Aligning artificial intelligence with climate change mitigation

Lynn H Kaack (1, 2, 3) , Priya L Donti (4, 5) , Emma Strubell (4) , George Kamiya (6) , Felix Creutzig (7, 8) , David Rolnick (9, 10)

1 Data Science Lab, Hertie School
2 D-GESS - Department of Humanities, Social and Political Sciences [ETH Zürich]
3 ETH Zürich - Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich]
4 School of Computer Science, Carnegie Mellon University
5 Department of Engineering and Public Policy, Carnegie Mellon University
6 International Energy Agency
7 MCC - Mercator Research Institute on Global Commons and Climate Change
8 Sustainability Economics of Human Settlements, Technical University Berlin
9 School of Computer Science, McGill University
10 Mila - Quebec AI Institute

Assessing and shaping the effects of artificial intelligence (AI) and machine learning (ML) on climate change mitigation demands a concerted effort across research, policy, and industry. However, there is great uncertainty regarding how ML may affect present and future greenhouse gas (GHG) emissions. This is owed in part to insufficient characterization of the different mechanisms through which such emissions impacts may occur, posing difficulties in measuring and forecasting them. We therefore introduce a systematic framework for describing ML's effects on GHG emissions, comprising three categories: (A) compute-related impacts, (B) immediate impacts of applying ML, and (C) system-level impacts. Using this framework, we assess and prioritize research and data needs for impact assessment and scenario analysis, and identify important policy levers.

Domaines: Etudes de l'environnement; Environnement et Société; Intelligence artificielle [cs.AI]

Lynn H Kaack, Priya L Donti, Emma Strubell, George Kamiya, Felix Creutzig, et al.. Aligning artificial intelligence with climate change mitigation. 2021. ⟨hal-03368037⟩