GHG emissions by country, yearly (Climate Watch)
Annual all-greenhouse-gas emissions panel from WRI Climate Watch (CAIT historical compilation, keyless API, CC BY 4.0): per-country totals including and excluding LULUCF in MtCO2e, 1990-2023, with net land-use flux, YoY and 10-year CAGR, emissions peak year, global share and rank. The global emissions layer for climate, energy-transition and country-risk models; joins on ISO country codes with the catalog's macro panels. Raw data: World Resources Institute.
Les titres et les descriptions proviennent des sources de données, en anglais.
- Lignes
- 6 596
- Colonnes
- 13
- Cadence de la source
- Annuelle
- Dernière actualisation
- 3 oct. 2026
- Thème
- climate
| Colonne | Type | Description |
|---|---|---|
| year | integer | Reference year of the emissions estimate (1990-2023). |
| country | string | Country name via the hub normalization layer. |
| country_code | string | ISO 3166-1 alpha-3 country code. |
| ghg_incl_lulucf_mtco2e | float | All-GHG emissions including LULUCF, megatonnes CO2-equivalent (Climate Watch sector 'Total including LULUCF'). (unit: MtCO2e) |
| ghg_excl_lulucf_mtco2e | float | All-GHG emissions excluding LULUCF, megatonnes CO2-equivalent (Climate Watch sector 'Total excluding LULUCF'). (unit: MtCO2e) |
| lulucf_net_mtco2e | float | Net land-use flux = incl-LULUCF minus excl-LULUCF; negative means the country's land sector is a net carbon sink. (unit: MtCO2e) |
| ghg_yoy_pct | float | Year-on-year percent change of the incl-LULUCF total; null for each country's first year. (unit: %) |
| ghg_cagr_10y_pct | float | 10-year compound annual growth rate of the incl-LULUCF total; null for the first 10 years (warm-up, never back-filled). (unit: %/yr) |
| peak_year | integer | Year the country's incl-LULUCF total peaked (max over the full series). |
| years_since_peak | integer | year minus peak_year; 0 at the peak, negative before it. |
| global_share | float | Country incl-LULUCF total divided by the world incl-LULUCF total for that year. |
| ghg_rank | integer | Per-year rank on the incl-LULUCF total (1 = largest emitter). |
| row_hash | string | Deterministic 12-hex row identity hash over source, country, year and both totals. |
10 premières lignes d’exemple — un aperçu, pas le jeu de données complet.
| year | country | country_code | ghg_incl_lulucf_mtco2e | ghg_excl_lulucf_mtco2e | lulucf_net_mtco2e | ghg_yoy_pct | ghg_cagr_10y_pct | peak_year | years_since_peak | global_share | ghg_rank | row_hash |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 990 | United States | USA | 5 183,62 | 5 919,82 | -736,2 | — | — | 2 000 | -10 | 0,153 | 1 | 27e62e26d03f |
| 1 990 | China | CHN | 3 059,8 | 3 406,28 | -346,48 | — | — | 2 023 | -33 | 0,09 | 2 | 9aab092bb611 |
| 1 990 | Brazil | BRA | 2 142,12 | 621,62 | 1 520,5 | — | — | 2 000 | -10 | 0,063 | 3 | 74ea4a1dc819 |
| 1 990 | Russia | RUS | 1 987,92 | 2 985,04 | -997,12 | — | — | 1 990 | 0 | 0,059 | 4 | 068d48e8cebb |
| 1 990 | Indonesia | IDN | 1 924,58 | 482,651 | 1 441,929 | — | — | 1 997 | -7 | 0,057 | 5 | 32da974ee5ce |
| 1 990 | India | IND | 1 131,28 | 1 349,24 | -217,96 | — | — | 2 023 | -33 | 0,033 | 6 | 0bae3acf9e3a |
| 1 990 | Japan | JPN | 1 127,32 | 1 203,33 | -76,01 | — | — | 2 013 | -23 | 0,033 | 7 | d64b0fc7c1a7 |
| 1 990 | Germany | DEU | 1 096,59 | 1 129,33 | -32,74 | — | — | 1 990 | 0 | 0,032 | 8 | 27b45d0d5666 |
| 1 990 | Ukraine | UKR | 965,828 | 1 005,12 | -39,292 | — | — | 1 990 | 0 | 0,029 | 9 | 6df4010720d6 |
| 1 990 | United Kingdom | GBR | 777,15 | 764,461 | 12,689 | — | — | 1 991 | -1 | 0,023 | 10 | 9f45e0e7e8c9 |
- Actuelle
20261003T004830Z-6e145491b2c1 · sha256 6e145491b2c1…
6 596 lignes · premier instantané
Dirigez n’importe quel LLM vers le point d’accès des métadonnées — la documentation ci-dessus est aussi lisible par machine (JSON-LD + Croissant).
curl "https://datazimuts.com/v1/datasets/climatewatch_ghg_intel/ghg_country_yearly" | jq '{title, rows, columns_count, license}'import requests
ds = requests.get("https://datazimuts.com/v1/datasets/climatewatch_ghg_intel/ghg_country_yearly").json()
print(ds["title"], ds["rows"], "rows")
# Sample rows for an LLM context window
for row in ds.get("sample_rows", [])[:5]:
print(row)Point d’accès API : https://datazimuts.com/v1/datasets/climatewatch_ghg_intel/ghg_country_yearly
Astuce : récupérez /llms.txt pour le catalogue complet lisible par machine.
D’où viennent ces données et ce qui en a été fait. Le travail des autres apparaît sous forme de décomptes ; seuls les projets partagés sont nommés.
Citer cet instantané
Épinglé à l’instantané 20261003T004830Z-6e145491b2c1 et à son empreinte, pour que vos lecteurs obtiennent exactement les données utilisées.
Climate Watch GHG country intelligence. (2026). GHG emissions by country, yearly (Climate Watch) [Data set, snapshot 20261003T004830Z-6e145491b2c1, sha256 6e145491b2c1]. Datazimuts. Retrieved 2026-10-04, from https://datazimuts.com/fr/datasets/climatewatch_ghg_intel/ghg_country_yearly?snapshot=20261003T004830Z-6e145491b2c1
@misc{dz_climatewatch_ghg_intel_ghg_country_yearl_6e145491,
title = {{GHG emissions by country, yearly (Climate Watch)}},
author = {{Climate Watch GHG country intelligence}},
year = {2026},
publisher = {Datazimuts},
howpublished = {\url{https://datazimuts.com/fr/datasets/climatewatch_ghg_intel/ghg_country_yearly?snapshot=20261003T004830Z-6e145491b2c1}},
note = {Snapshot 20261003T004830Z-6e145491b2c1, sha256 6e145491b2c1b5ea068cd4e84f40a04e9d7112bf67ec6b01c71c93c9bd42ad05; accessed 2026-10-04}
}Intégrer un tableau ou un graphique
Collez ce code dans n’importe quelle page. L’intégration est épinglée au même instantané, suit le thème clair ou sombre du lecteur et affiche toujours la source, la licence et un lien de retour.
<iframe src="https://datazimuts.com/embed/chart?dataset=climatewatch_ghg_intel%2Fghg_country_yearly&lang=fr&theme=auto&snapshot=20261003T004830Z-6e145491b2c1&x=year&y=year&agg=avg" title="GHG emissions by country, yearly (Climate Watch)" width="100%" height="380" style="border:0" loading="lazy"></iframe>
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