# -*- coding: utf-8 -*-
# SPDX-FileCopyrightText: : 2020-2024 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: MIT
"""
Build total energy demands and carbon emissions per country using JRC IDEES,
eurostat, and EEA data.
- Country-specific data is read in :func:`build_eurostat`, :func:`build_idees` and `build_swiss`.
- :func:`build_energy_totals` then combines energy data from Eurostat, Swiss, and IDEES data and :func:`rescale_idees_from_eurostat` rescales IDEES data to match Eurostat data.
- :func:`build_district_heat_share` calculates the share of district heating for each country from IDEES data.
- Historical CO2 emissions are calculated in :func:`build_eea_co2` and :func:`build_eurostat_co2` and combined in :func:`build_co2_totals`.
Relevant Settings
-----------------
.. code:: yaml
countries:
energy:
Inputs
------
- `resources/<run_name>/nuts3_shapes.gejson`: NUTS3 shapes.
- `data/bundle/eea_UNFCCC_v23.csv`: CO2 emissions data from EEA.
- `data/switzerland-new_format-all_years.csv`: Swiss energy data.
- `data/gr-e-11.03.02.01.01-cc.csv`: Swiss transport data
- `data/bundle/jrc-idees`: JRC IDEES data.
- `data/district_heat_share.csv`: District heating shares.
- `data/eurostat/Balances-April2023`: Eurostat energy balances.
- `data/eurostat/eurostat-household_energy_balances-february_2024.csv`: Eurostat household energy balances.
Outputs
-------
- `resources/<run_name>/energy_totals.csv`: Energy totals per country, sector and year.
- `resources/<run_name>/co2_totals.csv`: CO2 emissions per country, sector and year.
- `resources/<run_name>/transport_data.csv`: Transport data per country and year.
- `resources/<run_name>/district_heat_share.csv`: District heating share per by country and year.
"""
import logging
import multiprocessing as mp
from functools import partial
from typing import List
import country_converter as coco
import geopandas as gpd
import numpy as np
import pandas as pd
from _helpers import configure_logging, mute_print, set_scenario_config
from tqdm import tqdm
cc = coco.CountryConverter()
logger = logging.getLogger(__name__)
idx = pd.IndexSlice
def cartesian(s1: pd.Series, s2: pd.Series) -> pd.DataFrame:
"""
Compute the Cartesian product of two pandas Series.
Parameters
----------
s1: pd.Series
The first pandas Series
s2: pd.Series:
The second pandas Series.
Returns
----------
pd.DataFrame
A DataFrame representing the Cartesian product of s1 and s2.
Examples
--------
>>> s1 = pd.Series([1, 2, 3], index=["a", "b", "c"])
>>> s2 = pd.Series([4, 5, 6], index=["d", "e", "f"])
>>> cartesian(s1, s2)
d e f
a 4 5 6
b 8 10 12
c 12 15 18
"""
return pd.DataFrame(np.outer(s1, s2), index=s1.index, columns=s2.index)
def reverse(dictionary: dict) -> dict:
"""
Reverses the keys and values of a dictionary.
Parameters
----------
dictionary : dict
The dictionary to be reversed.
Returns
-------
dict
A new dictionary with the keys and values reversed.
Examples
--------
>>> d = {"a": 1, "b": 2, "c": 3}
>>> reverse(d)
{1: 'a', 2: 'b', 3: 'c'}
"""
return {v: k for k, v in dictionary.items()}
idees_rename = {"GR": "EL", "GB": "UK"}
eu28 = cc.EU28as("ISO2").ISO2.tolist()
eu28_eea = eu28.copy()
eu28_eea.remove("GB")
eu28_eea.append("UK")
to_ipcc = {
"electricity": "1.A.1.a - Public Electricity and Heat Production",
"residential non-elec": "1.A.4.b - Residential",
"services non-elec": "1.A.4.a - Commercial/Institutional",
"rail non-elec": "1.A.3.c - Railways",
"road non-elec": "1.A.3.b - Road Transportation",
"domestic navigation": "1.A.3.d - Domestic Navigation",
"international navigation": "1.D.1.b - International Navigation",
"domestic aviation": "1.A.3.a - Domestic Aviation",
"international aviation": "1.D.1.a - International Aviation",
"total energy": "1 - Energy",
"industrial processes": "2 - Industrial Processes and Product Use",
"agriculture": "3 - Agriculture",
"agriculture, forestry and fishing": "1.A.4.c - Agriculture/Forestry/Fishing",
"LULUCF": "4 - Land Use, Land-Use Change and Forestry",
"waste management": "5 - Waste management",
"other": "6 - Other Sector",
"indirect": "ind_CO2 - Indirect CO2",
"total wL": "Total (with LULUCF)",
"total woL": "Total (without LULUCF)",
}
def eurostat_per_country(input_eurostat: str, country: str) -> pd.DataFrame:
"""
Read energy balance data for a specific country from Eurostat.
Parameters
----------
input_eurostat : str
Path to the directory containing Eurostat data files.
country : str
Country code for the specific country.
Returns
-------
pd.DataFrame
Concatenated energy balance data for the specified country.
Notes
-----
- The function reads `<input_eurostat>/<country>.-Energy-balance-sheets-April-2023-edition.xlsb`
- It removes the "Cover" sheet from the data and concatenates all the remaining sheets into a single DataFrame.
"""
filename = (
f"{input_eurostat}/{country}-Energy-balance-sheets-April-2023-edition.xlsb"
)
sheet = pd.read_excel(
filename,
engine="pyxlsb",
sheet_name=None,
skiprows=4,
index_col=list(range(4)),
)
sheet.pop("Cover")
return pd.concat(sheet)
def build_eurostat(
input_eurostat: str,
countries: List[str],
nprocesses: int = 1,
disable_progressbar: bool = False,
) -> pd.DataFrame:
"""
Return multi-index for all countries' energy data in TWh/a.
Parameters:
-----------
input_eurostat : str
Path to the Eurostat database.
countries : List[str]
List of countries for which energy data is to be retrieved.
nprocesses : int, optional
Number of processes to use for parallel execution, by default 1.
disable_progressbar : bool, optional
Whether to disable the progress bar, by default False.
Returns:
--------
pd.DataFrame
Multi-index DataFrame containing energy data for all countries in TWh/a.
Notes:
------
- The function first renames the countries in the input list using the `idees_rename` mapping and removes "CH".
- It then reads country-wise data using :func:`eurostat_per_country` into a single DataFrame.
- The data is reordered, converted to TWh/a, and missing values are filled.
"""
countries = {idees_rename.get(country, country) for country in countries} - {"CH"}
func = partial(eurostat_per_country, input_eurostat)
tqdm_kwargs = dict(
ascii=False,
unit=" country",
total=len(countries),
desc="Build from eurostat database",
disable=disable_progressbar,
)
with mute_print():
with mp.Pool(processes=nprocesses) as pool:
dfs = list(tqdm(pool.imap(func, countries), **tqdm_kwargs))
index_names = ["country", "year", "lvl1", "lvl2", "lvl3", "lvl4"]
df = pd.concat(dfs, keys=countries, names=index_names)
df.index = df.index.set_levels(df.index.levels[1].astype(int), level=1)
# drop columns with all NaNs
unnamed_cols = df.columns[df.columns.astype(str).str.startswith("Unnamed")]
df.drop(unnamed_cols, axis=1, inplace=True)
df.drop(list(range(1990, 2022)), axis=1, inplace=True, errors="ignore")
# make numeric values where possible
df.replace("Z", 0, inplace=True)
df = df.apply(pd.to_numeric, errors="coerce")
df = df.select_dtypes(include=[np.number])
# write 'International aviation' to the lower level of the multiindex
int_avia = df.index.get_level_values(3) == "International aviation"
temp = df.loc[int_avia]
temp.index = pd.MultiIndex.from_frame(
temp.index.to_frame().fillna("International aviation")
)
df = pd.concat([temp, df.loc[~int_avia]])
# Fill in missing data on "Domestic aviation" for each country.
for country in countries:
slicer = idx[country, :, :, :, "Domestic aviation"]
# For the Total and Fossil energy columns, fill in zeros with
# the closest non-zero value in the year index.
for col in ["Total", "Fossil energy"]:
df.loc[slicer, col] = (
df.loc[slicer, col].replace(0.0, np.nan).ffill().bfill()
)
# Renaming some indices
index_rename = {
"Households": "Residential",
"Commercial & public services": "Services",
"Domestic navigation": "Domestic Navigation",
"International maritime bunkers": "Bunkers",
"UK": "GB",
"EL": "GR",
}
columns_rename = {"Total": "Total all products"}
df.rename(index=index_rename, columns=columns_rename, inplace=True)
df.sort_index(inplace=True)
# convert to TWh/a from ktoe/a
df *= 11.63 / 1e3
return df
def build_swiss() -> pd.DataFrame:
"""
Return a pd.DataFrame of Swiss energy data in TWh/a.
Returns
--------
pd.DataFrame
Swiss energy data in TWh/a.
Notes
-----
- Reads Swiss energy data from `data/switzerland-new_format-all_years.csv`.
- Reshapes and renames data.
- Converts energy units from PJ/a to TWh/a.
"""
fn = snakemake.input.swiss
df = pd.read_csv(fn, index_col=[0, 1])
df.columns = df.columns.astype(int)
df.columns.name = "year"
df = df.stack().unstack("item")
df.columns.name = None
# convert PJ/a to TWh/a
df /= 3.6
return df
def idees_per_country(ct: str, base_dir: str) -> pd.DataFrame:
"""
Calculate energy totals per country using JRC-IDEES data.
Parameters
----------
ct : str
The country code.
base_dir : str
The base directory where the JRC-IDEES data files are located.
Returns
-------
pd.DataFrame
A DataFrame containing the energy totals per country. Columns are energy uses.
Notes
-----
- Retrieves JRC-IDEES data for the specified country from `base_dir` for residential, tertiary, and transport sectors.
- Calculates energy totals for each sector, stores them in a dictionary and returns them as data frame.
- Assertions ensure indices of JRC-IDEES data are as expected.
"""
ct_idees = idees_rename.get(ct, ct)
fn_residential = f"{base_dir}/JRC-IDEES-2015_Residential_{ct_idees}.xlsx"
fn_tertiary = f"{base_dir}/JRC-IDEES-2015_Tertiary_{ct_idees}.xlsx"
fn_transport = f"{base_dir}/JRC-IDEES-2015_Transport_{ct_idees}.xlsx"
ct_totals = {}
# residential
df = pd.read_excel(fn_residential, "RES_hh_fec", index_col=0)
rows = ["Advanced electric heating", "Conventional electric heating"]
ct_totals["electricity residential space"] = df.loc[rows].sum()
ct_totals["total residential space"] = df.loc["Space heating"]
ct_totals["total residential water"] = df.loc["Water heating"]
assert df.index[23] == "Electricity"
ct_totals["electricity residential water"] = df.iloc[23]
ct_totals["total residential cooking"] = df.loc["Cooking"]
assert df.index[30] == "Electricity"
ct_totals["electricity residential cooking"] = df.iloc[30]
df = pd.read_excel(fn_residential, "RES_summary", index_col=0)
row = "Energy consumption by fuel - Eurostat structure (ktoe)"
ct_totals["total residential"] = df.loc[row]
assert df.index[47] == "Electricity"
ct_totals["electricity residential"] = df.iloc[47]
assert df.index[46] == "Derived heat"
ct_totals["derived heat residential"] = df.iloc[46]
assert df.index[50] == "Thermal uses"
ct_totals["thermal uses residential"] = df.iloc[50]
# services
df = pd.read_excel(fn_tertiary, "SER_hh_fec", index_col=0)
ct_totals["total services space"] = df.loc["Space heating"]
rows = ["Advanced electric heating", "Conventional electric heating"]
ct_totals["electricity services space"] = df.loc[rows].sum()
ct_totals["total services water"] = df.loc["Hot water"]
assert df.index[24] == "Electricity"
ct_totals["electricity services water"] = df.iloc[24]
ct_totals["total services cooking"] = df.loc["Catering"]
assert df.index[31] == "Electricity"
ct_totals["electricity services cooking"] = df.iloc[31]
df = pd.read_excel(fn_tertiary, "SER_summary", index_col=0)
row = "Energy consumption by fuel - Eurostat structure (ktoe)"
ct_totals["total services"] = df.loc[row]
assert df.index[50] == "Electricity"
ct_totals["electricity services"] = df.iloc[50]
assert df.index[49] == "Derived heat"
ct_totals["derived heat services"] = df.iloc[49]
assert df.index[53] == "Thermal uses"
ct_totals["thermal uses services"] = df.iloc[53]
# agriculture, forestry and fishing
start = "Detailed split of energy consumption (ktoe)"
end = "Market shares of energy uses (%)"
df = pd.read_excel(fn_tertiary, "AGR_fec", index_col=0).loc[start:end]
rows = [
"Lighting",
"Ventilation",
"Specific electricity uses",
"Pumping devices (electric)",
]
ct_totals["total agriculture electricity"] = df.loc[rows].sum()
rows = ["Specific heat uses", "Low enthalpy heat"]
ct_totals["total agriculture heat"] = df.loc[rows].sum()
rows = [
"Motor drives",
"Farming machine drives (diesel oil incl. biofuels)",
"Pumping devices (diesel oil incl. biofuels)",
]
ct_totals["total agriculture machinery"] = df.loc[rows].sum()
row = "Agriculture, forestry and fishing"
ct_totals["total agriculture"] = df.loc[row]
# transport
df = pd.read_excel(fn_transport, "TrRoad_ene", index_col=0)
ct_totals["total road"] = df.loc["by fuel (EUROSTAT DATA)"]
ct_totals["electricity road"] = df.loc["Electricity"]
ct_totals["total two-wheel"] = df.loc["Powered 2-wheelers (Gasoline)"]
assert df.index[19] == "Passenger cars"
ct_totals["total passenger cars"] = df.iloc[19]
assert df.index[30] == "Battery electric vehicles"
ct_totals["electricity passenger cars"] = df.iloc[30]
assert df.index[31] == "Motor coaches, buses and trolley buses"
ct_totals["total other road passenger"] = df.iloc[31]
assert df.index[39] == "Battery electric vehicles"
ct_totals["electricity other road passenger"] = df.iloc[39]
assert df.index[41] == "Light duty vehicles"
ct_totals["total light duty road freight"] = df.iloc[41]
assert df.index[49] == "Battery electric vehicles"
ct_totals["electricity light duty road freight"] = df.iloc[49]
row = "Heavy duty vehicles (Diesel oil incl. biofuels)"
ct_totals["total heavy duty road freight"] = df.loc[row]
assert df.index[61] == "Passenger cars"
ct_totals["passenger car efficiency"] = df.iloc[61]
df = pd.read_excel(fn_transport, "TrRail_ene", index_col=0)
ct_totals["total rail"] = df.loc["by fuel (EUROSTAT DATA)"]
ct_totals["electricity rail"] = df.loc["Electricity"]
assert df.index[15] == "Passenger transport"
ct_totals["total rail passenger"] = df.iloc[15]
assert df.index[16] == "Metro and tram, urban light rail"
assert df.index[19] == "Electric"
assert df.index[20] == "High speed passenger trains"
ct_totals["electricity rail passenger"] = df.iloc[[16, 19, 20]].sum()
assert df.index[21] == "Freight transport"
ct_totals["total rail freight"] = df.iloc[21]
assert df.index[23] == "Electric"
ct_totals["electricity rail freight"] = df.iloc[23]
df = pd.read_excel(fn_transport, "TrAvia_ene", index_col=0)
assert df.index[6] == "Passenger transport"
ct_totals["total aviation passenger"] = df.iloc[6]
assert df.index[10] == "Freight transport"
ct_totals["total aviation freight"] = df.iloc[10]
assert df.index[7] == "Domestic"
ct_totals["total domestic aviation passenger"] = df.iloc[7]
assert df.index[8] == "International - Intra-EU"
assert df.index[9] == "International - Extra-EU"
ct_totals["total international aviation passenger"] = df.iloc[[8, 9]].sum()
assert df.index[11] == "Domestic and International - Intra-EU"
ct_totals["total domestic aviation freight"] = df.iloc[11]
assert df.index[12] == "International - Extra-EU"
ct_totals["total international aviation freight"] = df.iloc[12]
ct_totals["total domestic aviation"] = (
ct_totals["total domestic aviation freight"]
+ ct_totals["total domestic aviation passenger"]
)
ct_totals["total international aviation"] = (
ct_totals["total international aviation freight"]
+ ct_totals["total international aviation passenger"]
)
df = pd.read_excel(fn_transport, "TrNavi_ene", index_col=0)
# coastal and inland
ct_totals["total domestic navigation"] = df.loc["by fuel (EUROSTAT DATA)"]
df = pd.read_excel(fn_transport, "TrRoad_act", index_col=0)
assert df.index[85] == "Passenger cars"
ct_totals["passenger cars"] = df.iloc[85]
return pd.DataFrame(ct_totals)
def build_idees(countries: List[str]) -> pd.DataFrame:
"""
Build energy totals from IDEES database for the given list of countries
using :func:`idees_per_country`.
Parameters
----------
countries : List[str]
List of country names for which energy totals need to be built.
Returns
-------
pd.DataFrame
Energy totals for the given countries.
Notes
-----
- Retrieves energy totals per country and year using :func:`idees_per_country`.
- Returns a DataFrame with columns: country, year, and energy totals for different categories.
"""
nprocesses = snakemake.threads
disable_progress = snakemake.config["run"].get("disable_progressbar", False)
func = partial(idees_per_country, base_dir=snakemake.input.idees)
tqdm_kwargs = dict(
ascii=False,
unit=" country",
total=len(countries),
desc="Build from IDEES database",
disable=disable_progress,
)
with mute_print():
with mp.Pool(processes=nprocesses) as pool:
totals_list = list(tqdm(pool.imap(func, countries), **tqdm_kwargs))
totals = pd.concat(
totals_list,
keys=countries,
names=["country", "year"],
)
# efficiency kgoe/100km -> ktoe/100km so that after conversion TWh/100km
totals.loc[:, "passenger car efficiency"] /= 1e6
# convert ktoe to TWh
exclude = totals.columns.str.fullmatch("passenger cars")
totals.loc[:, ~exclude] *= 11.63 / 1e3
return totals
def build_energy_totals(
countries: List[str],
eurostat: pd.DataFrame,
swiss: pd.DataFrame,
idees: pd.DataFrame,
) -> pd.DataFrame:
"""
Combine energy totals for the specified countries from Eurostat, Swiss, and
IDEES data.
Parameters
----------
countries : List[str]
List of country codes for which energy totals are to be calculated.
eurostat : pd.DataFrame
Eurostat energy balances dataframe.
swiss : pd.DataFrame
Swiss energy data dataframe.
idees : pd.DataFrame
IDEES energy data dataframe.
Returns
-------
pd.DataFrame
Energy totals dataframe for the given countries.
Notes
-----
- Missing values are filled based on Eurostat energy balances and average values in EU28.
- The function also performs specific calculations for Norway and splits road, rail, and aviation traffic for non-IDEES data.
References
----------
- `Norway heating data <http://www.ssb.no/en/energi-og-industri/statistikker/husenergi/hvert-3-aar/2014-07-14>`_
"""
eurostat_fuels = {"electricity": "Electricity", "total": "Total all products"}
eurostat_countries = eurostat.index.levels[0]
eurostat_years = eurostat.index.levels[1]
to_drop = ["passenger cars", "passenger car efficiency"]
new_index = pd.MultiIndex.from_product(
[countries, eurostat_years], names=["country", "year"]
)
df = idees.reindex(new_index).drop(to_drop, axis=1)
in_eurostat = df.index.levels[0].intersection(eurostat_countries)
# add international navigation
slicer = idx[in_eurostat, :, :, "Bunkers", :]
fill_values = eurostat.loc[slicer, "Total all products"].groupby(level=[0, 1]).sum()
df.loc[in_eurostat, "total international navigation"] = fill_values
# add swiss energy data
df = pd.concat([df.drop("CH", errors="ignore"), swiss]).sort_index()
# get values for missing countries based on Eurostat EnergyBalances
# divide cooking/space/water according to averages in EU28
uses = ["space", "cooking", "water"]
to_fill = df.index[
df["total residential"].isna()
& df.index.get_level_values("country").isin(eurostat_countries)
]
c = to_fill.get_level_values("country")
y = to_fill.get_level_values("year")
for sector in ["residential", "services", "road", "rail"]:
eurostat_sector = sector.capitalize()
# fuel use
for fuel in ["electricity", "total"]:
slicer = idx[c, y, :, :, eurostat_sector]
fill_values = (
eurostat.loc[slicer, eurostat_fuels[fuel]].groupby(level=[0, 1]).sum()
)
df.loc[to_fill, f"{fuel} {sector}"] = fill_values
for sector in ["residential", "services"]:
# electric use
for use in uses:
fuel_use = df[f"electricity {sector} {use}"]
fuel = df[f"electricity {sector}"]
avg = fuel_use.div(fuel).mean()
logger.debug(
f"{sector}: average fraction of electricity for {use} is {avg:.3f}"
)
df.loc[to_fill, f"electricity {sector} {use}"] = (
avg * df.loc[to_fill, f"electricity {sector}"]
)
# non-electric use
for use in uses:
nonelectric_use = (
df[f"total {sector} {use}"] - df[f"electricity {sector} {use}"]
)
nonelectric = df[f"total {sector}"] - df[f"electricity {sector}"]
avg = nonelectric_use.div(nonelectric).mean()
logger.debug(
f"{sector}: average fraction of non-electric for {use} is {avg:.3f}"
)
electric_use = df.loc[to_fill, f"electricity {sector} {use}"]
nonelectric = (
df.loc[to_fill, f"total {sector}"]
- df.loc[to_fill, f"electricity {sector}"]
)
df.loc[to_fill, f"total {sector} {use}"] = electric_use + avg * nonelectric
# Fix Norway space and water heating fractions
# http://www.ssb.no/en/energi-og-industri/statistikker/husenergi/hvert-3-aar/2014-07-14
# The main heating source for about 73 per cent of the households is based on electricity
# => 26% is non-electric
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: List[str], idees: pd.DataFrame) -> pd.Series:
"""
Calculate the share of district heating for each country.
Parameters
----------
countries : List[str]
List of country codes for which to calculate district heating share.
idees : pd.DataFrame
IDEES energy data dataframe.
Returns
-------
pd.Series
Series with the district heating share for each country.
Notes
-----
- The function calculates the district heating share as the sum of residential and services derived heat, divided by the sum of residential and services thermal uses.
- The district heating share is then reindexed to match the provided list of countries.
- Missing district heating shares are filled from `data/district_heat_share.csv`.
- The function makes a conservative assumption and takes the minimum district heating share from both the IDEES data and `data/district_heat_share.csv`.
"""
# 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: str, year: int = 1990, emissions_scope: str = "CO2"
) -> pd.DataFrame:
"""
Calculate CO2 emissions for a given year based on EEA data in Mt.
Parameters
----------
input_co2 : str
Path to the input CSV file with CO2 data.
year : int, optional
Year for which to calculate emissions, by default 1990.
emissions_scope : str, optional
Scope of the emissions to consider, by default "CO2".
Returns
-------
pd.DataFrame
DataFrame with CO2 emissions for the given year.
Notes
-----
- The function reads the `input_co2` data and for a specific `year` and `emission scope`
- It calculates "industrial non-elec" and "agriculture" emissions from that data
- It drops unneeded columns and converts the emissions to Mt.
References
---------
- `EEA CO2 data <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 Gt to Mt
return emissions / 1e3
def build_eurostat_co2(eurostat: pd.DataFrame, year: int = 1990) -> pd.Series:
"""
Calculate CO2 emissions for a given year based on Eurostat fuel consumption
data and fuel-specific emissions.
Parameters
----------
eurostat : pd.DataFrame
DataFrame with Eurostat data.
year : int, optional
Year for which to calculate emissions, by default 1990.
Returns
-------
pd.Series
Series with CO2 emissions for the given year.
Notes
-----
- The function hard-sets fuel-specific emissions:
- solid fuels: 0.36 tCO2_equi/MW_th (approximates coal)
- oil: 0.285 tCO2_equi/MW_th (average of distillate and residue)
- natural gas: 0.2 tCO2_equi/MW_th
- It then multiplies the Eurostat fuel consumption data for `year` by the specific emissions and sums the result.
References
----------
- Oil values from `EIA <https://www.eia.gov/tools/faqs/faq.cfm?id=74&t=11>`_
- Distillate oil (No. 2) 0.276
- Residual oil (No. 6) 0.298
- `EIA Electricity Annual <https://www.eia.gov/electricity/annual/html/epa_a_03.html>`_
"""
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
return eurostat_year.multiply(specific_emissions).sum(axis=1)
def build_co2_totals(
countries: List[str], eea_co2: pd.DataFrame, eurostat_co2: pd.DataFrame
) -> pd.DataFrame:
"""
Combine CO2 emissions data from EEA and Eurostat for a list of countries.
Parameters
----------
countries : List[str]
List of country codes for which CO2 totals need to be built.
eea_co2 : pd.DataFrame
DataFrame with EEA CO2 emissions data.
eurostat_co2 : pd.DataFrame
DataFrame with Eurostat CO2 emissions data.
Returns
-------
pd.DataFrame
Combined CO2 emissions data for the given countries.
Notes
-----
- The function combines the CO2 emissions from EEA and Eurostat into a single DataFrame for the given countries.
"""
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: List[str], population: pd.DataFrame, idees: pd.DataFrame
) -> pd.DataFrame:
"""
Build transport data for a set of countries based on IDEES data.
Parameters
----------
countries : List[str]
List of country codes.
population : pd.DataFrame
DataFrame with population data.
idees : pd.DataFrame
DataFrame with IDEES data.
Returns
-------
pd.DataFrame
DataFrame with transport data.
Notes
-----
- The function first collects the number of passenger cars.
- For Switzerland, it reads the data from `data/gr-e-11.03.02.01.01-cc.csv`.
- It fills missing data on the number of cars and fuel efficiency with average data.
References
----------
- Swiss transport data: `BFS <https://www.bfs.admin.ch/bfs/en/home/statistics/mobility-transport/transport-infrastructure-vehicles/vehicles/road-vehicles-stock-level-motorisation.html>`_
"""
# 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)
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 MWh/100km, taking passengar car efficiency in TWh/100km
transport_data["average fuel efficiency"] = idees["passenger car efficiency"] * 1e6
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: List[str], energy: pd.DataFrame, eurostat: pd.DataFrame
) -> pd.DataFrame:
"""
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']
Parameters
----------
idees_countries : List[str]
List of IDEES country codes.
energy : pd.DataFrame
DataFrame with JRC IDEES data.
eurostat : pd.DataFrame
DataFrame with Eurostat data.
Returns
-------
pd.DataFrame
DataFrame with rescaled IDEES data.
Notes
-----
- The function first reads in the Eurostat data for 2015 and calculates the ratio of that data with other Eurostat data.
- This ratio is mapped to the IDEES data.
References
----------
- JRC IDEES data: `JRC IDEES <https://ec.europa.eu/jrc/en/publication/eur-scientific-and-technical-research-reports/jrc-idees>`_
- Eurostat data: `Eurostat <https://ec.europa.eu/eurostat/data/database>`_
"""
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",
"total heavy 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",
]
# international navigation is already read in from the eurostat data directly
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
# set the total of agriculture/road to the sum of all agriculture/road categories (corresponding to the IDEES data)
rows = idx[country, :]
cols = [
"total agriculture electricity",
"total agriculture heat",
"total agriculture machinery",
]
energy.loc[rows, "total agriculture"] = energy.loc[rows, cols].sum(axis=1)
cols = [
"total passenger cars",
"total other road passenger",
"total light duty road freight",
"total heavy duty road freight",
]
energy.loc[rows, "total road"] = energy.loc[rows, cols].sum(axis=1)
return energy
def update_residential_from_eurostat(energy: pd.DataFrame) -> pd.DataFrame:
"""
Updates energy balances for residential from disaggregated data from
Eurostat by mutating input data DataFrame.
Parameters
----------
energy : pd.DataFrame
DataFrame with energy data.
Returns
-------
pd.DataFrame
DataFrame with updated energy balances.
Notes
-----
- The function first reads in the Eurostat data for households and maps the energy types to the corresponding Eurostat codes.
- For each energy type, it selects the corresponding data, converts units, and drops unnecessary data.
"""
eurostat_households = pd.read_csv(snakemake.input.eurostat_households)
# Column mapping for energy type
nrg_type = {
"total residential": ("FC_OTH_HH_E", "TOTAL"),
"total residential space": ("FC_OTH_HH_E_SH", "TOTAL"),
"total residential water": ("FC_OTH_HH_E_WH", "TOTAL"),
"total residential cooking": ("FC_OTH_HH_E_CK", "TOTAL"),
"electricity residential": ("FC_OTH_HH_E", "E7000"),
"electricity residential space": ("FC_OTH_HH_E_SH", "E7000"),
"electricity residential water": ("FC_OTH_HH_E_WH", "E7000"),
"electricity residential cooking": ("FC_OTH_HH_E_CK", "E7000"),
}
for nrg_name, (code, siec) in nrg_type.items():
# Select energy balance type, rename columns and countries to match IDEES data,
# convert TJ to TWh, and drop XK data already since included in RS data
col_to_rename = {"geo": "country", "TIME_PERIOD": "year", "OBS_VALUE": nrg_name}
idx_to_rename = {v: k for k, v in idees_rename.items()}
drop_geo = ["EU27_2020", "EA20", "XK"]
nrg_data = eurostat_households.query(
"nrg_bal == @code and siec == @siec and geo not in @drop_geo and OBS_VALUE > 0"
).copy()
nrg_data.rename(columns=col_to_rename, inplace=True)
nrg_data = nrg_data.set_index(["country", "year"])[nrg_name] / 3.6e3
nrg_data.rename(index=idx_to_rename, inplace=True)
# update energy balance from household-specific eurostat data
idx = nrg_data.index.intersection(energy.index)
energy.loc[idx, nrg_name] = nrg_data[idx]
logger.info(
"Updated energy balances for residential using disaggregate final energy consumption data in Households from Eurostat"
)
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,
nprocesses=snakemake.threads,
disable_progressbar=snakemake.config["run"].get("disable_progressbar", False),
)
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)
update_residential_from_eurostat(energy)
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)
base_year_emissions = params["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(eurostat, 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)