Aligning artificial intelligence with climate change mitigation
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Following traditional hardware development practices alone will not suffice to meet the demands for efficient computing power in AI systems, whether deployed in data centers or at the edge.
“Overall, these observations suggest that following traditional hardware development practices alone will not suffice to meet the demands for efficient computing power in artificial intelligence (AI) systems [26], whether they are deployed in data centers or at the edge.”
From Brain-inspired computing systems: a systematic literature review (2024) · cites this paper as [26]
Gains in efficiency can spur higher overall consumption, a phenomenon known as rebound effects.
“Recent work on indirect impacts [63] warns of potential 'rebound effects', whereby gains in efficiency spur higher overall consumption.”
From From Efficiency Gains to Rebound Effects: The Problem of Jevons' Paradox in AI's Polarized Environmental Debate (2025) · cites this paper as [63]
Direct economic rebound effects are the most common type of indirect effects discussed in conjunction with many technological innovations.
“They are also the most common type of indirect effects that are discussed in conjunction with many technological innovations (see [63, 135]).”
From From Efficiency Gains to Rebound Effects: The Problem of Jevons' Paradox in AI's Polarized Environmental Debate (2025) · cites this paper as [63]
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On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜
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“NAtuRE CLImAtE CHANGE | www.nature.com/natureclimatechange centres towards large cloud and hyperscale data centres 23,67 , which have higher virtualization, more efficient cooling and increased use of specialized ‘AI accelerator’ hardware such as application-specific integrated circuits (ASICs) and…”
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“Modern ML models that use neural networks are particularly energy intensive in the development phase as they have many more possible model configurations than their predecessors, and it is not”
The Energy and Carbon Footprint of the Global ICT and E&M Sectors 2010–2015
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Digitalization and the Anthropocene
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“From a policy perspective, Kaack et al. (2022) provide a comprehensive taxonomy of how AI systems can both contribute to and help mitigate climate change, identifying energy-efficient inference as a key lever for reducing AI’s direct environmental footprint.”
- Authors
- Kaack, L. H., Donti, P. L., Strubell, E., Kamiya, G., Creutzig, F., & Rolnick, D.
- Affiliations
- Hertie School (DE)
- Carnegie Mellon University (US)
- Agence internationale de l'énergie (FR)
- Mercator Research Institute on Global Commons and Climate Change (DE)
- Mila - Quebec Artificial Intelligence Institute (CA)
- Corresponding author
- Kaack, Lynn H.
- Venue
- Nature Climate Change
- Published
- 2022-06
- Volume
- 12
- Issue
- 6
- Pages
- 518-527
- ISSN
- 1758-678X (ISSN-L)
- 1758-6798
- OpenAlex
- W4281786142
- Cited by
- 570
- References
- 12
- Citations by year
- 2026: 193 · 2025: 180 · 2024: 118 · 2023: 63 · 2022: 16
- FWCI
- 25.82
- Type
- journal article
- Language
- en
- Open access
- green
- hal.science
- Licence
- Locations
- Nature Climate Change (publishedVersion)
- HAL (publishedVersion, other-oa)
- RePEc: Research Papers in Economics (submittedVersion)
- OPUS 4 (Zuse Institute Berlin) (submittedVersion)
- Publication Database PIK (Potsdam Institute for Climate Impact Research (PIK)) (submittedVersion)
- Funders
- For this report, LHK, PLD and DR received funding from the Environmental Law Institute and Alfred P. Sloan Foundation.
- U.S. Department of Energy (DE-FG02-97ER25308)
- National Science Foundation (SES-00949710)
- Thomas and Stacey Siebel Foundation
- Canada CIFAR AI Chairs program
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Green IT and Sustainability
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Climate Change Policy and Economics
- Physical Sciences › Computer Science › Hardware and Architecture › Energy Efficiency in Computing
- Keywords
- artificial intelligence, machine learning, greenhouse gas emissions, climate change mitigation, impact assessment framework, scenario analysis, AI policy
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