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Aligning artificial intelligence with climate change mitigation

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.

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Fig. 1: Framework for assessing the GHG emissions impacts of ML.
Fig. 2: Computing-related GHG emissions impacts of ML.
Fig. 3: Immediate application impacts of ML.

References

  1. Zhang, D. et al. Artificial Intelligence Index Report 2021 (AI Index Steering Committee, Human-Centered AI Institute, 2021).

  2. Digital Technology and the Planet: Harnessing Computing to Achieve Net Zero (Royal Society, 2020); https://royalsociety.org/-/media/policy/projects/digital-technology-and-the-planet/digital-technology-and-the-planet-report.pdf

  3. Kaack, L. H., Donti, P. L., Strubell, E. & Rolnick, D. Artificial Intelligence and Climate Change: Opportunities, Considerations, and Policy Levers to Align AI with Climate Change Goals (Heinrich-Böll-Stiftung, 2020); https://eu.boell.org/en/2020/12/03/artificial-intelligence-and-climate-change

  4. Harnessing Artificial Intelligence to Accelerate the Energy Transition (World Economic Forum, 2021); https://www.weforum.org/whitepapers/harnessing-artificial-intelligence-to-accelerate-the-energy-transition

  5. Berkhout, F. & Hertin, J. De-materialising and re-materialising: digital technologies and the environment. Futures 36, 903–920 (2004).

    Article  Google Scholar 

  6. Hilty, L. M. & Aebischer, B. in ICT Innovations for Sustainability (eds Hilty, L. M. & Aebischer, B) 3–36 (Springer, 2015).

  7. Rolnick, D. et al. Tackling climate change with machine learning. ACM Comput. Surv. https://doi.org/10.1145/3485128 (2022).

  8. Oil in the Cloud: How Tech Companies are Helping Big Oil Profit from Climate Destruction (Greenpeace, 2019); https://www.greenpeace.org/usa/reports/oil-in-the-cloud/

  9. Dobbe, R. & Whittaker, M. AI and climate change: how they’re connected, and what we can do about it. Medium https://medium.com/@AINowInstitute/ai-and-climate-change-how-theyre-connected-and-what-we-can-do-about-it-6aa8d0f5b32c (2019).

  10. Strubell, E., Ganesh, A. & McCallum, A. Energy and policy considerations for deep learning in NLP. In Proc. 57th Annual Meeting of the Association for Computational Linguistics 3645–3650 (Association for Computational Linguistics, 2019); https://doi.org/10.18653/v1/P19-1355

  11. Schwartz, R., Dodge, J., Smith, N. A. & Etzioni, O. Green AI. Commun. ACM 63, 54–63 (2020).

    Article  Google Scholar 

  12. Dauvergne, P. Is artificial intelligence greening global supply chains? Exposing the political economy of environmental costs. Rev. Int. Polit. Econ. https://doi.org/10.1080/09692290.2020.1814381 (2020).

  13. Coeckelbergh, M. AI for climate: freedom, justice, and other ethical and political challenges. AI Ethics 1, 67–72 (2021).

    Article  Google Scholar 

  14. Gunther, H. & Rose, J. Governing AI: the importance of environmentally sustainable and equitable innovation. Environ. Law Rep. 50, 10888 (2020).

  15. Stein, A. L. Artificial intelligence and climate change. Yale J. Reg. 37, 890–939 (2020).

    Google Scholar 

  16. Cowls, J., Tsamados, A., Taddeo, M. & Floridi, L. The AI gambit-leveraging artificial intelligence to combat climate change: opportunities, challenges, and recommendations. SSRN https://ssrn.com/abstract=3804983 (2021).

  17. FAccT ’21: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (ACM, 2021).

  18. Recommendation on the Ethics of Artificial Intelligence (UNESCO, 2021); https://unesdoc.unesco.org/ark:/48223/pf0000380455

  19. Areas for Future Action in the Responsible AI Ecosystem (The Future Society, GPAI Responsible Development, Use and Governance of AI Working Group & CEIMIA, 2020); https://www.gpai.ai/projects/responsible-ai/areas-for-future-action-in-responsible-ai.pdf

  20. Horner, N. C., Shehabi, A. & Azevedo, I. L. Known unknowns: indirect energy effects of information and communication technology. Environ. Res. Lett. 11, 103001 (2016).

    Article  Google Scholar 

  21. Bieser, J. & Hilty, L. Indirect effects of the digital transformation on environmental sustainability: methodological challenges in assessing the greenhouse gas abatement potential of ICT. In 5th International Conference on Information and Communication Technology for Sustainability 68–81 (EasyChair, 2018); https://doi.org/10.29007/lx7q

  22. Pohl, J., Hilty, L. M. & Finkbeiner, M. How LCA contributes to the environmental assessment of higher order effects of ICT application: a review of different approaches. J. Clean. Prod. 219, 698–712 (2019).

    Article  Google Scholar 

  23. Digitalization & Energy (OECD/IEA, 2017); https://www.iea.org/reports/digitalisation-and-energy

  24. Sivaram, V. et al. Digital Decarbonization Promoting Digital Innovations to Advance Clean Energy Systems (Council on Foreign Relations, 2018); https://www.cfr.org/report/digital-decarbonization

  25. Wilson, C., Kerr, L., Sprei, F., Vrain, E. & Wilson, M. Potential climate benefits of digital consumer innovations. Annu. Rev. Environ. Resour. 45, 113–144 (2020).

    Article  Google Scholar 

  26. Canziani, A., Paszke, A. & Culurciello, E. An analysis of deep neural network models for practical applications. Preprint at https://arxiv.org/abs/1605.07678 (2017).

  27. AI and compute. OpenAI https://openai.com/blog/ai-and-compute (16 May 2018).

  28. Bommasani, R. et al. On the opportunities and risks of foundation models. Preprint at https://arxiv.org/abs/2108.07258 (2021).

  29. Joulin, A., Grave, E., Bojanowski, P. & Mikolov, T. Bag of tricks for efficient text classification. In Proc. 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2 427–431 (Association for Computational Linguistics, 2017); https://www.aclweb.org/anthology/E17-2068

  30. Hazelwood, K. et al. Applied machine learning at Facebook: a datacenter infrastructure perspective. In 2018 IEEE International Symposium on High Performance Computer Architecture 620–629 (IEEE, 2018); https://doi.org/10.1109/HPCA.2018.00059

  31. Sandler, M., Howard, A., Zhu, M., Zhmoginov, A. & Chen, L.-C. MobileNetV2: inverted residuals and linear bottlenecks. In IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2018).

  32. Turovsky, B. Ten years of Google Translate. The Keyword https://blog.google/products/translate/ten-years-of-google-translate (2016).

  33. Wu, C. J. et al. Sustainable AI: environmental implications, challenges and opportunities. In Proc. Machine Learning and Systems 4 795–813 (MLSys, 2022).

  34. Jiang, A. H. et al. Accelerating deep learning by focusing on the biggest losers. Preprint at https://arxiv.org/abs/1910.00762 (2019).

  35. He, K., Zhang, X., Ren, S. & Sun, J. Deep residual learning for image recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition 770–778 (IEEE, 2016); https://doi.org/10.1109/CVPR.2016.90

  36. Albanie, S. ConvNet-Burden: estimates of memory consumption and FLOP counts for various convolutional neural networks. GitHub https://github.com/albanie/convnet-burden (2019).

  37. Bender, E. M., Gebru, T., McMillan-Major, A. & Shmitchell, S. On the dangers of stochastic parrots: can language models be too big? In Proc. 2021 ACM Conference on Fairness, Accountability, and Transparency 610–623 (ACM, 2021).

  38. Gupta, A., Lanteigne, C. & Kingsley, S. SECure: a social and environmental certificate for AI Systems. Preprint at https://arxiv.org/abs/2006.06217 (2020).

  39. Tomašev, N. et al. AI for social good: unlocking the opportunity for positive impact. Nat. Commun. 11, 2468 (2020).

    Article  Google Scholar 

  40. Henderson, P. et al. Towards the systematic reporting of the energy and carbon footprints of machine learning. J. Mach. Learn. Res. 21, 1–43 (2020).

    Google Scholar 

  41. Schmidt, V. et al. CodeCarbon: estimate and track carbon emissions from machine learning computing. Zenodo https://doi.org/10.5281/zenodo.4658424 (2021).

  42. Anthony, L. F. W., Kanding, B. & Selvan, R. Carbontracker: tracking and predicting the carbon footprint of training deep learning models. Preprint at https://arxiv.org/abs/2007.03051 (2020).

  43. Cai, E., Juan, D., Stamoulis, D. & Marculescu, D. NeuralPower: predict and deploy energy-efficient convolutional neural networks. In 9th Asian Conference on Machine Learning (ACML, 2017).

  44. Dodge, J., Gururangan, S., Card, D., Schwartz, R. & Smith, N. A. Show your work: improved reporting of experimental results. In Proc. 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing 2185–2194 (Association for Computational Linguistics, 2019); https://doi.org/10.18653/v1/D19-1224

  45. Mattson, P. et al. (eds) Proc. Machine Learning and Systems 2 336–349 (MLSys, 2020).

  46. Reddi, V. J. et al. MLPerf inference benchmark. In 2020 ACM/IEEE 47th Annual International Symposium on Computer Architecture 446–459 (IEEE, 2020); https://doi.org/10.1109/ISCA45697.2020.00045

  47. Hinton, G., Vinyals, O. & Dean, J. Distilling the knowledge in a neural network. In NeurIPS Deep Learning Workshop (NeurIPS, 2014).

  48. Schaul, T., Quan, J., Antonoglou, I. & Silver, D. Prioritized experience replay. In International Conference on Learning Representations (ICLR, 2016).

  49. Hubara, I., Courbariaux, M., Soudry, D., El-Yaniv, R. & Bengio, Y. Quantized neural networks: training neural networks with low precision weights and activations. J. Mach. Learn. Res. 18, 1–30 (2018).

    Google Scholar 

  50. Pfeiffer, J. et al. Adapterhub: a framework for adapting transformers. In Proc. 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations 46–54 (Association for Computational Linguistics, 2020).

  51. Cai, H., Gan, C., Wang, T., Zhang, Z. & Han, S. Once-for-all: train one network and specialize it for efficient deployment. In International Conference on Learning Representations (ICLR, 2020).

  52. Lepikhin, D. et al. GShard: scaling giant models with conditional computation and automatic sharding. In International Conference on Learning Representations (ICLR, 2021).

  53. Hooker, S., Moorosi, N., Clark, G., Bengio, S. & Denton, E. Characterizing and mitigating bias in compact models. In ICML Workshop on Human Interpretability in Machine Learning (ICML, 2020).

  54. Greenhouse Gas Emissions Trajectories for the Information and Communication Technology Sector Compatible with the UNFCCC Paris Agreement (International Telecommunication Union, 2020); http://handle.itu.int/11.1002/1000/14084

  55. Malmodin, J. & Lundén, D. The energy and carbon footprint of the global ICT and E&M sectors 2010–2015. Sustainability 10, 3027 (2018).

    Article  Google Scholar 

  56. Masanet, E., Shehabi, A., Lei, N., Smith, S. & Koomey, J. Recalibrating global data center energy-use estimates. Science 367, 984–986 (2020).

    Article  CAS  Google Scholar 

  57. Data Centres and Data Transmission Networks (International Energy Agency, 2021); https://www.iea.org/reports/data-centres-and-data-transmission-networks

  58. Montevecchi, F., Stickler, T., Hintemann, R. & Hinterholzer, S. Energy-Efficient Cloud Computing Technologies and Policies for an Eco-Friendly Cloud Market (2020); https://ec.europa.eu/newsroom/dae/document.cfm?doc_id=71330

  59. Cisco Global Cloud Index: Forecast and Methodology, 2016–2021 (Cisco, 2018); https://www.cisco.com/c/en/us/solutions/collateral/service-provider/global-cloud-index-gci/white-paper-c11-738085.pdf

  60. Compton, C. Cisco’s Global Cloud Index study: acceleration of the multicloud era. Cisco Blogs https://blogs.cisco.com/news/acceleration-of-multicloud-era (2018).

  61. Wu, C. et al. Machine learning at Facebook: understanding inference at the edge. In 2019 IEEE International Symposium on High Performance Computer Architecture 331–344 (IEEE, 2019).

  62. Koomey, J., Berard, S., Sanchez, M. & Wong, H. Implications of historical trends in the electrical efficiency of computing. IEEE Ann. Hist. Comput. 33, 46–54 (2010).

    Article  Google Scholar 

  63. Koomey, J. & Naffziger, S. Moore's law might be slowing down, but not energy efficiency. IEEE Spectrum 52, 35 (2015).

    Google Scholar 

  64. Facebook Sustainability Data 2020 (Facebook, 2021); https://sustainability.fb.com/wp-content/uploads/2021/06/2020_FB_Sustainability-Data.pdf

  65. Naumov, M. et al. Deep learning training in Facebook data centers: design of scale-up and scale-out systems. Preprint at https://arxiv.org/abs/2003.09518 (2020).

  66. Park, J. et al. Deep learning inference in Facebook data centers: characterization, performance optimizations and hardware implications. Preprint at https://arxiv.org/abs/1811.09886 (2018).

  67. Shehabi, A. et al. United States Data Center Energy Usage Report (Lawrence Berkeley National Laboratory, 2016); https://eta.lbl.gov/publications/united-states-data-center-energy

  68. Jouppi, N. P. et al. In-datacenter performance analysis of a tensor processing unit. In Proc. 44th Annual International Symposium on Computer Architecture 1–12 (ACM, 2017).

  69. Radovanovic, A. Our data centers now work harder when the sun shines and wind blows. The Keyword https://blog.google/inside-google/infrastructure/data-centers-work-harder-sun-shines-wind-blows (2020).

  70. Whitehead, B., Andrews, D. & Shah, A. The life cycle assessment of a UK data centre. Int. J. Life Cycle Assess. 20, 332–349 (2015).

    Article  Google Scholar 

  71. Masanet, E., Shehabi, A. & Koomey, J. Characteristics of low-carbon data centres. Nat. Clim. Change 3, 627–630 (2013).

    Article  Google Scholar 

  72. Hischier, R., Coroama, V. C., Schien, D. & Achachlouei, M. A. in ICT Innovations for Sustainability (eds Hilty, L. M. & Aebischer, B.) 171–189 (Springer, 2015).

  73. André Barroso, L., Clidaras, J. & Hölzle, U. The datacenter as a computer: an introduction to the design of warehouse-scale machines. Synth. Lect. Comput. Arch. 8, 1–154 (2013).

    Google Scholar 

  74. Gupta, U. et al. Chasing carbon: the elusive environmental footprint of computing. In IEEE International Symposium on High-Performance Computer Architecture 854–867 (IEEE, 2021).

  75. Finer, M. et al. Combating deforestation: from satellite to intervention. Science 360, 1303–1305 (2018).

    Article  CAS  Google Scholar 

  76. Kulp, S. A. & Strauss, B. H. New elevation data triple estimates of global vulnerability to sea-level rise and coastal flooding. Nat. Commun. 10, 4844 (2019).

    Article  CAS  Google Scholar 

  77. Friederich, D., Kaack, L. H., Luccioni, A. & Steffen, B. Automated identification of climate risk disclosures in annual corporate reports. Preprint at https://arxiv.org/abs/2108.01415 (2021).

  78. Liu, Y., Guo, B., Zou, X., Li, Y. & Shi, S. Machine learning assisted materials design and discovery for rechargeable batteries. Energy Storage Mater. 31, 434–450 (2020).

    Article  Google Scholar 

  79. Ahmed, R., Sreeram, V., Mishra, Y. & Arif, M. D. A review and evaluation of the state-of-the-art in PV solar power forecasting: techniques and optimization. Renew. Sustain. Energy Rev. 124, 109792 (2020).

    Article  Google Scholar 

  80. You, J., Li, X., Low, M., Lobell, D. & Ermon, S. Deep Gaussian process for crop yield prediction based on remote sensing data. In Thirty-First AAAI Conference on Artificial Intelligence 4559–4565 (ACM, 2017).

  81. Toqué, F., Khouadjia, M., Come, E., Trepanier, M. & Oukhellou, L. Short & long term forecasting of multimodal transport passenger flows with machine learning methods. In 2017 IEEE 20th International Conference on Intelligent Transportation Systems 560–566 (IEEE, 2017).

  82. Evans, R. & Gao, J. DeepMind AI reduces Google data centre cooling bill by 40%. DeepMind https://www.deepmind.com/blog/deepmind-ai-reduces-google-data-centre-cooling-bill-by-40 (2016).

  83. Roman, N. D., Bre, F., Fachinotti, V. D. & Lamberts, R. Application and characterization of metamodels based on artificial neural networks for building performance simulation: a systematic review. Energy Build. 217, 109972 (2020).

    Article  Google Scholar 

  84. Irrgang, C. et al. Towards neural Earth system modelling by integrating artificial intelligence in Earth system science. Nat. Mach. Intell. 3, 667–674 (2021).

    Article  Google Scholar 

  85. Jenssen, R. et al. Automatic autonomous vision-based power line inspection: a review of current status and the potential role of deep learning. Int. J. Electr. Power Energy Syst. 99, 107–120 (2018).

    Article  Google Scholar 

  86. Rudin, C. et al. Interpretable machine learning: fundamental principles and 10 grand challenges. Stat. Surv. 16, 1–85 (2022).

    Article  Google Scholar 

  87. Ghahramani, Z. Probabilistic machine learning and artificial intelligence. Nature 521, 452–459 (2015).

    Article  CAS  Google Scholar 

  88. Willard, J., Jia, X., Xu, S., Steinbach, M. & Kumar, V. Integrating physics-based modeling with machine learning: a survey. Preprint at https://arxiv.org/abs/2003.04919 (2020).

  89. Zhuang, F. et al. A comprehensive survey on transfer learning. Proc. IEEE 109, 43–76 (2020).

    Article  Google Scholar 

  90. Adams-Progar, A., Fink, G.A.,Walker, E. & Llewellyn, D. in Security and Privacy in Cyber‐Physical Systems: Foundations, Principles and Applications (eds Song, H. et al.) Ch. 18 (Wiley, 2017); https://doi.org/10.1002/9781119226079.ch18

  91. Charles, H. et al. Meat consumption, health, and the environment. Science 361, eaam5324 (2018).

    Article  Google Scholar 

  92. Herweijer, C., Combes, B. & Gillham, J. How AI Can Enable a Sustainable Future (Microsoft & PWC, 2018); https://www.pwc.co.uk/services/sustainability-climate-change/insights/how-ai-future-can-enable-sustainable-future.html

  93. Climate AI: How Artificial Intelligence Can Power Your Climate Action Strategy (Capgemini, 2020); https://www.capgemini.com/research/climate-ai/

  94. Degot, C., Duranton, S., Frédeau, M. & Hutchinson, R. Reduce carbon and costs with the power of AI. BCG https://www.bcg.com/en-us/publications/2021/ai-to-reduce-carbon-emissions (2021).

  95. Azevedo, I. M. L. Consumer end-use energy efficiency and rebound effects. Annu. Rev. Environ. Resour. 39, 393–418 (2014).

    Article  Google Scholar 

  96. Lange, S., Pohl, J. & Santarius, T. Digitalization and energy consumption. Does ICT reduce energy demand? Ecol. Econ. 176, 106760 (2020).

    Article  Google Scholar 

  97. Anderson, J. M. et al. Autonomous Vehicle Technology: A Guide for Policymakers (RAND Corporation, 2016); https://doi.org/10.7249/RR443-2

  98. Creutzig, F. et al. Leveraging digitalization for sustainability in urban transport. Glob. Sustain. 2, e14 (2019).

    Article  Google Scholar 

  99. Wadud, Z., MacKenzie, D. & Leiby, P. Help or hindrance? The travel, energy and carbon impacts of highly automated vehicles. Transport. Res. A 86, 1–18 (2016).

    Google Scholar 

  100. Chase, N., Maples, J. & Schipper, M. Autonomous Vehicles: Uncertainties and Energy Implications (EIA, 2018); https://www.eia.gov/outlooks/aeo/av.php

  101. Arthur, W. B. Competing technologies, increasing returns, and lock-in by historical events. Econ. J. 99, 116–131 (1989).

    Article  Google Scholar 

  102. Cox, E., Royston, S. & Selby, J. Impact of Non-Energy Policies on Energy Systems (UK Energy Research Centre, 2016); https://ukerc.ac.uk/publications/impact-of-non-energy-policies-on-energy-systems/

  103. Stilgoe, J., Owen, R. & Macnaghten, P. Developing a framework for responsible innovation. Res. Policy 42, 1568–1580 (2013).

    Article  Google Scholar 

  104. Jirotka, M., Grimpe, B., Stahl, B., Eden, G. & Hartswood, M. Responsible research and innovation in the digital age. Commun. ACM 60, 62–68 (2017).

    Article  Google Scholar 

  105. Itten, R. et al. Digital transformation-life cycle assessment of digital services, multifunctional devices and cloud computing. Int. J. Life Cycle Assess. 25, 2093–2098 (2020).

    Article  Google Scholar 

  106. Coroamă, V. C., Bergmark, P., Höjer, M. & Malmodin, J. A methodology for assessing the environmental effects induced by ICT services: part I. Single services. In Proc. 7th International Conference on ICT for Sustainability 36–45 (ACM, 2020).

  107. Bergmark, P., Coroamă, V. C., Höjer, M. & Donovan, C. A Methodology for assessing the environmental effects induced by ICT services: part I. Multiple services and companies. In Proc. 7th International Conference on ICT for Sustainability 46–55 (ACM, 2020).

  108. Haataja, M. & Bryson, J. J. What costs should we expect from the EU’s AI Act? Preprint at SocArXiv https://osf.io/preprints/socarxiv/8nzb4 (2021).

  109. Mytton, D. Hiding greenhouse gas emissions in the cloud. Nat. Clim. Change 10, 701–701 (2020).

    Article  Google Scholar 

  110. Proposal for a Regulation of the European Parliament and of the Council Laying Down Harmonised Rules on Artificial Intelligence (Artificial Intelligence Act) and Amending Certain Union Legislative Acts (European Commission, 2021); https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:52021PC0206

  111. Hilbert, M. Big data for development: a review of promises and challenges. Dev. Policy Rev. 34, 135–174 (2016).

    Article  Google Scholar 

  112. Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K. & Galstyan, A. A survey on bias and fairness in machine learning. in ACM Computing Surveys Vol. 54, 1–35 (ACM, 2021).

  113. Bondi, E., Xu, L., Acosta-Navas, D. & Killian, J. A. Envisioning communities: a participatory approach towards AI for social good. In Proc. 2021 AAAI/ACM Conference on AI, Ethics, and Society 425–436 (ACM, 2021).

  114. Pinch, T. J. & Bijker, W. E. The social construction of facts and artefacts: or how the sociology of science and the sociology of technology might benefit each other. Soc. Stud. Sci. 14, 399–441 (1984).

    Article  Google Scholar 

  115. Klein, H. K. & Lee Kleinman, D. The social construction of technology: structural considerations. Sci. Technol. Human Values 27, 28–52 (2002).

    Article  Google Scholar 

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Acknowledgements

We thank the Environmental Law Institute and Alfred P. Sloan Foundation for their support of this work. P.L.D. was also supported by a US Department of Energy Computational Science Graduate Fellowship (DE-FG02-97ER25308), the Center for Climate and Energy Decision Making through a cooperative agreement between the US National Science Foundation and Carnegie Mellon University (grant number SES-00949710) and the Siebel Scholars programme. D.R. was supported by the Canada CIFAR AI Chairs programme. We are grateful to W.Y. for assistance with the figures. We thank P. Bergmark, V. Coroama, D. Daniels, J. Dunietz, L. Klaaβen, S. Luccioni, J. Malmodin, D. Rejeski, C. Samaras, N. Schmid, T. S. Schmidt, M. Scheutz, S. Sewerin, B. Steffen and M. Voss for their input and comments on the manuscript. Opinions are G.K.’s own and do not reflect those of the OECD, the IEA or their member countries.

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P.L.D., L.H.K. and D.R. conceived the idea for this manuscript. All authors wrote and edited the manuscript text and figures, with primary contributions from E.S. and G.K. to the section on computing-related impacts, from D.R., F.C. and L.H.K. to the sections on application-related impacts, from P.L.D. to the section on shaping ML’s impacts and from L.H.K. to the introduction, roadmap for assessing impacts and overall conceptual framing.

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Correspondence to Lynn H. Kaack.

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Kaack, L.H., Donti, P.L., Strubell, E. et al. Aligning artificial intelligence with climate change mitigation. Nat. Clim. Chang. 12, 518–527 (2022). https://doi.org/10.1038/s41558-022-01377-7

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