diff --git a/src/ITR/data/base_providers.py b/src/ITR/data/base_providers.py
index e306daa9..2c05da75 100644
--- a/src/ITR/data/base_providers.py
+++ b/src/ITR/data/base_providers.py
@@ -2,7 +2,7 @@
 import warnings  # needed until quantile behaves better with Pint quantities in arrays
 from functools import partial, reduce
 from operator import add
-from typing import Any, Dict, List, Optional, Type
+from typing import Any, Callable, Dict, List, Optional, Type, cast
 
 import numpy as np
 import pandas as pd
@@ -889,45 +889,32 @@ def get_company_projected_trajectories(self, company_ids: List[str], year=None)
         :param year: values for a specific year, or all years if None
         :return: A pandas DataFrame with projected intensity trajectories per company, indexed by company_id and scope
         """
-        company_ids, scopes, projections = list(
-            map(
-                list,
-                zip(
-                    *[
-                        (
-                            c.company_id,
-                            EScope[scope_name],
-                            c.projected_intensities[scope_name].projections,
-                        )
-                        # FIXME: we should make _companies a dict so we can look things up rather than searching every time!
-                        for c in self._companies
-                        for scope_name in EScope.get_scopes()
-                        if c.company_id in company_ids
-                        if c.projected_intensities[scope_name]
-                    ]
-                ),
-            )
-        )
-        if projections:
-            index = pd.MultiIndex.from_tuples(zip(company_ids, scopes), names=["company_id", "scope"])
-            if year is not None:
-                if isinstance(projections[0], ICompanyEIProjectionsScopes):
-                    values = [yvp.value for pt in projections for yvp in pt if yvp.year == year]
-                else:
-                    values = list(map(lambda x: x[year].squeeze(), projections))
-                with warnings.catch_warnings():
-                    warnings.simplefilter("ignore")
-                    # pint units don't like columns of heterogeneous data...tough!
-                    return pd.Series(data=values, index=index, name=year)
-            else:
-                if isinstance(projections[0], ICompanyEIProjectionsScopes):
-                    values = [{yvp.year: yvp.value for yvp in pt} for pt in projections]
-                else:
-                    values = projections
-                with warnings.catch_warnings():
-                    warnings.simplefilter("ignore")
-                    return pd.DataFrame(data=values, index=index)
-        return pd.DataFrame()
+        c_ids: List[str] = []
+        scopes: List[EScope] = []
+        projections: List[DF_ICompanyEIProjections] = []
+
+        for c in self._companies:
+            if c.company_id in company_ids:
+                for scope_name in EScope.get_scopes():
+                    if c.projected_intensities[scope_name]:
+                        c_ids.append(c.company_id)
+                        scopes.append(EScope[scope_name])
+                        projections.append(c.projected_intensities[scope_name].projections)
+
+        if len(projections) == 0:
+            return pd.DataFrame()
+        index = pd.MultiIndex.from_tuples(zip(c_ids, scopes), names=["company_id", "scope"])
+        if year is not None:
+            values = list(map(cast(Callable[[pd.Series], Any], lambda x: x[year].squeeze()), projections))
+            with warnings.catch_warnings():
+                warnings.simplefilter("ignore")
+                # pint units don't like columns of heterogeneous data...tough!
+                return pd.Series(data=values, index=index, name=year)
+        else:
+            values = projections
+            with warnings.catch_warnings():
+                warnings.simplefilter("ignore")
+                return pd.DataFrame(data=values, index=index)
 
     def get_company_projected_targets(self, company_ids: List[str], year=None) -> pd.DataFrame:
         """