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Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning

arXiv 2002.05651 ↗First posted 31 Jan 2020cs.CY cs.LGcited by 246

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Abstract (arXiv)

Accurate reporting of energy and carbon usage is essential for understanding the potential climate impacts of machine learning research. We introduce a framework that makes this easier by providing a simple interface for tracking realtime energy consumption and carbon emissions, as well as generating standardized online appendices. Utilizing this framework, we create a leaderboard for energy efficient reinforcement learning algorithms to incentivize responsible research in this area as an example for other areas of machine learning. Finally, based on case studies using our framework, we propose strategies for mitigation of carbon emissions and reduction of energy consumption. By making accounting easier, we hope to further the sustainable development of machine learning experiments and spur more research into energy efficient algorithms.

Key facts. Cited by 246 papers in the map (data through 7 Oct 2026). First posted on arXiv 31 Jan 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 26; 2022: 45; 2023: 40; 2024: 37; 2025: 49; 2026: 49.

246 papers.

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  1. 2021Carbon Emissions and Large Neural Network Trainingcited by 561
  2. 2021AI and the Everything in the Whole Wide World Benchmarkcited by 340
  3. 2021DocFormer: End-to-End Transformer for Document Understandingcited by 254
  4. 2021Sustainable AI: Environmental Implications, Challenges and Opportunitiescited by 242
  5. 2022The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrinkcited by 183
  6. 2022Pile of Law: Learning Responsible Data Filtering from the Law and a 256GB Open-Source Legal Datasetcited by 113
  7. 2023Art and the science of generative AI: A deeper divecited by 93
  8. 2023Evaluating the Social Impact of Generative AI Systems in Systems and Societycited by 91
  9. 2022Zeus: Understanding and Optimizing GPU Energy Consumption of DNN Trainingcited by 78
  10. 2022Efficient Methods for Natural Language Processing: A Surveycited by 78
  11. 2023A Systematic Review of Green AIcited by 77
  12. 2024Foundation Model for Advancing Healthcare: Challenges, Opportunities, and Future Directionscited by 77
38 more
  1. 2023LLMCarbon: Modeling the end-to-end Carbon Footprint of Large Language Modelscited by 71
  2. 2022Impact of Tokenization on Language Models: An Analysis for Turkishcited by 62
  3. 2021Compute and Energy Consumption Trends in Deep Learning Inferencecited by 61
  4. 2021Distributed Deep Learning in Open Collaborationscited by 58
  5. 2022Eco2AI: carbon emissions tracking of machine learning models as the first step towards sustainable AIcited by 58
  6. 2021Dynaboard: An Evaluation-As-A-Service Platform for Holistic Next-Generation Benchmarkingcited by 53
  7. 2021Temporal Adaptation of BERT and Performance on Downstream Document Classification: Insights from Social Mediacited by 51
  8. 2022Evaluation Gaps in Machine Learning Practicecited by 48
  9. 2022A Survey on Model Compression and Acceleration for Pretrained Language Modelscited by 45
  10. 2023Making AI Less "Thirsty": Uncovering and Addressing the Secret Water Footprint of AI Modelscited by 44
  11. 2023Counting Carbon: A Survey of Factors Influencing the Emissions of Machine Learningcited by 42
  12. 2022Compute-Efficient Deep Learning: Algorithmic Trends and Opportunitiescited by 38
  13. 2021How I failed machine learning in medical imaging -- shortcomings and recommendationscited by 37
  14. 2023How to estimate carbon footprint when training deep learning models? A guide and reviewcited by 34
  15. 2021Unraveling the Hidden Environmental Impacts of AI Solutions for Environmentcited by 32
  16. 2023Ecosystem Graphs: The Social Footprint of Foundation Modelscited by 31
  17. 2022Square One Bias in NLP: Towards a Multi-Dimensional Exploration of the Research Manifoldcited by 29
  18. 2025Think Smarter not Harder: Adaptive Reasoning with Inference Aware Optimizationcited by 29
  19. 2024Offline Energy-Optimal LLM Serving: Workload-Based Energy Models for LLM Inference on Heterogeneous Systemscited by 27
  20. 2021How Robust are Model Rankings: A Leaderboard Customization Approach for Equitable Evaluationcited by 24
  21. 2022Does Simultaneous Speech Translation need Simultaneous Models?cited by 22
  22. 2024Reinforcement Learning from Multi-role Debates as Feedback for Bias Mitigation in LLMscited by 22
  23. 2024The Price of Prompting: Profiling Energy Use in Large Language Models Inferencecited by 22
  24. 2022Great Power, Great Responsibility: Recommendations for Reducing Energy for Training Language Modelscited by 21
  25. 2022Towards Climate Awareness in NLP Researchcited by 19
  26. 2023A Material Lens on Coloniality in NLPcited by 19
  27. 2022Carbon Footprint of Selecting and Training Deep Learning Models for Medical Image Analysiscited by 18
  28. 2022Subgroup Robustness Grows On Trees: An Empirical Baseline Investigationcited by 17
  29. 2023A Synthesis of Green Architectural Tactics for ML-Enabled Systemscited by 17
  30. 2024Hybrid Heterogeneous Clusters Can Lower the Energy Consumption of LLM Inference Workloadscited by 17
  31. 2021Shrinking Bigfoot: Reducing wav2vec 2.0 footprintcited by 16
  32. 2022Vote'n'Rank: Revision of Benchmarking with Social Choice Theorycited by 15
  33. 2024MLPerf Power: Benchmarking the Energy Efficiency of Machine Learning Systems from Microwatts to Megawatts for Sustainable AIcited by 15
  34. 2024adaptMLLM: Fine-Tuning Multilingual Language Models on Low-Resource Languages with Integrated LLM Playgroundscited by 14
  35. 2023Towards energy-efficient Deep Learning: An overview of energy-efficient approaches along the Deep Learning Lifecyclecited by 13
  36. 2025Intelligence per Watt: Measuring Intelligence Efficiency of Local AIcited by 13
  37. 2021Edge-Native Intelligence for 6G Communications Driven by Federated Learning: A Survey of Trends and Challengescited by 12
  38. 2022Towards a multi-stakeholder value-based assessment framework for algorithmic systemscited by 12

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