mlsanityData to 14 Oct 2026

AI, Climate, and Regulation: From Data Centers to the AI Act

arXiv 2410.06681 ↗First posted 9 Oct 2024cs.CY cs.AIcited by 4

Not on the map. Why?

Abstract (arXiv)

We live in a world that is experiencing an unprecedented boom of AI applications that increasingly penetrate and enhance all sectors of private and public life, from education, media, medicine, and mobility to the industrial and professional workspace, and -- potentially particularly consequentially -- robotics. As this world is simultaneously grappling with climate change, the climate and environmental implications of the development and use of AI have become an important subject of public and academic debate. In this paper, we aim to provide guidance on the climate-related regulation for data centers and AI specifically, and discuss how to operationalize these requirements. We also highlight challenges and room for improvement, and make a number of policy proposals to this end. In particular, we propose a specific interpretation of the AI Act to bring reporting on the previously unadressed energy consumption from AI inferences back into the scope. We also find that the AI Act fails to address indirect greenhouse gas emissions from AI applications. Furthermore, for the purpose of energy consumption reporting, we compare levels of measurement within data centers and recommend measurement at the cumulative server level. We also argue for an interpretation of the AI Act that includes environmental concerns in the mandatory risk assessment (sustainability risk assessment, SIA), and provide guidance on its operationalization. The EU data center regulation proves to be a good first step but requires further development by including binding renewable energy and efficiency targets for data centers. Overall, we make twelve concrete policy proposals, in four main areas: Energy and Environmental Reporting Obligations; Legal and Regulatory Clarifications; Transparency and Accountability Mechanisms; and Future Far-Reaching Measures beyond Transparency.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 20 of its 85 references that are in the map. First posted on arXiv 9 Oct 2024.

Builds on

20 of 85 references.

References · 20

  1. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,380
  2. 2020Scaling Laws for Neural Language Modelscited by 6,586
  3. 2021Scaling Language Models: Methods, Analysis & Insights from Training Gophercited by 1,202
  4. 2021Efficient Large-Scale Language Model Training on GPU Clusters Using Megatron-LMcited by 632
  5. 2019Green AIcited by 627
  6. 2021Carbon Emissions and Large Neural Network Trainingcited by 567
  7. 2019Tackling Climate Change with Machine Learningcited by 265
  8. 2022Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Modelcited by 265
  9. 2021Sustainable AI: Environmental Implications, Challenges and Opportunitiescited by 242
  10. 2019A Study of BFLOAT16 for Deep Learning Trainingcited by 232
  11. 2023Power Hungry Processing: Watts Driving the Cost of AI Deployment?cited by 191
  12. 2023Efficient Large Language Models: A Surveycited by 186
8 more
  1. 2022Measuring the Carbon Intensity of AI in Cloud Instancescited by 111
  2. 2023Counting Carbon: A Survey of Factors Influencing the Emissions of Machine Learningcited by 42
  3. 2020HULK: An Energy Efficiency Benchmark Platform for Responsible Natural Language Processingcited by 16
  4. 2023Algorithms as Social-Ecological-Technological Systems: an Environmental Justice Lens on Algorithmic Auditscited by 12
  5. 2023Towards Environmentally Equitable AI via Geographical Load Balancingcited by 10
  6. 2023Beyond CO2 Emissions: The Overlooked Impact of Water Consumption of Information Retrieval Modelscited by 6
  7. 2024AI, Climate, and Transparency: Operationalizing and Improving the AI Actcited by 4
  8. 2021Artificial intelligence for Sustainable Energy: A Contextual Topic Modeling and Content Analysiscited by 3

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 1.

4 papers.