AI carbon footprint in EU Netherlands: emissions per token and per query

Factor version soma-ai-ef-2026.09-lifecycle-v2, dated September 2026. Location-based grid averages, not renewable contracts. Estimates from published benchmarks.

How much CO₂ does an AI query emit when served from EU Netherlands?

A query of 400 tokens to a mid-size AI model (class B, such as gpt-4o) served from EU Netherlands emits about 0.02 g CO₂e, counting electricity, the share of server hardware and amortised training; a million tokens emit 0.0501 kg CO₂e. A small model (class A) emits 0.005 g per query and a frontier model (class C) 0.031 g.

EU Netherlands ranks 6 of 15 regions for class B, where 1 is the lowest carbon. Compared with EU Sweden, the same query here emits 3.8 times as much; compared with Japan, 0.6 times as much. The grid, not the model, sets most of the difference.

Emission factors for EU Netherlands by model class

Per million tokens unless stated. Total = electricity + embodied hardware + amortised training. Per query = 400 tokens.
Model class	Electricity kg	Hardware kg	Training kg	Total kg CO₂e	g per query	Water L	mL per query
Class A (small)	0.011	0.0008	0.0008	0.013	0.005	0.140	0.056
Class B (mid-size)	0.043	0.0030	0.0041	0.050	0.02	0.565	0.23
Class C (frontier)	0.055	0.0039	0.0179	0.077	0.031	0.719	0.29

Example: a company of 100 people where 80 use a class B assistant for 2,000 messages a month each sends about 768 million tokens a year. Served from EU Netherlands, that is 38 kg CO₂e a year, against 10 kg in EU Sweden.

How much water does AI usage consume in EU Netherlands?

A 400-token query to a class B model served from EU Netherlands consumes about 0.23 mL of water, and a million tokens 0.565 litres. The figure combines the water evaporated on site for cooling with the water embedded in generating the grid electricity. Regions that Li et al. 2025 do not cover directly use an estimated on-site intensity, marked as such in the dataset.

How do the other regions compare?

Class B, total kg CO₂e per million tokens, lowest first: EU Sweden 0.0131, EU France 0.0161, EU Finland 0.0201, EU Belgium 0.0291, UK (London) 0.0451, EU Netherlands 0.0501, US East 0.0511, EU Ireland 0.0531, US West (Oregon) 0.0531, US Texas 0.0611, EU Germany 0.0661, US South 0.0691, US Central (Iowa) 0.0741, Singapore 0.0831, Japan 0.0871.

Where do these numbers come from and how do I cite them?

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 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). Uncertainty on the electricity term is ±40% for class A and ±50% for classes B and C.

Dataset: AI Inference Emission and Resource Factors for Corporate GHG Inventories, Guillermo Llopis, SOMA AI, September 2026, CC BY 4.0, concept DOI 10.5281/zenodo.20443585 (this version 10.5281/zenodo.22767475). Method: Accounting for AI Inference in Corporate GHG Inventories: A Four-Tier Methodology for Scope 3 Category 1 Reporting, arXiv:2606.10660, under peer review.

Partners can fetch any row from the AI factors API.

When citing a single value, name the region and class, for example "0.0501 kg CO₂e per million tokens, class B, EU Netherlands, SOMA AI factors September 2026", and link to app.somaai.earth/ai-factors/region/eu-netherlands.