diff --git a/Snakefile b/Snakefile
index 5c325699..9d62dfe2 100644
--- a/Snakefile
+++ b/Snakefile
@@ -226,7 +226,7 @@ rule add_electricity:
         geth_hydro_capacities='data/geth2015_hydro_capacities.csv',
         load='resources/load.csv',
         nuts3_shapes='resources/nuts3_shapes.geojson',
-        gdp='data/bundle/GDP_PPP_30arcsec_v3.nc',
+        ua_md_gdp='data/bundle/GDP_PPP_30arcsec_v3_mapped.csv',
         **{f"profile_{tech}": f"resources/profile_{tech}.nc"
            for tech in config['renewable']}
     output: "networks/elec.nc"
diff --git a/scripts/add_electricity.py b/scripts/add_electricity.py
index 3945c04a..35e2bc98 100755
--- a/scripts/add_electricity.py
+++ b/scripts/add_electricity.py
@@ -190,7 +190,7 @@ def load_powerplants(ppl_fn):
             .replace({'carrier': carrier_dict}))
 
 
-def attach_load(n, regions, load, nuts3_shapes, gdp, countries, scaling=1.):
+def attach_load(n, regions, load, nuts3_shapes, ua_md_gdp, countries, scaling=1.):
 
     substation_lv_i = n.buses.index[n.buses['substation_lv']]
     regions = (gpd.read_file(regions).set_index('name')
@@ -198,17 +198,7 @@ def attach_load(n, regions, load, nuts3_shapes, gdp, countries, scaling=1.):
     opsd_load = (pd.read_csv(load, index_col=0, parse_dates=True)
                 .filter(items=countries))
 
-    #ToDo: adapt time+slices from config etc. (cover all data)
-    gdp = (xr.open_dataset(gdp)
-           .sel(time=2015)
-           .sel(longitude=slice(10,30))
-           .sel(latitude=slice(50, 30)))
-    weightmap = xa.pixel_overlaps(gdp, regions.iloc[0:2])
-    aggregated = xa.aggregate(gdp, weightmap)
-
-    print(aggregated.to_dataset().name)
-    print(aggregated.to_dataset().GDP_per_capita_PPP)
-    print(martha)
+    ua_md_gdp = pd.read_csv(ua_md_gdp, dtype={'name': 'str'}).set_index('name')
 
     logger.info(f"Load data scaled with scalling factor {scaling}.")
     opsd_load *= scaling
@@ -233,8 +223,8 @@ def attach_load(n, regions, load, nuts3_shapes, gdp, countries, scaling=1.):
             # regression on the country to continent load data
             factors = normed(0.6 * normed(gdp_n) + 0.4 * normed(pop_n))
             if cntry in ['UA', 'MD']:
-                #generate new factors in this case
-                print('ToDo: adjust load for UA and MD here')
+                # overwrite factor because nuts3 provides no data for UA+MD
+                factors = normed(ua_md_gdp.loc[group.index, "GDP_PPP"].squeeze())
 
             return pd.DataFrame(factors.values * l.values[:,np.newaxis],
                                 index=l.index, columns=factors.index)
@@ -242,8 +232,6 @@ def attach_load(n, regions, load, nuts3_shapes, gdp, countries, scaling=1.):
     load = pd.concat([upsample(cntry, group) for cntry, group
                       in regions.geometry.groupby(regions.country)], axis=1)
 
-    print(some_error)
-
     n.madd("Load", substation_lv_i, bus=substation_lv_i, p_set=load)
 
 
@@ -571,7 +559,7 @@ if __name__ == "__main__":
     ppl = load_powerplants(snakemake.input.powerplants)
 
     attach_load(n, snakemake.input.regions, snakemake.input.load, snakemake.input.nuts3_shapes,
-                snakemake.input.gdp, snakemake.config['countries'], snakemake.config['load']['scaling_factor'])
+                snakemake.input.ua_md_gdp, snakemake.config['countries'], snakemake.config['load']['scaling_factor'])
 
     update_transmission_costs(n, costs, snakemake.config['lines']['length_factor'])