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Unraveling the Hidden Environmental Impacts of AI Solutions for Environment Life Cycle Assessment of AI Solutions

Author

Listed:
  • Anne-Laure Ligozat

    (Université Paris-Saclay, CNRS, ENSIIE, Laboratoire Interdisciplinaire des Sciences du Numérique, 91400 Orsay, France)

  • Julien Lefevre

    (Aix Marseille Univ., CNRS, INT, Inst Neurosci Timone, 13005 Marseille, France)

  • Aurélie Bugeau

    (Univ. Bordeaux, CNRS, Bordeaux INP, LaBRI, UMR5800, 33400 Talence, France)

  • Jacques Combaz

    (Univ. Grenoble Alpes, CNRS, Grenoble INP, VERIMAG, 38000 Grenoble, France)

Abstract

In the past ten years, artificial intelligence has encountered such dramatic progress that it is now seen as a tool of choice to solve environmental issues and, in the first place, greenhouse gas emissions (GHG). At the same time, the deep learning community began to realize that training models with more and more parameters require a lot of energy and, as a consequence, GHG emissions. To our knowledge, questioning the complete net environmental impacts of AI solutions for the environment (AI for Green) and not only GHG, has never been addressed directly. In this article, we propose to study the possible negative impacts of AI for Green. First, we review the different types of AI impacts; then, we present the different methodologies used to assess those impacts and show how to apply life cycle assessment to AI services. Finally, we discuss how to assess the environmental usefulness of a general AI service and point out the limitations of existing work in AI for Green.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:9:p:5172-:d:801671
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    References listed on IDEAS

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    1. 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.
    2. Berkhout, Peter H. G. & Muskens, Jos C. & W. Velthuijsen, Jan, 2000. "Defining the rebound effect," Energy Policy, Elsevier, vol. 28(6-7), pages 425-432, June.
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    Cited by:

    1. Aimee van Wynsberghe & Tijs Vandemeulebroucke & Larissa Bolte & Jamila Nachid, 2022. "Special Issue “Towards the Sustainability of AI; Multi-Disciplinary Approaches to Investigate the Hidden Costs of AI”," Sustainability, MDPI, vol. 14(24), pages 1-4, December.
    2. Xuwei Wang & Kaiwen Ji & Tongping Xie, 2023. "AI Carbon Footprint Management with Multi-Agent Participation: A Tripartite Evolutionary Game Analysis Based on a Case in China," Sustainability, MDPI, vol. 15(11), pages 1-23, June.
    3. Chen, Yan & Zhang, Ruiqian & Lyu, Jiayi & Hou, Yuqi, 2024. "AI and Nuclear: A perfect intersection of danger and potential?," Energy Economics, Elsevier, vol. 133(C).
    4. Badr Moutik & John Summerscales & Jasper Graham-Jones & Richard Pemberton, 2023. "Life Cycle Assessment Research Trends and Implications: A Bibliometric Analysis," Sustainability, MDPI, vol. 15(18), pages 1-45, September.
    5. Naudé, Wim, 2023. "We Already Live in a Degrowth World, and We Do Not like It," IZA Discussion Papers 16191, Institute of Labor Economics (IZA).

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