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adapt transport data to multiyear setup with new swiss data

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Fabian Neumann 2024-03-13 13:42:03 +01:00
parent 2eb159af7c
commit 5b5d308bf7
3 changed files with 62 additions and 6 deletions
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45
data/gr-e-11.03.02.01.01-cc.csv Normal file
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@ -0,0 +1,45 @@
year,passenger cars,passenger vehicles,goods vehicles,agricultural vehicles,industrial vehicles,motorcycles,mopeds (incl. fast e-bikes)¹
1980,2246752,11087,169402,137685,0,137340,671473
1981,2394455,11122,167846,151238,0,152508,687517
1982,2473318,11341,178313,156631,0,178398,656102
1983,2520610,11255,189920,165332,0,187090,674710
1984,2552132,10853,192708,164078,0,199302,647391
1985,2617164,10771,200537,175161,0,217974,644175
1986,2678911,10800,207014,183689,0,225676,627523
1987,2732720,11027,217750,189984,0,240102,613093
1988,2819548,26869,236649,152693,43519,219987,581270
1989,2895842,29270,241488,157867,44326,261715,551808
1990,2985397,31180,252136,162932,45920,299264,464609
1991,3057798,32968,257646,165571,46938,319779,418251
1992,3091228,34136,256611,169277,47281,336448,381236
1993,3109523,34852,253461,171414,47229,348159,358732
1994,3165042,35676,256285,172300,47373,357252,336367
1995,3229176,36517,262352,174026,47693,370700,317783
1996,3268093,37662,263020,174247,47622,381986,301009
1997,3323455,38508,264200,175689,47743,410750,280467
1998,3383307,39012,267380,176712,47754,435042,265422
1999,3467311,39692,273954,177148,48265,464357,246018
2000,3545247,40260,278518,177963,48949,493781,218932
2001,3629713,41342,285246,179321,49549,521390,199033
2002,3700951,42401,290142,180063,50227,545132,186811
2003,3753890,43629,292329,180295,50795,567358,173486
2004,3811351,44784,298193,180898,50957,583010,165000
2005,3863807,45785,307264,182093,51860,592194,156095
2006,3899917,46445,314020,185450,53437,608648,150563
2007,3955787,48026,324153,184062,55149,619166,144704
2008,3989811,48536,326232,188218,55808,636540,141549
2009,4009602,50675,327808,185902,56533,642777,139220
2010,4075825,52751,335200,186485,58492,651202,139548
2011,4163003,55422,348553,187130,60324,665870,142834
2012,4254725,58278,361926,188358,62219,679822,145984
2013,4320885,60151,371361,189305,63950,687990,147247
2014,4384490,62436,382281,190095,65563,699219,152962
2015,4458069,65720,393598,191132,67101,710022,161292
2016,4524029,69676,405566,192139,68721,720381,176030
2017,4570823,73814,416501,192858,70113,729149,188053
2018,4602688,77985,428808,193283,71683,739344,201423
2019,4623952,83054,440795,193834,74085,744542,211480
2020,4658335,88293,452186,195082,75659,771586,229421
2021,4709366,97805,466857,196530,77672,791323,244572
2022,4721280,105158,475714,196942,79691,789794,257753
2023,4760948,114299,485303,197678,81241,805653,
1 year passenger cars passenger vehicles goods vehicles agricultural vehicles industrial vehicles motorcycles mopeds (incl. fast e-bikes)¹
2 1980 2246752 11087 169402 137685 0 137340 671473
3 1981 2394455 11122 167846 151238 0 152508 687517
4 1982 2473318 11341 178313 156631 0 178398 656102
5 1983 2520610 11255 189920 165332 0 187090 674710
6 1984 2552132 10853 192708 164078 0 199302 647391
7 1985 2617164 10771 200537 175161 0 217974 644175
8 1986 2678911 10800 207014 183689 0 225676 627523
9 1987 2732720 11027 217750 189984 0 240102 613093
10 1988 2819548 26869 236649 152693 43519 219987 581270
11 1989 2895842 29270 241488 157867 44326 261715 551808
12 1990 2985397 31180 252136 162932 45920 299264 464609
13 1991 3057798 32968 257646 165571 46938 319779 418251
14 1992 3091228 34136 256611 169277 47281 336448 381236
15 1993 3109523 34852 253461 171414 47229 348159 358732
16 1994 3165042 35676 256285 172300 47373 357252 336367
17 1995 3229176 36517 262352 174026 47693 370700 317783
18 1996 3268093 37662 263020 174247 47622 381986 301009
19 1997 3323455 38508 264200 175689 47743 410750 280467
20 1998 3383307 39012 267380 176712 47754 435042 265422
21 1999 3467311 39692 273954 177148 48265 464357 246018
22 2000 3545247 40260 278518 177963 48949 493781 218932
23 2001 3629713 41342 285246 179321 49549 521390 199033
24 2002 3700951 42401 290142 180063 50227 545132 186811
25 2003 3753890 43629 292329 180295 50795 567358 173486
26 2004 3811351 44784 298193 180898 50957 583010 165000
27 2005 3863807 45785 307264 182093 51860 592194 156095
28 2006 3899917 46445 314020 185450 53437 608648 150563
29 2007 3955787 48026 324153 184062 55149 619166 144704
30 2008 3989811 48536 326232 188218 55808 636540 141549
31 2009 4009602 50675 327808 185902 56533 642777 139220
32 2010 4075825 52751 335200 186485 58492 651202 139548
33 2011 4163003 55422 348553 187130 60324 665870 142834
34 2012 4254725 58278 361926 188358 62219 679822 145984
35 2013 4320885 60151 371361 189305 63950 687990 147247
36 2014 4384490 62436 382281 190095 65563 699219 152962
37 2015 4458069 65720 393598 191132 67101 710022 161292
38 2016 4524029 69676 405566 192139 68721 720381 176030
39 2017 4570823 73814 416501 192858 70113 729149 188053
40 2018 4602688 77985 428808 193283 71683 739344 201423
41 2019 4623952 83054 440795 193834 74085 744542 211480
42 2020 4658335 88293 452186 195082 75659 771586 229421
43 2021 4709366 97805 466857 196530 77672 791323 244572
44 2022 4721280 105158 475714 196942 79691 789794 257753
45 2023 4760948 114299 485303 197678 81241 805653

1
rules/build_sector.smk
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@ -289,6 +289,7 @@ rule build_energy_totals:
nuts3_shapes=resources("nuts3_shapes.geojson"),
co2="data/bundle-sector/eea/UNFCCC_v23.csv",
swiss="data/switzerland-new_format-all_years.csv",
swiss_transport="data/gr-e-11.03.02.01.01-cc.csv",
idees="data/bundle-sector/jrc-idees-2015",
district_heat_share="data/district_heat_share.csv",
eurostat="data/eurostat/eurostat-energy_balances-april_2023_edition",

22
scripts/build_energy_totals.py
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@ -712,15 +712,25 @@ def build_co2_totals(countries, eea_co2, eurostat_co2):
def build_transport_data(countries, population, idees):
transport_data = pd.DataFrame(index=countries)
# first collect number of cars
# collect number of cars
transport_data = pd.DataFrame(idees["passenger 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
# https://www.bfs.admin.ch/bfs/en/home/statistics/mobility-transport/transport-infrastructure-vehicles/vehicles/road-vehicles-stock-level-motorisation.html
if "CH" in countries:
transport_data.at["CH", "number cars"] = 4.136e6
fn = snakemake.input.swiss_transport
swiss_cars = pd.read_csv(fn, index_col=0).loc[1990:2021, ["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: