from functools import partial from tqdm import tqdm import multiprocessing as mp import pandas as pd import geopandas as gpd import numpy as np idx = pd.IndexSlice def cartesian(s1, s2): """Cartesian product of two pd.Series""" return pd.DataFrame(np.outer(s1, s2), index=s1.index, columns=s2.index) def reverse(dictionary): """reverses a keys and values of a dictionary""" return {v: k for k, v in dictionary.items()} # translations for Eurostat eurostat_country_to_alpha2 = { "EU28": "EU", "EA19": "EA", "Belgium": "BE", "Bulgaria": "BG", "Czech Republic": "CZ", "Denmark": "DK", "Germany": "DE", "Estonia": "EE", "Ireland": "IE", "Greece": "GR", "Spain": "ES", "France": "FR", "Croatia": "HR", "Italy": "IT", "Cyprus": "CY", "Latvia": "LV", "Lithuania": "LT", "Luxembourg": "LU", "Hungary": "HU", "Malta": "MA", "Netherlands": "NL", "Austria": "AT", "Poland": "PL", "Portugal": "PT", "Romania": "RO", "Slovenia": "SI", "Slovakia": "SK", "Finland": "FI", "Sweden": "SE", "United Kingdom": "GB", "Iceland": "IS", "Norway": "NO", "Montenegro": "ME", "FYR of Macedonia": "MK", "Albania": "AL", "Serbia": "RS", "Turkey": "TU", "Bosnia and Herzegovina": "BA", "Kosovo\n(UNSCR 1244/99)": "KO", # 2017 version # 2016 version "Kosovo\n(under United Nations Security Council Resolution 1244/99)": "KO", "Moldova": "MO", "Ukraine": "UK", "Switzerland": "CH", } non_EU = ["NO", "CH", "ME", "MK", "RS", "BA", "AL"] idees_rename = {"GR": "EL", "GB": "UK"} eu28 = [ "FR", "DE", "GB", "IT", "ES", "PL", "SE", "NL", "BE", "FI", "CZ", "DK", "PT", "RO", "AT", "BG", "EE", "GR", "LV", "HU", "IE", "SK", "LT", "HR", "LU", "SI", ] + ["CY", "MT"] eu28_eea = eu28.copy() eu28_eea.remove("GB") eu28_eea.append("UK") to_ipcc = { "electricity": "1.A.1.a - Public Electricity and Heat Production", "residential non-elec": "1.A.4.b - Residential", "services non-elec": "1.A.4.a - Commercial/Institutional", "rail non-elec": "1.A.3.c - Railways", "road non-elec": "1.A.3.b - Road Transportation", "domestic navigation": "1.A.3.d - Domestic Navigation", "international navigation": "1.D.1.b - International Navigation", "domestic aviation": "1.A.3.a - Domestic Aviation", "international aviation": "1.D.1.a - International Aviation", "total energy": "1 - Energy", "industrial processes": "2 - Industrial Processes and Product Use", "agriculture": "3 - Agriculture", "agriculture, forestry and fishing": '1.A.4.c - Agriculture/Forestry/Fishing', "LULUCF": "4 - Land Use, Land-Use Change and Forestry", "waste management": "5 - Waste management", "other": "6 - Other Sector", "indirect": "ind_CO2 - Indirect CO2", "total wL": "Total (with LULUCF)", "total woL": "Total (without LULUCF)", } def build_eurostat(input_eurostat, countries, report_year, year): """Return multi-index for all countries' energy data in TWh/a.""" filenames = { 2016: f"/{year}-Energy-Balances-June2016edition.xlsx", 2017: f"/{year}-ENERGY-BALANCES-June2017edition.xlsx" } dfs = pd.read_excel( input_eurostat + filenames[report_year], sheet_name=None, skiprows=1, index_col=list(range(4)), ) # sorted_index necessary for slicing lookup = eurostat_country_to_alpha2 labelled_dfs = {lookup[df.columns[0]]: df for df in dfs.values() if lookup[df.columns[0]] in countries} df = pd.concat(labelled_dfs, sort=True).sort_index() # drop non-numeric and country columns non_numeric_cols = df.columns[df.dtypes != float] country_cols = df.columns.intersection(lookup.keys()) to_drop = non_numeric_cols.union(country_cols) df.drop(to_drop, axis=1, inplace=True) # convert ktoe/a to TWh/a df *= 11.63 / 1e3 return df def build_swiss(year): """Return a pd.Series of Swiss energy data in TWh/a""" fn = snakemake.input.swiss df = pd.read_csv(fn, index_col=[0,1]).loc["CH", str(year)] # convert PJ/a to TWh/a df /= 3.6 return df def idees_per_country(ct, year): base_dir = snakemake.input.idees ct_totals = {} ct_idees = idees_rename.get(ct, ct) fn_residential = f"{base_dir}/JRC-IDEES-2015_Residential_{ct_idees}.xlsx" fn_tertiary = f"{base_dir}/JRC-IDEES-2015_Tertiary_{ct_idees}.xlsx" fn_transport = f"{base_dir}/JRC-IDEES-2015_Transport_{ct_idees}.xlsx" # residential df = pd.read_excel(fn_residential, "RES_hh_fec", index_col=0)[year] ct_totals["total residential space"] = df["Space heating"] rows = ["Advanced electric heating", "Conventional electric heating"] ct_totals["electricity residential space"] = df[rows].sum() ct_totals["total residential water"] = df.at["Water heating"] assert df.index[23] == "Electricity" ct_totals["electricity residential water"] = df[23] ct_totals["total residential cooking"] = df["Cooking"] assert df.index[30] == "Electricity" ct_totals["electricity residential cooking"] = df[30] df = pd.read_excel(fn_residential, "RES_summary", index_col=0)[year] row = "Energy consumption by fuel - Eurostat structure (ktoe)" ct_totals["total residential"] = df[row] assert df.index[47] == "Electricity" ct_totals["electricity residential"] = df[47] assert df.index[46] == "Derived heat" ct_totals["derived heat residential"] = df[46] assert df.index[50] == 'Thermal uses' ct_totals["thermal uses residential"] = df[50] # services df = pd.read_excel(fn_tertiary, "SER_hh_fec", index_col=0)[year] ct_totals["total services space"] = df["Space heating"] rows = ["Advanced electric heating", "Conventional electric heating"] ct_totals["electricity services space"] = df[rows].sum() ct_totals["total services water"] = df["Hot water"] assert df.index[24] == "Electricity" ct_totals["electricity services water"] = df[24] ct_totals["total services cooking"] = df["Catering"] assert df.index[31] == "Electricity" ct_totals["electricity services cooking"] = df[31] df = pd.read_excel(fn_tertiary, "SER_summary", index_col=0)[year] row = "Energy consumption by fuel - Eurostat structure (ktoe)" ct_totals["total services"] = df[row] assert df.index[50] == "Electricity" ct_totals["electricity services"] = df[50] assert df.index[49] == "Derived heat" ct_totals["derived heat services"] = df[49] assert df.index[53] == 'Thermal uses' ct_totals["thermal uses services"] = df[53] # agriculture, forestry and fishing start = "Detailed split of energy consumption (ktoe)" end = "Market shares of energy uses (%)" df = pd.read_excel(fn_tertiary, "AGR_fec", index_col=0).loc[start:end, year] rows = [ "Lighting", "Ventilation", "Specific electricity uses", "Pumping devices (electric)" ] ct_totals["total agriculture electricity"] = df[rows].sum() rows = ["Specific heat uses", "Low enthalpy heat"] ct_totals["total agriculture heat"] = df[rows].sum() rows = [ "Motor drives", "Farming machine drives (diesel oil incl. biofuels)", "Pumping devices (diesel oil incl. biofuels)", ] ct_totals["total agriculture machinery"] = df[rows].sum() row = "Agriculture, forestry and fishing" ct_totals["total agriculture"] = df[row] # transport df = pd.read_excel(fn_transport, "TrRoad_ene", index_col=0)[year] ct_totals["total road"] = df["by fuel (EUROSTAT DATA)"] ct_totals["electricity road"] = df["Electricity"] ct_totals["total two-wheel"] = df["Powered 2-wheelers (Gasoline)"] assert df.index[19] == "Passenger cars" ct_totals["total passenger cars"] = df[19] assert df.index[30] == "Battery electric vehicles" ct_totals["electricity passenger cars"] = df[30] assert df.index[31] == "Motor coaches, buses and trolley buses" ct_totals["total other road passenger"] = df[31] assert df.index[39] == "Battery electric vehicles" ct_totals["electricity other road passenger"] = df[39] assert df.index[41] == "Light duty vehicles" ct_totals["total light duty road freight"] = df[41] assert df.index[49] == "Battery electric vehicles" ct_totals["electricity light duty road freight"] = df[49] row = "Heavy duty vehicles (Diesel oil incl. biofuels)" ct_totals["total heavy duty road freight"] = df[row] assert df.index[61] == "Passenger cars" ct_totals["passenger car efficiency"] = df[61] df = pd.read_excel(fn_transport, "TrRail_ene", index_col=0)[year] ct_totals["total rail"] = df["by fuel (EUROSTAT DATA)"] ct_totals["electricity rail"] = df["Electricity"] assert df.index[15] == "Passenger transport" ct_totals["total rail passenger"] = df[15] assert df.index[16] == "Metro and tram, urban light rail" assert df.index[19] == "Electric" assert df.index[20] == "High speed passenger trains" ct_totals["electricity rail passenger"] = df[[16, 19, 20]].sum() assert df.index[21] == "Freight transport" ct_totals["total rail freight"] = df[21] assert df.index[23] == "Electric" ct_totals["electricity rail freight"] = df[23] df = pd.read_excel(fn_transport, "TrAvia_ene", index_col=0)[year] assert df.index[6] == "Passenger transport" ct_totals["total aviation passenger"] = df[6] assert df.index[10] == "Freight transport" ct_totals["total aviation freight"] = df[10] assert df.index[7] == "Domestic" ct_totals["total domestic aviation passenger"] = df[7] assert df.index[8] == "International - Intra-EU" assert df.index[9] == "International - Extra-EU" ct_totals["total international aviation passenger"] = df[[8,9]].sum() assert df.index[11] == "Domestic and International - Intra-EU" ct_totals["total domestic aviation freight"] = df[11] assert df.index[12] == "International - Extra-EU" ct_totals["total international aviation freight"] = df[12] ct_totals["total domestic aviation"] = ct_totals["total domestic aviation freight"] \ + ct_totals["total domestic aviation passenger"] ct_totals["total international aviation"] = ct_totals["total international aviation freight"] \ + ct_totals["total international aviation passenger"] df = pd.read_excel(fn_transport, "TrNavi_ene", index_col=0)[year] # coastal and inland ct_totals["total domestic navigation"] = df["by fuel (EUROSTAT DATA)"] df = pd.read_excel(fn_transport, "TrRoad_act", index_col=0)[year] assert df.index[85] == "Passenger cars" ct_totals["passenger cars"] = df[85] return pd.Series(ct_totals, name=ct) def build_idees(countries, year): nprocesses = snakemake.threads func = partial(idees_per_country, year=year) tqdm_kwargs = dict(ascii=False, unit=' country', total=len(countries), desc='Build from IDEES database') with mp.Pool(processes=nprocesses) as pool: totals_list = list(tqdm(pool.imap(func, countries), **tqdm_kwargs)) totals = pd.concat(totals_list, axis=1) # convert ktoe to TWh exclude = totals.index.str.fullmatch("passenger cars") totals.loc[~exclude] *= 11.63 / 1e3 # convert TWh/100km to kWh/km totals.loc["passenger car efficiency"] *= 10 # district heating share district_heat = totals.loc[["derived heat residential", "derived heat services"]].sum() total_heat = totals.loc[["thermal uses residential", "thermal uses services"]].sum() totals.loc["district heat share"] = district_heat.div(total_heat) return totals.T def build_energy_totals(countries, eurostat, swiss, idees): eurostat_fuels = {"electricity": "Electricity", "total": "Total all products"} to_drop = ["passenger cars", "passenger car efficiency"] df = idees.reindex(countries).drop(to_drop, axis=1) eurostat_countries = eurostat.index.levels[0] in_eurostat = df.index.intersection(eurostat_countries) # add international navigation slicer = idx[in_eurostat, :, "Bunkers", :] fill_values = eurostat.loc[slicer, "Total all products"].groupby(level=0).sum() df.loc[in_eurostat, "total international navigation"] = fill_values # add swiss energy data df.loc["CH"] = swiss # get values for missing countries based on Eurostat EnergyBalances # divide cooking/space/water according to averages in EU28 missing = df.index[df["total residential"].isna()] to_fill = missing.intersection(eurostat_countries) uses = ["space", "cooking", "water"] for sector in ["residential", "services", "road", "rail"]: eurostat_sector = sector.capitalize() # fuel use for fuel in ["electricity", "total"]: slicer = idx[to_fill, :, :, eurostat_sector] fill_values = eurostat.loc[slicer, eurostat_fuels[fuel]].groupby(level=0).sum() df.loc[to_fill, f"{fuel} {sector}"] = fill_values for sector in ["residential", "services"]: # electric use for use in uses: fuel_use = df[f"electricity {sector} {use}"] fuel = df[f"electricity {sector}"] avg = fuel_use.div(fuel).mean() print(f"{sector}: average fraction of electricity for {use} is {avg:.3f}") df.loc[to_fill, f"electricity {sector} {use}"] = avg * df.loc[to_fill, f"electricity {sector}"] # non-electric use for use in uses: nonelectric_use = df[f"total {sector} {use}"] - df[f"electricity {sector} {use}"] nonelectric = df[f"total {sector}"] - df[f"electricity {sector}"] avg = nonelectric_use.div(nonelectric).mean() print(f"{sector}: average fraction of non-electric for {use} is {avg:.3f}") electric_use = df.loc[to_fill, f"electricity {sector} {use}"] nonelectric = df.loc[to_fill, f"total {sector}"] - df.loc[to_fill, f"electricity {sector}"] df.loc[to_fill, f"total {sector} {use}"] = electric_use + avg * nonelectric # Fix Norway space and water heating fractions # http://www.ssb.no/en/energi-og-industri/statistikker/husenergi/hvert-3-aar/2014-07-14 # The main heating source for about 73 per cent of the households is based on electricity # => 26% is non-electric elec_fraction = 0.73 no_norway = df.drop("NO") for sector in ["residential", "services"]: # assume non-electric is heating nonelectric = df.loc["NO", f"total {sector}"] - df.loc["NO", f"electricity {sector}"] total_heating = nonelectric / (1 - elec_fraction) for use in uses: nonelectric_use = no_norway[f"total {sector} {use}"] - no_norway[f"electricity {sector} {use}"] nonelectric = no_norway[f"total {sector}"] - no_norway[f"electricity {sector}"] fraction = nonelectric_use.div(nonelectric).mean() df.loc["NO", f"total {sector} {use}"] = total_heating * fraction df.loc["NO", f"electricity {sector} {use}"] = total_heating * fraction * elec_fraction # Missing aviation slicer = idx[to_fill, :, :, "Domestic aviation"] fill_values = eurostat.loc[slicer, "Total all products"].groupby(level=0).sum() df.loc[to_fill, "total domestic aviation"] = fill_values slicer = idx[to_fill, :, :, "International aviation"] fill_values = eurostat.loc[slicer, "Total all products"].groupby(level=0).sum() df.loc[to_fill, "total international aviation"] = fill_values # missing domestic navigation slicer = idx[to_fill, :, :, "Domestic Navigation"] fill_values = eurostat.loc[slicer, "Total all products"].groupby(level=0).sum() df.loc[to_fill, "total domestic navigation"] = fill_values # split road traffic for non-IDEES missing = df.index[df["total passenger cars"].isna()] for fuel in ["total", "electricity"]: selection = [ f"{fuel} passenger cars", f"{fuel} other road passenger", f"{fuel} light duty road freight", ] if fuel == "total": selection.extend([ f"{fuel} two-wheel", f"{fuel} heavy duty road freight" ]) road = df[selection].sum() road_fraction = road / road.sum() fill_values = cartesian(df.loc[missing, f"{fuel} road"], road_fraction) df.loc[missing, road_fraction.index] = fill_values # split rail traffic for non-IDEES missing = df.index[df["total rail passenger"].isna()] for fuel in ["total", "electricity"]: selection = [f"{fuel} rail passenger", f"{fuel} rail freight"] rail = df[selection].sum() rail_fraction = rail / rail.sum() fill_values = cartesian(df.loc[missing, f"{fuel} rail"], rail_fraction) df.loc[missing, rail_fraction.index] = fill_values # split aviation traffic for non-IDEES missing = df.index[df["total domestic aviation passenger"].isna()] for destination in ["domestic", "international"]: selection = [ f"total {destination} aviation passenger", f"total {destination} aviation freight", ] aviation = df[selection].sum() aviation_fraction = aviation / aviation.sum() fill_values = cartesian(df.loc[missing, f"total {destination} aviation"], aviation_fraction) df.loc[missing, aviation_fraction.index] = fill_values for purpose in ["passenger", "freight"]: attrs = [f"total domestic aviation {purpose}", f"total international aviation {purpose}"] df.loc[missing, f"total aviation {purpose}"] = df.loc[missing, attrs].sum(axis=1) if "BA" in df.index: # fill missing data for BA (services and road energy data) # proportional to RS with ratio of total residential demand missing = df.loc["BA"] == 0.0 ratio = df.at["BA", "total residential"] / df.at["RS", "total residential"] df.loc['BA', missing] = ratio * df.loc["RS", missing] # Missing district heating share dh_share = pd.read_csv(snakemake.input.district_heat_share, index_col=0, usecols=[0, 1]) # make conservative assumption and take minimum from both data sets df["district heat share"] = (pd.concat([df["district heat share"], dh_share.reindex(index=df.index)/100], axis=1).min(axis=1)) return df def build_eea_co2(input_co2, year=1990, emissions_scope="CO2"): # https://www.eea.europa.eu/data-and-maps/data/national-emissions-reported-to-the-unfccc-and-to-the-eu-greenhouse-gas-monitoring-mechanism-16 # downloaded 201228 (modified by EEA last on 201221) df = pd.read_csv(input_co2, encoding="latin-1") df.replace(dict(Year="1985-1987"), 1986, inplace=True) df.Year = df.Year.astype(int) index_col = ["Country_code", "Pollutant_name", "Year", "Sector_name"] df = df.set_index(index_col).sort_index() emissions_scope = emissions_scope cts = ["CH", "EUA", "NO"] + eu28_eea slicer = idx[cts, emissions_scope, year, to_ipcc.values()] emissions = ( df.loc[slicer, "emissions"] .unstack("Sector_name") .rename(columns=reverse(to_ipcc)) .droplevel([1,2]) ) emissions.rename(index={"EUA": "EU28", "UK": "GB"}, inplace=True) to_subtract = [ "electricity", "services non-elec", "residential non-elec", "road non-elec", "rail non-elec", "domestic aviation", "international aviation", "domestic navigation", "international navigation", "agriculture, forestry and fishing" ] emissions["industrial non-elec"] = emissions["total energy"] - emissions[to_subtract].sum(axis=1) emissions["agriculture"] += emissions["agriculture, forestry and fishing"] to_drop = ["total energy", "total wL", "total woL", "agriculture, forestry and fishing"] emissions.drop(columns=to_drop, inplace=True) # convert from Gg to Mt return emissions / 1e3 def build_eurostat_co2(input_eurostat, countries, report_year, year=1990): eurostat = build_eurostat(input_eurostat, countries, report_year, year) specific_emissions = pd.Series(index=eurostat.columns, dtype=float) # emissions in tCO2_equiv per MWh_th specific_emissions["Solid fuels"] = 0.36 # Approximates coal specific_emissions["Oil (total)"] = 0.285 # Average of distillate and residue specific_emissions["Gas"] = 0.2 # For natural gas # oil values from https://www.eia.gov/tools/faqs/faq.cfm?id=74&t=11 # Distillate oil (No. 2) 0.276 # Residual oil (No. 6) 0.298 # https://www.eia.gov/electricity/annual/html/epa_a_03.html return eurostat.multiply(specific_emissions).sum(axis=1) def build_co2_totals(countries, eea_co2, eurostat_co2): co2 = eea_co2.reindex(countries) for ct in countries.intersection(["BA", "RS", "AL", "ME", "MK"]): mappings = { "electricity": (ct, "+", "Conventional Thermal Power Stations", "of which From Coal"), "residential non-elec": (ct, "+", "+", "Residential"), "services non-elec": (ct, "+", "+", "Services"), "road non-elec": (ct, "+", "+", "Road"), "rail non-elec": (ct, "+", "+", "Rail"), "domestic navigation": (ct, "+", "+", "Domestic Navigation"), "international navigation": (ct, "-", "Bunkers"), "domestic aviation": (ct, "+", "+", "Domestic aviation"), "international aviation": (ct, "+", "+", "International aviation"), # does not include industrial process emissions or fuel processing/refining "industrial non-elec": (ct, "+", "Industry"), # does not include non-energy emissions "agriculture": (eurostat_co2.index.get_level_values(0) == ct) & eurostat_co2.index.isin(["Agriculture / Forestry", "Fishing"], level=3), } for i, mi in mappings.items(): co2.at[ct, i] = eurostat_co2.loc[mi].sum() return co2 def build_transport_data(countries, population, idees): transport_data = pd.DataFrame(index=countries) # collect number of cars transport_data["number cars"] = idees["passenger cars"] # CH from http://ec.europa.eu/eurostat/statistics-explained/index.php/Passenger_cars_in_the_EU#Luxembourg_has_the_highest_number_of_passenger_cars_per_inhabitant transport_data.at["CH", "number cars"] = 4.136e6 missing = transport_data.index[transport_data["number cars"].isna()] print(f"Missing data on cars from:\n{list(missing)}\nFilling gaps with averaged data.") cars_pp = transport_data["number cars"] / population transport_data.loc[missing, "number cars"] = cars_pp.mean() * population # collect average fuel efficiency in kWh/km transport_data["average fuel efficiency"] = idees["passenger car efficiency"] missing = transport_data.index[transport_data["average fuel efficiency"].isna()] print(f"Missing data on fuel efficiency from:\n{list(missing)}\nFilling gapswith averaged data.") fill_values = transport_data["average fuel efficiency"].mean() transport_data.loc[missing, "average fuel efficiency"] = fill_values return transport_data if __name__ == "__main__": if 'snakemake' not in globals(): from helper import mock_snakemake snakemake = mock_snakemake('build_energy_totals') config = snakemake.config["energy"] nuts3 = gpd.read_file(snakemake.input.nuts3_shapes).set_index("index") population = nuts3["pop"].groupby(nuts3.country).sum() countries = population.index idees_countries = countries.intersection(eu28) data_year = config["energy_totals_year"] report_year = snakemake.config["energy"]["eurostat_report_year"] input_eurostat = snakemake.input.eurostat eurostat = build_eurostat(input_eurostat, countries, report_year, data_year) swiss = build_swiss(data_year) idees = build_idees(idees_countries, data_year) energy = build_energy_totals(countries, eurostat, swiss, idees) energy.to_csv(snakemake.output.energy_name) base_year_emissions = config["base_emissions_year"] emissions_scope = snakemake.config["energy"]["emissions"] eea_co2 = build_eea_co2(snakemake.input.co2, base_year_emissions, emissions_scope) eurostat_co2 = build_eurostat_co2(input_eurostat, countries, report_year, base_year_emissions) co2 = build_co2_totals(countries, eea_co2, eurostat_co2) co2.to_csv(snakemake.output.co2_name) transport = build_transport_data(countries, population, idees) transport.to_csv(snakemake.output.transport_name)