New open-source method shows the same AI request emits 8.5 times more CO₂ in Texas than in Sweden

Estimates based on the average emissions intensity of each regional electricity grid*

STOCKHOLM, 21 October 2026. Swedish AI provider Berget AI today releases an open-source method for calculating the carbon emissions of every individual AI request, developed with researchers from Stockholm Environment Institute (SEI) and the tech non-profits WattTime and Klimatkollen. Berget AI's customers can follow their own emissions in the company's console as they use the service. By making the method free for anyone to use, Berget AI wants more AI providers to report emissions per request, so that buyers can compare providers on real figures and choose lower-carbon options.

"Most companies today have no way of knowing what their use of AI emits. With this method our customers can follow their emissions request by request and compare providers on data instead of marketing. We know most European buyers would choose differently if they could see the numbers," says Andreas Lundmark, CEO of Berget AI.

A typical request to the open model Kimi K3 emits about 0.4 grams of CO₂ in Sweden, 1.6 grams in Ireland, 2.5 grams in Germany and 3.2 grams in Texas, on identical hardware — 8.5 times more in Texas than in Sweden. The result also depends on the model, the time of day and whether part of the prompt is cached, so the method calculates each request separately instead of applying an average. Berget AI reports location-based figures, using the average grid mix where the servers run, and includes the emissions from manufacturing the hardware.

Buyers rarely get emission figures from their AI providers, and the public figures that exist are mostly averages estimated from the outside. Open-weight models, which can run in any data centre, handle a growing share of AI traffic: 29% of tokens on Vercel's AI Gateway in June 2026, up from 11% in April. For those models, where a request runs is the buyer's choice, provided the emissions are visible.

"Most of the alarming figures about AI's footprint come from multiplying an average by a guess at the volume, and nobody can check them. The only fair comparison is to calculate each request and add them up. We have published our judgement calls so anyone can check and use them," says Christian Landgren, CTO and co-founder of Berget AI.

"The International Energy Agency projects data-centre electricity consumption to more than double to about 945 TWh by 2030, with AI as the principal driver. Any promise of efficiency gains is likely to be outweighed by higher demand, negating any emission reductions from renewables. Methodologies like this offer a transparent basis for informed decisions. Their strength is that they clearly demonstrate how the location of the grid and critical infrastructure matter when it comes to how sustainable AI can be," says Somya Joshi, Research Director at SEI.

About the method

The partners mapped and measured the environmental impact of Berget AI's infrastructure, including energy, water and hardware, as well as the carbon footprint of Klimatkollen's Garbo AI, which runs on it. They combined the data with current research on translating it into carbon emissions and broader environmental impact.

The method follows the Green Software Foundation's SCI for AI specification and is available under the MIT licence on GitHub. The calculator at co2.berget.ai applies it to any model and region. It builds on open-source estimators such as EcoLogits and the Sustainable AI Group's CLEER dashboard, which estimate emissions from outside the provider. As far as we know, Berget AI is the first inference provider to calculate emissions for every request on its own infrastructure and publish the method. The partners will keep developing it, for example to cover the water and energy footprint of chip manufacturing, which the current version does not include.

* Emissions estimates are based on the current average emissions intensity of each regional electricity grid, which has been the norm in greenhouse gas accounting. An emerging approach uses marginal emission rates, which estimate the impact of additional load on both the use of existing power plants and the new generators built to meet increased demand. Marginal estimates will be added to the calculator next month and may significantly change the measured greenhouse gas intensity of data centres in Sweden.

Correction, 21 October 2026: an earlier version of this release, distributed under embargo, stated 1.2 g (Ireland), 2.1 g (Germany) and 3.9 g (Texas) per request and a ten-fold difference between Texas and Sweden. A source review of the grid intensities published with this release corrected them to Ireland ~224, Germany ~337 and Texas ~333 g CO₂e/kWh; the corrected per-request figures appear above and the Texas–Sweden difference is 8.5×. Sweden's figure is unchanged.

Facts


Grid intensity	Sweden ~8 g CO₂e/kWh (IEA 2024) · Ireland ~224 (SEAI 2024) · Germany ~337 (Ember 2024) · Texas (ERCOT) ~333 (EPA eGRID 2023) · global average ~470 (Ember 2024)
Embodied hardware	~1,000 kg CO₂ per data centre GPU; refurbished counted as zero
Idle power	An idle B300 still draws 236 W (measured over seven days in production)
Cooling	Nordic free air: PUE ~1.15, no water. Hot climates: toward 1.8, up to 2 L water/kWh
Scope	Training excluded: self-reported figures vary ±50%
Open-model traffic	29% of tokens on Vercel AI Gateway, June 2026 (about 11% in April); ~69% of named-model tokens on OpenRouter
Kimi K3, one typical request	Sweden 0.38 g CO₂e (89% from hardware manufacturing). Texas 3.2 g (90% from electricity), plus about 6 mL of cooling water. Energy about 4 Wh in both. Calculated with @bergetai/co2-calculator 1.1.1: B300 node, shared deployment, production-typical request of 5,000 input and 387 output tokens, weighted by a typical day's traffic.

About Berget AI

Berget AI champions AI sovereignty to unlock innovation and growth in Europe. Founded in Sweden in 2024, Berget AI offers inference and AI infrastructure for private and public sector organisations. Since 2025 it has reported the CO₂ emissions of every API request.

About SEI, WattTime and Klimatkollen

Partners: Stockholm Environment Institute (knowledge partner), WattTime (scientific partner), Klimatkollen (partner).

Stockholm Environment Institute is an international non-profit research institute that tackles climate, environment and sustainable development challenges. To learn more, visit www.sei.org.

Klimatkollen.se is a Swedish citizen platform that presents climate data about municipalities and companies. The purpose is to contribute to more engaged citizens, a more fact-based climate debate, and measures that reduce emissions in line with the Paris Agreement. Klimatkollen is run by the non-profit association Klimatbyrån.

WattTime is an environmental tech nonprofit that empowers all people, companies, policymakers, and countries to slash emissions and choose cleaner energy. Founded by UC Berkeley researchers, we research, develop, and share data-driven tools and policies that help anyone who is interested to reduce more emissions, faster. www.watttime.org

Press contacts

Andreas Lundmark, CEO, andreas@berget.ai, +46 730 794 471
Christian Landgren, CTO, christian@berget.ai, +46 70 775 58 31