diff --git a/doc/configtables/licenses-sector.csv b/doc/configtables/licenses-sector.csv
index a297b2f6..7f20b5a6 100644
--- a/doc/configtables/licenses-sector.csv
+++ b/doc/configtables/licenses-sector.csv
@@ -10,7 +10,7 @@ BASt emobility statistics,emobility/,unknown,http://www.bast.de/DE/Verkehrstechn
 BDEW heating profile,heat_load_profile_BDEW.csv,unknown,https://github.com/oemof/demandlib
 heating profiles for Aarhus,heat_load_profile_DK_AdamJensen.csv,unknown,Adam Jensen MA thesis at Aarhus University
 co2 budgets,co2_budget.csv,CC BY 4.0,https://arxiv.org/abs/2004.11009
-existing heating potentials,existing_infrastructure/existing_heating_raw.csv,unknown,https://ec.europa.eu/energy/studies/mapping-and-analyses-current-and-future-2020-2030-heatingcooling-fuel-deployment_en?redir=1
+existing heating potentials,existing_infrastructure/existing_heating_raw.csv,unknown,https://energy.ec.europa.eu/publications/mapping-and-analyses-current-and-future-2020-2030-heatingcooling-fuel-deployment-fossilrenewables-1_en
 IRENA existing VRE capacities,existing_infrastructure/{solar|onwind|offwind}_capcity_IRENA.csv,unknown,https://www.irena.org/Statistics/Download-Data
 USGS ammonia production,myb1-2017-nitro.xls,unknown,https://www.usgs.gov/centers/nmic/nitrogen-statistics-and-information
 hydrogen salt cavern potentials,h2_salt_caverns_GWh_per_sqkm.geojson,CC BY 4.0,https://doi.org/10.1016/j.ijhydene.2019.12.161 https://doi.org/10.20944/preprints201910.0187.v1
diff --git a/scripts/add_existing_baseyear.py b/scripts/add_existing_baseyear.py
index 4f476b4d..3934079e 100644
--- a/scripts/add_existing_baseyear.py
+++ b/scripts/add_existing_baseyear.py
@@ -402,16 +402,10 @@ def add_heating_capacities_installed_before_baseyear(
     """
     logger.debug(f"Adding heating capacities installed before {baseyear}")
 
-    # Add existing heating capacities, data comes from the study
-    # "Mapping and analyses of the current and future (2020 - 2030)
-    # heating/cooling fuel deployment (fossil/renewables) "
-    # https://ec.europa.eu/energy/studies/mapping-and-analyses-current-and-future-2020-2030-heatingcooling-fuel-deployment_en?redir=1
-    # file: "WP2_DataAnnex_1_BuildingTechs_ForPublication_201603.xls" -> "existing_heating_raw.csv".
-    # TODO start from original file
+    existing_heating = pd.read_csv(snakemake.input.existing_heating_distribution,
+                                   header=[0,1],
+                                   index_col=0)
 
-    existing_heating = pd.read_csv(
-        snakemake.input.existing_heating_distribution, header=[0, 1], index_col=0
-    )
 
     techs = existing_heating.columns.get_level_values(1).unique()
 
diff --git a/scripts/build_existing_heating_distribution.py b/scripts/build_existing_heating_distribution.py
index 67993c29..12bfd4da 100644
--- a/scripts/build_existing_heating_distribution.py
+++ b/scripts/build_existing_heating_distribution.py
@@ -16,9 +16,17 @@ cc = coco.CountryConverter()
 def build_existing_heating():
     # retrieve existing heating capacities
 
-    existing_heating = pd.read_csv(
-        snakemake.input.existing_heating, index_col=0, header=0
-    )
+    # Add existing heating capacities, data comes from the study
+    # "Mapping and analyses of the current and future (2020 - 2030)
+    # heating/cooling fuel deployment (fossil/renewables) "
+    # https://energy.ec.europa.eu/publications/mapping-and-analyses-current-and-future-2020-2030-heatingcooling-fuel-deployment-fossilrenewables-1_en
+    # file: "WP2_DataAnnex_1_BuildingTechs_ForPublication_201603.xls" -> "existing_heating_raw.csv".
+    # data is for buildings only (i.e. NOT district heating) and represents the year 2012
+    # TODO start from original file
+
+    existing_heating = pd.read_csv(snakemake.input.existing_heating,
+                                   index_col=0,
+                                   header=0)
 
     # data for Albania, Montenegro and Macedonia not included in database
     existing_heating.loc["Albania"] = np.nan
@@ -67,17 +75,14 @@ def build_existing_heating():
         nodal_sectoral_totals.sum(axis=1), axis=0
     )
 
-    nodal_heat_name_fraction = pd.DataFrame(dtype=float)
+    nodal_heat_name_fraction = pd.DataFrame(index=district_heat_info.index,
+                                            dtype=float)
 
-    nodal_heat_name_fraction["urban central"] = dist_fraction
+    nodal_heat_name_fraction["urban central"] = 0.
 
     for sector in sectors:
-        nodal_heat_name_fraction[f"{sector} rural"] = nodal_sectoral_fraction[
-            sector
-        ] * (1 - urban_fraction)
-        nodal_heat_name_fraction[f"{sector} urban decentral"] = nodal_sectoral_fraction[
-            sector
-        ] * (urban_fraction - dist_fraction)
+        nodal_heat_name_fraction[f"{sector} rural"] = nodal_sectoral_fraction[sector]*(1 - urban_fraction)
+        nodal_heat_name_fraction[f"{sector} urban decentral"] = nodal_sectoral_fraction[sector]*urban_fraction
 
     nodal_heat_name_tech = pd.concat(
         {