AI and sustainability

I collect noteworthy resources about the topic of AI and environmental sustainability.

Quicklinks:

News | Papers | Reports | Norwegian media | Tools | Leaderboards | Other resources

News

• 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

Papers

• 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]

Reports

• The Environmental Impacts of AI - Policy Primer (Hugging Face).

Norwegian media (Norsk media)

• 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]
• 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]

Leaderboards

• [LEADERBOARD]: LLM-Perf Leaderboard
• [LEADERBOARD]: ML.ENERGY Leaderboard

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