Emission Totals - Emissions (N2O) - Burning - Crop residues 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...
What the numbers show
Emission Totals - Emissions (N2O) - Burning - Crop residues is currently reported for 185 countries. The highest value is 4 in China; the lowest is 0 in Samoa.
The median across all reporting countries is 0.0175, and the mean is 0.1398.
Over the past decade 1 countries rose and 1 fell. The largest increase was in Sudan (up 32.4%), and the largest decrease in South Sudan (down 70.5%).
Emission Totals - Emissions (N2O) - Burning - Crop residues: full country ranking
| # | Country | Latest | Year | 10-year change | Trend |
|---|---|---|---|---|---|
| 1 | China | 4 | 2050 | β | rising |
| 2 | United States | 3.28 | 2050 | β | rising |
| 3 | India | 2.71 | 2050 | β | rising |
| 4 | Brazil | 1.65 | 2050 | β | rising |
| 5 | Indonesia | 0.9458 | 2050 | β | rising |
| 6 | Nigeria | 0.5825 | 2050 | β | volatile |
| 7 | Russia | 0.5499 | 2050 | β | rising |
| 8 | Mexico | 0.5449 | 2050 | β | flat |
| 9 | Argentina | 0.5418 | 2050 | β | rising |
| 10 | Bangladesh | 0.4722 | 2050 | β | rising |
| 11 | Australia | 0.4621 | 2050 | β | rising |
| 12 | Myanmar | 0.4555 | 2050 | β | rising |
| 13 | Vietnam | 0.4368 | 2050 | β | rising |
| 14 | Thailand | 0.432 | 2050 | β | rising |
| 15 | Canada | 0.3763 | 2050 | β | rising |
| 16 | Philippines | 0.3716 | 2050 | β | rising |
| 17 | South Africa | 0.3412 | 2050 | β | falling |
| 18 | Pakistan | 0.3405 | 2050 | β | rising |
| 19 | Ethiopia | 0.3362 | 2050 | β | rising |
| 20 | Ukraine | 0.2827 | 2050 | β | rising |
| 21 | Tanzania | 0.2584 | 2050 | β | volatile |
| 22 | Kazakhstan | 0.2525 | 2050 | β | falling |
| 23 | France | 0.2488 | 2050 | β | rising |
| 24 | Turkey | 0.2269 | 2050 | β | falling |
| 25 | Democratic Republic of Congo | 0.2179 | 2050 | β | volatile |
| 25 | Romania | 0.2179 | 2050 | β | falling |
| 27 | Iran | 0.2091 | 2050 | β | rising |
| 28 | Cambodia | 0.1968 | 2050 | β | rising |
| 29 | Mozambique | 0.1819 | 2050 | β | volatile |
| 30 | Kenya | 0.1803 | 2050 | β | rising |
| 31 | Nepal | 0.1768 | 2050 | β | rising |
| 32 | Egypt | 0.1683 | 2050 | β | rising |
| 33 | Peru | 0.1525 | 2050 | β | rising |
| 34 | Colombia | 0.1507 | 2050 | β | rising |
| 35 | Italy | 0.1365 | 2050 | β | falling |
| 36 | Malawi | 0.1303 | 2050 | β | rising |
| 37 | Ghana | 0.1168 | 2050 | β | volatile |
| 38 | Germany | 0.1136 | 2050 | β | rising |
| 39 | Morocco | 0.1087 | 2050 | β | rising |
| 40 | Hungary | 0.106 | 2050 | β | falling |
| 41 | Madagascar | 0.1053 | 2050 | β | rising |
| 42 | Venezuela | 0.1023 | 2050 | β | rising |
| 43 | Angola | 0.1019 | 2050 | β | volatile |
| 44 | Serbia | 0.0993 | 2050 | β | flat |
| 45 | Guinea | 0.0941 | 2050 | β | volatile |
| 46 | North Korea | 0.0917 | 2050 | β | falling |
| 47 | Afghanistan | 0.0904 | 2050 | β | falling |
| 47 | Syria | 0.0904 | 2050 | β | rising |
| 49 | Benin | 0.0882 | 2050 | β | rising |
| 50 | Uganda | 0.0876 | 2050 | β | volatile |
| 51 | Spain | 0.0843 | 2050 | β | falling |
| 52 | Mali | 0.0818 | 2050 | β | volatile |
| 53 | Poland | 0.0802 | 2050 | β | rising |
| 54 | Cote d'Ivoire | 0.0792 | 2050 | β | rising |
| 55 | Iraq | 0.0736 | 2050 | β | rising |
| 56 | Ecuador | 0.0726 | 2050 | β | rising |
| 57 | Cameroon | 0.0641 | 2050 | β | volatile |
| 58 | Paraguay | 0.0622 | 2050 | β | volatile |
| 59 | Algeria | 0.0616 | 2050 | β | falling |
| 60 | Burkina Faso | 0.0609 | 2050 | β | volatile |
| 61 | Laos | 0.0587 | 2050 | β | rising |
| 62 | Bolivia | 0.0569 | 2050 | β | rising |
| 63 | Guatemala | 0.056 | 2050 | β | rising |
| 64 | Japan | 0.0529 | 2050 | β | falling |
| 65 | United Kingdom | 0.0517 | 2050 | β | rising |
| 66 | Cuba | 0.0502 | 2050 | β | falling |
| 67 | Bulgaria | 0.0483 | 2050 | β | falling |
| 68 | Moldova | 0.0478 | 2050 | β | rising |
| 69 | Togo | 0.039 | 2050 | β | volatile |
| 70 | Nicaragua | 0.0385 | 2050 | β | rising |
| 71 | Greece | 0.0382 | 2050 | β | falling |
| 72 | El Salvador | 0.0367 | 2050 | β | rising |
| 73 | Honduras | 0.0353 | 2050 | β | rising |
| 74 | Sri Lanka | 0.0334 | 2050 | β | rising |
| 74 | Uzbekistan | 0.0334 | 2050 | β | falling |
| 76 | Malaysia | 0.0324 | 2050 | β | rising |
| 77 | Sierra Leone | 0.0317 | 2050 | β | rising |
| 78 | Uruguay | 0.0316 | 2050 | β | falling |
| 79 | Chile | 0.0301 | 2050 | β | falling |
| 80 | South Korea | 0.0294 | 2050 | β | falling |
| 81 | Czechia | 0.029 | 2050 | β | rising |
| 82 | Chad | 0.0281 | 2050 | β | volatile |
| 83 | Croatia | 0.0259 | 2050 | β | falling |
| 84 | Senegal | 0.0257 | 2050 | β | volatile |
| 85 | Haiti | 0.0236 | 2050 | β | rising |
| 86 | Central African Republic | 0.0223 | 2050 | β | rising |
| 87 | Turkmenistan | 0.0203 | 2050 | β | rising |
| 88 | Austria | 0.0202 | 2050 | β | rising |
| 89 | Slovakia | 0.02 | 2050 | β | rising |
| 89 | Tunisia | 0.02 | 2050 | β | falling |
| 91 | Denmark | 0.0192 | 2050 | β | volatile |
| 92 | Rwanda | 0.0188 | 2050 | β | volatile |
| 93 | Dominican Republic | 0.0175 | 2050 | β | rising |
| 94 | Bosnia and Herzegovina | 0.0161 | 2050 | β | rising |
| 95 | Liberia | 0.016 | 2050 | β | rising |
| 96 | Kyrgyzstan | 0.0148 | 2050 | β | flat |
| 97 | Georgia | 0.0147 | 2050 | β | falling |
| 98 | Azerbaijan | 0.0139 | 2050 | β | rising |
| 98 | Saudi Arabia | 0.0139 | 2050 | β | volatile |
| 98 | Sudan | 0.0139 | 2019 | up 32.4% | rising |
| 98 | Taiwan | 0.0139 | 2050 | β | falling |
| 102 | Belarus | 0.0133 | 2050 | β | volatile |
| 103 | Burundi | 0.0124 | 2050 | β | rising |
| 104 | Somalia | 0.0115 | 2050 | β | falling |
| 105 | Yemen | 0.0109 | 2050 | β | rising |
| 106 | Portugal | 0.0104 | 2050 | β | volatile |
| 107 | Lithuania | 0.0102 | 2050 | β | rising |
| 108 | Sweden | 0.0101 | 2050 | β | rising |
| 109 | Tajikistan | 0.01 | 2050 | β | rising |
| 110 | Panama | 0.0099 | 2050 | β | falling |
| 111 | Eswatini | 0.0097 | 2050 | β | rising |
| 112 | Lesotho | 0.0096 | 2050 | β | falling |
| 112 | East Timor | 0.0096 | 2050 | β | rising |
| 114 | Belgium | 0.0093 | 2050 | β | flat |
| 115 | Niger | 0.0079 | 2050 | β | volatile |
| 116 | Guyana | 0.0075 | 2050 | β | rising |
| 117 | Guinea-Bissau | 0.0074 | 2050 | β | rising |
| 118 | Costa Rica | 0.007 | 2050 | β | falling |
| 119 | Mauritius | 0.0063 | 2050 | β | falling |
| 120 | Latvia | 0.0059 | 2050 | β | rising |
| 120 | South Sudan | 0.0059 | 2019 | down 70.5% | volatile |
| 122 | Finland | 0.0057 | 2050 | β | flat |
| 123 | Albania | 0.0054 | 2050 | β | falling |
| 124 | Netherlands | 0.0052 | 2050 | β | rising |
| 125 | North Macedonia | 0.005 | 2050 | β | falling |
| 126 | Mauritania | 0.0049 | 2050 | β | volatile |
| 127 | Gambia | 0.0044 | 2050 | β | volatile |
| 128 | Bhutan | 0.0041 | 2050 | β | falling |
| 129 | Eritrea | 0.004 | 2050 | β | rising |
| 130 | Switzerland | 0.0037 | 2050 | β | rising |
| 131 | Slovenia | 0.0035 | 2050 | β | falling |
| 132 | Fiji | 0.0034 | 2050 | β | flat |
| 133 | Congo | 0.0033 | 2050 | β | rising |
| 133 | Libya | 0.0033 | 2050 | β | falling |
| 135 | Belize | 0.0031 | 2050 | β | rising |
| 136 | Armenia | 0.003 | 2050 | β | flat |
| 136 | Botswana | 0.003 | 2050 | β | volatile |
| 136 | New Zealand | 0.003 | 2050 | β | falling |
| 139 | Gabon | 0.0027 | 2050 | β | volatile |
| 140 | Estonia | 0.0026 | 2050 | β | rising |
| 140 | Mongolia | 0.0026 | 2050 | β | falling |
| 140 | Namibia | 0.0026 | 2050 | β | rising |
| 143 | Cape Verde | 0.0025 | 2050 | β | rising |
| 143 | Ireland | 0.0025 | 2050 | β | rising |
| 143 | Israel | 0.0025 | 2050 | β | falling |
| 143 | Suriname | 0.0025 | 2050 | β | rising |
| 147 | Norway | 0.0022 | 2050 | β | volatile |
| 148 | Jamaica | 0.0019 | 2050 | β | falling |
| 149 | Lebanon | 0.0018 | 2050 | β | falling |
| 150 | Comoros | 0.0012 | 2050 | β | rising |
| 151 | Trinidad and Tobago | 0.001 | 2050 | β | volatile |
| 152 | Jordan | 0.0009 | 2050 | β | volatile |
| 153 | Papua New Guinea | 0.0006 | 2050 | β | volatile |
| 154 | Palestine | 0.0005 | 2050 | β | falling |
| 155 | Barbados | 0.0004 | 2050 | β | volatile |
| 155 | Luxembourg | 0.0004 | 2050 | β | rising |
| 157 | Oman | 0.0002 | 2050 | β | volatile |
| 158 | Bahamas | 0.0001 | 2050 | β | rising |
| 158 | Cyprus | 0.0001 | 2050 | β | volatile |
| 158 | Saint Kitts and Nevis | 0.0001 | 2050 | β | falling |
| 158 | Kuwait | 0.0001 | 2050 | β | volatile |
| 158 | Malta | 0.0001 | 2050 | β | volatile |
| 158 | Montenegro | 0.0001 | 2050 | β | volatile |
| 158 | New Caledonia | 0.0001 | 2050 | β | volatile |
| 158 | Sao Tome and Principe | 0.0001 | 2050 | β | volatile |
| 158 | Saint Vincent and the Grenadines | 0.0001 | 2050 | β | volatile |
| 158 | Vanuatu | 0.0001 | 2050 | β | rising |
| 168 | United Arab Emirates | 0 | 2050 | β | volatile |
| 168 | American Samoa | 0 | 2050 | β | flat |
| 168 | Antigua and Barbuda | 0 | 2050 | β | volatile |
| 168 | Brunei | 0 | 2050 | β | volatile |
| 168 | Dominica | 0 | 2050 | β | flat |
| 168 | Micronesia (country) | 0 | 2050 | β | flat |
| 168 | Grenada | 0 | 2050 | β | volatile |
| 168 | Guam | 0 | 2050 | β | flat |
| 168 | Hong Kong | 0 | 2050 | β | volatile |
| 168 | Saint Lucia | 0 | 1980 | β | flat |
| 168 | Maldives | 0 | 2050 | β | flat |
| 168 | Puerto Rico | 0 | 2050 | β | volatile |
| 168 | French Polynesia | 0 | 2050 | β | flat |
| 168 | Qatar | 0 | 2050 | β | flat |
| 168 | Singapore | 0 | 1992 | β | flat |
| 168 | Solomon Islands | 0 | 2050 | β | volatile |
| 168 | United States Virgin Islands | 0 | 1992 | β | volatile |
| 168 | Samoa | 0 | 2050 | β | 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 1961β2019 with projections for 2030 and 2050 for some categories of emissions or 1990β2019 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).