diff --git a/da/preprocessing/emissionmodel.py b/da/preprocessing/emissionmodel.py
index 7fe21ac17b13357f43b2163dd3f39459b5e4d6aa..593e77572faa1933616ffcf42c5de5a9dab939c4 100755
--- a/da/preprocessing/emissionmodel.py
+++ b/da/preprocessing/emissionmodel.py
@@ -949,6 +949,7 @@ class EmisModel(Regional):
                         if isinstance(public_power_tp, pd.core.frame.DataFrame):
                             category_distribution_country *= emission_factors[species][category]
                             public_power_spatial += category_distribution_country * self.hourperyear * public_power_tp['Total'].values[:, None, None]
+                            #logging.debug(np.nansum(category_distribution_country * self.hourperyear))
                         else: 
                             # TODO: add here also a scaling factor?
                             fill_with_cams.append((country, category, public_power_tp, (species, emission_factors[species][category])))
@@ -959,7 +960,7 @@ class EmisModel(Regional):
     
                     # # For public power specific: Get data from other sources
                     elif gnfr == 'A':
-                        public_power_time = self.energydict[country]
+                        #public_power_time = self.energydict[country]
                         public_power_spatial += category_distribution_country * self.hourperyear
                     else:
                         spatial_distr = np.nan_to_num(category_distribution_country * yremis[i, j])