Sustainability / AI and sustainability

I collect noteworthy resources about the topic of AI and environmental sustainability. See also my page on Sustainable computing and Artificial intelligence.

News

• 2025-10-27 Europeans agree: Time to Put Limits on Big Tech Data Centres. A polling conducted in five European countries shows that a majority wants "rules to limit new data centre impacts on energy, water and economy".
• 2025-10-20 # Data Centers Look to Old Airplane Engines for Power (IEEE).
  • Related (2025-10-22): Data centers turn to commercial aircraft jet engines bolted onto trailers as AI power crunch bites.
• 2025-10-16 Scottish data centres powering AI already using enough water to fill 27 million bottles a year.
• 2025-09-30 Responding to the climate impact of generative AI. An overview of methods used to reduce the emissions of AI.
• 2025-08-29 How AI Is Transforming Data Centers and Ramping Up Power Demand. AI workloads are estimated to be about 13% of the current power demand of 62 GW. AI os projected to be about 28% of the overall demand by 2027.
• 2025-08-21 Google: How much energy does Google’s AI use? We did the math.
  • 2025-08-21 MIT Technology Review: In a first, Google has released data on how much energy an AI prompt uses.
  • 2025-08-23 Ketan Joshi: Big tech’s selective disclosure masks AI’s real climate impact.
• 2025-05-20 MIT Technology Review: We did the math on AI’s energy footprint. Here’s the story you haven’t heard..
• 08 Feb 2025: Ultra-efficient AI won’t solve data centers’ climate problem. This might.. Summary: Adaptively use less resource-demanding inference processes when the carbon intensity of the power grid is high.
• 15 Jan 2025: Enterprises in for a shock when they realize power and cooling demands of AI
• 13 Jan 2025: Water shortage fears as Labour’s first AI growth zone sited close to new reservoir
• 09 Jan 2025: To save the energy grid from AI, use open source AI software

Reports

• 2025-09-17: Water use in AI and Data Centres (Government Digital Sustainability Alliance, UK). Full report (PDF)

  The Government Digital Sustainability Alliance’s (GDSA) report highlights that AI is predicted to lead to an increase in global water usage from 1.1bn to 6.6bn cubic metres by 2027. This is equivalent to more than half of the UK’s total water usage.

• 2025-04-10: Energy and AI (IEA).
• 2025-03-05: Extracting Profits from the Public: How Utility Ratepayers Are Paying for Big Tech’s Power (Martin and Peskoe, Harvard Law School).
• 2024-12-20: 2024 United States Data Center Energy Usage Report. Projects that by 2028, more than half of the energy going to data centers will be used for AI.
• 2024-12-09: Data Centers in Virginia
• 2024-09-03: The Environmental Impacts of AI - Policy Primer (Hugging Face).

Reports from Big Tech:

• Meta 2025 Sustainability Report
• Meta 2024 Sustainability Report

Research groups and organizations.

• Hertie Schoole, Centre of Sustainability: AI and Climate Technology Policy

See also Sustainable computing / Research groups and organizations.

Blog posts

• 2025-11-05 Power, Heat, and Intelligence - AI Data Centers Explained

Norwegian media

• 2025-11-03 Kunstig intelligens får skylden for et enormt strømforbruk den ikke har
  • Appendix til debattinnlegget
• 26 des 2024: Spørsmål til ChatGPT sluker vatn: – Burde vere ei øvre grense for tøys og tull
• 16 okt 2024: Atomkraft + KI = sant?
• 02 okt 2024: KI forurenser 10 ganger mer enn Google – slik bør du bruke det

Tools

A. Tools for Measuring and Quantifying Footprint

• AIPowerMeter [Website] [Source code]
• CarbonAI [Source code]
• carbontracker [Source code] [Paper]
• CodeCarbon [Website] [Source code] [Paper]
• d2m [Website] [Source code]
• Eco2AI [Source code] [Paper]
• EcoLogits [Source code] [Documentation]
• EnergyMeter [Source code] [Paper]
• experiment-impact-tracker [Source code] [Paper]
• powermeter [Source code]
• PowerJoular [Website]
• pyJoules [Source code]
• tracarbon [Source code]
• zeus [Website] [Source code] [Paper]

B. Tools for Calculation/Estimation of Footprint

• Green Algorithms [Website] [Paper]
• ML CO2 Impact [Website] [Paper]

C. Tools for "greener" prompting

• PromptZero. Cannot find research showing that this works (2025-11-19).
• ROCKS method. Cannot find research showing that this actually reduces the energy consumption

Leaderboards

• LLM-Perf Leaderboard
• ML.ENERGY Leaderboard

Papers

• The Problem of Sustainable AI: A Critical Assessment of an Emerging Phenomenon (Schütze 2024).
• Video Killed the Energy Budget: Characterizing the Latency and Power Regimes of Open Text-to-Video Models (Delavande et al. 2025).
• 2025-06-18: Artificial intelligence: Supply chain constraints and energy implications.
  • Blog post by the author about the paper

    [...] AI systems were responsible for up to 20% of global data center power demand by the end of last year. Moreover, this share could approach half of data center power demand by the end of this year, as the power demand of AI systems could rise to 23 gigawatts. This exceeds the current power demand of cryptocurrency mining and is equivalent to twice the power needed to keep my home country the Netherlands running.

• From Efficiency Gains to Rebound Effects: The Problem of Jevons’ Paradox in AI’s Polarized Environmental Debate (Luccioni et al. 2025) [Paper]
• SPROUT: Green Generative AI with Carbon-Efficient LLM Inference (Li et al. 2024) Paper.
• Artificial Intelligence in Climate Change Mitigation: A Review of Predictive Modeling and Data-Driven Solutions for Reducing Greenhouse Gas Emissions (Adegbite et al. 2024) Paper
• Addition is All You Need for Energy-Efficient Language Models (Luo et al. 2024) [Paper]
• LLMCO2: Advancing Accurate Carbon Footprint Prediction for LLM Inferences (Fu et al. 2024) [Paper]
• AI, Climate, and Regulation: From Data Centers to the AI Act (Erbert et al. 2024) [Paper]
• Offline Energy-Optimal LLM Serving: Workload-Based Energy Models for LLM Inference on Heterogeneous Systems (Wilkins et al. 2024) [Paper]
• Hybrid Heterogeneous Clusters Can Lower the Energy Consumption of LLM Inference Workloads (Wilkins et al. 2024) [Paper]
• The Price of Prompting: Profiling Energy Use in Large Language Models Inference (Husom et al. 2024) [Paper]
• Towards Greener LLMs: Bringing Energy-Efficiency to the Forefront of LLM Inference (Stojkovic et al. 2024) [Paper]
• Towards Efficient Generative Large Language Model Serving: A Survey from Algorithms to Systems (Miao et al. 2024) [Paper]
• Beyond Efficiency: Scaling AI Sustainably (Wu et al. 2024) [Paper]
• A Simplified Machine Learning Product Carbon Footprint Evaluation Tool (Lang et al. 2024) [Paper]
• Green AI: Exploring Carbon Footprints, Mitigation Strategies, and Trade Offs in Large Language Model Training (Liu et al. 2024) [Paper]
• Measuring and Improving the Energy Efficiency of Large Language Models Inference (Argerich et al. 2024) [Paper] [GitHub]
• Engineering Carbon Emissions Aware Machine Learning Pipelines (Husom et al. 2024) [Paper]
• Timeshifting Strategies for Carbon-Efficient Long-Running Large Language Model Training (Jagannadharao et al. 2024) [Paper]
• Perseus: Reducing Energy Bloat in Large Model Training (Chung et al. 2024) [Paper]
• Counting Carbon: A Survey of Factors Influencing the Emissions of Machine Learning (Luccioni et al. 2023) [Paper]
• A Systematic Review of Green AI (Verdecchia et al. 2023) [Paper]
• Power Hungry Processing: Watts Driving the Cost of AI Deployment? (Luccioni et al. 2023) [Paper]
• LLMCarbon: Modeling the End-To-End Carbon Footprint of Large Language Models (Faiz et al. 2023) [Paper]
• Chasing Low-Carbon Electricity for Practical and Sustainable DNN Training (Yang et al. 2023) [Paper]
• Zeus: Understanding and Optimizing GPU Energy Consumption of DNN Training (You et al. 2023) [Paper]
• Making AI Less "Thirsty": Uncovering and Addressing the Secret Water Footprint of AI Models (Li et al. 2023) [Paper]
• Method and Evaluations of the Effective Gain of Artificial Intelligence Models for Reducing CO2 Emissions (Delanoë et al. 2023) [Paper]
• Measuring the Environmental Impacts of Artificial Intelligence Compute and Applications (OECD 2022) [Paper]
• Towards Sustainable Artificial Intelligence: An Overview of Environmental Protection Uses and Issues (Pachot et al. 2022) [Paper]
• Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning (Henderson et al. 2022) [Paper]
• The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrink (Patterson et al. 2022) [Paper]
• A First Look into the Carbon Footprint of Federated Learning (Qiu et al. 2022) [Paper]
• Sustainable AI: Environmental Implications, Challenges and Opportunities (Wu et al. 2022) [Paper]
• Environmental Assessment of Projects Involving AI Methods (Lefèvre et al. 2022) [Paper]
• Eco2AI: Carbon Emissions Tracking of Machine Learning Models (Budennyy et al. 2022) [Paper]
• Bridging Fairness and Environmental Sustainability in Natural Language Processing (Hessenthaler et al. 2022) [Paper]
• Estimating the Carbon Footprint of BLOOM: A 176B Parameter Language Model (Luccioni et al. 2022) [Paper]
• Measuring the Carbon Intensity of AI in Cloud Instances (Dodge et al. 2022) [Paper]
• Unraveling the Hidden Environmental Impacts of AI Solutions for Life Cycle Assessment (Ligozat et al. 2022) [Paper]
• A Survey on Green Deep Learning (Xu et al. 2021) [Paper]
• Evaluating the Carbon Footprint of NLP Methods: A Survey and Analysis of Existing Tools (Bannour et al. 2021) [Paper]
• New Universal Sustainability Metrics to Assess Edge Intelligence (Lenherr et al. 2021) [Paper]
• Aligning Artificial Intelligence with Climate Change Mitigation (Kaack et al. 2021) [Paper]
• A Framework for Energy and Carbon Footprint Analysis of Distributed and Federated Edge Learning (Savazzi et al. 2021) [Paper]
• A Practical Guide to Quantifying Carbon Emissions for Machine Learning Researchers and Practitioners (Ligozat et al. 2021) [Paper]
• Green Algorithms: Quantifying the Carbon Footprint of Computation (Lannelongue et al. 2021) [Paper]
• Carbon Emissions and Large Neural Network Training (Patterson et al. 2021) [Paper]
• Chasing Carbon: The Elusive Environmental Footprint of Computing (Gupta et al. 2020) [Paper]
• Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models (Anthony et al. 2020) [Paper]
• Green AI (Schwartz et al. 2020) [Paper]
• Quantifying the Carbon Emissions of Machine Learning (Lacoste et al. 2019) [Paper]
• Energy and Policy Considerations for Deep Learning in NLP (Strubell et al. 2019) [Paper]

Other resources

• Green Software Foundation – Promoting Sustainable Software Development [Website]
• ENFIELD – European Lighthouse to Manifest Trustworthy and Green AI, a Centre of Excellence for Green AI [Website]
• Awesome Green AI by samuelrince