adjust mapping energy demand per ct

scripts/build_industrial_energy_demand_per_country_today.py

@@ -36,7 +36,7 @@ For each country and each subcategory of
 - Integrated steelworks
 - Machinery Equipment
 - Methanol
-- Other Industrial Sectors
+- Other industrial sectors
 - Other chemicals
 - Other non-ferrous metals
 - Paper production
@@ -74,46 +74,49 @@ ktoe_to_twh = 0.011630
 
 # name in JRC-IDEES Energy Balances
 sector_sheets = {
-    "Integrated steelworks": "cisb",
+    "Integrated steelworks": "FC_IND_IS_BF_E",
-    "Electric arc": "cise",
+    "Electric arc": "FC_IND_IS_EA_E",
-    "Alumina production": "cnfa",
+    "Alumina production": "FC_IND_NFM_AM_E",
-    "Aluminium - primary production": "cnfp",
+    "Aluminium - primary production": "FC_IND_NFM_PA_E",
-    "Aluminium - secondary production": "cnfs",
+    "Aluminium - secondary production": "FC_IND_NFM_SA_E",
-    "Other non-ferrous metals": "cnfo",
+    "Other non-ferrous metals": "FC_IND_NFM_OM_E",
-    "Basic chemicals": "cbch",
+    "Basic chemicals": "FC_IND_CPC_BC_E",
-    "Other chemicals": "coch",
+    "Other chemicals": "FC_IND_CPC_OC_E",
-    "Pharmaceutical products etc.": "cpha",
+    "Pharmaceutical products etc.": "FC_IND_CPC_PH_E",
-    "Basic chemicals feedstock": "cpch",
+    "Basic chemicals feedstock": "FC_IND_CPC_BC_E",
-    "Cement": "ccem",
+    "Cement": "FC_IND_NMM_CM_E",
-    "Ceramics & other NMM": "ccer",
+    "Ceramics & other NMM": "FC_IND_NMM_CR_E",
-    "Glass production": "cgla",
+    "Glass production": "FC_IND_NMM_GL_E",
-    "Pulp production": "cpul",
+    "Pulp production": "FC_IND_PPP_PU_E",
-    "Paper production": "cpap",
+    "Paper production": "FC_IND_PPP_PA_E",
-    "Printing and media reproduction": "cprp",
+    "Printing and media reproduction": "FC_IND_PPP_PR_E",
-    "Food, beverages and tobacco": "cfbt",
+    "Food, beverages and tobacco": "FC_IND_FBT_E",
-    "Transport Equipment": "ctre",
+    "Transport equipment": "FC_IND_TE_E",
-    "Machinery Equipment": "cmae",
+    "Machinery equipment": "FC_IND_MAC_E",
-    "Textiles and leather": "ctel",
+    "Textiles and leather": "FC_IND_TL_E",
-    "Wood and wood products": "cwwp",
+    "Wood and wood products": "FC_IND_WP_E",
-    "Mining and quarrying": "cmiq",
+    "Mining and quarrying": "FC_IND_MQ_E",
-    "Construction": "ccon",
+    "Construction": "FC_IND_CON_E",
-    "Non-specified": "cnsi",
+    "Non-specified": "FC_IND_NSP_E",
 }
 
 
 fuels = {
-    "All Products": "all",
+    "Total": "all",
-    "Solid Fuels": "solid",
+    "Solid fossil fuels": "solid",
-    "Total petroleum products (without biofuels)": "liquid",
+    "Peat and peat products": "solid",
-    "Gases": "gas",
+    "Oil shale and oil sands": "solid",
+    "Oil and petroleum products": "liquid",
+    "Manufactured gases": "gas",
+    "Natural gas": "gas",
     "Nuclear heat": "heat",
-    "Derived heat": "heat",
+    "Heat": "heat",
-    "Biomass and Renewable wastes": "biomass",
+    "Renewables and biofuels": "biomass",
-    "Wastes (non-renewable)": "waste",
+    "Non-renewable waste": "waste",
     "Electricity": "electricity",
 }
 
-eu28 = cc.EU28as("ISO2").ISO2.tolist()
+eu27 = cc.EU27as("ISO2").ISO2.tolist()
 
 jrc_names = {"GR": "EL", "GB": "UK"}
 
@@ -139,7 +142,7 @@ def industrial_energy_demand_per_country(country, year, jrc_dir):
     )
 
     sel = ["Mining and quarrying", "Construction", "Non-specified"]
-    df["Other Industrial Sectors"] = df[sel].sum(axis=1)
+    df["Other industrial sectors"] = df[sel].sum(axis=1)
     df["Basic chemicals"] += df["Basic chemicals feedstock"]
 
     df.drop(columns=sel + ["Basic chemicals feedstock"], index="all", inplace=True)
@@ -189,20 +192,20 @@ def separate_basic_chemicals(demand, production):
     return demand
 
 
-def add_non_eu28_industrial_energy_demand(countries, demand, production):
+def add_non_eu27_industrial_energy_demand(countries, demand, production):
-    non_eu28 = countries.difference(eu28)
+    non_eu27 = countries.difference(eu27)
-    if non_eu28.empty:
+    if non_eu27.empty:
         return demand
 
-    eu28_production = production.loc[countries.intersection(eu28)].sum()
+    eu27_production = production.loc[countries.intersection(eu27)].sum()
-    eu28_energy = demand.groupby(level=1).sum()
+    eu27_energy = demand.groupby(level=1).sum()
-    eu28_averages = eu28_energy / eu28_production
+    eu27_averages = eu27_energy / eu27_production
 
-    demand_non_eu28 = pd.concat(
+    demand_non_eu27 = pd.concat(
-        {k: v * eu28_averages for k, v in production.loc[non_eu28].iterrows()}
+        {k: v * eu27_averages for k, v in production.loc[non_eu27].iterrows()}
     )
 
-    return pd.concat([demand, demand_non_eu28])
+    return pd.concat([demand, demand_non_eu27])
 
 
 def industrial_energy_demand(countries, year):
@@ -232,10 +235,10 @@ if __name__ == "__main__":
     set_scenario_config(snakemake)
 
     params = snakemake.params.industry
-    year = params.get("reference_year", 2015)
+    year = params.get("reference_year", 2019)
     countries = pd.Index(snakemake.params.countries)
 
-    demand = industrial_energy_demand(countries.intersection(eu28), year)
+    demand = industrial_energy_demand(countries.intersection(eu27), year)
 
     # output in MtMaterial/a
     production = (
@@ -245,7 +248,7 @@ if __name__ == "__main__":
 
     demand = separate_basic_chemicals(demand, production)
 
-    demand = add_non_eu28_industrial_energy_demand(countries, demand, production)
+    demand = add_non_eu27_industrial_energy_demand(countries, demand, production)
 
     # for format compatibility
     demand = demand.stack(future_stack=True).unstack(level=[0, 2])