Ranking emissions by country

Oct 29, 2022   #OurWorldInData 

In issues of policy and negotiations between countries on actions to be taken to mitigate climate change, the country-level figures that get discussed are either total emissions or per capita emissions. While total emissions is an important figure, per capita emissions gets all the attention. I’m starting to think that might be the wrong number to focus on.

The reason it feels like the wrong number are many fold –

  1. We’re interested in minimizing the world’s emissions not per human but in total.

  2. If policies are being made by drawing a line down the ranked per-capita-emissions table, a country can manipulate its position in the table by altering its population. A country with a high population growth rate is more problematic than one with a low to negative growth rate.

  3. Thinking of the emissions problem as though the earth were burning, we want to put out the fires where it is most intense and then go down the list. Even this is simplistic, but works better as a mental model than saying “let’s convince every human to cut down emissions” when the notion of “every human” is in constant flux.

So I computed the per-square-km annual emissions by country for the year 2020 and ranked countries by “emissions per unit area” which is in the units of “tonnes of CO2 per annum per sq. km”. I got the source data from https://ourworldindata.org and much thanks to them for making information like this very accessible. I combined two tables - one giving the annual total CO2 emissions by country and another giving the land area of countries.

Advantages of considering emissions per unit area

  1. It can be better visualized as different intensities of “burn” happening on the planet that need to be put out.

  2. It cannot be manipulated by a country by growing or shrinking its population without affecting total emissions.

  3. Reducing per area emissions amounts to reducing total emissions for each country, since a country’s area remains mostly constant.

  4. If combined with, perhaps, GDP per unit area, it might give a better picture of the investment neecssary to help put out the fire, and where that investment better come from.

  5. It can reflect an average level of experienced pollution in a given country. This may be useful to determine which country’s policies are working and which countries may be targets for migration by those who value low emission rates.

Drawbacks of considering emissions per unit area

  1. Small countries like Singapore are perhaps better compared against cities than other much larger (by land area) countries.

  2. Very large high GDP countries with relatively small population will turn up low on the list.

So this ranking is skewed at both ends from a policy making perspective. In between though, it does look viable as a number to focus on for policy making instead of per capita emissions.

Cumulative emissions till 1990

A couple of my colleagues at Krea suggested we look at cumulative “production” emissions prior to globalization. 1990 is rough date for that and, again, ourworldindata has the necessary numbers. This makes sense (to me) from a policy perspective because that’s the key argument against industrialized countries appearing to push greenification agenda on those countries that industrialized much later.

CSV files –

  1. Cumulative emissions per unit area till 2020
  2. Cumulative emissions per unit area till 1990
  3. Cumulative emissions per unit area till 1970

Annual emissions (for year 2020)

Some surprises (for me)

  1. Many European countries have recently benefited from a narrative that they’re somehow “cleaner and greener” than the rest. They feature fairly high up on this list though. Germany, Netherlands, Belgium are all above India in this ranking. (My interest here is in understanding the climate decisions India is making.)

  2. Many middle-eastern countries essentially top this ranking.

  3. Malaysia, India, Vietnam and Switzerland are pretty close to each other in emissions per unit area.

  4. The world average ranks at 87 on this list. Not too far off from the median.

  5. Australia and New Zealand rank well below the world average, with Australia right alongside Bhutan. However the country wide average for Australia is perhaps not representative, considering its sparsely inhabited central lands. That’s perhaps applicable to Russia as well.

  6. Finland looks good too from this perspective relative to its European peers, but that wasn’t a surprise.

The data

It might be more useful to remove small countries like Singapore from this ranking as the impact of emission control there is unlikely to impact the world at large. However, in the interest of completeness, I’ve decided to keep all the data as is.

All numbers are truncated to two decimal places. Some numbers use scientific notation like “1.24e6” which means “1240000”.

Entity Code Annual CO2 emissions Land area (sq. km) EmissionsPerUnitArea Rank
Singapore SGP 45503904 709.0 64180.40 1
Bahrain BHR 34960075 780.0 44820.60 2
Macao MAC 1262755 32.90 38381.60 3
Hong Kong HKG 31238788 1050.0 29751.22 4
Sint Maarten (Dutch part) SXM 623462 34.0 18337.11 5
Bermuda BMU 624448 54.0 11563.85 6
Qatar QAT 106654967 11490.0 9282.41 7
Curacao CUW 3335066 444.0 7511.40 8
Trinidad and Tobago TTO 35509414 5130.0 6921.91 9
South Korea KOR 597605055 97520.0 6128.02 10
Maldives MDV 1796469 300.0 5988.23 11
Kuwait KWT 88935077 17820.0 4990.74 12
Malta MLT 1594903 320.0 4984.07 13
Aruba ABW 753218 180.0 4184.54 14
Netherlands NLD 138100074 33670.0 4101.57 15
Luxembourg LUX 8174648 2430.0 3364.05 16
Nauru NRU 56731 20.0 2836.55 17
Japan JPN 1030775384 364500.0 2827.91 18
Belgium BEL 83748962 30280.0 2765.81 19
Israel ISR 56350861 21640.0 2604.01 20
Lebanon LBN 25969298 10230.0 2538.54 21
Barbados BRB 1086743 430.0 2527.30 22
United Arab Emirates ARE 150268152 71020.0 2115.85 23
Mauritius MUS 3979358 2030.0 1960.27 24
Brunei BRN 10158494 5270.0 1927.60 25
Germany DEU 644310352 349380.0 1844.15 26
United Kingdom GBR 329578911 241930.0 1362.29 27
Czechia CZE 87974706 77200.0 1139.56 28
China CHN 10667887453 9,424,703 1131.90 29
Seychelles SYC 491067 460.0 1067.53 30
Italy ITA 303815294 297730.0 1020.43 31
Andorra AND 466294 470.0 992.11 32
Poland POL 299592767 306170.0 978.51 33
Antigua and Barbuda ATG 430410 440.0 978.20 34
British Virgin Islands VGB 139250 150.0 928.33 35
Liechtenstein LIE 141012 160.0 881.32 36
Grenada GRD 294834 340.0 867.15 37
Marshall Islands MHL 151282 180.0 840.45 38
Malaysia MYS 272607434 328550.0 829.72 39
India IND 2441792313 2,973,190 821.27 40
Vietnam VNM 254303169 310070.0 820.14 41
Switzerland CHE 32298333 39516.03 817.34 42
Saint Kitts and Nevis KNA 212040 260.0 815.53 43
Austria AUT 60634876 82520.0 734.79 44
Saint Lucia LCA 439905 610.0 721.15 45
Bangladesh BGD 92841625 130170.0 713.23 46
Cyprus CYP 6496162 9240.0 703.04 47
Jamaica JAM 7429454 10830.0 686.00 48
Denmark DNK 26194912 40000.0 654.87 49
Slovakia SVK 30730385 48080.0 639.15 50
Slovenia SVN 12562986 20136.40 623.89 51
Dominican Republic DOM 27769310 48310.0 574.81 52
Saint Vincent and the Grenadines VCT 208876 390.0 535.57 53
Hungary HUN 48275451 91260.0 528.98 54
United States USA 4712770573 9,147,420 515.20 55
Turkey TUR 392794051 769630.0 510.36 56
France FRA 276633893 547557.0 505.21 57
Thailand THA 257765782 510890.0 504.54 58
Serbia SRB 43135397 87460.0 493.20 59
Faeroe Islands FRO 683898 1396.0 489.89 60
Iraq IRQ 210829146 434128.0 485.63 61
Ireland IRL 33348699 68890.0 484.08 62
Palestine PSE 2898754 6020.0 481.52 63
Palau PLW 219358 460.0 476.86 64
New Caledonia NCL 8692686 18280.0 475.52 65
Iran IRN 745035109 1.62e6 457.42 66
Azerbaijan AZE 37720462 82654.0 456.36 67
Philippines PHL 136017950 298170.0 456.17 68
Portugal PRT 40387817 91605.60 440.88 69
Bosnia and Herzegovina BIH 21417961 51200.0 418.31 70
Spain ESP 208914968 499603.46 418.16 71
Greece GRC 52235141 128900.0 405.23 72
South Africa ZAF 451957087 1.21e6 372.56 73
Ukraine UKR 213908873 579400.0 369.19 74
Equatorial Guinea GNQ 10265267 28050.0 365.96 75
Bulgaria BGR 37444110 108560.0 344.91 76
Sri Lanka LKA 21106254 61864.0 341.17 77
Indonesia IDN 589500368 1.87e6 313.97 78
Romania ROU 71475046 230080.0 310.65 79
Pakistan PAK 234754740 770880.0 304.52 80
Croatia HRV 16982010 56590.0 300.08 81
El Salvador SLV 6123716 20720.0 295.54 82
Saudi Arabia SAU 625507882 2.14e6 290.97 83
Jordan JOR 25487430 88780.0 287.08 84
North Macedonia MKD 7146509 25220.0 283.36 85
Belarus BLR 57445417 202980.0 283.01 86
World OWID_WRL 34807259099 1.29e8 267.85 87
Uzbekistan UZB 112784345 440555.0 256.00 88
Tuvalu TUV 7564 30.0 252.13 89
North Korea PRK 29311104 120410.0 243.42 90
Estonia EST 10452414 43470.0 240.45 91
French Polynesia PYF 828267 3520.0 235.30 92
Bahamas BHS 2337684 10010.0 233.53 93
Lithuania LTU 13799480 62630.0 220.33 94
Egypt EGY 213456746 995450.0 214.43 95
Turks and Caicos Islands TCA 202546 950.0 213.20 96
Micronesia (country) FSM 147500 700.0 210.71 97
Armenia ARM 5890292 28470.0 206.89 98
Oman OMN 62162570 309500.0 200.84 99
Tonga TON 143718 720.0 199.60 100
Cuba CUB 20152280 103800.0 194.14 101
Dominica DMA 139250 750.0 185.66 102
Mexico MEX 356968119 1.94e6 183.63 103
Tunisia TUN 28126702 155360.0 181.04 104
Guatemala GTM 18937908 107160.0 176.72 105
Montenegro MNE 2309894 13450.0 171.73 106
Syria SYR 30531928 183630.0 166.26 107
Albania ALB 4534673 27400.0 165.49 108
Turkmenistan TKM 75337709 469930.0 160.31 109
Moldova MDA 5146876 32885.30 156.50 110
Costa Rica CRI 7907389 51060.0 154.86 111
Laos LAO 33846818 230800.0 146.64 112
Panama PAN 10779522 74177.0 145.32 113
Morocco MAR 64536253 446300.0 144.60 114
Georgia GEO 9968094 69490.0 143.44 115
Comoros COM 258456 1861.0 138.88 116
Nigeria NGA 125462961 910770.0 137.75 117
Cape Verde CPV 549930 4030.0 136.45 118
Finland FIN 39287631 303920.0 129.26 119
New Zealand NZL 33475158 263310.0 127.13 120
Ecuador ECU 30931763 248360.0 124.54 121
Nepal NPL 16957668 143350.0 118.29 122
Sao Tome and Principe STP 112744 960.0 117.44 123
Norway NOR 41283000 365107.84 113.07 124
Chile CHL 81171490 743532.0 109.17 125
Latvia LVA 6772778 62090.0 109.08 126
Kazakhstan KAZ 291335929 2.69e6 107.91 127
Haiti HTI 2919510 27560.0 105.93 128
Russia RUS 1577136041 1.63e7 96.30 129
Venezuela VEN 84609478 882050.0 95.92 130
Sweden SWE 38634794 407310.0 94.85 131
Samoa WSM 245833 2830.0 86.86 132
Cambodia KHM 15325618 176520.0 86.82 133
Honduras HND 9659570 111890.0 86.33 134
Kiribati KIR 68077 810.0 84.04 135
Colombia COL 89104941 1.10e6 80.31 136
Fiji FJI 1393413 18270.0 76.26 137
Lesotho LSO 2183421 30360.0 71.91 138
Ghana GHA 16001330 227540.0 70.32 139
Tajikistan TJK 9447656 138790.0 68.07 140
Algeria DZA 154995460 2.38e6 65.07 141
Kyrgyzstan KGZ 11507817 191800.0 59.99 142
Canada CAN 535822990 8.96e6 59.76 143
Benin BEN 6702765 112760.0 59.44 144
Argentina ARG 156978063 2.73e6 57.36 145
Mongolia MNG 88441761 1.55e6 56.79 146
Brazil BRA 467383500 8.35e6 55.91 147
Myanmar MMR 36325546 652790.0 55.64 148
Eswatini SWZ 955776 17200.0 55.56 149
Senegal SEN 10451209 192530.0 54.28 150
Australia AUS 391891928 7.69e6 50.94 151
Bhutan BTN 1925367 38140.0 50.48 152
Gambia GMB 499912 10120.0 49.39 153
Nicaragua NIC 5073650 120340.0 42.16 154
Rwanda RWA 1032737 24670.0 41.86 155
Togo TGO 2191571 54390.0 40.29 156
Timor TLS 525704 14870.0 35.35 157
Peru PER 44706055 1.28e6 34.92 158
Uruguay URY 5840060 175020.0 33.36 159
Cote d’Ivoire CIV 10070733 318000.0 31.66 160
Iceland ISL 2935990 100830.0 29.11 161
Libya LBY 50720577 1.75e6 28.82 162
Kenya KEN 16146074 569140.0 28.36 163
Zimbabwe ZWE 10531342 386850.0 27.22 164
Belize BLZ 582795 22810.0 25.54 165
Uganda UGA 4892203 200520.0 24.39 166
Burundi BDI 602181 25680.0 23.44 167
Bolivia BOL 20700023 1.08e6 19.10 168
Paraguay PRY 7569706 397300.0 19.05 169
Afghanistan AFG 12160286 652860.0 18.62 170
Yemen YEM 9768313 527970.0 18.50 171
Angola AGO 22198161 1.24e6 17.80 172
Gabon GAB 4298177 257670.0 16.68 173
Djibouti DJI 351464 23180.0 15.16 174
Vanuatu VUT 181538 12190.0 14.89 175
Malawi MWI 1394983 94280.0 14.79 176
Papua New Guinea PNG 6652121 452860.0 14.68 177
Cameroon CMR 6889318 472710.0 14.57 178
Burkina Faso BFA 3969636 273600.0 14.50 179
Suriname SUR 2224178 156000.0 14.25 180
Guinea GIN 3394049 245720.0 13.81 181
Ethiopia ETH 14664773 1.12e6 12.98 182
Tanzania TZA 10938632 885800.0 12.34 183
Sierra Leone SLE 877190 72180.0 12.15 184
Botswana BWA 6518934 566730.0 11.50 185
Guyana GUY 2212691 196850.0 11.24 186
Solomon Islands SLB 298781 27990.0 10.67 187
Liberia LBR 1008984 96320.0 10.47 188
Sudan SDN 18859244 1.84e6 10.19 189
Guinea-Bissau GNB 286774 28120.0 10.19 190
Congo COG 3116760 341500.0 9.12 191
Zambia ZMB 6572938 743390.0 8.84 192
Mozambique MOZ 6570936 786380.0 8.35 193
Eritrea ERI 722332 101000.0 7.15 194
Madagascar MDG 3679618 581800.0 6.32 195
Namibia NAM 3877243 823290.0 4.70 196
Mauritania MRT 3377180 1.03e6 3.27 197
Mali MLI 3390011 1.22e6 2.77 198
South Sudan SSD 1179207 631928.12 1.86 199
Niger NER 1690423 1.26e6 1.33 200
Greenland GRL 514413 410450.0 1.25 201
Democratic Republic of Congo COD 2477334 2.26e6 1.09 202
Somalia SOM 562143 627340.0 0.89 203
Chad TCD 912301 1.25e6 0.72 204
Central African Republic CAF 187906 622980.0 0.30 205