corrected issue with unit conversion factors in elementary flows contribution analysis

Author: matthieu-str

mescal/impact_assessment.py

@@ -342,26 +342,26 @@ def compute_impact_scores(
             )
 
     if contribution_analysis:
-        df_contrib_analysis_results_constr = pd.concat(  # concatenate the contribution analysis results
-            [df_contrib_analysis_results_constr]+df_comp_list,
-            ignore_index=True
-        )
+        df_comp_all = pd.concat(df_comp_list)  # concatenate composition results in a single dataframe
 
-        df_contrib_analysis_results_constr = pd.merge(
-            left=df_contrib_analysis_results_constr,
+        df_comp_all = pd.merge(  # adding the unit conversion factors to the dataframe
+            left=df_comp_all,
             right=unit_conversion[['Name', 'Type', 'Value']],
             left_on=['act_name', 'act_type'],
             right_on=['Name', 'Type'],
             how='left',
         )
 
-        # Multiply the score and amount columns by the conversion factor
-        df_contrib_analysis_results_constr['score'] = (df_contrib_analysis_results_constr['score']
-                                                       * df_contrib_analysis_results_constr['Value'])
-        df_contrib_analysis_results_constr['amount'] = (df_contrib_analysis_results_constr['amount']
-                                                        * df_contrib_analysis_results_constr['Value'])
+        # Multiply the score and amount columns by the conversion factor in the composition dataframe
+        df_comp_all['score'] = (df_comp_all['score'] * df_comp_all['Value'])
+        df_comp_all['amount'] = (df_comp_all['amount'] * df_comp_all['Value'])
 
-        df_contrib_analysis_results_constr.drop(columns=['Value', 'Name', 'Type'], inplace=True)
+        df_comp_all.drop(columns=['Value', 'Name', 'Type'], inplace=True)
+
+        df_contrib_analysis_results_constr = pd.concat(  # concatenate the contribution analysis results
+            [df_contrib_analysis_results_constr, df_comp_all],
+            ignore_index=True
+        )
 
     if lifetime is None:
         pass

tests/test_impact_assessment.py

@@ -6,6 +6,36 @@
 from mescal.filesystem_constants import BW_PROJECT_NAME
 
 dummy_esm_db = [
+    {
+        "name": "BATTERY, Construction",
+        "reference product": "battery, Li-ion, LFP, rechargeable, prismatic",
+        "location": "GLO",
+        "database": "dummy_esm_db",
+        "unit": "kilogram",
+        "code": "00000",
+        "exchanges": [
+            {
+                "name": "BATTERY, Construction",
+                "product": "battery, Li-ion, LFP, rechargeable, prismatic",
+                "location": "GLO",
+                "database": "dummy_esm_db",
+                "type": "production",
+                "amount": 1,
+                "unit": "kilogram",
+                "code": "00000"
+            },
+            {
+                "name": "market for battery, Li-ion, LFP, rechargeable, prismatic",
+                "product": "battery, Li-ion, LFP, rechargeable, prismatic",
+                "location": "GLO",
+                "database": "ecoinvent-3.9.1-cutoff",
+                "type": "technosphere",
+                "amount": 1,
+                "unit": "kilogram",
+                "code": 'f22991a7a4e1d7ecfa81cdd7453e83d2',
+            }
+        ]
+    },
     {
         "name": "TRAIN_FREIGHT_DIESEL_LOC, Construction",
         "reference product": "locomotive",
@@ -69,6 +99,8 @@
 ]
 
 mapping = [
+    ['BATTERY', 'Construction', 'battery, Li-ion, LFP, rechargeable, prismatic',
+     'market for battery, Li-ion, LFP, rechargeable, prismatic', 'GLO', 'ecoinvent-3.9.1-cutoff', '00000'],
     ['TRAIN_FREIGHT_DIESEL_LOC', 'Construction', 'locomotive', 'locomotive production', 'RER',
      'ecoinvent-3.9.1-cutoff', '00001'],
     ['TRAIN_FREIGHT_DIESEL_WAG', 'Construction', 'goods wagon', 'goods wagon production', 'RER',
@@ -80,12 +112,14 @@
 ]
 
 unit_conversion = [
+    ['BATTERY', 'Construction', 10, 'kilogram', 'kilowatt hour'],
     ['TRAIN_FREIGHT_DIESEL_LOC', 'Construction', 2, 'unit', 'unit'],
     ['TRAIN_FREIGHT_DIESEL_WAG', 'Construction', 20, 'unit', 'unit'],
     ['TRAIN_FREIGHT_DIESEL', 'Construction', 2.5e-5, 'unit', 'ton kilometer per hour'],
 ]
 
 lifetime = [
+    ['BATTERY', 30, 20],
     ['TRAIN_FREIGHT_DIESEL', 40, None],
     ['TRAIN_FREIGHT_DIESEL_LOC', None, 50],
     ['TRAIN_FREIGHT_DIESEL_WAG', None, 50],
@@ -128,21 +162,46 @@ def test_compute_impact_score():
         contribution_analysis=True,
     )
 
-    lcia_value = R[
+    lcia_value_train = R[
         (R.Impact_category == ('IPCC 2021', 'climate change', 'global warming potential (GWP100)'))
         & (R.Name == 'TRAIN_FREIGHT_DIESEL')
         & (R.Type == 'Construction')
         ].Value.iloc[0]
 
-    assert 44.40 <= lcia_value <= 44.41
+    assert 44.40 <= lcia_value_train <= 44.41
 
-    max_contrib = df_contrib_results[
+    contrib_train = df_contrib_results[
         (df_contrib_results.impact_category == ('IPCC 2021', 'climate change', 'global warming potential (GWP100)'))
         & (df_contrib_results.act_name == 'TRAIN_FREIGHT_DIESEL')
         & (df_contrib_results.act_type == 'Construction')
-        & (df_contrib_results.score == df_contrib_results.score.max())
     ]
 
-    assert max_contrib.ef_name.iloc[0] == 'Carbon dioxide, fossil'
-    assert max_contrib.ef_categories.iloc[0] == ('air', 'non-urban air or from high stacks')
-    assert 17.42 <= max_contrib.score.iloc[0] <= 17.43
+    max_contrib_train = contrib_train[
+        (contrib_train.score == contrib_train.score.max())
+    ]
+
+    assert max_contrib_train.ef_name.iloc[0] == 'Carbon dioxide, fossil'
+    assert max_contrib_train.ef_categories.iloc[0] == ('air', 'non-urban air or from high stacks')
+    assert 17.42 <= max_contrib_train.score.iloc[0] <= 17.43
+
+    lcia_value_batt = R[
+        (R.Impact_category == ('IPCC 2021', 'climate change', 'global warming potential (GWP100)'))
+        & (R.Name == 'BATTERY')
+        & (R.Type == 'Construction')
+        ].Value.iloc[0]
+
+    assert 167.60 <= lcia_value_batt <= 167.61
+
+    contrib_batt = df_contrib_results[
+        (df_contrib_results.impact_category == ('IPCC 2021', 'climate change', 'global warming potential (GWP100)'))
+        & (df_contrib_results.act_name == 'BATTERY')
+        & (df_contrib_results.act_type == 'Construction')
+    ]
+
+    max_contrib_batt = contrib_batt[
+        (contrib_batt.score == contrib_batt.score.max())
+    ]
+
+    assert max_contrib_batt.ef_name.iloc[0] == 'Carbon dioxide, fossil'
+    assert max_contrib_batt.ef_categories.iloc[0] == ('air', 'non-urban air or from high stacks')
+    assert 98.78 <= max_contrib_batt.score.iloc[0] <= 98.79