PreprintCited by 552Ethics and Social Impacts of AIInnovation, Sustainability, Human-Machine SystemsGreen IT and Sustainability

The AI Gambit — Leveraging Artificial Intelligence to Combat Climate Change: Opportunities, Challenges, and Recommendations

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Cowls, Josh, Tsamados, Andreas, Taddeo, Mariarosaria, Floridi, Luciano·2021·AI & Society

Abstract

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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  • Training only one single AI model can emit as many CO2 emissions as five cars in their entire lifetime.

    “For instance, training only one single AI model can emit as many CO2 emissions as five cars in their entire lifetime.”

    From Connecting the dots in trustworthy Artificial Intelligence: From AI principles, ethics, and key requirements to responsible AI systems and regulation (2023) · cites this paper as [95]

    Section 3.1 estimates that a single AI-model training run could emit as much as 49 or 61 typical US passenger cars emit in one year, depending on the cloud provider. It does not compare the emissions with five cars over their entire lifetimes. (LLM, conf=0.88)

    Experimental — accuracy not yet measured
  • AI can be an effective tool to mitigate climate change, but greenhouse gases emitted by the computationally intensive training processes of complex AI-based systems can exacerbate existing social and ethical challenges linked to AI.

    “Although they can be an effective tool to mitigate climate change [93,94], greenhouse gases emitted by the computationally intensive training processes of complex AI-based systems can exacerbate existing social and ethical challenges linked to AI [95].”

    From Connecting the dots in trustworthy Artificial Intelligence: From AI principles, ethics, and key requirements to responsible AI systems and regulation (2023) · cites this paper as [95]

    Section 3, “AI’s carbon footprint” (page not provided), says training AI models can generate greenhouse-gas emissions. The Introduction (page not provided) separately says introducing AI into climate applications risks amplifying existing social and ethical challenges. The paper does not say that emissions from training cause that amplification; it treats the carbon footprint and those ethical challenges as distinct risks. (LLM, conf=0.88)

    Experimental — accuracy not yet measured
  • Evaluating the factors that influence AI's greenhouse gas emissions is the first step towards mitigating its negative effects.

    “Evaluating the factors that influence AI's greenhouse gas emissions is the first step towards mitigating its negative effects [95].”

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Research works printed but not linked: 12

  1. ACM (2020) Artifact review and badging-current. https:// www. acm. org/ publi catio ns/ polic ies/ artif act-review-and-badgi ng-curre nt
  2. Alom MZ, Taha TM, Yakopcic C, Westberg S, Sidike P, Nasrin MS, Van Esesn BC, Awwal AAS, Asari VK (2018) The history began from AlexNet: a comprehensive survey on deep learning approaches. http:// arxiv. org/ abs/ 1803. 01164
  3. Brown TB, Mann B, Ryder N, Subbiah M, Kaplan J, Dhariwal P, Neelakantan A et al (2020) Language models are few-shot learn- ers. http:// arxiv. org/ abs/ 2005. 14165
  4. Coleman C, Kang D, Narayanan D, Nardi L, Zhao T, Zhang J, Bailis P, Olukotun K, Re C, Zaharia M (2019b) Analysis of DAWNBench, a time-to-accuracy machine learning performance benchmark. http:// arxiv. org/ abs/ 1806. 01427 [
  5. Dahlquist E (2020) The FUDIPO Project: AI systems in process industries. https:// cordis. europa. eu/ artic le/ id/ 415798-using-ai- to-impro ve-energy-and-resou rce-effic iency-in-vario us-indus tries
  6. Etchemendy J, Li FF (2020) National research cloud: ensuring the continuation of American innovation'. Stanford HAI. https:// hai. stanf ord. edu/ blog/ natio nal-resea rch-cloud-ensur ing-conti nuati on-ameri can-innov ation European Commission (2019) The European green deal. https:// eur-lex. europa. eu/ legal-conte nt/ EN/ TXT/? qid= 15761 50542 719& uri= COM% 3A2019% 3A640% 3AFIN European Commission (2020a) 2030 climate & energy framework. Climate Action-European Commission. https:// ec. europa. eu/ clima/ polic ies/ strat egies/ 2030_ en European Commission (2020b) A European strategy for data. Shap- ing Europe's Digital Future-European Commission. https:// ec. europa. eu/ digit al-single-market/ en/ polic ies/ build ing-europ ean-data-econo my European Commission (2020c) A new industrial strategy for a Green and Digital Europe. https:// ec. europa. eu/ commi ssion/ press cor- ner/ api/ files/ docum ent/ print/ en/ ip_ 20_ 416/ IP_ 20_ 416_ EN. pdf European Commission (2020d) Energy-efficient cloud computing tech- nologies and policies for an eco-friendly cloud market. Shaping Europe's Digital Future-European Commission. https:// ec. europa. eu/ digit al-single-market/ en/ news/ energy-effic ient-cloud- compu ting-techn ologi es-and-polic ies-eco-frien dly-cloud-market European Commission (2020e) Shaping Europe's digital future'. European Commission. https:// ec. europa. eu/ info/ strat egy/ prior ities-2019-2024/ europe-fit-digit al-age/ shapi ng-europe-digit al- future_ en European Commission (2020f) Towards a European strategy on busi- ness-to-government data sharing for the public interest European Commission (2020g) White paper on artificial intelligence: a European approach to excellence and trust. European Com- mission-European Commission. https:// ec. europa. eu/ info/ publi catio ns/ white-paper-artif icial-intel ligen ce-europ ean-appro ach- excel lence-and-trust_ en European Commission (2020h) Destination earth (DestinE) Shaping Europe's Digital Future-European Commission. https:// ec. europa. eu/ digit al-single-market/ en/ desti nation-earth-desti ne European Commission (2020i) Recovery plan for Europe. European Commission-European Commission. https:// ec. europa. eu/ info/ strat egy/ recov ery-plan-europe_ en
  7. Henderson P, Hu J, Romoff J, Brunskill E, Jurafsky D, Pineau J (2020) Towards the systematic reporting of the energy and carbon foot- prints of machine learning. J Mach Learn Res 21(248):1-43
  8. Hooker S (2020) The hardware lottery. http:// arxiv. org/ abs/ 2009. 06489
  9. Marcus G (2020) The next decade in AI: four steps towards robust artificial intelligence. http:// arxiv. org/ abs/ 2002. 06177
  10. McCarthy J, Minsky ML, Rochester N, Shannon CE (2006) A pro- posal for the Dartmouth summer research project on artificial intelligence, August 31, 1955. AI Mag 27(4):12-12
  11. NeurIPS (2020) OpenReview. https:// openr eview. net/ NVIDIA (2018) TESLA V100 performance guide Open Compute Project (2020) Open Compute Project. https:// www. openc ompute. org
  12. Schwartz R, Dodge J, Smith NA, Etzioni O (2019) Green AI. http:// arxiv. org/ abs/ 1907. 10597 [

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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
Publisher
Elsevier BV
Published
2021
Volume
38
Issue
1
Pages
283-307
ISSN
0951-5666 (ISSN-L)
OpenAlex
W3207559276
Cited by
552
References
117
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
Licence
cc-by
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

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