982 lines
34 KiB
Python
982 lines
34 KiB
Python
# -*- coding: utf-8 -*-
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# SPDX-FileCopyrightText: : 2020-2024 The PyPSA-Eur Authors
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#
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# SPDX-License-Identifier: MIT
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"""
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Build total energy demands per country using JRC IDEES, eurostat, and EEA data.
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"""
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import logging
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import multiprocessing as mp
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import os
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from functools import partial
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import country_converter as coco
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import geopandas as gpd
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import numpy as np
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import pandas as pd
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from _helpers import configure_logging, mute_print, set_scenario_config
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from tqdm import tqdm
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cc = coco.CountryConverter()
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logger = logging.getLogger(__name__)
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idx = pd.IndexSlice
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def cartesian(s1, s2):
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"""
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Cartesian product of two pd.Series.
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"""
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return pd.DataFrame(np.outer(s1, s2), index=s1.index, columns=s2.index)
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def reverse(dictionary):
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"""
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Reverses a keys and values of a dictionary.
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"""
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return {v: k for k, v in dictionary.items()}
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idees_rename = {"GR": "EL", "GB": "UK"}
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eu28 = cc.EU28as("ISO2").ISO2.tolist()
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eu28_eea = eu28.copy()
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eu28_eea.remove("GB")
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eu28_eea.append("UK")
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to_ipcc = {
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"electricity": "1.A.1.a - Public Electricity and Heat Production",
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"residential non-elec": "1.A.4.b - Residential",
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"services non-elec": "1.A.4.a - Commercial/Institutional",
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"rail non-elec": "1.A.3.c - Railways",
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"road non-elec": "1.A.3.b - Road Transportation",
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"domestic navigation": "1.A.3.d - Domestic Navigation",
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"international navigation": "1.D.1.b - International Navigation",
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"domestic aviation": "1.A.3.a - Domestic Aviation",
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"international aviation": "1.D.1.a - International Aviation",
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"total energy": "1 - Energy",
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"industrial processes": "2 - Industrial Processes and Product Use",
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"agriculture": "3 - Agriculture",
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"agriculture, forestry and fishing": "1.A.4.c - Agriculture/Forestry/Fishing",
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"LULUCF": "4 - Land Use, Land-Use Change and Forestry",
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"waste management": "5 - Waste management",
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"other": "6 - Other Sector",
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"indirect": "ind_CO2 - Indirect CO2",
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"total wL": "Total (with LULUCF)",
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"total woL": "Total (without LULUCF)",
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}
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def eurostat_per_country(input_eurostat, country):
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filename = (
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f"{input_eurostat}/{country}-Energy-balance-sheets-April-2023-edition.xlsb"
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)
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sheet = pd.read_excel(
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filename,
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engine="pyxlsb",
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sheet_name=None,
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skiprows=4,
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index_col=list(range(4)),
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)
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sheet.pop("Cover")
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return pd.concat(sheet)
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def build_eurostat(input_eurostat, countries):
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"""
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Return multi-index for all countries' energy data in TWh/a.
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"""
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countries = {idees_rename.get(country, country) for country in countries} - {"CH"}
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nprocesses = snakemake.threads
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disable_progress = snakemake.config["run"].get("disable_progressbar", False)
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func = partial(eurostat_per_country, input_eurostat)
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tqdm_kwargs = dict(
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ascii=False,
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unit=" country",
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total=len(countries),
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desc="Build from eurostat database",
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disable=disable_progress,
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)
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with mute_print():
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with mp.Pool(processes=nprocesses) as pool:
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dfs = list(tqdm(pool.imap(func, countries), **tqdm_kwargs))
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index_names = ["country", "year", "lvl1", "lvl2", "lvl3", "lvl4"]
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df = pd.concat(dfs, keys=countries, names=index_names)
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df.index = df.index.set_levels(df.index.levels[1].astype(int), level=1)
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# drop columns with all NaNs
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unnamed_cols = df.columns[df.columns.astype(str).str.startswith("Unnamed")]
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df.drop(unnamed_cols, axis=1, inplace=True)
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df.drop(list(range(1990, 2022)), axis=1, inplace=True, errors="ignore")
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# make numeric values where possible
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df.replace("Z", 0, inplace=True)
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df = df.apply(pd.to_numeric, errors="coerce")
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df = df.select_dtypes(include=[np.number])
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# write 'International aviation' to the lower level of the multiindex
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int_avia = df.index.get_level_values(3) == "International aviation"
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temp = df.loc[int_avia]
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temp.index = pd.MultiIndex.from_frame(
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temp.index.to_frame().fillna("International aviation")
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)
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df = pd.concat([temp, df.loc[~int_avia]])
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# Renaming some indices
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index_rename = {
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"Households": "Residential",
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"Commercial & public services": "Services",
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"Domestic navigation": "Domestic Navigation",
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"International maritime bunkers": "Bunkers",
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"UK": "GB",
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}
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columns_rename = {"Total": "Total all products"}
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df.rename(index=index_rename, columns=columns_rename, inplace=True)
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df.sort_index(inplace=True)
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# convert to TWh/a from ktoe/a
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df *= 11.63 / 1e3
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return df
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def build_swiss():
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"""
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Return a pd.DataFrame of Swiss energy data in TWh/a.
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"""
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fn = snakemake.input.swiss
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df = pd.read_csv(fn, index_col=[0, 1])
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df.columns = df.columns.astype(int)
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df.columns.name = "year"
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df = df.stack().unstack("item")
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df.columns.name = None
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# convert PJ/a to TWh/a
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df /= 3.6
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return df
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def idees_per_country(ct, base_dir):
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ct_idees = idees_rename.get(ct, ct)
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fn_residential = f"{base_dir}/JRC-IDEES-2015_Residential_{ct_idees}.xlsx"
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fn_tertiary = f"{base_dir}/JRC-IDEES-2015_Tertiary_{ct_idees}.xlsx"
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fn_transport = f"{base_dir}/JRC-IDEES-2015_Transport_{ct_idees}.xlsx"
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ct_totals = {}
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# residential
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df = pd.read_excel(fn_residential, "RES_hh_fec", index_col=0)
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rows = ["Advanced electric heating", "Conventional electric heating"]
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ct_totals["electricity residential space"] = df.loc[rows].sum()
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ct_totals["total residential space"] = df.loc["Space heating"]
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ct_totals["total residential water"] = df.loc["Water heating"]
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assert df.index[23] == "Electricity"
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ct_totals["electricity residential water"] = df.iloc[23]
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ct_totals["total residential cooking"] = df.loc["Cooking"]
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assert df.index[30] == "Electricity"
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ct_totals["electricity residential cooking"] = df.iloc[30]
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df = pd.read_excel(fn_residential, "RES_summary", index_col=0)
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row = "Energy consumption by fuel - Eurostat structure (ktoe)"
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ct_totals["total residential"] = df.loc[row]
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assert df.index[47] == "Electricity"
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ct_totals["electricity residential"] = df.iloc[47]
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assert df.index[46] == "Derived heat"
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ct_totals["derived heat residential"] = df.iloc[46]
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assert df.index[50] == "Thermal uses"
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ct_totals["thermal uses residential"] = df.iloc[50]
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# services
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df = pd.read_excel(fn_tertiary, "SER_hh_fec", index_col=0)
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ct_totals["total services space"] = df.loc["Space heating"]
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rows = ["Advanced electric heating", "Conventional electric heating"]
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ct_totals["electricity services space"] = df.loc[rows].sum()
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ct_totals["total services water"] = df.loc["Hot water"]
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assert df.index[24] == "Electricity"
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ct_totals["electricity services water"] = df.iloc[24]
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ct_totals["total services cooking"] = df.loc["Catering"]
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assert df.index[31] == "Electricity"
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ct_totals["electricity services cooking"] = df.iloc[31]
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df = pd.read_excel(fn_tertiary, "SER_summary", index_col=0)
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row = "Energy consumption by fuel - Eurostat structure (ktoe)"
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ct_totals["total services"] = df.loc[row]
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assert df.index[50] == "Electricity"
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ct_totals["electricity services"] = df.iloc[50]
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assert df.index[49] == "Derived heat"
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ct_totals["derived heat services"] = df.iloc[49]
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assert df.index[53] == "Thermal uses"
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ct_totals["thermal uses services"] = df.iloc[53]
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# agriculture, forestry and fishing
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start = "Detailed split of energy consumption (ktoe)"
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end = "Market shares of energy uses (%)"
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df = pd.read_excel(fn_tertiary, "AGR_fec", index_col=0).loc[start:end]
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rows = [
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"Lighting",
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"Ventilation",
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"Specific electricity uses",
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"Pumping devices (electric)",
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]
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ct_totals["total agriculture electricity"] = df.loc[rows].sum()
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rows = ["Specific heat uses", "Low enthalpy heat"]
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ct_totals["total agriculture heat"] = df.loc[rows].sum()
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rows = [
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"Motor drives",
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"Farming machine drives (diesel oil incl. biofuels)",
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"Pumping devices (diesel oil incl. biofuels)",
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]
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ct_totals["total agriculture machinery"] = df.loc[rows].sum()
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row = "Agriculture, forestry and fishing"
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ct_totals["total agriculture"] = df.loc[row]
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# transport
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df = pd.read_excel(fn_transport, "TrRoad_ene", index_col=0)
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ct_totals["total road"] = df.loc["by fuel (EUROSTAT DATA)"]
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ct_totals["electricity road"] = df.loc["Electricity"]
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ct_totals["total two-wheel"] = df.loc["Powered 2-wheelers (Gasoline)"]
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assert df.index[19] == "Passenger cars"
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ct_totals["total passenger cars"] = df.iloc[19]
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assert df.index[30] == "Battery electric vehicles"
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ct_totals["electricity passenger cars"] = df.iloc[30]
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assert df.index[31] == "Motor coaches, buses and trolley buses"
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ct_totals["total other road passenger"] = df.iloc[31]
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assert df.index[39] == "Battery electric vehicles"
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ct_totals["electricity other road passenger"] = df.iloc[39]
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assert df.index[41] == "Light duty vehicles"
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ct_totals["total light duty road freight"] = df.iloc[41]
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assert df.index[49] == "Battery electric vehicles"
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ct_totals["electricity light duty road freight"] = df.iloc[49]
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row = "Heavy duty vehicles (Diesel oil incl. biofuels)"
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ct_totals["total heavy duty road freight"] = df.loc[row]
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assert df.index[61] == "Passenger cars"
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ct_totals["passenger car efficiency"] = df.iloc[61]
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df = pd.read_excel(fn_transport, "TrRail_ene", index_col=0)
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ct_totals["total rail"] = df.loc["by fuel (EUROSTAT DATA)"]
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ct_totals["electricity rail"] = df.loc["Electricity"]
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assert df.index[15] == "Passenger transport"
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ct_totals["total rail passenger"] = df.iloc[15]
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assert df.index[16] == "Metro and tram, urban light rail"
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assert df.index[19] == "Electric"
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assert df.index[20] == "High speed passenger trains"
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ct_totals["electricity rail passenger"] = df.iloc[[16, 19, 20]].sum()
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assert df.index[21] == "Freight transport"
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ct_totals["total rail freight"] = df.iloc[21]
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assert df.index[23] == "Electric"
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ct_totals["electricity rail freight"] = df.iloc[23]
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df = pd.read_excel(fn_transport, "TrAvia_ene", index_col=0)
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assert df.index[6] == "Passenger transport"
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ct_totals["total aviation passenger"] = df.iloc[6]
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assert df.index[10] == "Freight transport"
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ct_totals["total aviation freight"] = df.iloc[10]
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assert df.index[7] == "Domestic"
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ct_totals["total domestic aviation passenger"] = df.iloc[7]
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assert df.index[8] == "International - Intra-EU"
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assert df.index[9] == "International - Extra-EU"
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ct_totals["total international aviation passenger"] = df.iloc[[8, 9]].sum()
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assert df.index[11] == "Domestic and International - Intra-EU"
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ct_totals["total domestic aviation freight"] = df.iloc[11]
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assert df.index[12] == "International - Extra-EU"
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ct_totals["total international aviation freight"] = df.iloc[12]
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ct_totals["total domestic aviation"] = (
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ct_totals["total domestic aviation freight"]
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+ ct_totals["total domestic aviation passenger"]
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)
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ct_totals["total international aviation"] = (
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ct_totals["total international aviation freight"]
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+ ct_totals["total international aviation passenger"]
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)
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df = pd.read_excel(fn_transport, "TrNavi_ene", index_col=0)
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# coastal and inland
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ct_totals["total domestic navigation"] = df.loc["by fuel (EUROSTAT DATA)"]
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df = pd.read_excel(fn_transport, "TrRoad_act", index_col=0)
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assert df.index[85] == "Passenger cars"
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ct_totals["passenger cars"] = df.iloc[85]
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return pd.DataFrame(ct_totals)
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def build_idees(countries):
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nprocesses = snakemake.threads
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disable_progress = snakemake.config["run"].get("disable_progressbar", False)
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func = partial(idees_per_country, base_dir=snakemake.input.idees)
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tqdm_kwargs = dict(
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ascii=False,
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unit=" country",
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total=len(countries),
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desc="Build from IDEES database",
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disable=disable_progress,
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)
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with mute_print():
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with mp.Pool(processes=nprocesses) as pool:
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totals_list = list(tqdm(pool.imap(func, countries), **tqdm_kwargs))
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totals = pd.concat(
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totals_list,
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keys=countries,
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names=["country", "year"],
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)
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# convert ktoe to TWh
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exclude = totals.columns.str.fullmatch("passenger cars")
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totals.loc[:, ~exclude] *= 11.63 / 1e3
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# convert TWh/100km to kWh/km
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totals.loc[:, "passenger car efficiency"] *= 10
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return totals
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def build_energy_totals(countries, eurostat, swiss, idees):
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eurostat_fuels = {"electricity": "Electricity", "total": "Total all products"}
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eurostat_countries = eurostat.index.levels[0]
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eurostat_years = eurostat.index.levels[1]
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to_drop = ["passenger cars", "passenger car efficiency"]
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new_index = pd.MultiIndex.from_product(
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[countries, eurostat_years], names=["country", "year"]
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)
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df = idees.reindex(new_index).drop(to_drop, axis=1)
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in_eurostat = df.index.levels[0].intersection(eurostat_countries)
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# add international navigation
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slicer = idx[in_eurostat, :, :, "Bunkers", :]
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fill_values = eurostat.loc[slicer, "Total all products"].groupby(level=[0, 1]).sum()
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df.loc[in_eurostat, "total international navigation"] = fill_values
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# add swiss energy data
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df = pd.concat([df.drop("CH"), swiss]).sort_index()
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# get values for missing countries based on Eurostat EnergyBalances
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# divide cooking/space/water according to averages in EU28
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uses = ["space", "cooking", "water"]
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to_fill = df.index[
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df["total residential"].isna()
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& df.index.get_level_values("country").isin(eurostat_countries)
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]
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c = to_fill.get_level_values("country")
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y = to_fill.get_level_values("year")
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for sector in ["residential", "services", "road", "rail"]:
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eurostat_sector = sector.capitalize()
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# fuel use
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for fuel in ["electricity", "total"]:
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slicer = idx[c, y, :, :, eurostat_sector]
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fill_values = (
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eurostat.loc[slicer, eurostat_fuels[fuel]].groupby(level=[0, 1]).sum()
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)
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df.loc[to_fill, f"{fuel} {sector}"] = fill_values
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for sector in ["residential", "services"]:
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# electric use
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for use in uses:
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fuel_use = df[f"electricity {sector} {use}"]
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fuel = df[f"electricity {sector}"]
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avg = fuel_use.div(fuel).mean()
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logger.debug(
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f"{sector}: average fraction of electricity for {use} is {avg:.3f}"
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)
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df.loc[to_fill, f"electricity {sector} {use}"] = (
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avg * df.loc[to_fill, f"electricity {sector}"]
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)
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# non-electric use
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for use in uses:
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nonelectric_use = (
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df[f"total {sector} {use}"] - df[f"electricity {sector} {use}"]
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)
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nonelectric = df[f"total {sector}"] - df[f"electricity {sector}"]
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avg = nonelectric_use.div(nonelectric).mean()
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logger.debug(
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f"{sector}: average fraction of non-electric for {use} is {avg:.3f}"
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)
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electric_use = df.loc[to_fill, f"electricity {sector} {use}"]
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nonelectric = (
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df.loc[to_fill, f"total {sector}"]
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- df.loc[to_fill, f"electricity {sector}"]
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)
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df.loc[to_fill, f"total {sector} {use}"] = electric_use + avg * nonelectric
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# Fix Norway space and water heating fractions
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# http://www.ssb.no/en/energi-og-industri/statistikker/husenergi/hvert-3-aar/2014-07-14
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# The main heating source for about 73 per cent of the households is based on electricity
|
|
# => 26% is non-electric
|
|
|
|
if "NO" in df.index:
|
|
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
|
|
).values
|
|
df.loc["NO", f"electricity {sector} {use}"] = (
|
|
total_heating * fraction * elec_fraction
|
|
).values
|
|
|
|
# Missing aviation
|
|
|
|
slicer = idx[c, y, :, :, "Domestic aviation"]
|
|
fill_values = eurostat.loc[slicer, "Total all products"].groupby(level=[0, 1]).sum()
|
|
df.loc[to_fill, "total domestic aviation"] = fill_values
|
|
|
|
slicer = idx[c, y, :, :, "International aviation"]
|
|
fill_values = eurostat.loc[slicer, "Total all products"].groupby(level=[0, 1]).sum()
|
|
df.loc[to_fill, "total international aviation"] = fill_values
|
|
|
|
# missing domestic navigation
|
|
|
|
slicer = idx[c, y, :, :, "Domestic Navigation"]
|
|
fill_values = eurostat.loc[slicer, "Total all products"].groupby(level=[0, 1]).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
|
|
mean_BA = df.loc["BA"].loc[2014:2021, "total residential"].mean()
|
|
mean_RS = df.loc["RS"].loc[2014:2021, "total residential"].mean()
|
|
ratio = mean_BA / mean_RS
|
|
df.loc["BA"] = df.loc["BA"].replace(0.0, np.nan).values
|
|
df.loc["BA"] = df.loc["BA"].combine_first(ratio * df.loc["RS"]).values
|
|
|
|
return df
|
|
|
|
|
|
def build_district_heat_share(countries, idees):
|
|
# district heating share
|
|
district_heat = idees[["derived heat residential", "derived heat services"]].sum(
|
|
axis=1
|
|
)
|
|
total_heat = idees[["thermal uses residential", "thermal uses services"]].sum(
|
|
axis=1
|
|
)
|
|
|
|
district_heat_share = district_heat / total_heat
|
|
|
|
district_heat_share = district_heat_share.reindex(countries, level="country")
|
|
|
|
# Missing district heating share
|
|
dh_share = (
|
|
pd.read_csv(snakemake.input.district_heat_share, index_col=0, usecols=[0, 1])
|
|
.div(100)
|
|
.squeeze()
|
|
)
|
|
# make conservative assumption and take minimum from both data sets
|
|
district_heat_share = pd.concat(
|
|
[district_heat_share, dh_share.reindex_like(district_heat_share)], axis=1
|
|
).min(axis=1)
|
|
|
|
district_heat_share.name = "district heat share"
|
|
|
|
# restrict to available years
|
|
district_heat_share = (
|
|
district_heat_share.unstack().dropna(how="all", axis=1).ffill(axis=1)
|
|
)
|
|
|
|
return district_heat_share
|
|
|
|
|
|
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", low_memory=False)
|
|
|
|
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()
|
|
|
|
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(eurostat, year=1990):
|
|
eurostat_year = eurostat.xs(year, level="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_year.multiply(specific_emissions).sum(axis=1)
|
|
|
|
|
|
def build_co2_totals(countries, eea_co2, eurostat_co2):
|
|
co2 = eea_co2.reindex(countries)
|
|
|
|
for ct in pd.Index(countries).intersection(["BA", "RS", "AL", "ME", "MK"]):
|
|
mappings = {
|
|
"electricity": (ct, "+", "Electricity & heat generation", np.nan),
|
|
"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 sector"),
|
|
# 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):
|
|
# first collect number of cars
|
|
|
|
transport_data = pd.DataFrame(idees["passenger cars"])
|
|
|
|
countries_without_ch = set(countries) - {"CH"}
|
|
new_index = pd.MultiIndex.from_product(
|
|
[countries_without_ch, transport_data.index.levels[1]],
|
|
names=["country", "year"]
|
|
)
|
|
|
|
transport_data = transport_data.reindex(index=new_index)
|
|
|
|
# https://www.bfs.admin.ch/bfs/en/home/statistics/mobility-transport/transport-infrastructure-vehicles/vehicles/road-vehicles-stock-level-motorisation.html
|
|
if "CH" in countries:
|
|
fn = snakemake.input.swiss_transport
|
|
swiss_cars = pd.read_csv(fn, index_col=0).loc[2000:2015, ["passenger cars"]]
|
|
|
|
swiss_cars.index = pd.MultiIndex.from_product(
|
|
[["CH"], swiss_cars.index], names=["country", "year"]
|
|
)
|
|
|
|
transport_data = pd.concat([transport_data, swiss_cars]).sort_index()
|
|
|
|
transport_data.rename(columns={"passenger cars": "number cars"}, inplace=True)
|
|
|
|
missing = transport_data.index[transport_data["number cars"].isna()]
|
|
if not missing.empty:
|
|
logger.info(
|
|
f"Missing data on cars from:\n{list(missing)}\nFilling gaps with averaged data."
|
|
)
|
|
|
|
cars_pp = transport_data["number cars"] / population
|
|
|
|
fill_values = {year: cars_pp.mean() * population for year in transport_data.index.levels[1]}
|
|
fill_values = pd.DataFrame(fill_values).stack()
|
|
fill_values = pd.DataFrame(fill_values, columns=["number cars"])
|
|
fill_values.index.names = ["country", "year"]
|
|
fill_values = fill_values.reindex(transport_data.index)
|
|
|
|
transport_data = transport_data.combine_first(fill_values)
|
|
|
|
# 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()]
|
|
if not missing.empty:
|
|
logger.info(
|
|
f"Missing data on fuel efficiency from:\n{list(missing)}\nFilling gaps with averaged data."
|
|
)
|
|
|
|
fill_values = transport_data["average fuel efficiency"].mean()
|
|
transport_data.loc[missing, "average fuel efficiency"] = fill_values
|
|
|
|
return transport_data
|
|
|
|
|
|
def rescale_idees_from_eurostat(
|
|
idees_countries,
|
|
energy,
|
|
eurostat,
|
|
):
|
|
"""
|
|
Takes JRC IDEES data from 2015 and rescales it by the ratio of the eurostat
|
|
data and the 2015 eurostat data.
|
|
|
|
missing data: ['passenger car efficiency', 'passenger cars']
|
|
"""
|
|
main_cols = ["Total all products", "Electricity"]
|
|
# read in the eurostat data for 2015
|
|
eurostat_2015 = eurostat.xs(2015, level="year")[main_cols]
|
|
# calculate the ratio of the two data sets
|
|
ratio = eurostat[main_cols] / eurostat_2015
|
|
ratio = ratio.droplevel([2, 5])
|
|
cols_rename = {"Total all products": "total", "Electricity": "ele"}
|
|
index_rename = {v: k for k, v in idees_rename.items()}
|
|
ratio.rename(columns=cols_rename, index=index_rename, inplace=True)
|
|
|
|
mappings = {
|
|
"Residential": {
|
|
"total": [
|
|
"total residential space",
|
|
"total residential water",
|
|
"total residential cooking",
|
|
"total residential",
|
|
"derived heat residential",
|
|
"thermal uses residential",
|
|
],
|
|
"elec": [
|
|
"electricity residential space",
|
|
"electricity residential water",
|
|
"electricity residential cooking",
|
|
"electricity residential",
|
|
],
|
|
},
|
|
"Services": {
|
|
"total": [
|
|
"total services space",
|
|
"total services water",
|
|
"total services cooking",
|
|
"total services",
|
|
"derived heat services",
|
|
"thermal uses services",
|
|
],
|
|
"elec": [
|
|
"electricity services space",
|
|
"electricity services water",
|
|
"electricity services cooking",
|
|
"electricity services",
|
|
],
|
|
},
|
|
"Agriculture & forestry": {
|
|
"total": [
|
|
"total agriculture heat",
|
|
"total agriculture machinery",
|
|
"total agriculture",
|
|
],
|
|
"elec": [
|
|
"total agriculture electricity",
|
|
],
|
|
},
|
|
"Road": {
|
|
"total": [
|
|
"total road",
|
|
"total passenger cars",
|
|
"total other road passenger",
|
|
"total light duty road freight",
|
|
],
|
|
"elec": [
|
|
"electricity road",
|
|
"electricity passenger cars",
|
|
"electricity other road passenger",
|
|
"electricity light duty road freight",
|
|
],
|
|
},
|
|
"Rail": {
|
|
"total": [
|
|
"total rail",
|
|
"total rail passenger",
|
|
"total rail freight",
|
|
],
|
|
"elec": [
|
|
"electricity rail",
|
|
"electricity rail passenger",
|
|
"electricity rail freight",
|
|
],
|
|
},
|
|
}
|
|
|
|
avia_inter = [
|
|
"total aviation passenger",
|
|
"total aviation freight",
|
|
"total international aviation passenger",
|
|
"total international aviation freight",
|
|
"total international aviation",
|
|
]
|
|
avia_domestic = [
|
|
"total domestic aviation passenger",
|
|
"total domestic aviation freight",
|
|
"total domestic aviation",
|
|
]
|
|
navigation = [
|
|
"total domestic navigation",
|
|
]
|
|
|
|
for country in idees_countries:
|
|
filling_years = [(2015, slice(2016, 2021)), (2000, slice(1990, 1999))]
|
|
|
|
for source_year, target_years in filling_years:
|
|
|
|
slicer_source = idx[country, source_year, :, :]
|
|
slicer_target = idx[country, target_years, :, :]
|
|
|
|
for sector, mapping in mappings.items():
|
|
sector_ratio = ratio.loc[
|
|
(country, slice(None), slice(None), sector)
|
|
].droplevel("lvl2")
|
|
|
|
energy.loc[slicer_target, mapping["total"]] = cartesian(
|
|
sector_ratio.loc[target_years, "total"],
|
|
energy.loc[slicer_source, mapping["total"]].squeeze(axis=0),
|
|
).values
|
|
energy.loc[slicer_target, mapping["elec"]] = cartesian(
|
|
sector_ratio.loc[target_years, "ele"],
|
|
energy.loc[slicer_source, mapping["elec"]].squeeze(axis=0),
|
|
).values
|
|
|
|
level_drops = ["country", "lvl2", "lvl3"]
|
|
|
|
slicer = idx[country, :, :, "Domestic aviation"]
|
|
avi_d = ratio.loc[slicer, "total"].droplevel(level_drops)
|
|
|
|
slicer = idx[country, :, :, "International aviation"]
|
|
avi_i = ratio.loc[slicer, "total"].droplevel(level_drops)
|
|
|
|
slicer = idx[country, :, :, "Domestic Navigation"]
|
|
nav = ratio.loc[slicer, "total"].droplevel(level_drops)
|
|
|
|
energy.loc[slicer_target, avia_inter] = cartesian(
|
|
avi_i.loc[target_years],
|
|
energy.loc[slicer_source, avia_inter].squeeze(axis=0),
|
|
).values
|
|
|
|
energy.loc[slicer_target, avia_domestic] = cartesian(
|
|
avi_d.loc[target_years],
|
|
energy.loc[slicer_source, avia_domestic].squeeze(axis=0),
|
|
).values
|
|
|
|
energy.loc[slicer_target, navigation] = cartesian(
|
|
nav.loc[target_years],
|
|
energy.loc[slicer_source, navigation].squeeze(axis=0),
|
|
).values
|
|
|
|
return energy
|
|
|
|
|
|
if __name__ == "__main__":
|
|
if "snakemake" not in globals():
|
|
from _helpers import mock_snakemake
|
|
|
|
snakemake = mock_snakemake("build_energy_totals")
|
|
|
|
configure_logging(snakemake)
|
|
set_scenario_config(snakemake)
|
|
|
|
params = snakemake.params.energy
|
|
|
|
nuts3 = gpd.read_file(snakemake.input.nuts3_shapes).set_index("index")
|
|
population = nuts3["pop"].groupby(nuts3.country).sum()
|
|
|
|
countries = snakemake.params.countries
|
|
idees_countries = pd.Index(countries).intersection(eu28)
|
|
|
|
input_eurostat = snakemake.input.eurostat
|
|
eurostat = build_eurostat(input_eurostat, countries)
|
|
swiss = build_swiss()
|
|
idees = build_idees(idees_countries)
|
|
|
|
energy = build_energy_totals(countries, eurostat, swiss, idees)
|
|
|
|
# Data from IDEES only exists from 2000-2015.
|
|
logger.info("Extrapolate IDEES data based on eurostat for years 2015-2021.")
|
|
energy = rescale_idees_from_eurostat(idees_countries, energy, eurostat)
|
|
|
|
energy.to_csv(snakemake.output.energy_name)
|
|
|
|
# use rescaled idees data to calculate district heat share
|
|
district_heat_share = build_district_heat_share(
|
|
countries, energy.loc[idees_countries]
|
|
)
|
|
district_heat_share.to_csv(snakemake.output.district_heat_share)
|
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base_year_emissions = params["base_emissions_year"]
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emissions_scope = snakemake.params.energy["emissions"]
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eea_co2 = build_eea_co2(snakemake.input.co2, base_year_emissions, emissions_scope)
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eurostat_co2 = build_eurostat_co2(eurostat, base_year_emissions)
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co2 = build_co2_totals(countries, eea_co2, eurostat_co2)
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co2.to_csv(snakemake.output.co2_name)
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transport = build_transport_data(countries, population, idees)
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transport.to_csv(snakemake.output.transport_name)
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