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build_eea_co2() now reads version23 of UNFCCC inventory.

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**This requires updating to UNFCCC_v23.csv in the data bundle**

This enables that the same function is used to read emissions in 2018, which are assumed to remain constant in 2019 and 2020 and subtracted from carbon budget (estimated from 2018 on).

I checked and 1990 emissions calculated with UNFCCC_v23 are very similar to those calculated with UNFCCC_v21 (<1% differences in all values at EU level).
Some countries show higher deviations, mainly in domestic aviation and navigation. I guess because those values started to be reported later and v23 should include more accurate values.
This commit is contained in:
martavp 2020-12-29 11:31:00 +01:00
parent afdfbf54e8
commit ceba265c0a
1 changed files with 11 additions and 16 deletions
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25
scripts/build_energy_totals.py
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@ -390,12 +390,12 @@ def build_energy_totals():
return clean_df
def build_eea_co2():
def build_eea_co2(year=1990):
# see ../notebooks/compute_1990_Europe_emissions_for_targets.ipynb
#https://www.eea.europa.eu/data-and-maps/data/national-emissions-reported-to-the-unfccc-and-to-the-eu-greenhouse-gas-monitoring-mechanism-14
#downloaded 190222 (modified by EEA last on 181130)
fn = "data/eea/UNFCCC_v21.csv"
#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)
fn = "data/eea/UNFCCC_v23.csv"
df = pd.read_csv(fn, encoding="latin-1")
df.loc[df["Year"] == "1985-1987","Year"] = 1986
df["Year"] = df["Year"].astype(int)
@ -418,16 +418,14 @@ def build_eea_co2():
e['waste management'] = '5 - Waste management'
e['other'] = '6 - Other Sector'
e['indirect'] = 'ind_CO2 - Indirect CO2'
e["total wL"] = "Total (with LULUCF, with indirect CO2)"
e["total woL"] = "Total (without LULUCF, with indirect CO2)"
e["total wL"] = "Total (with LULUCF)"
e["total woL"] = "Total (without LULUCF)"
pol = "CO2" #["All greenhouse gases - (CO2 equivalent)","CO2"]
cts = ["CH","EUA","NO"] + eu28_eea
year = 1990
emissions = df.loc[idx[cts,pol,year,e.values],"emissions"].unstack("Sector_name").rename(columns=pd.Series(e.index,e.values)).rename(index={"All greenhouse gases - (CO2 equivalent)" : "GHG"},level=1)
#only take level 0, since level 1 (pol) and level 2 (year) are trivial
@ -467,7 +465,7 @@ def build_eurostat_co2(year=1990):
return eurostat_co2
def build_co2_totals(year=1990):
def build_co2_totals(eea_co2, eurostat_co2, year=1990):
co2 = eea_co2.reindex(["EU28","NO","CH","BA","RS","AL","ME","MK"] + eu28)
@ -486,10 +484,6 @@ def build_co2_totals(year=1990):
#doesn't include non-energy emissions
co2.loc[ct,'agriculture'] = eurostat_co2[ct,"+","+","Agriculture / Forestry"].sum()
co2.to_csv(snakemake.output.co2_name)
return co2
@ -547,7 +541,7 @@ if __name__ == "__main__":
snakemake.output['transport_name'] = "data/transport_data.csv"
snakemake.input = Dict()
snakemake.input['nuts3_shapes'] = 'resources/nuts3_shapes.geojson'
snakemake.input['nuts3_shapes'] = '../pypsa-eur/resources/nuts3_shapes.geojson'
nuts3 = gpd.read_file(snakemake.input.nuts3_shapes).set_index('index')
population = nuts3['pop'].groupby(nuts3.country).sum()
@ -566,6 +560,7 @@ if __name__ == "__main__":
eurostat_co2 = build_eurostat_co2()
build_co2_totals()
co2=build_co2_totals(eea_co2, eurostat_co2, year)
co2.to_csv(snakemake.output.co2_name)
build_transport_data()