AI carbon footprint in EU West - UK (London): emissions per token and per query
Factor version soma-ai-ef-2026.10-dataset-v3.0.0. Last updated . Location-based grid averages, not renewable contracts. Estimates from published benchmarks.
Part of the SOMA AI Emission Factors cluster.
How much CO₂ does an AI query emit when served from EU West - UK (London)?
A 1210-token query to a class B model (gpt-4o) served from EU West - UK (London) emits about 0.029 g CO₂e, and a million tokens emit 0.0212 kg CO₂e.
Rank 6 of 16 for class B, lowest carbon first (full table). 3.0x the level of EU North - Sweden, Asia Pacific - Singapore is 1.9x higher.
Which grid supplies the electricity in EU West - UK (London)?
EU West - UK (London) maps to United Kingdom, a national grid, at 235.6 g CO₂e per kWh for 2023. That is 1.0 times the EU-27 average (236 g) and 0.5 times the global average (483.2 g). The source is Ember yearly electricity data, CO2 intensity 2023, 235.6 g/kWh (lifecycle, IPCC AR5).
Cloud regions on this grid, 4 in all (1 AWS, 2 Azure, 1 Google Cloud).
| Provider | Region id | Region name | Location | Mapping confidence | Mapping note |
|---|---|---|---|---|---|
| AWS | eu-west-2 | Europe (London) | London | high | |
| Azure | uksouth | UK South | London | high | |
| Azure | ukwest | UK West | Cardiff | high | |
| Google Cloud | europe-west2 | europe-west2 | London, England | high |
Which grids have an intensity close to United Kingdom?
| Difference g | Grid | Code | g CO₂e per kWh | Ratio |
|---|---|---|---|---|
| -3.5 | Lao People's Democratic Republic (the) | LAO | 232.1 | 0.985 |
| 7.6 | Romania | ROU | 243.2 | 1.032 |
| 9.3 | French Guiana | GUF | 244.9 | 1.039 |
| -10.5 | Slovenia | SVN | 225.1 | 0.955 |
| -11.2 | Cameroon | CMR | 224.4 | 0.952 |
| 14.4 | Cook Islands (the) | COK | 250 | 1.061 |
| -16.5 | WECC California | CAMX | 219.1 | 0.930 |
| 22.8 | Colombia | COL | 258.4 | 1.097 |
| 26.8 | Armenia | ARM | 262.4 | 1.114 |
| 30.6 | ASCC Miscellaneous | AKMS | 266.2 | 1.130 |
Emission factors for EU West - UK (London) by model class
| Model class | Electricity kg | Hardware kg | Construction kg | Training kg | Total kg CO₂e | g per query | Water L | mL per query |
|---|---|---|---|---|---|---|---|---|
| Class A (small) | 0.00513 | 0.00027 | 0.00014 | 0.00060 | 0.006 | 0.0084 | 0.053 | 0.073 |
| Class B (mid-size) | 0.01680 | 0.00101 | 0.00046 | 0.00293 | 0.021 | 0.029 | 0.173 | 0.24 |
| Class C (frontier) | 0.06254 | 0.00388 | 0.00169 | 0.01284 | 0.081 | 0.11 | 0.644 | 0.89 |
| Model class | Central | Class range low | Class range high | High case | B200 | Development |
|---|---|---|---|---|---|---|
| Class A | 0.00614 | 0.003896 | 0.008777 | 0.009603 | 0.005432 | 0.006994 |
| Class B | 0.0212 | 0.01414 | 0.02534 | 0.03257 | 0.01542 | 0.0229 |
| Class C | 0.08097 | 0.03571 | 0.1178 | 0.1233 | 0.08303 | 0.08529 |
How much does siting matter for a model served from EU West - UK (London)?
Electricity is 79.2% of the class B total here (class A 83.5%, class C 77.2%), with hardware at 4.8%, construction 2.1% and training 14%. Electricity carries most of the 0.0212 kg, so the grid is the biggest lever.
The example company (2,323 million class B tokens a year) emits 49 kg CO₂e from EU West - UK (London). Moving to EU South - Spain would save 11 kg (22%) or EU West - Belgium would save 17 kg (34%).
| Region | Grid g per kWh | Class A | Class B | Class C |
|---|---|---|---|---|
| EU South - Spain | 169.7 | 3.33 | 10.9 | 40.7 |
| EU West - Belgium | 134.7 | 5.1 | 16.7 | 62.2 |
How much water does AI usage consume in EU West - UK (London)?
A class B query of 1210 tokens from EU West - UK (London) consumes about 0.24 mL of water and a million tokens 0.173 litres, or 0.137 litres without the water attributed to hydropower.
| Input | Value |
|---|---|
| On-site WUE, L per kWh | 0.14 |
| WUE denominator | IT load |
| WUE basis as published | withdrawal |
| Withdrawal to consumption | 0.6667 |
| EWIF, L per kWh | 2.347 |
| EWIF without hydro, L per kWh | 1.850 |
| AWARE factor | 2.29 |
| Confidence | published-site |
| Source | AWS; AWS Region Europe (London) eu-west-2; 2025 |
| Model class | On site | Grid electricity | Total | Total without hydro | AWARE-weighted on site |
|---|---|---|---|---|---|
| Class A | 0.00169 | 0.0511 | 0.0528 | 0.042 | 0.00388 |
| Class B | 0.00554 | 0.167 | 0.173 | 0.137 | 0.0127 |
| Class C | 0.0206 | 0.623 | 0.644 | 0.512 | 0.0473 |
Where do these numbers come from and how do I cite them?
Figures per query are one plain exchange, before the Tier 2b reasoning uplift. Electricity term. GPU energy per token from the ML.ENERGY Leaderboard v3, a facility overhead of 1.20, and grid carbon intensity from EPA eGRID 2023 and Ember 2023. Embodied hardware, data-centre construction and amortised training terms are documented on the methodology page. Water combines on-site cooling water (Li et al. 2025) with the water embedded in grid electricity (Reig et al. 2020). The modelled range of the models inside a class, around the class value, is −41% to +48% for class A, −39% to +24% for class B and −67% to +55% for class C.
Dataset: AI Inference Emission and Resource Factors for Corporate GHG Inventories (SOMA AI Emission Factors (SAEF)), Llopis-Garcia, G., SOMA AI, 19 October 2026, CC BY 4.0, concept DOI 10.5281/zenodo.20443585, which resolves to the latest version (this version 10.5281/zenodo.23371447, earlier version 2 values at 10.5281/zenodo.22767475). Method: Accounting for Purchased AI Inference in Corporate GHG Inventories: A Token-Based Method, arXiv:2606.10660, under peer review.
Partners can fetch any row from the AI factors API, and the whole table is a CSV download.
Cite as "0.0212 kg CO₂e per million tokens, class B, EU West - UK (London), SAEF, 19 October 2026" and link somaai.earth/ai-footprint/factors/uk-london.