Carbon Emission Calculation

Carbon Emission Calculation#

import pandas as pd

The purpose of this calculation is to determine the appropriate total emission constraints based on the defined scope of the model. The objective is to establish the relationship between this target and the total emissions from the year 1990.

The countries included in the analysis are:

AT: Austria
BE: Belgium
CH: Switzerland
CZ: Czech Republic
DE: Germany
DK: Denmark
FR: France
IT: Italy
LU: Luxembourg
NL: Netherlands
PL: Poland
SE: Sweden

The sector scope for this analysis includes:

Power Sector
Transport Sector (fossil fuel emissions are excluded)
Heating Sector

Since the emissions of individual countries are being compared for the years 1990 and 2005, we first need to extract the emissions data for those years.

1. Extract the total emissions data for the years 1990 and 2005.#

ctrys = ['AT', 'BE', 'CH', 'CZ', 'DE', 'DK', 'FR', 'IT', 'LU', 'NL', 'PL', 'SE']

# 1990 emissions in MtCO2
co2_totals = pd.read_csv("../data/co2_totals_1990.csv", index_col=0).loc[ctrys]

# 2005 emissions in MtCO2
co2_totals_2005 = pd.read_csv("../data/co2_totals_2005.csv", index_col=0).loc[ctrys]

2. Calculate the linearly interpolated targets for 2035.#

Below are the emission reduction targets for each of these countries:

Table 1: Country Specific Emission Reduction Targets

Country	2030	2035	2040	2045	2050	Source
DE	65% below 1990 excl. LULUCF	-	85% below 1990 excl. LULUCF	100%	-	Source
AT	48% below 2005 excl. LULUCF	-	100%	-	-	Source1 Source2
BE	55% below 1990 excl. LULUCF	-	-	-	100%	Source
CH	50% below 1990 excl. LULUCF	-	-	-	100%	Source
CZ	26% below 2005 excl. LULUCF	-	-	-	100%	Source
DK	70% below 1990 incl. LULUCF and 50% below 2005 excl. LULUCF	-	-	100%	110%	Source
FR	47.5% below 2005 excl. LULUCF	-	-	-	100%	Source
IT	43.7% below 2005 excl. LULUCF	-	-	-	100%	Source1 Source2
LU	55% below 2005 excl. LULUCF	-	-	-	100%	Source
NL	55% below 1990	-	-	-	100%	Source
PL	17.7% below 2005 excl. LULUCF	-	-	-		Source
SE	63% below 1990	-	75% below 1990	100%		Source

Since Table 1 do not include the year 2035, we will interpolate the targets linearly to estimate the 2035 values.

Note: Polands 2035 target is deduced based on the EUs Effort Sharing Regulation (ESR) distrubution key in relation to the German interpolated target (source).

# EU Effort Sharing Regulation
esr_targets = {
    "AT": 0.48,
    "BE": 0.47,
    "CZ": 0.26,
    "DE": 0.5,
    "DK": 0.5,
    "FR": 0.475,
    "IT": 0.437,
    "LU": 0.5,
    "NL": 0.48,
    "PL": 0.177,
    "SE": 0.5,
}

# Emission reduction targets for countries with a base year of 1990
target2035_base1990 = dict(DE = 0.75,
                           BE = 0.55 + (1-0.55)*(2035-2030)/(2050-2030),
                           CH = 0.625,
                           DK = 0.7 + (1-0.7)*(2035-2030)/(2045-2030),
                           NL = 0.55 + (1-0.55)*(2035-2030)/(2050-2030),
                           SE = 0.69,
                           PL = (esr_targets["PL"]/esr_targets["DE"])*0.75
                          )

# Emission reduction targets for countries with a base year of 2005
target2035_base2005 = dict(AT = 0.74,
                           CZ = 0.26 + (1-0.26)*(2035-2030)/(2050-2030),
                           FR = 0.475 + (1-0.475)*(2035-2030)/(2050-2030),
                           IT = 0.437 + (1-0.437)*(2035-2030)/(2050-2030),
                           LU = 0.55 + (1-0.55)*(2035-2030)/(2050-2030)
                          )

target_2035 = pd.DataFrame(index=co2_totals.index)
target_2035["base1990"] = target2035_base1990
target_2035["base2005"] = target2035_base2005
target_2035

	base1990	base2005
Country_code
AT	NaN	0.74000
BE	0.6625	NaN
CH	0.6250	NaN
CZ	NaN	0.44500
DE	0.7500	NaN
DK	0.8000	NaN
FR	NaN	0.60625
IT	NaN	0.57775
LU	NaN	0.66250
NL	0.6625	NaN
PL	0.2655	NaN
SE	0.6900	NaN

3. Extract the data and exclude LULUCF from the calculation.#

LULUFC: Land Use, Land Use Change and Forestry

LULUCF is excluded for all the countries listed in Table 1.

# Total emissions incl. LULUCF
co2_totals_incl_lulucf = co2_totals.sum(axis=1)
# Total emissions incl. LULUCF 2005
co2_totals_incl_lulucf_2005 = co2_totals_2005.sum(axis=1)
# Total emissions excl. LULUCF
co2_totals_excl_lulucf = co2_totals.drop(["LULUCF"], axis=1).sum(axis=1)
# Total emissions excl. LULUCF
co2_totals_excl_lulucf_2005 = co2_totals_2005.drop(["LULUCF"], axis=1).sum(axis=1)

pd.DataFrame({"1990":co2_totals_incl_lulucf, "2005":co2_totals_incl_lulucf_2005, "1990 excl. LULUFC":co2_totals_excl_lulucf, "2005 excl. LULUFC":co2_totals_excl_lulucf_2005})

	1990	2005	1990 excl. LULUFC	2005 excl. LULUFC
Country_code
AT	49.968104	68.414161	62.124701	79.192432
BE	117.060288	124.024742	120.309343	125.661688
CH	42.515838	43.712103	44.534441	45.915746
CZ	160.298010	119.316791	166.068823	126.738399
DE	1021.884333	851.168952	1052.348544	866.388549
DK	60.858777	57.805099	54.685986	52.164863
FR	374.572263	377.355895	400.777042	426.412463
IT	432.346810	463.962937	438.008802	500.005676
LU	11.927560	11.462305	11.847635	12.105448
NL	169.786966	183.428661	163.302301	177.897237
PL	343.554029	271.748435	376.546518	323.161267
SE	21.112932	19.936933	57.348825	53.852857

4. Calculate the absolute emissions#

This is done by multiplying the remaining emissions (1 - target emissions) by the CO2 emissions for the respective year.

# Calculate aggregated 1990 emissions in MtCO2
co2_1990_aggregated = co2_totals_excl_lulucf.sum()

# Calculate absolute target emissions in 2035 based on shares
target_2035["absolute_emissions"] = ((1-target_2035.base1990) * co2_totals_excl_lulucf).combine_first((1-target_2035.base2005) * co2_totals_excl_lulucf_2005)
target_2035_aggregated = target_2035.absolute_emissions.sum()

display(target_2035)

# Calculate share of 1990 emissions
round(target_2035_aggregated/co2_1990_aggregated,3)

	base1990	base2005	absolute_emissions
Country_code
AT	NaN	0.74000	20.590032
BE	0.6625	NaN	40.604403
CH	0.6250	NaN	16.700415
CZ	NaN	0.44500	70.339812
DE	0.7500	NaN	263.087136
DK	0.8000	NaN	10.937197
FR	NaN	0.60625	167.899907
IT	NaN	0.57775	211.127397
LU	NaN	0.66250	4.085589
NL	0.6625	NaN	55.114527
PL	0.2655	NaN	276.573417
SE	0.6900	NaN	17.778136

0.392

Note: 39.2% (0.392) compared to 1990 emissions is the result when all sectors are included.

Since not all sector is included in the model, we need to divide the remaining emissions further based on the share of remaining emissions in each sector.

5. Extract the emission target by sector and interpolate to the year 2035#

Data is from Europe’s 2040 climate target (Source).

target_sector = pd.read_csv("../data/co2_total_sector.csv", index_col=0)

# calculate linearly interpolated 2035 targets
target_sector['2035 S2'] = (target_sector['2030'] + target_sector['2040 S2'])/2
target_sector_share = target_sector/target_sector.loc['Total Gross GHG Emissions',:]
target_sector

	2005	2015	2030	2040 S1	2040 S2	2040 S3	2040 LIFE	2050 S1	2050 S2	2050 S3	2050 LIFE	2035 S2
MtC02-eq
Total Gross GHG Emissions	4641	3914	2301	1273	943	748	740	416	413	411	360.00	1622.0
Power and district heating	1300	1012	339	123	42	23	34	21	22	19	15.00	190.5
Other Energy sectors*	277	237	133	71	59	53	57	39	39	38	36.00	96.0
Industry (Energy)	469	360	232	126	94	75	86	6	6	9	11.00	163.0
Domestic Transport	822	772	583	190	143	120	134	10	8	7	9.00	363.0
Residential and Services**	648	514	221	119	92	75	92	20	19	19	29.00	156.5
Industry (Non-Energy)	343	233	157	139	88	14	13	7	7	7	7.00	122.5
Other Non-Energv sectors***	101	130	56	33	26	25	25	23	22	22	22.00	41.0
Intra-EU aviation	35	38	43	31	29	28	14	14	12	11	10.00	36.0
Intra-EU navigation	31	27	25	7	6	4	0	0	0	0	0.00	15.5
50% extra-EU maritime MRV	50	42	44	14	11	9	0	0	0	0	0.00	27.5
Agriculture****	390	385	361	351	302	271	209	249	249	249	194.00	331.5
Waste	155	118	87	68	55	55	55	32	32	32	0.32	71.0
C02 calibration	15	43	24	3	-1	-1	-1	0	0	0	0.00	11.5
Non-C02 calibration	5	2	-3	-3	-3	-3	-3	-3	-3	-3	-3.00	-3.0

6. Select the included sector in the model#

In this list, we only model:

Power and district heating
Other Energy sectors*
Residential and Services**

Note:

Other Energy sectors includes emissions from the energy branch as well as other non-CO2 emissions from the energy sector.
The Residential and Services sector may include fossil fuel combustion from the agriculture, fishery, and forestry sectors.

include_sector = ['Power and district heating','Residential and Services**','Other Energy sectors*']

target_sector_share_2035 = target_sector_share.loc[include_sector,'2035 S2'].sum()
target_sector_share_2035

0.27311960542540076

Note: 27.3% (0.273) of the emissions come from the included sector, relative to the total emissions from all sectors.

7. Calculate the absolute emission of the included sector emission#

target_2035['included_sector_emissions'] = target_2035['absolute_emissions'] * target_sector_share_2035
display(target_2035)

# Calculate share of 1990 emissions
round(target_2035['included_sector_emissions'].sum()/co2_1990_aggregated,3)

	base1990	base2005	absolute_emissions	included_sector_emissions
Country_code
AT	NaN	0.74000	20.590032	5.623541
BE	0.6625	NaN	40.604403	11.089859
CH	0.6250	NaN	16.700415	4.561211
CZ	NaN	0.44500	70.339812	19.211182
DE	0.7500	NaN	263.087136	71.854255
DK	0.8000	NaN	10.937197	2.987163
FR	NaN	0.60625	167.899907	45.856756
IT	NaN	0.57775	211.127397	57.663031
LU	NaN	0.66250	4.085589	1.115854
NL	0.6625	NaN	55.114527	15.052858
PL	0.2655	NaN	276.573417	75.537623
SE	0.6900	NaN	17.778136	4.855557

0.107

10.7% (0.107) compared to 1990 emissions is the answer for the co2_budget for the year 2035