diff --git a/src/muse/agents/factories.py b/src/muse/agents/factories.py
index 7fcde0a9..5f86a071 100644
--- a/src/muse/agents/factories.py
+++ b/src/muse/agents/factories.py
@@ -169,7 +169,7 @@ def create_agent(agent_type: str, **kwargs) -> Agent:
 
 def agents_factory(
     params_or_path: str | Path | list,
-    capacity: xr.DataArray | str | Path,
+    capacity: xr.DataArray,
     technologies: xr.Dataset,
     regions: Sequence[str] | None = None,
     year: int | None = None,
@@ -179,14 +179,12 @@ def agents_factory(
     from copy import deepcopy
     from logging import getLogger
 
-    from muse.readers import read_csv_agent_parameters, read_initial_assets
+    from muse.readers import read_csv_agent_parameters
 
     if isinstance(params_or_path, (str, Path)):
         params = read_csv_agent_parameters(params_or_path)
     else:
         params = params_or_path
-    if isinstance(capacity, (str, Path)):
-        capacity = read_initial_assets(capacity)
     assert isinstance(capacity, xr.DataArray)
     if year is None:
         year = int(capacity.year.min())
diff --git a/src/muse/sectors/subsector.py b/src/muse/sectors/subsector.py
index 3b2ddee5..7852d689 100644
--- a/src/muse/sectors/subsector.py
+++ b/src/muse/sectors/subsector.py
@@ -79,6 +79,10 @@ def aggregate_lp(
         # Select commodity demands for the subsector
         demands = market.consumption.sel(commodity=self.commodities)
 
+        # Remove commodities that have no demand in the investment year
+        mask = (demands.isel(year=1, drop=True) > 0).any(dim=["timeslice", "region"])
+        demands = demands.sel(commodity=mask)
+
         # Split demand across agents
         demands = self.demand_share(
             agents=self.agents,
@@ -120,6 +124,7 @@ def factory(
         from muse import investments as iv
         from muse.agents import InvestingAgent, agents_factory
         from muse.commodities import is_enduse
+        from muse.readers import read_initial_assets
         from muse.readers.toml import undo_damage
 
         # Raise error for renamed asset_threshhold parameter (PR #447)
@@ -142,9 +147,13 @@ def factory(
             )
             getLogger(__name__).warning(msg)
 
+        # Read existing capacity file
+        existing_capacity = read_initial_assets(settings.existing_capacity)
+
+        # Create agents
         agents = agents_factory(
             settings.agents,
-            settings.existing_capacity,
+            capacity=existing_capacity,
             technologies=technologies,
             regions=regions,
             year=current_year or int(technologies.year.min()),
@@ -172,11 +181,14 @@ def factory(
             if np.sum(outputs) == 0.0:
                 raise RuntimeError(msg)
 
+        # Get list of commodities for the subsector
         if hasattr(settings, "commodities"):
             commodities = settings.commodities
         else:
+            # If commodities aren't explicitly specified, we infer the commodities from
+            # the existing capacity file
             commodities = aggregate_enduses(
-                [agent.assets for agent in agents], technologies
+                technologies.sel(technology=existing_capacity.technology.values)
             )
 
         # len(commodities) == 0 may happen only if
@@ -208,21 +220,14 @@ def factory(
         )
 
 
-def aggregate_enduses(
-    assets: Sequence[xr.Dataset | xr.DataArray], technologies: xr.Dataset
-) -> Sequence[str]:
-    """Aggregate enduse commodities for input assets.
+def aggregate_enduses(technologies: xr.Dataset) -> list[str]:
+    """Aggregate enduse commodities for a set of technologies.
 
-    This function is meant as a helper to figure out the commodities attached to a group
-    of agents.
+    Returns a list of all enduse commodities associated with the technologies in the
+    input dataset. Enduse commodities are determined using based on the `comm_usage`
+    attribute of the technologies, using the `is_enduse` function from the
+    `muse.commodities` module.
     """
     from muse.commodities import is_enduse
 
-    techs = set.union(*(set(data.technology.values) for data in assets))
-    outputs = technologies.fixed_outputs.sel(
-        commodity=is_enduse(technologies.comm_usage), technology=list(techs)
-    )
-
-    return outputs.commodity.sel(
-        commodity=outputs.any([u for u in outputs.dims if u != "commodity"])
-    ).values.tolist()
+    return technologies.commodity.values[is_enduse(technologies.comm_usage)].tolist()
diff --git a/tests/test_subsector.py b/tests/test_subsector.py
index 23406139..efcbc1fb 100644
--- a/tests/test_subsector.py
+++ b/tests/test_subsector.py
@@ -36,9 +36,7 @@ def test_subsector_investing_aggregation():
             agents = list(examples.sector(sname, model).agents)
             sector = next(sector for sector in mca.sectors if sector.name == sname)
             technologies = sector.technologies
-            commodities = aggregate_enduses(
-                (agent.assets for agent in agents), technologies
-            )
+            commodities = aggregate_enduses(technologies)
             market = mca.market.sel(
                 commodity=technologies.commodity, region=technologies.region
             ).interp(year=[2020, 2025])
@@ -89,7 +87,7 @@ def test_subsector_noninvesting_aggregation(market, model, technologies, tmp_pat
         param["decision"]["parameters"] = ("ALCOE", False, 1)
         param.pop("quantity")
     agents = [create_agent(technologies=technologies, **param) for param in params]
-    commodities = aggregate_enduses((agent.assets for agent in agents), technologies)
+    commodities = aggregate_enduses(technologies)
 
     subsector = Subsector(
         agents,