|
|
@@ -38,7 +38,7 @@ unit_inventory = ['Gg'] * len(header_inventory)
|
|
|
unit_inventory[9] = "GgCO2eq"
|
|
|
unit_inventory[10] = "GgCO2eq"
|
|
|
|
|
|
-year = 2019
|
|
|
+year = 2016
|
|
|
entity_row = 0
|
|
|
unit_row = 1
|
|
|
gwp_to_use = "AR4GWP100"
|
|
|
@@ -296,7 +296,7 @@ data_indirect_IF = pm2.pm2io.convert_wide_dataframe_if(
|
|
|
)
|
|
|
|
|
|
# ###
|
|
|
-# merge the tree datasets
|
|
|
+# merge the three datasets
|
|
|
# ###
|
|
|
data_inventory_pm2 = pm2.pm2io.from_interchange_format(data_inventory_IF)
|
|
|
data_main_sector_ts_pm2 = pm2.pm2io.from_interchange_format(data_main_sector_ts_IF)
|
|
|
@@ -306,11 +306,14 @@ data_all = data_inventory_pm2.pr.merge(data_main_sector_ts_pm2)
|
|
|
data_all = data_all.pr.merge(data_indirect_pm2)
|
|
|
|
|
|
# combine CO2 emissions and absorptions
|
|
|
-data_all["CO2"] = data_all['CO2 removals'] + data_all['CO2 emissions']
|
|
|
+data_CO2 = data_all[['CO2 emissions', 'CO2 removals']].\
|
|
|
+ to_array().pr.sum("variable", skipna=True, min_count=1)
|
|
|
+data_all["CO2"] = data_CO2
|
|
|
|
|
|
data_all_if = data_all.pr.to_interchange_format()
|
|
|
|
|
|
|
|
|
+
|
|
|
# ###
|
|
|
# convert to IPCC2006 categories
|
|
|
# ###
|
