Emission Totals - Emissions (CO2eq) from CH4 (AR5) - Forest fires by country
The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change...
Countries reporting
208
Highest
12,438
Democratic Republic of Congo
Lowest
0
Niue
Median
0.2443
Years covered
30
1990–2019
Data points
6,120
What the numbers show
Emission Totals - Emissions (CO2eq) from CH4 (AR5) - Forest fires is currently reported for 208 countries. The highest value is 12,438 in Democratic Republic of Congo; the lowest is 0 in Niue.
The median across all reporting countries is 0.2443, and the mean is 405.02.
Over the past decade 64 countries rose and 53 fell. The largest increase was in El Salvador (up 16,642.7%), and the largest decrease in Albania (down 100.0%).
Emission Totals - Emissions (CO2eq) from CH4 (AR5) - Forest fires: full country ranking
| # | Country | Latest | Year | 10-year change | Trend |
|---|---|---|---|---|---|
| 1 | Democratic Republic of Congo | 12,438 | 2019 | down 5.6% | falling |
| 2 | Australia | 10,897 | 2019 | up 504.5% | volatile |
| 3 | Mozambique | 9,930 | 2019 | down 10.9% | rising |
| 4 | Angola | 6,983 | 2019 | down 0.2% | falling |
| 5 | Central African Republic | 6,905 | 2019 | down 11.0% | rising |
| 6 | Bolivia | 5,659 | 2019 | up 564.9% | volatile |
| 7 | Brazil | 5,395 | 2019 | up 348.4% | volatile |
| 8 | Tanzania | 3,640 | 2019 | down 28.1% | rising |
| 9 | Russia | 3,481 | 2019 | up 36.7% | volatile |
| 10 | Myanmar | 3,398 | 2019 | down 54.7% | falling |
| 11 | India | 1,606 | 2019 | down 70.4% | rising |
| 12 | Cambodia | 1,450 | 2019 | down 53.7% | falling |
| 13 | Mexico | 1,351 | 2019 | up 66.5% | volatile |
| 14 | Indonesia | 1,348 | 2019 | up 26.0% | volatile |
| 15 | Canada | 1,120 | 2019 | up 906.0% | volatile |
| 16 | Thailand | 1,102 | 2019 | down 21.0% | falling |
| 17 | United States | 814.71 | 2019 | up 38.3% | volatile |
| 18 | Laos | 793.4 | 2019 | up 6.8% | volatile |
| 19 | Nepal | 647.3 | 2019 | up 66.3% | volatile |
| 20 | Paraguay | 579.51 | 2019 | up 577.1% | volatile |
| 21 | Cameroon | 485.63 | 2019 | up 23.2% | volatile |
| 22 | Venezuela | 390.82 | 2019 | up 817.9% | volatile |
| 23 | Chad | 371.51 | 2019 | up 18.3% | volatile |
| 24 | Ethiopia | 371.23 | 2019 | down 5.0% | volatile |
| 25 | Madagascar | 278.53 | 2019 | up 2.6% | volatile |
| 26 | South Sudan | 276.74 | 2019 | down 49.3% | falling |
| 27 | Vietnam | 238.54 | 2019 | down 33.5% | volatile |
| 28 | China | 227.3 | 2019 | down 44.3% | volatile |
| 29 | Congo | 215.17 | 2019 | up 60.1% | falling |
| 30 | Malawi | 134.71 | 2019 | up 30.4% | rising |
| 31 | Honduras | 120.32 | 2019 | down 28.0% | volatile |
| 32 | Ukraine | 117.14 | 2019 | up 49.5% | volatile |
| 33 | Colombia | 116.67 | 2019 | up 158.5% | volatile |
| 34 | Liberia | 112.2 | 2019 | down 66.4% | volatile |
| 35 | South Africa | 107.04 | 2019 | up 71.2% | volatile |
| 36 | Chile | 91.48 | 2019 | up 63.6% | volatile |
| 37 | Argentina | 83.97 | 2019 | down 76.8% | volatile |
| 38 | Nigeria | 82.79 | 2019 | up 4.3% | volatile |
| 39 | Guinea-Bissau | 77.29 | 2019 | down 44.2% | falling |
| 40 | Peru | 72.62 | 2019 | up 137.0% | volatile |
| 41 | Guatemala | 67.91 | 2019 | down 67.7% | volatile |
| 42 | Nicaragua | 55.08 | 2019 | up 481.4% | volatile |
| 43 | Belize | 35.87 | 2019 | up 216.4% | volatile |
| 44 | Benin | 30.67 | 2019 | up 22.2% | volatile |
| 45 | North Korea | 30.34 | 2019 | down 79.3% | volatile |
| 46 | Papua New Guinea | 29.8 | 2019 | up 25.7% | volatile |
| 47 | Guinea | 27.46 | 2019 | down 69.8% | volatile |
| 48 | Philippines | 26.24 | 2019 | up 188.1% | volatile |
| 49 | France | 26.17 | 2019 | up 82.9% | volatile |
| 50 | Gabon | 23.52 | 2019 | up 51.7% | falling |
| 51 | Malaysia | 23.47 | 2019 | down 70.3% | volatile |
| 52 | Georgia | 22.61 | 2019 | up 3,140.3% | volatile |
| 53 | Costa Rica | 21.31 | 2019 | up 302.7% | volatile |
| 54 | Guyana | 20.83 | 2019 | up 378.3% | volatile |
| 55 | Senegal | 19.33 | 2019 | down 65.8% | volatile |
| 56 | Turkey | 18.89 | 2019 | up 5,316.7% | volatile |
| 57 | Uganda | 18.74 | 2019 | up 35.4% | volatile |
| 58 | Sierra Leone | 16.84 | 2019 | down 34.5% | volatile |
| 59 | Algeria | 14.5 | 2019 | up 15.6% | volatile |
| 60 | Panama | 14 | 2019 | up 59.6% | volatile |
| 61 | Eswatini | 13.49 | 2019 | up 223.3% | volatile |
| 62 | Portugal | 11.65 | 2019 | up 255.2% | volatile |
| 63 | Ghana | 11.44 | 2019 | up 535.5% | volatile |
| 64 | Bangladesh | 11.4 | 2019 | down 88.4% | volatile |
| 65 | Spain | 11.23 | 2019 | up 61.0% | volatile |
| 66 | El Salvador | 9.73 | 2019 | up 16,642.7% | volatile |
| 67 | Ecuador | 9.67 | 2019 | up 206.3% | volatile |
| 68 | Serbia | 8.1 | 2019 | up 314.2% | volatile |
| 69 | Japan | 7.94 | 2019 | up 100.0% | flat |
| 70 | Bahamas | 7.69 | 2019 | down 50.8% | volatile |
| 71 | Greece | 7.53 | 2019 | up 5,298.6% | volatile |
| 72 | Suriname | 7.43 | 2019 | up 137.0% | volatile |
| 73 | East Timor | 6.5 | 2019 | up 106.8% | volatile |
| 74 | Belarus | 6.44 | 2019 | up 268.0% | volatile |
| 75 | Romania | 5.38 | 2019 | down 3.8% | volatile |
| 76 | New Caledonia | 4.65 | 2019 | — | volatile |
| 77 | Uruguay | 3.83 | 2019 | up 1,000.2% | volatile |
| 78 | Togo | 3.72 | 2019 | up 16.4% | volatile |
| 79 | Kenya | 3.39 | 2019 | down 22.5% | volatile |
| 80 | North Macedonia | 3.07 | 2019 | — | volatile |
| 81 | Cote d'Ivoire | 3.03 | 2019 | down 73.4% | volatile |
| 82 | Dominican Republic | 2.68 | 2019 | down 11.4% | volatile |
| 83 | Botswana | 2.64 | 2019 | up 1,166.5% | volatile |
| 84 | Italy | 2.37 | 2019 | down 60.0% | volatile |
| 85 | Namibia | 2.22 | 2019 | up 966.9% | volatile |
| 86 | Bosnia and Herzegovina | 2.09 | 2019 | up 233.3% | volatile |
| 87 | Sudan | 2.08 | 2019 | up 50.0% | rising |
| 88 | Morocco | 1.95 | 2019 | up 600.0% | volatile |
| 89 | Cuba | 1.91 | 2019 | down 95.7% | volatile |
| 90 | South Korea | 1.74 | 2019 | down 70.6% | volatile |
| 91 | Moldova | 1.68 | 2019 | — | volatile |
| 92 | Pakistan | 1.25 | 2019 | down 56.1% | volatile |
| 93 | Croatia | 1.12 | 2019 | up 100.0% | volatile |
| 94 | Bulgaria | 1.12 | 2019 | up 699.9% | volatile |
| 95 | Montenegro | 1.05 | 2019 | — | volatile |
| 96 | Bhutan | 0.8929 | 2019 | down 77.7% | volatile |
| 97 | United Kingdom | 0.8404 | 2019 | up 1,374.4% | volatile |
| 98 | Trinidad and Tobago | 0.8134 | 2019 | up 600.0% | volatile |
| 99 | Tunisia | 0.7666 | 2019 | — | volatile |
| 100 | New Zealand | 0.5581 | 2019 | up 59.8% | volatile |
| 101 | Fiji | 0.4651 | 2019 | down 96.2% | volatile |
| 102 | Burundi | 0.3468 | 2019 | up 25.0% | volatile |
| 103 | Armenia | 0.2791 | 2019 | — | volatile |
| 104 | Azerbaijan | 0.279 | 2019 | — | volatile |
| 105 | Austria | 0.2096 | 2019 | unchanged | volatile |
| 105 | Slovenia | 0.2096 | 2019 | down 25.0% | volatile |
| 107 | Iran | 0.2091 | 2019 | — | volatile |
| 108 | Syria | 0.209 | 2019 | — | volatile |
| 109 | Mali | 0.1743 | 2019 | up 50.0% | volatile |
| 110 | Hungary | 0.1398 | 2019 | down 77.8% | volatile |
| 111 | Finland | 0.114 | 2019 | up 100.0% | volatile |
| 112 | Kazakhstan | 0.0698 | 2019 | — | volatile |
| 113 | Brunei | 0.0581 | 2019 | down 83.3% | volatile |
| 113 | Comoros | 0.0581 | 2019 | down 83.3% | volatile |
| 115 | Mongolia | 0.0569 | 2019 | down 99.8% | volatile |
| 116 | Aruba | 0 | 2019 | — | flat |
| 116 | Afghanistan | 0 | 2019 | — | volatile |
| 116 | Albania | 0 | 2019 | down 100.0% | volatile |
| 116 | Andorra | 0 | 2019 | — | flat |
| 116 | United Arab Emirates | 0 | 2019 | — | flat |
| 116 | American Samoa | 0 | 2019 | — | flat |
| 116 | Antigua and Barbuda | 0 | 2019 | — | flat |
| 116 | Belgium | 0 | 2019 | — | volatile |
| 116 | Burkina Faso | 0 | 2019 | down 100.0% | volatile |
| 116 | Bahrain | 0 | 2019 | — | flat |
| 116 | Bermuda | 0 | 2019 | — | flat |
| 116 | Barbados | 0 | 2019 | — | flat |
| 116 | Switzerland | 0 | 2019 | down 100.0% | volatile |
| 116 | Cape Verde | 0 | 2019 | — | flat |
| 116 | Cayman Islands | 0 | 2019 | — | flat |
| 116 | Cyprus | 0 | 2019 | — | volatile |
| 116 | Czechia | 0 | 2019 | down 100.0% | volatile |
| 116 | Germany | 0 | 2019 | down 100.0% | volatile |
| 116 | Dominica | 0 | 2019 | — | flat |
| 116 | Denmark | 0 | 2019 | — | volatile |
| 116 | Egypt | 0 | 2019 | — | volatile |
| 116 | Eritrea | 0 | 2019 | — | volatile |
| 116 | Estonia | 0 | 2019 | — | volatile |
| 116 | Faroe Islands | 0 | 2019 | — | flat |
| 116 | Micronesia (country) | 0 | 2019 | — | flat |
| 116 | Gibraltar | 0 | 2019 | — | volatile |
| 116 | Gambia | 0 | 2019 | — | volatile |
| 116 | Equatorial Guinea | 0 | 2019 | — | volatile |
| 116 | Grenada | 0 | 2019 | — | volatile |
| 116 | Greenland | 0 | 2019 | — | flat |
| 116 | Guam | 0 | 2019 | — | flat |
| 116 | Hong Kong | 0 | 2019 | down 100.0% | volatile |
| 116 | Haiti | 0 | 2019 | — | volatile |
| 116 | Isle of Man | 0 | 2019 | — | volatile |
| 116 | Ireland | 0 | 2019 | — | volatile |
| 116 | Iraq | 0 | 2019 | — | volatile |
| 116 | Iceland | 0 | 2019 | — | flat |
| 116 | Israel | 0 | 2019 | — | volatile |
| 116 | Jamaica | 0 | 2019 | — | volatile |
| 116 | Jordan | 0 | 2019 | — | flat |
| 116 | Kyrgyzstan | 0 | 2019 | — | volatile |
| 116 | Kiribati | 0 | 2019 | — | flat |
| 116 | Saint Kitts and Nevis | 0 | 2019 | — | flat |
| 116 | Kuwait | 0 | 2019 | — | flat |
| 116 | Lebanon | 0 | 2019 | — | volatile |
| 116 | Libya | 0 | 2019 | — | flat |
| 116 | Saint Lucia | 0 | 2019 | — | flat |
| 116 | Sri Lanka | 0 | 2019 | down 100.0% | volatile |
| 116 | Lesotho | 0 | 2019 | — | volatile |
| 116 | Lithuania | 0 | 2019 | down 100.0% | volatile |
| 116 | Luxembourg | 0 | 2019 | — | volatile |
| 116 | Latvia | 0 | 2019 | down 100.0% | volatile |
| 116 | Macao | 0 | 2019 | — | flat |
| 116 | Maldives | 0 | 2019 | — | flat |
| 116 | Marshall Islands | 0 | 2019 | — | flat |
| 116 | Malta | 0 | 2019 | — | flat |
| 116 | Northern Mariana Islands | 0 | 2019 | — | flat |
| 116 | Mauritania | 0 | 2019 | — | volatile |
| 116 | Mauritius | 0 | 2019 | — | volatile |
| 116 | Niger | 0 | 2019 | — | volatile |
| 116 | Netherlands | 0 | 2019 | — | volatile |
| 116 | Norway | 0 | 2019 | — | volatile |
| 116 | Nauru | 0 | 2019 | — | flat |
| 116 | Oman | 0 | 2019 | — | flat |
| 116 | Palau | 0 | 2019 | — | flat |
| 116 | Poland | 0 | 2019 | down 100.0% | volatile |
| 116 | Puerto Rico | 0 | 2019 | — | volatile |
| 116 | Palestine | 0 | 2019 | — | flat |
| 116 | French Polynesia | 0 | 2019 | — | volatile |
| 116 | Qatar | 0 | 2019 | — | flat |
| 116 | Rwanda | 0 | 2019 | down 100.0% | volatile |
| 116 | Saudi Arabia | 0 | 2019 | — | flat |
| 116 | Singapore | 0 | 2019 | — | volatile |
| 116 | Solomon Islands | 0 | 2019 | — | volatile |
| 116 | Somalia | 0 | 2019 | — | volatile |
| 116 | Sao Tome and Principe | 0 | 2019 | — | flat |
| 116 | Slovakia | 0 | 2019 | down 100.0% | volatile |
| 116 | Sweden | 0 | 2019 | — | volatile |
| 116 | Seychelles | 0 | 2019 | — | flat |
| 116 | Turks and Caicos Islands | 0 | 2019 | — | volatile |
| 116 | Tajikistan | 0 | 2019 | — | volatile |
| 116 | Turkmenistan | 0 | 2019 | — | volatile |
| 116 | Tonga | 0 | 2019 | — | flat |
| 116 | Tuvalu | 0 | 2019 | — | flat |
| 116 | Uzbekistan | 0 | 2019 | — | volatile |
| 116 | Saint Vincent and the Grenadines | 0 | 2019 | — | flat |
| 116 | British Virgin Islands | 0 | 2019 | — | volatile |
| 116 | United States Virgin Islands | 0 | 2019 | — | flat |
| 116 | Vanuatu | 0 | 2019 | — | volatile |
| 116 | Samoa | 0 | 2019 | — | volatile |
| 116 | Taiwan | 0 | 2019 | down 100.0% | volatile |
| 116 | Cook Islands | 0 | 2019 | — | flat |
| 116 | Niue | 0 | 2019 | — | flat |
About this data
The FAOSTAT domain Emissions Totals summarizes the greenhouse gas (GHG) emissions generated from agriculture and forest land. They consist of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from crop and livestock activities, forest management and include land use and land use change processes. Data are computed at Tier 1 of the IPCC Guidelines for National greenhouse gas (GHG) Inventories (IPCC, 1996; 1997; 2000; 2002; 2006; 2014). Estimates are available by country, with global coverage for the period 1961–2019 with projections for 2030 and 2050 for some categories of emissions or 1990–2019 for others. The database is updated annually. The FAOSTAT domain Emissions Totals disseminates information estimates of CH4, N2O and CO2 emissions/removals and their aggregates in CO2eq in units of kilotonnes (kt, or 106 kg). The latter are computed by using the IPCC Fifth Assessment report global warming potentials, AR5 (IPCC, 2014).