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.
- Rows
- 6,596
- Columns
- 13
- Source cadence
- Yearly
- Last refreshed
- Oct 3, 2026
- Theme
- climate
| Column | 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. |
First 10 sample rows — a preview, not the complete dataset.
| 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 |
- Current
20261003T004830Z-6e145491b2c1 · sha256 6e145491b2c1…
6,596 rows · first snapshot
Point any LLM at the metadata endpoint — the documentation above is machine-readable too (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)API endpoint: https://datazimuts.com/v1/datasets/climatewatch_ghg_intel/ghg_country_yearly
Tip: fetch /llms.txt for the full machine-readable catalog.
Where this data comes from and what was made from it. Other people's work shows as counts; only shared projects are named.
Cite this snapshot
Pinned to snapshot 20261003T004830Z-6e145491b2c1 and its content hash, so readers get exactly the data you used.
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/en/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/en/datasets/climatewatch_ghg_intel/ghg_country_yearly?snapshot=20261003T004830Z-6e145491b2c1}},
note = {Snapshot 20261003T004830Z-6e145491b2c1, sha256 6e145491b2c1b5ea068cd4e84f40a04e9d7112bf67ec6b01c71c93c9bd42ad05; accessed 2026-10-04}
}Embed a table or a chart
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<iframe src="https://datazimuts.com/embed/chart?dataset=climatewatch_ghg_intel%2Fghg_country_yearly&lang=en&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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