mlsanityData to 14 Oct 2026

Unraveling the Hidden Environmental Impacts of AI Solutions for Environment

arXiv 2110.11822 ↗First posted 22 Oct 2021cs.AI cs.CYcited by 32

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Abstract (arXiv)

In the past ten years, artificial intelligence has encountered such dramatic progress that it is now seen as a tool of choice to solve environmental issues and in the first place greenhouse gas emissions (GHG). At the same time the deep learning community began to realize that training models with more and more parameters requires a lot of energy and as a consequence GHG emissions. To our knowledge, questioning the complete net environmental impacts of AI solutions for the environment (AI for Green), and not only GHG, has never been addressed directly. In this article, we propose to study the possible negative impacts of AI for Green. First, we review the different types of AI impacts, then we present the different methodologies used to assess those impacts, and show how to apply life cycle assessment to AI services. Finally, we discuss how to assess the environmental usefulness of a general AI service, and point out the limitations of existing work in AI for Green.

Key facts. Cited by 32 papers in the map (data through 14 Oct 2026). Builds on 12 of its 36 references that are in the map. First posted on arXiv 22 Oct 2021.

Builds on

12 of 36 references.

References · 12

  1. 2021On the Opportunities and Risks of Foundation Modelscited by 4,033
  2. 2019Energy and Policy Considerations for Deep Learning in NLPcited by 1,254
  3. 2019Green AIcited by 627
  4. 2021Carbon Emissions and Large Neural Network Trainingcited by 567
  5. 2019Quantifying the Carbon Emissions of Machine Learningcited by 521
  6. 2019Tackling Climate Change with Machine Learningcited by 265
  7. 2020Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learningcited by 249
  8. 2020Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Modelscited by 169
  9. 2019The role of artificial intelligence in achieving the Sustainable Development Goalscited by 100
  10. 2020Chasing Carbon: The Elusive Environmental Footprint of Computingcited by 72
  11. 2019A Review on Energy, Environmental, and Sustainability Implications of Connected and Automated Vehiclescited by 7
  12. 2020Green Algorithms: Quantifying the carbon footprint of computationcited by 7

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 3; 2023: 10; 2024: 5; 2025: 8; 2026: 6.

32 papers.

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  1. 2023DoReMi: Optimizing Data Mixtures Speeds Up Language Model Pretrainingcited by 399
  2. 2023Data Selection for Language Models via Importance Resamplingcited by 329
  3. 2022Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Modelcited by 265
  4. 2023Large Language Model Alignment: A Surveycited by 243
  5. 2022Measuring the Carbon Intensity of AI in Cloud Instancescited by 111
  6. 2023A Systematic Review of Green AIcited by 77
  7. 2023Counting Carbon: A Survey of Factors Influencing the Emissions of Machine Learningcited by 42
  8. 2023How to estimate carbon footprint when training deep learning models? A guide and reviewcited by 34
  9. 2024The largest EEG-based BCI reproducibility study for open science: the MOABB benchmarkcited by 29
  10. 2023Green Edge AI: A Contemporary Surveycited by 25
  11. 2025HiMAE: Hierarchical Masked Autoencoders Discover Resolution-Specific Structure in Wearable Time Seriescited by 19
  12. 2023Watt For What: Rethinking Deep Learning's Energy-Performance Relationshipcited by 16
20 more
  1. 2023Efficiency is Not Enough: A Critical Perspective of Environmentally Sustainable AIcited by 12
  2. 2025EigenLoRAx: Recycling Adapters to Find Principal Subspaces for Resource-Efficient Adaptation and Inferencecited by 9
  3. 2025What do Large Language Models Say About Animals? Investigating Risks of Animal Harm in Generated Textcited by 9
  4. 2025LACONIC: A 3D Layout Adapter for Controllable Image Creationcited by 6
  5. 2024On the Ethical Considerations of Generative Agentscited by 4
  6. 2025AI persuading AI vs AI persuading Humans: LLMs' Differential Effectiveness in Promoting Pro-Environmental Behaviorcited by 4
  7. 2022Environmental and Social Sustainability of Creative-Aicited by 3
  8. 2025A Design Framework for operationalizing Trustworthy Artificial Intelligence in Healthcare: Requirements, Tradeoffs and Challenges for its Clinical Adoptioncited by 3
  9. 2026Shared LoRA Subspaces for almost Strict Continual Learningcited by 2
  10. 2024How do Hyenas deal with Human Speech? Speech Recognition and Translation with ConfHyenacited by 1
  11. 2023Explainable Sustainability for AI in the Artscited by 0
  12. 2024Green Runner: A tool for efficient deep learning component selectioncited by 0
  13. 2024Connecting Algorithmic Fairness to Quality Dimensions in Machine Learning in Official Statistics and Survey Productioncited by 0
  14. 2025Modular, On-Site Solutions with Lightweight Anomaly Detection for Sustainable Nutrient Management in Agriculturecited by 0
  15. 2025Green AI: A systematic review and meta-analysis of its definitions, lifecycle models, hardware and measurement attemptscited by 0
  16. 2026Where Does AI Leave a Footprint? Children's Reasoning About AI's Environmental Costscited by 0
  17. 2026Environmental Footprint of GenAI Research: Insights from the Moshi Foundation Modelcited by 0
  18. 2026CADS: Conformal Adaptive Decision System for Cost-Efficient Image Classificationcited by 0
  19. 2026The Rising Unsustainability of AI Graphics Cards Productioncited by 0
  20. 2026Life Cycle Assessment of Pre-training the Lucie 7B Open-Source Large Language Model on the Jean Zay Supercomputercited by 0