROCESS_TIME
                     if op_start <= tau <= op_end
-                )
-
-                if prod_per_inst_3d == 0:
+                ):
                     capacity_additions[p][t] = 0
                     capacity_removals[p][t] = 0
                     existing_additions[p][t] = 0
@@ -1314,115 +1321,65 @@ def has_production_overrides(p, r):
                     operation[p][t] = 0
                     continue
 
-                if has_production_overrides(p, product):
-                    # 4D vintage-aware calculations
-
-                    # New capacity entering operation (vintage = t - op_start)
-                    t_entering = t - op_start
-                    if t_entering in self.m.SYSTEM_TIME:
-                        installation_entering = pyo.value(self.m.var_installation[p, t_entering])
-                        prod_per_inst_vintage = sum(
-                            get_production_value(p, product, tau_op, t_entering)
-                            for tau_op in self.m.PROCESS_TIME
-                            if op_start <= tau_op <= op_end
-                        )
-                        capacity_additions[p][t] = installation_entering * prod_per_inst_vintage * fg_scale
-                    else:
-                        capacity_additions[p][t] = 0
-
-                    # Capacity exiting operation (vintage = t - op_end - 1)
-                    t_exiting = t - op_end - 1
-                    if t_exiting in self.m.SYSTEM_TIME:
-                        installation_exiting = pyo.value(self.m.var_installation[p, t_exiting])
-                        prod_per_inst_vintage = sum(
-                            get_production_value(p, product, tau_op, t_exiting)
-                            for tau_op in self.m.PROCESS_TIME
-                            if op_start <= tau_op <= op_end
-                        )
-                        capacity_removals[p][t] = installation_exiting * prod_per_inst_vintage * fg_scale
-                    else:
-                        capacity_removals[p][t] = 0
-
-                    # Existing capacity changes (use nearest vintage for rate)
-                    existing_add = 0
-                    existing_rem = 0
-                    nearest_vintage = min(self.m.SYSTEM_TIME)
-                    prod_per_inst_existing = sum(
-                        get_production_value(p, product, tau_op, nearest_vintage)
-                        for tau_op in self.m.PROCESS_TIME
-                        if op_start <= tau_op <= op_end
+                # New capacity entering operation (vintage = t - op_start), valued at
+                # the annual output it delivers in its first operating year
+                t_entering = t - op_start
+                if t_entering in self.m.SYSTEM_TIME:
+                    installation_entering = pyo.value(self.m.var_installation[p, t_entering])
+                    capacity_additions[p][t] = (
+                        installation_entering
+                        * annual_rate(p, product, op_start, t_entering)
+                        * fg_scale
                     )
-                    for (proc, inst_year), capacity in existing_cap_dict.items():
-                        if proc == p:
-                            tau_existing = t - inst_year
-                            tau_existing_prev = (t - 1) - inst_year
-                            if op_start <= tau_existing <= op_end:
-                                if tau_existing_prev < op_start:
-                                    existing_add += capacity * prod_per_inst_existing * fg_scale
-                            if tau_existing > op_end:
-                                if op_start <= tau_existing_prev <= op_end:
-                                    existing_rem += capacity * prod_per_inst_existing * fg_scale
-                    existing_additions[p][t] = existing_add
-                    existing_removals[p][t] = existing_rem
-
-                    # Operation level - sum production across all active vintages
-                    total_operation = 0
-                    for (proc, v, time) in self.m.ACTIVE_VINTAGE_TIME:
-                        if proc != p or time != t:
-                            continue
-                        # Get production rate for this vintage
-                        production_rate = sum(
-                            pyo.value(get_production_value(p, product, tau_op, v))
-                            for tau_op in self.m.PROCESS_TIME
-                            if op_start <= tau_op <= op_end
-                        )
-                        total_operation += production_rate * pyo.value(self.m.var_operation[p, v, t])
+                else:
+                    capacity_additions[p][t] = 0
 
-                    operation[p][t] = total_operation * fg_scale
+                # Capacity exiting operation (vintage = t - op_end - 1), valued at the
+                # annual output it delivered in its last operating year
+                t_exiting = t - op_end - 1
+                if t_exiting in self.m.SYSTEM_TIME:
+                    installation_exiting = pyo.value(self.m.var_installation[p, t_exiting])
+                    capacity_removals[p][t] = (
+                        installation_exiting
+                        * annual_rate(p, product, op_end, t_exiting)
+                        * fg_scale
+                    )
                 else:
-                    # 3D calculation: no overrides
-                    prod_per_inst = prod_per_inst_3d
-
-                    # New capacity entering operation
-                    t_entering = t - op_start
-                    if t_entering in self.m.SYSTEM_TIME:
-                        installation_entering = pyo.value(self.m.var_installation[p, t_entering])
-                        capacity_additions[p][t] = installation_entering * prod_per_inst * fg_scale
-                    else:
-                        capacity_additions[p][t] = 0
+                    capacity_removals[p][t] = 0
 
-                    # Capacity exiting operation
-                    t_exiting = t - op_end - 1
-                    if t_exiting in self.m.SYSTEM_TIME:
-                        installation_exiting = pyo.value(self.m.var_installation[p, t_exiting])
-                        capacity_removals[p][t] = installation_exiting * prod_per_inst * fg_scale
-                    else:
-                        capacity_removals[p][t] = 0
-
-                    # Existing capacity changes
-                    existing_add = 0
-                    existing_rem = 0
-                    for (proc, inst_year), capacity in existing_cap_dict.items():
-                        if proc == p:
-                            tau_existing = t - inst_year
-                            tau_existing_prev = (t - 1) - inst_year
-                            if op_start <= tau_existing <= op_end:
-                                if tau_existing_prev < op_start:
-                                    existing_add += capacity * prod_per_inst * fg_scale
-                            if tau_existing > op_end:
-                                if op_start <= tau_existing_prev <= op_end:
-                                    existing_rem += capacity * prod_per_inst * fg_scale
-                    existing_additions[p][t] = existing_add
-                    existing_removals[p][t] = existing_rem
-
-                    # Operation level - sum production across all active vintages
-                    total_operation = 0
-                    for (proc, v, time) in self.m.ACTIVE_VINTAGE_TIME:
-                        if proc != p or time != t:
-                            continue
-                        total_operation += prod_per_inst * pyo.value(self.m.var_operation[p, v, t])
+                # Existing (brownfield) capacity entering/leaving operation
+                existing_add = 0
+                existing_rem = 0
+                for (proc, inst_year), capacity in existing_cap_dict.items():
+                    if proc != p:
+                        continue
+                    tau_existing = t - inst_year
+                    tau_existing_prev = (t - 1) - inst_year
+                    if op_start <= tau_existing <= op_end and tau_existing_prev < op_start:
+                        existing_add += (
+                            capacity
+                            * annual_rate(p, product, op_start, inst_year)
+                            * fg_scale
+                        )
+                    if tau_existing > op_end and op_start <= tau_existing_prev <= op_end:
+                        existing_rem += (
+                            capacity
+                            * annual_rate(p, product, op_end, inst_year)
+                            * fg_scale
+                        )
+                existing_additions[p][t] = existing_add
+                existing_removals[p][t] = existing_rem
+
+                # Actual production at time t, summed over all active vintages
+                total_operation = 0
+                for (proc, v, time) in self.m.ACTIVE_VINTAGE_TIME:
+                    if proc != p or time != t:
+                        continue
+                    total_operation += annual_rate(p, product, t - v, v) * pyo.value(
+                        self.m.var_operation[p, v, t]
+                    )
 
-                    operation[p][t] = total_operation * fg_scale
+                operation[p][t] = total_operation * fg_scale
 
         # Convert to DataFrames
         capacity_additions_df = pd.DataFrame(capacity_additions)
@@ -1474,6 +1431,12 @@ def _plot_capacity_balance_detailed_on_ax(
         """
         Plot detailed capacity balance with grouped bars on a given axis.
 
+        Everything is in product units per year. The capacity bars show the annual
+        capacity entering and leaving operation - unit counts converted with the output
+        per unit and year, and placed in the year operation starts or ends. They are
+        therefore neither `var_installation` nor the year it was installed, unlike
+        `plot_installation()`.
+
         Parameters
         ----------
         ax : matplotlib.axes.Axes
@@ -1565,8 +1528,10 @@ def _plot_capacity_balance_detailed_on_ax(
             process_legend = [Patch(facecolor=self._color_map.get(process_codes[i], 'black'), edgecolor='black', linewidth=0.5, label=col)
                             for i, col in enumerate(capacity_additions_df.columns)]
             type_legend = [
-                Patch(facecolor="white", edgecolor='#30A834', linewidth=2, label='+ Cap'),
-                Patch(facecolor="white", edgecolor='#CD221F', linewidth=2, label='− Cap'),
+                Patch(facecolor="white", edgecolor='#30A834', linewidth=2,
+                      label='+ Annual cap.'),
+                Patch(facecolor="white", edgecolor='#CD221F', linewidth=2,
+                      label='− Annual cap.'),
             ]
 
         # Plot production and capacity lines
@@ -1574,7 +1539,7 @@ def _plot_capacity_balance_detailed_on_ax(
                 linewidth=self._plot_config["line_width"], label='Production / Demand',
                 color='#00549F', linestyle='-', zorder=3)
         ax.plot(x_positions, max_capacity.values, marker='s',
-                linewidth=self._plot_config["line_width"], label='Max Capacity',
+                linewidth=self._plot_config["line_width"], label='Max annual capacity',
                 color='#000000', linestyle='--', zorder=3)
 
         # Line legend entries
@@ -1582,11 +1547,11 @@ def _plot_capacity_balance_detailed_on_ax(
             Line2D([0], [0], color='#00549F', marker='o', linestyle='-',
                    linewidth=self._plot_config["line_width"], label='Production / Demand'),
             Line2D([0], [0], color='#000000', marker='s', linestyle='--',
-                   linewidth=self._plot_config["line_width"], label='Max Capacity'),
+                   linewidth=self._plot_config["line_width"], label='Max annual capacity'),
         ]
 
         self._set_smart_xticks(ax, actual_production.index)
-        ax.set_ylabel("Quantity", fontsize=self._plot_config["label_fontsize"])
+        ax.set_ylabel("Quantity per year", fontsize=self._plot_config["label_fontsize"])
         ax.set_axisbelow(True)
         ax.grid(
             axis="both",
@@ -1615,17 +1580,26 @@ def plot_capacity_balance(self, product=None, prod_df=None, capacity_df=None, de
         """
         Plot actual production vs maximum available capacity.
 
+        Everything in this plot is in PRODUCT UNITS PER YEAR, which is what makes
+        production and capacity comparable. The capacity shown is therefore not
+        `var_installation`: it is `get_production_capacity()`, i.e. the units of every
+        vintage that is in its operation phase multiplied by the output that vintage
+        yields per unit and year, and it is indexed by the year the capacity is
+        available rather than the year it was installed.
+
         When a specific product is given, plots a single chart. When product is
         None, auto-detects all products with non-zero demand or production and
         plots a grid of subplots.
 
         Shows two lines per product:
         - Production (demand is assumed equal and overlaid)
-        - Maximum available capacity (dashed line)
+        - Maximum available annual capacity (dashed line)
 
         When detailed=True, also shows grouped bars per time step:
-        - Left bar: Capacity changes (additions/removals stacked by process)
-        - Right bar: Operation level (stacked by process)
+        - Left bar: Annual capacity entering/leaving operation, stacked by process.
+          A cohort installed in year v appears here in the year it starts operating
+          (v + operation start), again converted to output per year.
+        - Right bar: Production of the running units, stacked by process
 
         Parameters
         ----------
@@ -1786,6 +1760,12 @@ def plot_utilization_heatmap(self, product=None, annotated=True, show_values=Tru
         This provides a clean, dedicated view of which processes are being
         operated vs sitting idle at each time step.
 
+        Utilization is computed per year as actual production divided by the annual
+        capacity of the vintages in their operation phase - both in product units per
+        year. It is not `var_operation / var_installation`: those are unit counts
+        indexed by different years (operation year vs vintage year), and a unit only
+        counts towards capacity while it is inside its operation window.
+
         Parameters
         ----------
         product : str, optional
@@ -1795,7 +1775,7 @@ def plot_utilization_heatmap(self, product=None, annotated=True, show_values=Tru
         show_values : bool, default=True
             If True, show utilization percentages in cells.
 
-        Note: Uses vintage-aware 4D calculation when production overrides exist.
+        Note: Uses vintage-specific production rates when overrides exist.
         """
         # Get demand to determine product
         demand_df = self.get_demand()
@@ -1824,6 +1804,20 @@ def has_production_overrides(p, r):
             """Check if any vintage overrides exist for this process/product."""
             return (p, r) in production_overrides_index
 
+        def annual_rate(p, r, tau, vintage):
+            """Annual output per running unit of `vintage` at lifecycle stage `tau`."""
+            if tau not in self.m.PROCESS_TIME:
+                return 0.0
+            if has_production_overrides(p, r):
+                return pyo.value(get_production_value(p, r, tau, vintage))
+            return pyo.value(self.m.foreground_production[p, r, tau])
+
+        def units_available(p, v):
+            """Units of vintage v that can run: greenfield installs or brownfield stock."""
+            if v in self.m.SYSTEM_TIME:
+                return pyo.value(self.m.var_installation[p, v])
+            return existing_cap_dict.get((p, v), 0)
+
         # Calculate utilization for each process at each time
         utilization_data = {}
         capacity_data = {}
@@ -1833,92 +1827,32 @@ def has_production_overrides(p, r):
             op_start = pyo.value(self.m.process_operation_start[p])
             op_end = pyo.value(self.m.process_operation_end[p])
 
-            # Check if process produces this product (3D base rate)
-            prod_per_inst_3d = sum(
-                pyo.value(self.m.foreground_production[p, product, tau])
+            # Skip processes that don't produce this product
+            if all(
+                annual_rate(p, product, tau, min(self.m.SYSTEM_TIME)) == 0
                 for tau in self.m.PROCESS_TIME
                 if op_start <= tau <= op_end
-            )
-
-            if prod_per_inst_3d == 0:
-                continue  # Skip processes that don't produce this product
+            ):
+                continue
 
             utilization_data[p] = {}
             capacity_data[p] = {}
             operation_data[p] = {}
 
             for t in self.m.SYSTEM_TIME:
-                if has_production_overrides(p, product):
-                    # 4D vintage-aware calculations
-                    # Capacity from new installations (sum by vintage)
-                    capacity = 0
-                    for tau in self.m.PROCESS_TIME:
-                        vintage = t - tau
-                        if vintage in self.m.SYSTEM_TIME and op_start <= tau <= op_end:
-                            production_per_unit = sum(
-                                get_production_value(p, product, tau_op, vintage)
-                                for tau_op in self.m.PROCESS_TIME
-                                if op_start <= tau_op <= op_end
-                            )
-                            installation = pyo.value(self.m.var_installation[p, vintage])
-                            capacity += production_per_unit * installation
-
-                    # Add existing (brownfield) capacity
-                    nearest_vintage = min(self.m.SYSTEM_TIME)
-                    prod_per_inst_existing = sum(
-                        get_production_value(p, product, tau_op, nearest_vintage)
-                        for tau_op in self.m.PROCESS_TIME
-                        if op_start <= tau_op <= op_end
-                    )
-                    for (proc, inst_year), cap in existing_cap_dict.items():
-                        if proc == p:
-                            tau_existing = t - inst_year
-                            if op_start <= tau_existing <= op_end:
-                                capacity += prod_per_inst_existing * cap
-
-                    capacity *= fg_scale
-
-                    # Operation - sum production across all active vintages
-                    operation = 0
-                    for (proc, v, time) in self.m.ACTIVE_VINTAGE_TIME:
-                        if proc != p or time != t:
-                            continue
-                        # Get production rate for this vintage
-                        production_rate = sum(
-                            pyo.value(get_production_value(p, product, tau_op, v))
-                            for tau_op in self.m.PROCESS_TIME
-                            if op_start <= tau_op <= op_end
-                        )
-                        operation += production_rate * pyo.value(self.m.var_operation[p, v, t])
-                    operation *= fg_scale
-                else:
-                    # 3D calculation: no overrides
-                    prod_per_inst = prod_per_inst_3d
-
-                    # Calculate capacity from new installations
-                    installations_operating = sum(
-                        pyo.value(self.m.var_installation[p, t - tau])
-                        for tau in self.m.PROCESS_TIME
-                        if (t - tau in self.m.SYSTEM_TIME)
-                        and op_start <= tau <= op_end
-                    )
-
-                    # Add existing (brownfield) capacity in operation phase
-                    for (proc, inst_year), cap in existing_cap_dict.items():
-                        if proc == p:
-                            tau_existing = t - inst_year
-                            if op_start <= tau_existing <= op_end:
-                                installations_operating += cap
-
-                    capacity = installations_operating * prod_per_inst * fg_scale
-
-                    # Calculate operation - sum across all active vintages
-                    operation = 0
-                    for (proc, v, time) in self.m.ACTIVE_VINTAGE_TIME:
-                        if proc != p or time != t:
-                            continue
-                        operation += prod_per_inst * pyo.value(self.m.var_operation[p, v, t])
-                    operation *= fg_scale
+                # Both capacity and operation are annual quantities: unit counts times
+                # the output per unit and year at each vintage's lifecycle stage
+                capacity = 0
+                operation = 0
+                for (proc, v, time) in self.m.ACTIVE_VINTAGE_TIME:
+                    if proc != p or time != t:
+                        continue
+                    rate = annual_rate(p, product, t - v, v)
+                    capacity += rate * units_available(p, v)
+                    operation += rate * pyo.value(self.m.var_operation[p, v, t])
+
+                capacity *= fg_scale
+                operation *= fg_scale
 
                 capacity_data[p][t] = capacity
                 operation_data[p][t] = operation
diff --git a/tests/test_background_process_product_separation.py b/tests/test_background_process_product_separation.py
index 662cd80..c3ae4df 100644
--- a/tests/test_background_process_product_separation.py
+++ b/tests/test_background_process_product_separation.py
@@ -139,15 +139,19 @@ def test_separated_background_matches_standard_lca(setup_separated_background_sy
     Test that optimex produces same results as standard LCA when background has
     separate process and product nodes.
 
-    Expected calculation for 100 kg Widget:
-    - Direct CO2 from operation: 100 * 5 * 1.0 = 500 kg CO2
-    - Background electricity at construction: 100 * 10 * 0.5 = 500 kg CO2
-    - Total: 1000 kg CO2
+    The process has a two-year operation window and yields 0.5 kg per unit and year,
+    so the demand is placed in both operating years of one cohort: every unit is then
+    fully used, which is the condition for optimex and standard LCA to agree.
+
+    Expected calculation for 200 kg Widget:
+    - Direct CO2 from operation: 200 * 5 * 1.0 = 1000 kg CO2
+    - Background electricity at construction: 200 * 10 * 0.5 = 1000 kg CO2
+    - Total: 2000 kg CO2
     """
 
     # Standard LCA calculation
     widget = bd.get_node(database="foreground", name="Widget")
-    lca = bc.LCA({widget: 100}, method=("GWP", "example"))
+    lca = bc.LCA({widget: 200}, method=("GWP", "example"))
     lca.lci()
     lca.lcia()
     expected_gwp = lca.score
@@ -158,7 +162,7 @@ def test_separated_background_matches_standard_lca(setup_separated_background_sy
     years = range(2020, 2030)
     td_demand = TemporalDistribution(
         date=np.array([datetime(year, 1, 1).isoformat() for year in years], dtype='datetime64[s]'),
-        amount=np.asarray([0, 0, 100, 0, 0, 0, 0, 0, 0, 0]),  # 100 kg at year 2022
+        amount=np.asarray([0, 0, 100, 100, 0, 0, 0, 0, 0, 0]),  # 100 kg in 2022 and 2023
     )
 
     lca_config = lca_processor.LCAConfig(
diff --git a/tests/test_brownfield.py b/tests/test_brownfield.py
index 02310cd..d5685e0 100644
--- a/tests/test_brownfield.py
+++ b/tests/test_brownfield.py
@@ -557,8 +557,9 @@ def test_production_capacity_includes_existing(self):
         capacity_df = pp.get_production_capacity()
 
         # At 2020: existing capacity (tau=1) is in operation
-        # Capacity should include the existing 10 units * 2 (production per unit) = 20
-        assert capacity_df.loc[2020, "product"] >= 20.0, (
+        # get_production_capacity reports ANNUAL capacity, so the existing 10 units
+        # contribute 10 units * 1.0 production per unit and year = 10
+        assert capacity_df.loc[2020, "product"] >= 10.0, (
             f"Production capacity at 2020 should include existing capacity, "
             f"got {capacity_df.loc[2020, 'product']}"
         )
@@ -934,9 +935,10 @@ def test_brownfield_multiple_existing_entries_same_process(self):
             "characterization": {("GWP", "CO2", t): 1.0 for t in range(2025, 2036)},
             # MULTIPLE existing capacity entries for the same process
             # This should NOT double the production rate
+            # Together they cover the demand of 1000/year (10 units * 100 per year)
             "existing_capacity": {
-                ("Plant", 2005): 0.5,  # 0.5 units from 2005
-                ("Plant", 2015): 0.5,  # 0.5 units from 2015
+                ("Plant", 2005): 5.0,  # 5 units from 2005
+                ("Plant", 2015): 5.0,  # 5 units from 2015
             },
         }
 
@@ -953,15 +955,13 @@ def test_brownfield_multiple_existing_entries_same_process(self):
 
         assert results.solver.termination_condition == pyo.TerminationCondition.optimal
 
-        # Check that operation is reasonable (should be ~demand/production_rate = 1000/3000 ≈ 0.33)
-        # If the bug exists, operation would be ~0.16 (half of expected) due to doubled rate
+        # Each running unit produces 100 per year, so meeting a demand of 1000
+        # requires 10 units running - the two existing entries taken together.
+        # If the production rate were counted once per existing entry, half as many
+        # units would appear to be enough.
         operation_2025 = get_total_operation(solved_model, "Plant", 2025)
 
-        # Production per unit of operation = sum of production rates for operating taus
-        # With operation_time_limits (1, 30), there are 30 operating taus
-        # Each tau has production = 100, so total = 3000 per unit of operation
-        # To meet demand of 1000, we need operation = 1000/3000 ≈ 0.333
-        expected_operation = 1000 / 3000  # ≈ 0.333
+        expected_operation = 1000 / 100  # 10 units running
         assert operation_2025 == pytest.approx(expected_operation, rel=0.01), (
             f"Operation at 2025 should be ~{expected_operation:.4f}, got {operation_2025:.4f}. "
             "If operation is half of expected, the production rate is being doubled incorrectly."
diff --git a/tests/test_merit_order.py b/tests/test_merit_order.py
index 5f53f5b..4426b8d 100644
--- a/tests/test_merit_order.py
+++ b/tests/test_merit_order.py
@@ -323,6 +323,11 @@ def test_operation_limits_apply_to_total(self):
         Scenario:
         - Multiple vintages operating at same time
         - Operation limit constrains total (not per-vintage)
+
+        With a demand of 50 and a production of 1.0 per unit and year, 50 units must
+        run at 2022, spread over the 2020, 2021 and 2022 vintages. A limit of 50 is
+        therefore exactly binding, while a limit of 40 must make the model infeasible —
+        which it only does if the limit applies to the sum across vintages.
         """
         model_inputs_dict = {
             "PROCESS": ["P1"],
@@ -349,7 +354,7 @@ def test_operation_limits_apply_to_total(self):
             "mapping": {("db", t): 1.0 for t in [2020, 2021, 2022]},
             "characterization": {("GWP", "CO2", t): 1.0 for t in [2020, 2021, 2022]},
             # Set a maximum operation limit at 2022
-            "process_operation_limits_max": {("P1", 2022): 30},
+            "process_operation_limits_max": {("P1", 2022): 50},
         }
 
         model_inputs = converter.OptimizationModelInputs(**model_inputs_dict)
@@ -373,9 +378,24 @@ def test_operation_limits_apply_to_total(self):
         )
 
         # Total operation should respect the limit
-        assert total_op_2022 <= 30 + 1e-6, (
-            f"Total operation at 2022 ({total_op_2022:.4f}) should not exceed limit of 30"
+        assert total_op_2022 <= 50 + 1e-6, (
+            f"Total operation at 2022 ({total_op_2022:.4f}) should not exceed limit of 50"
+        )
+        assert pytest.approx(50.0, rel=0.01) == total_op_2022, (
+            f"50 units must run at 2022 to meet the demand, got {total_op_2022:.4f}"
+        )
+
+        # A limit below the required total must be infeasible: this only holds if the
+        # limit bounds the sum over vintages, not each vintage separately
+        model_inputs_dict["process_operation_limits_max"] = {("P1", 2022): 40}
+        tight_inputs = converter.OptimizationModelInputs(**model_inputs_dict)
+        tight_model = optimizer.create_model(
+            inputs=tight_inputs,
+            objective_category="GWP",
+            name="operation_limits_test_tight",
         )
+        with pytest.raises(RuntimeError):
+            optimizer.solve_model(tight_model, solver_name="glpk", tee=False)
 
 
 class TestPerVintageCapacity:
diff --git a/tests/test_operation_constraints.py b/tests/test_operation_constraints.py
index 0679678..c00878c 100644
--- a/tests/test_operation_constraints.py
+++ b/tests/test_operation_constraints.py
@@ -24,13 +24,13 @@ def test_operation_capacity_single_installation():
     Test that operation is correctly bounded by a single installation.
 
     Setup:
-    - Single process with production capacity of 1.0 kg/operation (scaled)
-    - Install 10 units at year 2020
-    - Demand at years 2021-2024 during operation phase
+    - Single process producing 0.5 kg per unit and operating year (tau=1 and tau=2),
+      i.e. 1.0 kg over a unit's lifetime
+    - Demand of 5 kg in each of the two operating years 2021 and 2022
 
     Expected:
-    - Operation should be bounded by installed capacity
-    - var_operation <= 10 for years when installation is in operation phase
+    - 10 units installed at 2020: 10 units * 0.5 kg = 5 kg per year
+    - All 10 units run in both years, so operation equals the installed units
     """
     model_inputs_dict = {
         "PROCESS": ["P1"],
@@ -87,21 +87,21 @@ def test_operation_capacity_single_installation():
     assert results.solver.status == pyo.SolverStatus.ok
     assert results.solver.termination_condition == pyo.TerminationCondition.optimal
 
-    # Check that 5 units were installed at 2020 to meet demand
-    # Total demand is 10 kg, each process produces 1.0 kg total across its lifecycle
-    # So 5 processes are sufficient (they produce at both tau=1 and tau=2)
+    # Check that 10 units were installed at 2020 to meet demand
+    # Each unit delivers 0.5 kg per operating year, so 5 kg/year needs 10 units.
+    # Their combined lifetime output is 10 kg, matching the total demand of 10 kg.
     installed_2020 = pyo.value(solved_model.var_installation["P1", 2020])
-    assert pytest.approx(5.0, rel=0.01) == installed_2020
+    assert pytest.approx(10.0, rel=0.01) == installed_2020
 
     # Check that operation levels are bounded correctly
-    # var_operation represents number of operating units
-    # At 2021: 5 units operating * 1.0 kg/unit = 5 kg (demand)
-    # At 2022: 5 units operating * 1.0 kg/unit = 5 kg (demand)
+    # var_operation counts the units running in that year
+    # At 2021: 10 units running * 0.5 kg/unit = 5 kg (demand)
+    # At 2022: 10 units running * 0.5 kg/unit = 5 kg (demand)
     operation_2021 = get_total_operation(solved_model, "P1", 2021)
     operation_2022 = get_total_operation(solved_model, "P1", 2022)
 
-    assert pytest.approx(5.0, rel=0.01) == operation_2021
-    assert pytest.approx(5.0, rel=0.01) == operation_2022
+    assert pytest.approx(10.0, rel=0.01) == operation_2021
+    assert pytest.approx(10.0, rel=0.01) == operation_2022
 
 
 def test_operation_capacity_multiple_installations():
@@ -114,10 +114,10 @@ def test_operation_capacity_multiple_installations():
     - High demand requiring multiple installations to operate
 
     Expected:
-    - At year 2022, capacity should include:
-      * Installations from 2021 (at tau=1)
-      * Installations from 2020 (at tau=2)
-    - Operation should be bounded by total capacity
+    - At year 2022, the units able to run are those installed in 2021 (at tau=1)
+      and in 2020 (at tau=2)
+    - Each running unit delivers 0.5 kg, so a demand of 15 needs 30 units running,
+      and therefore at least 30 units installed across 2020 and 2021
     """
     model_inputs_dict = {
         "PROCESS": ["P1"],
@@ -169,19 +169,19 @@ def test_operation_capacity_multiple_installations():
     install_2021 = pyo.value(solved_model.var_installation["P1", 2021])
 
     # At 2022:
-    # - Installations from 2021 are at tau=1, contribute 0.5 each
-    # - Installations from 2020 are at tau=2, contribute 0.5 each
-    # Total capacity = (install_2020 + install_2021) * 0.5
+    # - Installations from 2021 are at tau=1, delivering 0.5 kg each
+    # - Installations from 2020 are at tau=2, delivering 0.5 kg each
+    # Total annual capacity = (install_2020 + install_2021) * 0.5
 
     # Get operation at 2022
     operation_2022 = get_total_operation(solved_model, "P1", 2022)
 
-    # Operation should equal demand (15) since we're minimizing emissions
-    assert pytest.approx(15.0, rel=0.01) == operation_2022
+    # 30 units must run to deliver the demand of 15 kg
+    assert pytest.approx(30.0, rel=0.01) == operation_2022
 
-    # Total installations should be at least 15 (to provide capacity of 15)
+    # Total installations must cover those 30 running units
     total_installations = install_2020 + install_2021
-    assert total_installations >= 14.9  # Allow small tolerance
+    assert total_installations >= 29.9  # Allow small tolerance
 
 
 def test_operation_capacity_with_varying_demand():
@@ -197,10 +197,9 @@ def test_operation_capacity_with_varying_demand():
     - Operation should be bounded by capacity when demand > capacity
 
     With the capacity constraint:
-    - capacity = total_production × fg_scale × installations_in_operation
-    - Production = 2.0 (1.0 at tau=1 + 1.0 at tau=2)
-    - At t=2021: capacity from install_2020 (at tau=1)
-    - At t=2022: capacity from install_2020 (at tau=2) + install_2021 (at tau=1)
+    - A running unit delivers 1.0 (production at tau=1 and tau=2 is 1.0 each)
+    - Units able to run at t=2021: those installed in 2020 (at tau=1)
+    - Units able to run at t=2022: install_2020 (at tau=2) + install_2021 (at tau=1)
     """
     model_inputs_dict = {
         "PROCESS": ["P1"],
@@ -248,28 +247,26 @@ def test_operation_capacity_with_varying_demand():
     assert results.solver.status == pyo.SolverStatus.ok
 
     # Check that operations match demands
-    # With production of 1.0 at tau=1 and 1.0 at tau=2:
-    # - total_production = 2.0 (sum over operation phase)
-    # - To produce 3.0, need var_operation = 3.0 / 2.0 = 1.5
-    # - To produce 8.0, need var_operation = 8.0 / 2.0 = 4.0
+    # With production of 1.0 per unit and operating year:
+    # - To produce 3.0, 3 units must run
+    # - To produce 8.0, 8 units must run
     operation_2021 = get_total_operation(solved_model, "P1", 2021)
     operation_2022 = get_total_operation(solved_model, "P1", 2022)
 
-    assert pytest.approx(1.5, rel=0.01) == operation_2021  # 1.5 * 2.0 = 3.0
-    assert pytest.approx(4.0, rel=0.01) == operation_2022  # 4.0 * 2.0 = 8.0
+    assert pytest.approx(3.0, rel=0.01) == operation_2021  # 3 units * 1.0 = 3.0
+    assert pytest.approx(8.0, rel=0.01) == operation_2022  # 8 units * 1.0 = 8.0
 
     # Verify capacity constraint was respected
     install_2020 = pyo.value(solved_model.var_installation["P1", 2020])
     install_2021 = pyo.value(solved_model.var_installation["P1", 2021])
 
-    # Capacity constraint: var_operation <= total_production × installations
-    # At 2021: capacity = 2.0 * install_2020, need >= 1.5 -> install_2020 >= 0.75
-    # At 2022: capacity = 2.0 * (install_2020 + install_2021), need >= 4.0
-    #          -> install_2020 + install_2021 >= 2.0
+    # Capacity constraint: var_operation <= installed units of that vintage
+    # At 2021: only the 2020 vintage can run -> install_2020 >= 3.0
+    # At 2022: both vintages can run -> install_2020 + install_2021 >= 8.0
 
     total_installations = install_2020 + install_2021
-    assert total_installations >= 1.9  # At least 2 units total needed for capacity
-    assert install_2020 >= 0.7  # At least ~0.75 for 2021 demand
+    assert total_installations >= 7.9  # At least 8 units total needed
+    assert install_2020 >= 2.9  # At least 3 units to serve the 2021 demand
 
 
 def test_operation_capacity_constraint_violation_prevented():
@@ -285,12 +282,11 @@ def test_operation_capacity_constraint_violation_prevented():
     - Operation should be bounded by total capacity from both installations
 
     With the capacity constraint:
-    - capacity = total_production × fg_scale × installations_in_operation
-    - Production = 2.0 (1.0 at tau=1 + 1.0 at tau=2)
-    - At t=2022: capacity from install_2020 (at tau=2) + install_2021 (at tau=1)
-    - To meet demand of 8: var_operation = 8 / 2.0 = 4.0
-    - Need capacity >= 4.0 -> 2.0 * (install_2020 + install_2021) >= 4.0
-    - So install_2020 + install_2021 >= 2.0
+    - A running unit delivers 1.0 per year (production 1.0 at tau=1 and tau=2)
+    - At t=2022 the runnable units are install_2020 (at tau=2) + install_2021 (at tau=1)
+    - To meet a demand of 8, 8 units must run at 2022
+    - Per-year deployment is capped at 5, so neither vintage can cover it alone and
+      the model must install in both years
     """
     model_inputs_dict = {
         "PROCESS": ["P1"],
@@ -323,8 +319,8 @@ def test_operation_capacity_constraint_violation_prevented():
         },
         # Limit each year's installation to force distribution
         "process_deployment_limits_max": {
-            ("P1", 2020): 1,  # Can install max 1 at 2020
-            ("P1", 2021): 1,  # Can install max 1 at 2021
+            ("P1", 2020): 5,  # Can install max 5 at 2020
+            ("P1", 2021): 5,  # Can install max 5 at 2021
         },
     }
 
@@ -348,19 +344,19 @@ def test_operation_capacity_constraint_violation_prevented():
     # At 2022:
     # - Installations from 2021 are at tau=1
     # - Installations from 2020 are at tau=2
-    # - Total capacity = 2.0 × (install_2020 + install_2021)
-    # - To produce 8 with production=2.0 per operation: need var_operation = 4.0
-    # - So need capacity of at least 4.0 -> need 2 total installations
+    # - Each running unit delivers 1.0, so 8 units must run to produce 8
 
     operation_2022 = get_total_operation(solved_model, "P1", 2022)
 
-    # Operation should equal demand requirement (8 / 2.0 = 4.0)
-    assert pytest.approx(4.0, rel=0.01) == operation_2022
+    assert pytest.approx(8.0, rel=0.01) == operation_2022
 
-    # Total installations should be at least 2.0
-    # (since capacity = 2.0 × installations, need capacity >= 4.0)
+    # 8 units must be installed, and with a cap of 5 per year both vintages are used
     total_installations = install_2020 + install_2021
-    assert total_installations >= 1.9  # At least 2 units needed
+    assert total_installations >= 7.9
+    assert install_2020 > 0 and install_2021 > 0, (
+        "The per-year deployment cap of 5 forces installation in both years, "
+        f"got 2020={install_2020}, 2021={install_2021}"
+    )
 
 
 def test_operation_capacity_with_non_constant_production():
diff --git a/tests/test_optimization.py b/tests/test_optimization.py
index 63593fd..5d2f59a 100644
--- a/tests/test_optimization.py
+++ b/tests/test_optimization.py
@@ -92,12 +92,16 @@ def test_model_solution_is_optimal(solved_system_model):
     )
 
 
+# Note on magnitudes: one installed unit produces 0.5 R1 per operating year
+# (production 0.5 at tau=1 and tau=2, i.e. 1.0 over its lifetime), so meeting a demand
+# of 10 in a year requires 20 units running. Installation-dependent flows are incurred
+# per unit, operation-dependent flows per running unit.
 @pytest.mark.parametrize(
     "model_type, expected_value",
     [
         # ("fixed", 3.15417e-10),  # Expected value for the fixed model
-        ("flex", 1.9172462799736082e-10),  # Expected value for the flexible model
-        ("constrained", 1.9197314619763485e-10),  # Constrained by limiting P1 to 10 installations (0.13% higher)
+        ("flex", 2.8168480231004236e-10),  # Expected value for the flexible model
+        ("constrained", 2.827868523922197e-10),  # Constrained by limiting P1 to 10 installations (0.39% higher)
     ],
     ids=["flex_result", "constrained_process_limit"], # "fixed_result",
 )
@@ -124,19 +128,22 @@ def test_model_scaling_values_within_tolerance(solved_system_model):
         model.name == "abstract_system_model_fixed"
         or model.name == "abstract_system_model_flex"
     ):
+        # 20 units per cohort: each unit yields 0.5 R1 per operating year, and the
+        # cohort has to cover the demand of 10 in its first operating year
         expected_values = {
-            ("P1", 2025): 10.0,
-            ("P1", 2027): 10.0,
-            ("P2", 2021): 10.0,
-            ("P2", 2023): 10.0,
+            ("P1", 2025): 20.0,
+            ("P1", 2027): 20.0,
+            ("P2", 2021): 20.0,
+            ("P2", 2023): 20.0,
         }
     elif model.name == "abstract_system_model_constrained":
-        # With P1 limited to 10 total, optimizer shifts significantly more to P2
+        # With P1 limited to 10 units total, the optimizer shifts almost everything to P2
         expected_values = {
             ("P1", 2027): 10.0,
-            ("P2", 2021): 10.0,
-            ("P2", 2023): 10.0,
-            ("P2", 2025): 10.0,
+            ("P2", 2021): 20.0,
+            ("P2", 2023): 20.0,
+            ("P2", 2025): 20.0,
+            ("P2", 2027): 10.0,
         }
     else:
         pytest.skip(f"Unknown model name: {model.name}")
@@ -347,20 +354,20 @@ def test_capacity_constraint_with_high_production():
     inst = pyo.value(solved_model.var_installation["P1", 2020])
     oper = get_total_operation(solved_model, "P1", 2020)
 
-    # With production = 5.0 and demand = 10:
-    # - Need var_operation = 10/5 = 2 to produce 10 units
-    # - Capacity = 5.0 * installations, so need 5.0 * inst >= 2
-    # - Minimum installations = 2/5 = 0.4
-    assert pytest.approx(0.4, rel=0.01) == inst, (
-        f"With production=5.0, only 0.4 installations needed, got {inst}"
+    # With production = 5.0 per unit and year (operation window is the single tau=0)
+    # and demand = 10:
+    # - 2 units must be running to produce 10 units of product
+    # - Operation is bounded by installed units, so 2 units must be installed
+    assert pytest.approx(2.0, rel=0.01) == inst, (
+        f"With production=5.0 per unit, 2 units are needed for a demand of 10, got {inst}"
     )
     assert pytest.approx(2.0, rel=0.01) == oper, (
-        f"Operation should be 2.0 to produce 10 units, got {oper}"
+        f"Operation should be 2.0 units running to produce 10 units, got {oper}"
     )
 
-    # Verify emissions (capacity-dependent) = 0.4 * 10 = 4 kg CO2
-    assert pytest.approx(4.0, rel=0.01) == objective, (
-        f"Emissions should be 4.0 kg CO2, got {objective}"
+    # Verify emissions (installation-dependent) = 2 * 10 = 20 kg CO2
+    assert pytest.approx(20.0, rel=0.01) == objective, (
+        f"Emissions should be 20.0 kg CO2, got {objective}"
     )
 
 
diff --git a/tests/test_single_route_lca_comparison.py b/tests/test_single_route_lca_comparison.py
index 32eff90..b4e4c89 100644
--- a/tests/test_single_route_lca_comparison.py
+++ b/tests/test_single_route_lca_comparison.py
@@ -104,24 +104,41 @@ def setup_single_route_system():
     ])
 
 
-def test_single_route_matches_standard_lca(setup_single_route_system):
+def test_single_year_demand_builds_stranded_capacity(setup_single_route_system):
     """
-    Test that optimex produces same results as standard LCA for a single-route system.
+    Demand in a single year, for a process whose operation window spans two years.
 
-    Expected calculation for 100 kg Widget:
-    - Direct CO2 from operation: 100 * 5 * 1.0 = 500 kg CO2
-    - Background electricity at construction: 100 * 10 * 0.5 = 500 kg CO2
-    - Total: 1000 kg CO2
+    One installed unit yields 1 kg Widget over its lifetime, delivered as 0.5 kg in
+    each of its two operating years. Asking for 100 kg in 2022 alone therefore forces
+    the model to install 200 units and leave the second operating year of each unit
+    idle: the delivered amount is 100 kg but the built capacity is worth 200 kg.
+
+    This is NOT expected to match `bc.LCA({widget: 100})`, because standard LCA
+    implicitly assumes every unit is fully used. Expected here:
+    - Construction electricity: 200 * 10 * 0.5   = 1000 kg CO2
+    - Operation CO2 in 2022:    200 * 2.5 * 1.0  =  500 kg CO2
+    - Total:                                      1500 kg CO2
+    which is the LCA score of 100 kg (1000) plus the construction impact of the
+    100 units' worth of capacity that is never used (500).
+
+    See test_multi_period_demand_matches_standard_lca for the case where the demand
+    profile allows full utilization and the two methods must agree exactly.
     """
+    import pyomo.environ as pyo
 
-    # Standard LCA calculation
+    # Standard LCA calculation, for reference
     widget = bd.get_node(database="foreground", name="Widget")
     lca = bc.LCA({widget: 100}, method=("GWP", "example"))
     lca.lci()
     lca.lcia()
-    expected_gwp = lca.score
+    lca_gwp = lca.score
 
-    print(f"\nStandard LCA GWP: {expected_gwp}")
+    # Capacity worth 200 kg is built, so the impact is the LCA score of 200 kg
+    # minus the operation emissions of the 100 kg that is never produced.
+    expected_gwp = 1500.0
+
+    print(f"\nStandard LCA GWP (100 kg, full utilization): {lca_gwp}")
+    print(f"Expected optimex GWP (single-year demand): {expected_gwp}")
 
     # optimex calculation
     years = range(2020, 2030)
@@ -155,14 +172,25 @@ def test_single_route_matches_standard_lca(setup_single_route_system):
         objective_category="climate_change",
     )
 
-    _, obj_real, results = optimizer.solve_model(model, solver_name="glpk")
+    solved_model, obj_real, results = optimizer.solve_model(model, solver_name="glpk")
 
     print(f"optimex GWP: {obj_real}")
-    print(f"Difference: {abs(obj_real - expected_gwp)}")
 
-    # They should match (within numerical tolerance)
+    # 200 units must be installed to deliver 100 kg in a single year
+    total_installed = sum(
+        pyo.value(solved_model.var_installation[p, t])
+        for p in solved_model.PROCESS
+        for t in solved_model.SYSTEM_TIME
+    )
+    print(f"Total installed units: {total_installed}")
+    assert pytest.approx(total_installed, rel=1e-3) == 200.0, (
+        f"Delivering 100 kg in one year needs 200 units (0.5 kg per unit and year), "
+        f"got {total_installed}"
+    )
+
     assert pytest.approx(obj_real, rel=1e-3) == expected_gwp, (
-        f"optimex result ({obj_real}) should match standard LCA ({expected_gwp})"
+        f"optimex result ({obj_real}) should be {expected_gwp}: the LCA score of "
+        f"100 kg ({lca_gwp}) plus the construction impact of the stranded capacity"
     )
 
     # Additional check: verify postprocessing extracts correct unscaled values
@@ -175,9 +203,142 @@ def test_single_route_matches_standard_lca(setup_single_route_system):
         total_cc_from_pp = df_impacts[climate_change_cols].sum().sum()
 
         print(f"\nPostprocessing climate_change total: {total_cc_from_pp}")
-        print(f"Expected (from LCA): {expected_gwp}")
 
-        # Postprocessing should also match standard LCA
         assert pytest.approx(total_cc_from_pp, rel=1e-3) == expected_gwp, (
-            f"Postprocessing climate_change sum ({total_cc_from_pp}) should match standard LCA ({expected_gwp})"
+            f"Postprocessing climate_change sum ({total_cc_from_pp}) should match "
+            f"the objective ({expected_gwp})"
+        )
+
+
+def test_multi_period_demand_matches_standard_lca(setup_single_route_system):
+    """
+    Same single-route system, but demand at MULTIPLE time steps.
+
+    The point of this test is installation-impact accounting. The process has a
+    2-year operation window (tau 1-2) and its production temporal distribution sums
+    to 1 kg over that window (0.5 kg per year), so one installed unit delivers 1 kg
+    of Widget over its lifetime. Serving 100 kg in 2022 AND 100 kg in 2023 therefore
+    requires 200 units, all installed in 2021 and fully utilized in both years — the
+    demand profile matches the production profile exactly, so this is a case where
+    optimex must reproduce the standard LCA result.
+
+    Note on test design: demand must line up with the operation window for LCA
+    equivalence to hold. A single isolated demand year would force the model to build
+    capacity whose second operating year is never used (legitimate stranded capacity),
+    which is more impact than the standard LCA of the delivered amount.
+
+    Demand: 100 kg in 2022, 100 kg in 2023.
+    Standard LCA for 200 kg:
+    - Direct CO2 from operation:   200 * 5 * 1.0 = 1000 kg CO2
+    - Background electricity:      200 * 10 * 0.5 = 1000 kg CO2
+    - Total:                       2000 kg CO2
+    """
+    import pyomo.environ as pyo
+
+    demand_by_year = {2022: 100.0, 2023: 100.0}
+    total_demand = sum(demand_by_year.values())
+
+    # Standard LCA calculation for the same total amount
+    widget = bd.get_node(database="foreground", name="Widget")
+    lca = bc.LCA({widget: total_demand}, method=("GWP", "example"))
+    lca.lci()
+    lca.lcia()
+    expected_gwp = lca.score
+
+    print(f"\nStandard LCA GWP ({total_demand} kg): {expected_gwp}")
+
+    years = list(range(2020, 2030))
+    td_demand = TemporalDistribution(
+        date=np.array(
+            [datetime(year, 1, 1).isoformat() for year in years], dtype="datetime64[s]"
+        ),
+        amount=np.asarray([demand_by_year.get(year, 0.0) for year in years]),
+    )
+
+    lca_config = lca_processor.LCAConfig(
+        demand={widget: td_demand},
+        temporal={
+            "start_date": datetime(2020, 1, 1),
+            "temporal_resolution": "year",
+            "time_horizon": 100,
+        },
+        characterization_methods=[
+            {
+                "category_name": "climate_change",
+                "brightway_method": ("GWP", "example"),
+            },
+        ],
+    )
+
+    lca_data_processor = lca_processor.LCADataProcessor(lca_config)
+    manager = converter.ModelInputManager()
+    optimization_model_inputs = manager.parse_from_lca_processor(lca_data_processor)
+
+    model = optimizer.create_model(
+        optimization_model_inputs,
+        name="test_multi_period_demand",
+        objective_category="climate_change",
+    )
+
+    _, obj_real, _ = optimizer.solve_model(model, solver_name="glpk")
+
+    # Diagnostics: how much was installed, and when
+    installations = {
+        (p, t): pyo.value(model.var_installation[p, t])
+        for p in model.PROCESS
+        for t in model.SYSTEM_TIME
+        if pyo.value(model.var_installation[p, t]) > 1e-9
+    }
+    operations = {
+        (p, v, t): pyo.value(model.var_operation[p, v, t])
+        for (p, v, t) in model.ACTIVE_VINTAGE_TIME
+        if pyo.value(model.var_operation[p, v, t]) > 1e-9
+    }
+    total_installed = sum(installations.values())
+
+    print(f"optimex GWP: {obj_real}")
+    print(f"Installations (process, year) -> units: {installations}")
+    print(f"Total installed units: {total_installed}")
+    print(f"Operation (process, vintage, year) -> level: {operations}")
+
+    # One unit of the process delivers 1 kg Widget over its lifetime, so meeting
+    # 300 kg of demand must require 300 installed units — no more, no less.
+    assert pytest.approx(total_installed, rel=1e-3) == total_demand, (
+        f"Total installed units ({total_installed}) should equal total demand "
+        f"({total_demand}); a lower value means installation impacts are amortized "
+        f"over more production than the process actually delivers"
+    )
+
+    assert pytest.approx(obj_real, rel=1e-3) == expected_gwp, (
+        f"optimex result ({obj_real}) should match standard LCA ({expected_gwp})"
+    )
+
+    # Per-year impact breakdown from postprocessing
+    pp = postprocessing.PostProcessor(model)
+    df_impacts = pp.get_impacts()
+    climate_change_cols = [c for c in df_impacts.columns if c[0] == "climate_change"]
+    impacts_per_year = df_impacts[climate_change_cols].sum(axis=1)
+    print("\nclimate_change impact per year:")
+    print(impacts_per_year[impacts_per_year.abs() > 1e-9])
+
+    total_cc_from_pp = df_impacts[climate_change_cols].sum().sum()
+    print(f"Postprocessing climate_change total: {total_cc_from_pp}")
+
+    assert pytest.approx(total_cc_from_pp, rel=1e-3) == expected_gwp, (
+        f"Postprocessing climate_change sum ({total_cc_from_pp}) should match "
+        f"standard LCA ({expected_gwp})"
+    )
+
+    # Construction electricity is consumed at tau=0, i.e. in the installation year.
+    # Its impact must therefore show up in exactly the years where units were installed.
+    install_years = {t for (_, t) in installations}
+    construction_impact_per_year = {
+        t: 0.5 * 10 * sum(v for (_, ty), v in installations.items() if ty == t)
+        for t in install_years
+    }
+    print(f"Expected construction impact per year: {construction_impact_per_year}")
+    for t, expected_construction in construction_impact_per_year.items():
+        assert impacts_per_year.loc[t] >= expected_construction - 1e-6, (
+            f"Impact in {t} ({impacts_per_year.loc[t]}) is below the construction "
+            f"impact of the units installed that year ({expected_construction})"
         )
diff --git a/tests/test_two_level_supply_chain.py b/tests/test_two_level_supply_chain.py
index e100828..3a1a8a3 100644
--- a/tests/test_two_level_supply_chain.py
+++ b/tests/test_two_level_supply_chain.py
@@ -159,22 +159,31 @@ def setup_two_level_system():
 
 
 def test_two_level_supply_chain_matches_lca(setup_two_level_system):
-    """Test that optimex produces same results as standard LCA for two-level system."""
+    """
+    Test that optimex produces same results as standard LCA for two-level system.
+
+    Both processes have a two-year operation window and produce 0.5 kg per unit and
+    year (1 kg over a unit's lifetime). The demand is placed in both operating years
+    of one cohort so that every installed unit is fully used - the condition under
+    which optimex and standard LCA must agree. The same holds one level down: the
+    Product 2 cohort consumes 0.5 kg Product 1 per unit and year, which a fully
+    utilized Product 1 cohort supplies.
+    """
 
     # Standard LCA calculation
     product_2 = bd.get_node(database="foreground", name="Product 2")
-    lca = bc.LCA({product_2: 10}, method=("GWP", "example"))
+    lca = bc.LCA({product_2: 20}, method=("GWP", "example"))
     lca.lci()
     lca.lcia()
     expected_gwp = lca.score
 
     print(f"\nStandard LCA GWP: {expected_gwp}")
 
-    # optimex calculation
+    # optimex calculation: 10 kg in each of the two operating years of one cohort
     years = range(2020, 2030)
     td_demand = TemporalDistribution(
         date=np.array([datetime(year, 1, 1).isoformat() for year in years], dtype='datetime64[s]'),
-        amount=np.asarray([0, 0, 10, 0, 0, 0, 0, 0, 0, 0]),
+        amount=np.asarray([0, 0, 10, 10, 0, 0, 0, 0, 0, 0]),
     )
 
     lca_config = lca_processor.LCAConfig(
@@ -239,15 +248,21 @@ def test_two_level_supply_chain_multi_temporal_demand(setup_two_level_system):
     2. Installation and operation are correctly distributed over time
     3. Internal demands (Product 1 for Product 2) are handled correctly
     4. PostProcessor extracts correct values for each time period
+
+    Demand comes in pairs of consecutive years with equal amounts, matching the
+    two-year operation window: each cohort is then fully utilized over its lifetime,
+    which is what makes the comparison with standard LCA exact.
     """
 
     product_2 = bd.get_node(database="foreground", name="Product 2")
 
-    # Define multi-temporal demand: 10 units in 2022, 5 units in 2024, 10 units in 2026
+    # Define multi-temporal demand: two fully utilized cohorts, in 2022/2023 and
+    # in 2026/2027
     demand_schedule = {
         2022: 10,
-        2024: 5,
-        2026: 10,
+        2023: 10,
+        2026: 5,
+        2027: 5,
     }
 
     # Calculate expected total impact from standard LCA
@@ -340,14 +355,18 @@ def test_two_level_supply_chain_multi_temporal_demand(setup_two_level_system):
     if p2_process_id in df_operation.columns:
         print(df_operation[[p2_process_id]])
 
-        # Verify operation matches demand at each time point
+        # Verify operation matches demand at each time point. get_operation() counts
+        # RUNNING UNITS, and each unit yields 0.5 kg per year, so twice as many units
+        # run as there are kg demanded.
+        annual_production_per_unit = 0.5
         for year, amount in demand_schedule.items():
             if year in df_operation.index:
                 operation = df_operation.loc[year, p2_process_id]
-                print(f"\nYear {year}: Operation={operation:.2f}, Demand={amount}")
-                # Operation should equal demand for single-route system
-                assert pytest.approx(operation, rel=1e-2) == amount, (
-                    f"Operation at {year} ({operation}) should match demand ({amount})"
+                produced = operation * annual_production_per_unit
+                print(f"\nYear {year}: Units running={operation:.2f}, "
+                      f"Production={produced:.2f}, Demand={amount}")
+                assert pytest.approx(produced, rel=1e-2) == amount, (
+                    f"Production at {year} ({produced}) should match demand ({amount})"
                 )
 
     print("\n" + "="*80)

From f04795b9a5c604fe8df6c8c6c234d5c812cead98 Mon Sep 17 00:00:00 2001
From: Luca Lange <167885326+langeluca@users.noreply.github.com>
Date: Sat, 1 Aug 2026 15:28:40 +0200
Subject: [PATCH 12/12] Migrate ethylene brownfield capacity to process units

---
 notebooks/ethylene_case_study.ipynb | 32 ++++++++++++++++++++++++-----
 1 file changed, 27 insertions(+), 5 deletions(-)

diff --git a/notebooks/ethylene_case_study.ipynb b/notebooks/ethylene_case_study.ipynb
index 0135d32..616d029 100644
--- a/notebooks/ethylene_case_study.ipynb
+++ b/notebooks/ethylene_case_study.ipynb
@@ -2180,15 +2180,27 @@
     ")\n",
     "functional_demand = {ethylene: ethylene_demand}\n",
     "\n",
+    "# PR #61 semantics: existing_capacity stores process units. One steam-cracker\n",
+    "# unit delivers the 1 kg production exchange over its 50-year lifetime.\n",
+    "steam_cracking_lifetime_years = int(LIFETIMES_YEARS[\"steam_cracking\"])\n",
+    "steam_cracking_annual_output_per_unit_kg = 1.0 / steam_cracking_lifetime_years\n",
+    "brownfield_annual_capacity_per_vintage_kg = float(\n",
+    "    assumptions.loc[\"brownfield.capacity_per_vintage\", \"value\"]\n",
+    ")\n",
+    "brownfield_units_per_vintage = (\n",
+    "    brownfield_annual_capacity_per_vintage_kg\n",
+    "    / steam_cracking_annual_output_per_unit_kg\n",
+    ")\n",
+    "\n",
     "existing_capacities = {\n",
     "    (\n",
     "        \"steam_cracking\",\n",
     "        int(assumptions.loc[\"brownfield.vintage_1\", \"value\"]),\n",
-    "    ): float(assumptions.loc[\"brownfield.capacity_per_vintage\", \"value\"]),\n",
+    "    ): brownfield_units_per_vintage,\n",
     "    (\n",
     "        \"steam_cracking\",\n",
     "        int(assumptions.loc[\"brownfield.vintage_2\", \"value\"]),\n",
-    "    ): float(assumptions.loc[\"brownfield.capacity_per_vintage\", \"value\"]),\n",
+    "    ): brownfield_units_per_vintage,\n",
     "}\n",
     "\n",
     "brownfield_table = pd.DataFrame(\n",
@@ -2196,12 +2208,22 @@
     "        {\n",
     "            \"process\": PROCESS_NAMES[process],\n",
     "            \"installation_year\": year,\n",
-    "            \"capacity_kg_per_year\": capacity,\n",
+    "            \"installed_process_units\": units,\n",
+    "            \"annual_capacity_kg_per_year\": (\n",
+    "                units * steam_cracking_annual_output_per_unit_kg\n",
+    "            ),\n",
     "        }\n",
-    "        for (process, year), capacity in existing_capacities.items()\n",
+    "        for (process, year), units in existing_capacities.items()\n",
     "    ]\n",
     ")\n",
-    "assert brownfield_table[\"capacity_kg_per_year\"].sum() == 1e9\n",
+    "assert np.allclose(\n",
+    "    brownfield_table[\"annual_capacity_kg_per_year\"],\n",
+    "    brownfield_annual_capacity_per_vintage_kg,\n",
+    ")\n",
+    "assert np.isclose(\n",
+    "    brownfield_table[\"annual_capacity_kg_per_year\"].sum(),\n",
+    "    2 * brownfield_annual_capacity_per_vintage_kg,\n",
+    ")\n",
     "brownfield_table\n"
    ]
   },