Aligning artificial intelligence with climate change mitigation

Lynn H. Kaack (), Priya L. Donti, Emma Strubell, George Kamiya, Felix Creutzig and David Rolnick
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Lynn H. Kaack: Hertie School
Priya L. Donti: Carnegie Mellon University
Emma Strubell: Carnegie Mellon University
George Kamiya: International Energy Agency
Felix Creutzig: Mercator Research Institute on Global Commons and Climate Change
David Rolnick: McGill University

Nature Climate Change, 2022, vol. 12, issue 6, 518-527

Abstract: Abstract 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.

Date: 2022
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DOI: 10.1038/s41558-022-01377-7

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