The AI gambit: leveraging artificial intelligence to combat climate change—opportunities, challenges, and recommendations
Open accessAbstract
In this article, we analyse the role that artificial intelligence (AI) could play, and is playing, to combat global climate change. We identify two crucial opportunities that AI offers in this domain: it can help improve and expand current understanding of climate change, and it can contribute to combatting the climate crisis effectively. However, the development of AI also raises two sets of problems when considering climate change: the possible exacerbation of social and ethical challenges already associated with AI, and the contribution to climate change of the greenhouse gases emitted by training data and computation-intensive AI systems. We assess the carbon footprint of AI research, and the factors that influence AI’s greenhouse gas (GHG) emissions in this domain. We find that the carbon footprint of AI research may be significant and highlight the need for more evidence concerning the trade-off between the GHG emissions generated by AI research and the energy and resource efficiency gains that AI can offer. In light of our analysis, we argue that leveraging the opportunities offered by AI for global climate change whilst limiting its risks is a gambit which requires responsive, evidence-based, and effective governance to become a winning strategy. We conclude by identifying the European Union as being especially well-placed to play a leading role in this policy response and provide 13 recommendations that are designed to identify and harness the opportunities of AI for combatting climate change, while reducing its impact on the environment.
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Data collection, analysis, and storage processes in AI development require much computational power, which consumes enormous amounts of energy.
“Likewise, data collection, analysis, and storage processes in AI development require much computational power, which consumes enormous amounts of energy (Cowls et al. 2021;Kouhizadeh et al. 2019).”
From Artificial intelligence in support of the circular economy: ethical considerations and a path forward (2024) · cites this paper as Cowls et al. 2021
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- Authors
- Cowls, J., Tsamados, A., Taddeo, M., & Floridi, L.
- Affiliations
- University of Oxford (GB)
- Corresponding author
- Floridi, Luciano
- Venue
- AI & SOCIETY
- Published
- 2021-10-18
- Volume
- 38
- Issue
- 1
- Pages
- 283-307
- ISSN
- 0951-5666 (ISSN-L)
- 1435-5655
- PMC
- PMC8522259
- OpenAlex
- W3207559276
- Cited by
- 552
- References
- 116
- Citations by year
- 2026: 109 · 2025: 196 · 2024: 131 · 2023: 84 · 2022: 25 · 2021: 7
- FWCI
- 77.21
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- AI & Society (publishedVersion, cc-by)
- SSRN Electronic Journal (acceptedVersion)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- PhilPapers (PhilPapers Foundation) (submittedVersion, other-oa)
- PhilPapers (PhilPapers Foundation) (submittedVersion)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Sociology and Political Science › Innovation, Sustainability, Human-Machine Systems
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Green IT and Sustainability
- Keywords
- artificial intelligence, climate change mitigation, carbon footprint of AI, greenhouse gas emissions, energy efficiency, AI ethics, AI governance, EU climate policy, environmental impact of computing
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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