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Aligning artificial intelligence with climate change mitigation


There is great interest in how the growth of artificial intelligence and machine learning may affect global GHG emissions. However, such emissions impacts remain uncertain, owing in part to the diverse mechanisms through which they occur, posing difficulties for measurement and forecasting. Here we introduce a systematic framework for describing the effects of machine learning (ML) on GHG emissions, encompassing three categories: computing-related impacts, immediate impacts of applying ML and system-level impacts. Using this framework, we identify priorities for impact assessment and scenario analysis, and suggest policy levers for better understanding and shaping the effects of ML on climate change mitigation.

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Fig. 1: Framework for assessing the GHG emissions impacts of ML.
Fig. 2: Computing-related GHG emissions impacts of ML.
Fig. 3: Immediate application impacts of ML.


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We thank the Environmental Law Institute and Alfred P. Sloan Foundation for their support of this work. P.L.D. was also supported by a US Department of Energy Computational Science Graduate Fellowship (DE-FG02-97ER25308), the Center for Climate and Energy Decision Making through a cooperative agreement between the US National Science Foundation and Carnegie Mellon University (grant number SES-00949710) and the Siebel Scholars programme. D.R. was supported by the Canada CIFAR AI Chairs programme. We are grateful to W.Y. for assistance with the figures. We thank P. Bergmark, V. Coroama, D. Daniels, J. Dunietz, L. Klaaβen, S. Luccioni, J. Malmodin, D. Rejeski, C. Samaras, N. Schmid, T. S. Schmidt, M. Scheutz, S. Sewerin, B. Steffen and M. Voss for their input and comments on the manuscript. Opinions are G.K.’s own and do not reflect those of the OECD, the IEA or their member countries.

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P.L.D., L.H.K. and D.R. conceived the idea for this manuscript. All authors wrote and edited the manuscript text and figures, with primary contributions from E.S. and G.K. to the section on computing-related impacts, from D.R., F.C. and L.H.K. to the sections on application-related impacts, from P.L.D. to the section on shaping ML’s impacts and from L.H.K. to the introduction, roadmap for assessing impacts and overall conceptual framing.

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Correspondence to Lynn H. Kaack.

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Nature Climate Change thanks Lorenz Hilty, Christopher Irrgang and Yang Yu for their contribution to the peer review of this work.

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Kaack, L.H., Donti, P.L., Strubell, E. et al. Aligning artificial intelligence with climate change mitigation. Nat. Clim. Chang. 12, 518–527 (2022).

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