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Aligning artificial intelligence with climate change mitigation

Author

Listed:
  • Lynn H. Kaack

    (Hertie School
    ETH Zurich
    ETH Zurich)

  • Priya L. Donti

    (Carnegie Mellon University
    Carnegie Mellon University)

  • Emma Strubell

    (Carnegie Mellon University)

  • George Kamiya

    (International Energy Agency)

  • Felix Creutzig

    (Mercator Research Institute on Global Commons and Climate Change
    Technical University Berlin)

  • David Rolnick

    (McGill University
    Mila – Quebec AI Institute)

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.

Suggested Citation

  • Lynn H. Kaack & Priya L. Donti & Emma Strubell & George Kamiya & Felix Creutzig & David Rolnick, 2022. "Aligning artificial intelligence with climate change mitigation," Nature Climate Change, Nature, vol. 12(6), pages 518-527, June.
    • Handle: RePEc:nat:natcli:v:12:y:2022:i:6:d:10.1038_s41558-022-01377-7
    DOI: 10.1038/s41558-022-01377-7
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    Citations

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    Cited by:

    1. Anne-Laure Ligozat & Julien Lefevre & Aurélie Bugeau & Jacques Combaz, 2022. "Unraveling the Hidden Environmental Impacts of AI Solutions for Environment Life Cycle Assessment of AI Solutions," Sustainability, MDPI, vol. 14(9), pages 1-14, April.
    2. Daria Gritsenko & Jon Aaen & Bent Flyvbjerg, 2024. "Rethinking Digitalization and Climate: Don't Predict, Mitigate," Papers 2407.15016, arXiv.org.
    3. Puertas, Rosa & Guaita-Martinez, José M. & Marti, Luisa, 2023. "Analysis of the impact of university policies on society's environmental perception," Socio-Economic Planning Sciences, Elsevier, vol. 88(C).
    4. Behnam Zakeri & Katsia Paulavets & Leonardo Barreto-Gomez & Luis Gomez Echeverri & Shonali Pachauri & Benigna Boza-Kiss & Caroline Zimm & Joeri Rogelj & Felix Creutzig & Diana Ürge-Vorsatz & David G. , 2022. "Pandemic, War, and Global Energy Transitions," Energies, MDPI, vol. 15(17), pages 1-23, August.
    5. Qahtan, Talal F. & Alade, Ibrahim O. & Rahaman, Md Safiqur & Saleh, Tawfik A., 2023. "Mapping the research landscape of hydrogen production through electrocatalysis: A decade of progress and key trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 184(C).
    6. Podrecca, Matteo & Culot, Giovanna & Tavassoli, Sam & Orzes, Guido, 2024. "Artificial intelligence for climate change: a patent analysis in the manufacturing sector," Papers in Innovation Studies 2024/12, Lund University, CIRCLE - Centre for Innovation Research.
    7. Danilo Urzedo & Zarrin Tasnim Sworna & Andrew J. Hoskins & Cathy J. Robinson, 2024. "AI chatbots contribute to global conservation injustices," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-8, December.

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