228 lines
9.4 KiB
Python
228 lines
9.4 KiB
Python
"""Build industrial production per country."""
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import pandas as pd
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import numpy as np
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import multiprocessing as mp
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from tqdm import tqdm
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from helper import mute
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tj_to_ktoe = 0.0238845
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ktoe_to_twh = 0.01163
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sub_sheet_name_dict = {'Iron and steel': 'ISI',
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'Chemicals Industry': 'CHI',
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'Non-metallic mineral products': 'NMM',
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'Pulp, paper and printing': 'PPA',
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'Food, beverages and tobacco': 'FBT',
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'Non Ferrous Metals': 'NFM',
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'Transport Equipment': 'TRE',
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'Machinery Equipment': 'MAE',
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'Textiles and leather': 'TEL',
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'Wood and wood products': 'WWP',
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'Other Industrial Sectors': 'OIS'}
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non_EU = ['NO', 'CH', 'ME', 'MK', 'RS', 'BA', 'AL']
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jrc_names = {"GR": "EL", "GB": "UK"}
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eu28 = ['FR', 'DE', 'GB', 'IT', 'ES', 'PL', 'SE', 'NL', 'BE', 'FI',
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'DK', 'PT', 'RO', 'AT', 'BG', 'EE', 'GR', 'LV', 'CZ',
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'HU', 'IE', 'SK', 'LT', 'HR', 'LU', 'SI', 'CY', 'MT']
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sect2sub = {'Iron and steel': ['Electric arc', 'Integrated steelworks'],
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'Chemicals Industry': ['Basic chemicals', 'Other chemicals', 'Pharmaceutical products etc.'],
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'Non-metallic mineral products': ['Cement', 'Ceramics & other NMM', 'Glass production'],
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'Pulp, paper and printing': ['Pulp production', 'Paper production', 'Printing and media reproduction'],
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'Food, beverages and tobacco': ['Food, beverages and tobacco'],
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'Non Ferrous Metals': ['Alumina production', 'Aluminium - primary production', 'Aluminium - secondary production', 'Other non-ferrous metals'],
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'Transport Equipment': ['Transport Equipment'],
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'Machinery Equipment': ['Machinery Equipment'],
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'Textiles and leather': ['Textiles and leather'],
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'Wood and wood products': ['Wood and wood products'],
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'Other Industrial Sectors': ['Other Industrial Sectors']}
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sub2sect = {v: k for k, vv in sect2sub.items() for v in vv}
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fields = {'Electric arc': 'Electric arc',
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'Integrated steelworks': 'Integrated steelworks',
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'Basic chemicals': 'Basic chemicals (kt ethylene eq.)',
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'Other chemicals': 'Other chemicals (kt ethylene eq.)',
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'Pharmaceutical products etc.': 'Pharmaceutical products etc. (kt ethylene eq.)',
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'Cement': 'Cement (kt)',
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'Ceramics & other NMM': 'Ceramics & other NMM (kt bricks eq.)',
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'Glass production': 'Glass production (kt)',
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'Pulp production': 'Pulp production (kt)',
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'Paper production': 'Paper production (kt)',
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'Printing and media reproduction': 'Printing and media reproduction (kt paper eq.)',
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'Food, beverages and tobacco': 'Physical output (index)',
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'Alumina production': 'Alumina production (kt)',
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'Aluminium - primary production': 'Aluminium - primary production',
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'Aluminium - secondary production': 'Aluminium - secondary production',
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'Other non-ferrous metals': 'Other non-ferrous metals (kt lead eq.)',
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'Transport Equipment': 'Physical output (index)',
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'Machinery Equipment': 'Physical output (index)',
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'Textiles and leather': 'Physical output (index)',
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'Wood and wood products': 'Physical output (index)',
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'Other Industrial Sectors': 'Physical output (index)'}
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eb_names = {'NO': 'Norway', 'AL': 'Albania', 'BA': 'Bosnia and Herzegovina',
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'MK': 'FYR of Macedonia', 'GE': 'Georgia', 'IS': 'Iceland',
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'KO': 'Kosovo', 'MD': 'Moldova', 'ME': 'Montenegro', 'RS': 'Serbia',
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'UA': 'Ukraine', 'TR': 'Turkey', }
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eb_sectors = {'Iron & steel industry': 'Iron and steel',
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'Chemical and Petrochemical industry': 'Chemicals Industry',
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'Non-ferrous metal industry': 'Non-metallic mineral products',
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'Paper, Pulp and Print': 'Pulp, paper and printing',
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'Food and Tabacco': 'Food, beverages and tobacco',
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'Non-metallic Minerals (Glass, pottery & building mat. Industry)': 'Non Ferrous Metals',
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'Transport Equipment': 'Transport Equipment',
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'Machinery': 'Machinery Equipment',
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'Textile and Leather': 'Textiles and leather',
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'Wood and Wood Products': 'Wood and wood products',
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'Non-specified (Industry)': 'Other Industrial Sectors'}
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# TODO: this should go in a csv in `data`
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# Annual energy consumption in Switzerland by sector in 2015 (in TJ)
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# From: Energieverbrauch in der Industrie und im Dienstleistungssektor, Der Bundesrat
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# http://www.bfe.admin.ch/themen/00526/00541/00543/index.html?lang=de&dossier_id=00775
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e_switzerland = pd.Series({'Iron and steel': 7889.,
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'Chemicals Industry': 26871.,
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'Non-metallic mineral products': 15513.+3820.,
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'Pulp, paper and printing': 12004.,
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'Food, beverages and tobacco': 17728.,
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'Non Ferrous Metals': 3037.,
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'Transport Equipment': 14993.,
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'Machinery Equipment': 4724.,
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'Textiles and leather': 1742.,
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'Wood and wood products': 0.,
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'Other Industrial Sectors': 10825.,
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'current electricity': 53760.})
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def find_physical_output(df):
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start = np.where(df.index.str.contains('Physical output', na=''))[0][0]
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empty_row = np.where(df.index.isnull())[0]
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end = empty_row[np.argmax(empty_row > start)]
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return slice(start, end)
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def get_energy_ratio(country):
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if country == 'CH':
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e_country = e_switzerland * tj_to_ktoe
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else:
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# estimate physical output, energy consumption in the sector and country
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fn = f"{eurostat_dir}/{eb_names[country]}.XLSX"
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df = pd.read_excel(fn, sheet_name='2016', index_col=2,
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header=0, skiprows=1).squeeze('columns')
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e_country = df.loc[eb_sectors.keys(
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), 'Total all products'].rename(eb_sectors)
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fn = f'{jrc_dir}/JRC-IDEES-2015_Industry_EU28.xlsx'
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df = pd.read_excel(fn, sheet_name='Ind_Summary',
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index_col=0, header=0).squeeze('columns')
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assert df.index[48] == "by sector"
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year_i = df.columns.get_loc(year)
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e_eu28 = df.iloc[49:76, year_i]
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e_eu28.index = e_eu28.index.str.lstrip()
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e_ratio = e_country / e_eu28
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return pd.Series({k: e_ratio[v] for k, v in sub2sect.items()})
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def industry_production_per_country(country):
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def get_sector_data(sector, country):
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jrc_country = jrc_names.get(country, country)
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fn = f'{jrc_dir}/JRC-IDEES-2015_Industry_{jrc_country}.xlsx'
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sheet = sub_sheet_name_dict[sector]
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df = pd.read_excel(fn, sheet_name=sheet,
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index_col=0, header=0).squeeze('columns')
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year_i = df.columns.get_loc(year)
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df = df.iloc[find_physical_output(df), year_i]
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df = df.loc[map(fields.get, sect2sub[sector])]
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df.index = sect2sub[sector]
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return df
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ct = "EU28" if country in non_EU else country
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demand = pd.concat([get_sector_data(s, ct) for s in sect2sub.keys()])
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if country in non_EU:
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demand *= get_energy_ratio(country)
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demand.name = country
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return demand
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def industry_production(countries):
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nprocesses = snakemake.threads
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func = industry_production_per_country
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tqdm_kwargs = dict(ascii=False, unit=' country', total=len(countries),
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desc="Build industry production")
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with mp.Pool(processes=nprocesses, initializer=mute) as pool:
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demand_l = list(tqdm(pool.imap(func, countries), **tqdm_kwargs))
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demand = pd.concat(demand_l, axis=1).T
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demand.index.name = "kton/a"
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return demand
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def separate_basic_chemicals(demand):
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"""Separate basic chemicals into ammonia, chlorine, methanol and HVC."""
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ammonia = pd.read_csv(snakemake.input.ammonia_production, index_col=0)
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there = ammonia.index.intersection(demand.index)
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missing = demand.index.symmetric_difference(there)
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print("Following countries have no ammonia demand:", missing)
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demand["Ammonia"] = 0.
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demand.loc[there, "Ammonia"] = ammonia.loc[there, str(year)]
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demand["Basic chemicals"] -= demand["Ammonia"]
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# EE, HR and LT got negative demand through subtraction - poor data
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demand['Basic chemicals'].clip(lower=0., inplace=True)
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# assume HVC, methanol, chlorine production proportional to non-ammonia basic chemicals
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distribution_key = demand["Basic chemicals"] / demand["Basic chemicals"].sum()
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demand["HVC"] = config["HVC_production_today"] * 1e3 * distribution_key
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demand["Chlorine"] = config["chlorine_production_today"] * 1e3 * distribution_key
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demand["Methanol"] = config["methanol_production_today"] * 1e3 * distribution_key
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demand.drop(columns=["Basic chemicals"], inplace=True)
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if __name__ == '__main__':
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if 'snakemake' not in globals():
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from helper import mock_snakemake
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snakemake = mock_snakemake('build_industrial_production_per_country')
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countries = non_EU + eu28
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year = snakemake.config['industry']['reference_year']
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config = snakemake.config["industry"]
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jrc_dir = snakemake.input.jrc
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eurostat_dir = snakemake.input.eurostat
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demand = industry_production(countries)
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separate_basic_chemicals(demand)
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fn = snakemake.output.industrial_production_per_country
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demand.to_csv(fn, float_format='%.2f')
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