770 lines
26 KiB
Python
770 lines
26 KiB
Python
# -*- coding: utf-8 -*-
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# SPDX-FileCopyrightText: : 2020-2023 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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logger = logging.getLogger(__name__)
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import multiprocessing as mp
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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 mute_print
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from tqdm import tqdm
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cc = coco.CountryConverter()
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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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eurostat_codes = {
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"EU28": "EU",
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"EA19": "EA",
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"Belgium": "BE",
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"Bulgaria": "BG",
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"Czech Republic": "CZ",
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"Denmark": "DK",
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"Germany": "DE",
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"Estonia": "EE",
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"Ireland": "IE",
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"Greece": "GR",
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"Spain": "ES",
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"France": "FR",
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"Croatia": "HR",
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"Italy": "IT",
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"Cyprus": "CY",
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"Latvia": "LV",
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"Lithuania": "LT",
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"Luxembourg": "LU",
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"Hungary": "HU",
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"Malta": "MA",
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"Netherlands": "NL",
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"Austria": "AT",
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"Poland": "PL",
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"Portugal": "PT",
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"Romania": "RO",
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"Slovenia": "SI",
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"Slovakia": "SK",
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"Finland": "FI",
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"Sweden": "SE",
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"United Kingdom": "GB",
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"Iceland": "IS",
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"Norway": "NO",
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"Montenegro": "ME",
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"FYR of Macedonia": "MK",
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"Albania": "AL",
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"Serbia": "RS",
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"Turkey": "TU",
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"Bosnia and Herzegovina": "BA",
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"Kosovo\n(UNSCR 1244/99)": "KO", # 2017 version
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# 2016 version
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"Kosovo\n(under United Nations Security Council Resolution 1244/99)": "KO",
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"Moldova": "MO",
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"Ukraine": "UK",
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"Switzerland": "CH",
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}
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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 build_eurostat(input_eurostat, countries, report_year, year):
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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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filenames = {
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2016: f"/{year}-Energy-Balances-June2016edition.xlsx",
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2017: f"/{year}-ENERGY-BALANCES-June2017edition.xlsx",
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}
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with mute_print():
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dfs = pd.read_excel(
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input_eurostat + filenames[report_year],
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sheet_name=None,
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skiprows=1,
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index_col=list(range(4)),
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)
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# sorted_index necessary for slicing
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lookup = eurostat_codes
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labelled_dfs = {
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lookup[df.columns[0]]: df
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for df in dfs.values()
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if lookup[df.columns[0]] in countries
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}
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df = pd.concat(labelled_dfs, sort=True).sort_index()
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# drop non-numeric and country columns
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non_numeric_cols = df.columns[df.dtypes != float]
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country_cols = df.columns.intersection(lookup.keys())
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to_drop = non_numeric_cols.union(country_cols)
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df.drop(to_drop, axis=1, inplace=True)
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# convert ktoe/a to TWh/a
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df *= 11.63 / 1e3
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return df
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def build_swiss(year):
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"""
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Return a pd.Series 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]).loc["CH", str(year)]
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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, year, base_dir):
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ct_totals = {}
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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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# residential
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df = pd.read_excel(fn_residential, "RES_hh_fec", index_col=0)[year]
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ct_totals["total residential space"] = df["Space heating"]
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rows = ["Advanced electric heating", "Conventional electric heating"]
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ct_totals["electricity residential space"] = df[rows].sum()
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ct_totals["total residential water"] = df.at["Water heating"]
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assert df.index[23] == "Electricity"
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ct_totals["electricity residential water"] = df[23]
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ct_totals["total residential cooking"] = df["Cooking"]
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assert df.index[30] == "Electricity"
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ct_totals["electricity residential cooking"] = df[30]
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df = pd.read_excel(fn_residential, "RES_summary", index_col=0)[year]
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row = "Energy consumption by fuel - Eurostat structure (ktoe)"
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ct_totals["total residential"] = df[row]
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assert df.index[47] == "Electricity"
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ct_totals["electricity residential"] = df[47]
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assert df.index[46] == "Derived heat"
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ct_totals["derived heat residential"] = df[46]
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assert df.index[50] == "Thermal uses"
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ct_totals["thermal uses residential"] = df[50]
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# services
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df = pd.read_excel(fn_tertiary, "SER_hh_fec", index_col=0)[year]
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ct_totals["total services space"] = df["Space heating"]
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rows = ["Advanced electric heating", "Conventional electric heating"]
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ct_totals["electricity services space"] = df[rows].sum()
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ct_totals["total services water"] = df["Hot water"]
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assert df.index[24] == "Electricity"
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ct_totals["electricity services water"] = df[24]
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ct_totals["total services cooking"] = df["Catering"]
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assert df.index[31] == "Electricity"
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ct_totals["electricity services cooking"] = df[31]
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df = pd.read_excel(fn_tertiary, "SER_summary", index_col=0)[year]
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row = "Energy consumption by fuel - Eurostat structure (ktoe)"
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ct_totals["total services"] = df[row]
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assert df.index[50] == "Electricity"
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ct_totals["electricity services"] = df[50]
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assert df.index[49] == "Derived heat"
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ct_totals["derived heat services"] = df[49]
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assert df.index[53] == "Thermal uses"
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ct_totals["thermal uses services"] = df[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, year]
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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[rows].sum()
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rows = ["Specific heat uses", "Low enthalpy heat"]
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ct_totals["total agriculture heat"] = df[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[rows].sum()
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row = "Agriculture, forestry and fishing"
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ct_totals["total agriculture"] = df[row]
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# transport
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df = pd.read_excel(fn_transport, "TrRoad_ene", index_col=0)[year]
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ct_totals["total road"] = df["by fuel (EUROSTAT DATA)"]
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ct_totals["electricity road"] = df["Electricity"]
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ct_totals["total two-wheel"] = df["Powered 2-wheelers (Gasoline)"]
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assert df.index[19] == "Passenger cars"
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ct_totals["total passenger cars"] = df[19]
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assert df.index[30] == "Battery electric vehicles"
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ct_totals["electricity passenger cars"] = df[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[31]
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assert df.index[39] == "Battery electric vehicles"
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ct_totals["electricity other road passenger"] = df[39]
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assert df.index[41] == "Light duty vehicles"
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ct_totals["total light duty road freight"] = df[41]
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assert df.index[49] == "Battery electric vehicles"
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ct_totals["electricity light duty road freight"] = df[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[row]
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assert df.index[61] == "Passenger cars"
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ct_totals["passenger car efficiency"] = df[61]
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df = pd.read_excel(fn_transport, "TrRail_ene", index_col=0)[year]
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ct_totals["total rail"] = df["by fuel (EUROSTAT DATA)"]
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ct_totals["electricity rail"] = df["Electricity"]
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assert df.index[15] == "Passenger transport"
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ct_totals["total rail passenger"] = df[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[[16, 19, 20]].sum()
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assert df.index[21] == "Freight transport"
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ct_totals["total rail freight"] = df[21]
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assert df.index[23] == "Electric"
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ct_totals["electricity rail freight"] = df[23]
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df = pd.read_excel(fn_transport, "TrAvia_ene", index_col=0)[year]
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assert df.index[6] == "Passenger transport"
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ct_totals["total aviation passenger"] = df[6]
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assert df.index[10] == "Freight transport"
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ct_totals["total aviation freight"] = df[10]
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assert df.index[7] == "Domestic"
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ct_totals["total domestic aviation passenger"] = df[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[[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[11]
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assert df.index[12] == "International - Extra-EU"
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ct_totals["total international aviation freight"] = df[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)[year]
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# coastal and inland
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ct_totals["total domestic navigation"] = df["by fuel (EUROSTAT DATA)"]
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df = pd.read_excel(fn_transport, "TrRoad_act", index_col=0)[year]
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assert df.index[85] == "Passenger cars"
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ct_totals["passenger cars"] = df[85]
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return pd.Series(ct_totals, name=ct)
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def build_idees(countries, year):
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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, year=year, 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(totals_list, axis=1)
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# convert ktoe to TWh
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exclude = totals.index.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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# district heating share
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district_heat = totals.loc[
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["derived heat residential", "derived heat services"]
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].sum()
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total_heat = totals.loc[["thermal uses residential", "thermal uses services"]].sum()
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totals.loc["district heat share"] = district_heat.div(total_heat)
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return totals.T
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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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to_drop = ["passenger cars", "passenger car efficiency"]
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df = idees.reindex(countries).drop(to_drop, axis=1)
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eurostat_countries = eurostat.index.levels[0]
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in_eurostat = df.index.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).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.loc["CH"] = swiss
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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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missing = df.index[df["total residential"].isna()]
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to_fill = missing.intersection(eurostat_countries)
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uses = ["space", "cooking", "water"]
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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[to_fill, :, :, eurostat_sector]
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fill_values = (
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eurostat.loc[slicer, eurostat_fuels[fuel]].groupby(level=0).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
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# => 26% is non-electric
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if "NO" in df:
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elec_fraction = 0.73
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no_norway = df.drop("NO")
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for sector in ["residential", "services"]:
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# assume non-electric is heating
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nonelectric = (
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df.loc["NO", f"total {sector}"] - df.loc["NO", f"electricity {sector}"]
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)
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total_heating = nonelectric / (1 - elec_fraction)
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for use in uses:
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|
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", 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()
|
|
|
|
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 pd.Index(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
|
|
if "CH" in countries:
|
|
transport_data.at["CH", "number cars"] = 4.136e6
|
|
|
|
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
|
|
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()]
|
|
if not missing.empty:
|
|
logger.info(
|
|
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 _helpers import mock_snakemake
|
|
|
|
snakemake = mock_snakemake("build_energy_totals")
|
|
|
|
logging.basicConfig(level=snakemake.config["logging"]["level"])
|
|
|
|
config = 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)
|
|
|
|
data_year = config["energy_totals_year"]
|
|
report_year = snakemake.params["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.params["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)
|