How AI may help or harm the climate: A historical and sectoral review
DOI:
https://doi.org/10.58445/rars.3207Keywords:
Energy Consumption of AI, Climate Change Mitigation, Carbon Footprint of AI, Green AI, Computational SustainabilityAbstract
As AI capabilities have accelerated over the past decade, so have the questions surrounding their environmental impact. This paper traces sector-specific perceptions of AI’s climate effects throughout the past decade, focusing on three key eras of development from 2014. Drawing on peer-reviewed literature, corporate sustainability reports, and public publishing outlets, we developed a custom Sentiment Concern Index (SCI) to quantify shifts in optimism and concern across academia, industry, and publishing houses.
The findings suggest that while early academic and industrial discourse framed AI as a promising but untested tool, more recent years have seen both increased deployment and growing criticism, especially regarding the energy demands of large-scale models. Despite these concerns, the landscape is shifting toward “green AI,” carbon-aware infrastructure, and environmentally responsible development practices. The paper concludes with a forward-looking discussion of integrated strategies, emphasizing the need for coordinated policy, technical innovation, public transparency, and cross-sector collaboration. As AI becomes further embedded in society, ensuring that it functions as a climate asset and not liability, will be one of the defining sustainability challenges of the coming decade.
References
Agent builder. (n.d.). IBM. Retrieved July 15, 2025, from https://www.ibm.com/kr-ko/products/watsonx-orchestrate/ai-agent-builder?lnk=hpls1kr
AI's Role in Boosting Crop Productivity. (n.d.). Bloomberg. Retrieved July 7, 2025, from https://sponsored.bloomberg.com/article/google-sustainability/ai-s-role-in-boosting-crop-productivity
AI-powered pathways: Advancing SDG7 for a sustainable energy future. (2024, July). Google Sustainability. Retrieved July 9, 2025, from https://sustainability.google/stories/ai-powered-pathways/
Amasyali, K., & El-Gohary, N. (2018). Machine learning-based occupant energy use behavior optimization. Construction Research Congress, 379-389. https://doi.org/10.1061/9780784481301.038
Amiri, Z., Heidari, A., & Navimipour, N. J. (2024). Comprehensive survey of artificial intelligence techniques and strategies for climate change mitigation. Energy, 308. https://doi.org/10.1016/j.energy.2024.132827.
Amodei, D., & Hernandez, D. (2018, May 16). AI and compute. OpenAI. Retrieved July 14, 2025, from https://openai.com/index/ai-and-compute/
Baji, T. (2017). GPU: The biggest key processor for AI and parallel processing. In Proceedings Volume 10454, Photomask Japan 2017: XXIV Symposium on Photomask and Next-Generation Lithography Mask Technology.
Bathla, G., Bhadane, K., Singh, R. K., Kumar, R., Aluvalu, R., Krishnamurthi, R., Kumar, A., Thakur, R. N., & Basheer, S. (2022). Autonomous Vehicles and Intelligent Automation: Applications, Challenges, and Opportunities. Mobile Information Systems, 2022(1). https://doi.org/10.1155/2022/7632892
Bengio, Y. (2016, June 1). Springtime for AI: The Rise of Deep Learning. Scientific American. Retrieved July 7, 2025, from https://www.scientificamerican.com/article/springtime-for-ai-the-rise-of-deep-learning/
Bilton, N. (2014, November 5). Artificial intelligence as a threat. The New York Times. https://www.nytimes.com/2014/11/06/fashion/artificial-intelligence-as-a-threat.html
Bommasani, R., Hudson, D.A., Adeli, E., Altman, R., Arora, S., Arx, S.V., Bernstein, M.S., Bohg, J., Bosselut, A., Brunskill, E., Brynjolfsson, E., Buch, S., Card, D., Castellon, R., Chatterji, N., Chen, A., Creel, K., Davis, J.Q., Demszky, D., Donahue, C., Doumbouya, M., Durmus, E., Ermon, S., Etchemendy, J., Ethayarajh, K., Fei-Fei, L., Finn, C., Gale, T., Gillespie, L., Goel, K., Goodman, N., Grossman, S., Guha, N., Hashimoto, T., Henderson, P., Hewitt, J., Ho, D.E., Hong, J., Hsu, K., Huang, J., Icard, T., Jain, S., Jurafsky, D., Kalluri, P., Karamcheti, S., Keeling, G., Khani, F., Khattab, O., Koh, P.W., Krass, M., Krishna, R., Kuditipudi, R., Kumar, A., Ladhak, F., Lee, M., Lee, T., Leskovec, J., Levent, I., Li, X.L., Li, X., Ma, T., Malik, A., Manning, C.D., Mirchandani, S., Mitchell, E., Munyikwa, Z., Nair, S., Narayan, A., Narayanan, D., Newman, B., Nie, A., Niebles, J.C., Nilforoshan, H., Nyarko, J., Ogut, G., Orr, L., Papadimitriou, I., Park, J.S., Piech, C., Portelance, E., Potts, C., Raghunathan, A., Reich, R., Ren, H., Rong, F., Roohani, Y., Ruiz, C., Ryan, J., Ré, C., Sadigh, D., Sagawa, S., Santhanam, K., Shih, A., Srinivasan, K., Tamkin, A., Taori, R., Thomas, A.W., Tramèr, F., Wang, R.E., Wang, W., Wu, B., Wu, J., Wu, Y., Xie, S.M., Yasunaga, M., You, J., Zaharia, M., Zhang, M., Zhang, T., Zhang, X., Zhang, Y., Zheng, L., Zhou, K., Liang, P. (2021). On the Opportunities and Risks of Foundation Models. arXiv preprint arXiv:2108.07258.
Brodtkorb, A. R., Hagen, T. R., & Sætra, M. L. (2013). Graphics processing unit (GPU) programming strategies and trends in GPU computing. Journal of Parallel and Distributed Computing, 73(1), 4-13. https://doi.org/10.1016/j.jpdc.2012.04.003
Bzdok, D., Krzywinski, M., & Altman, N. (2017). Points of Significance: Machine learning: a primer. Nature methods, 14(12), 1119?1120. https://doi.org/10.1038/nmeth.4526
Campos, R. M., Krasnopolsky, V., Alves, J.-H. G. M., & Penny, S. G. (2019). Nonlinear wave ensemble averaging in the gulf of mexico using neural networks. Journal of Atmospheric and Oceanic Technology, 36(1), 113-127. https://doi.org/10.1175/jtech-d-18-0099.1
Causes and effects of climate change. (n.d.). United Nations. Retrieved July 3, 2025, from https://www.un.org/en/climatechange/science/causes-effects-climate-change
Chen, Y.-H., Emer, J., Sze, V., & Yang, T.-J. (2018). Understanding the limitations of existing energy-efficient design approaches for deep neural networks. SysML.
Columbus, L. (2018, August 26). 25 machine learning startups to watch in 2018. Forbes. Retrieved July 19, 2025, from https://www.forbes.com/sites/louiscolumbus/2018/08/26/25-machine-learning-startups-to-watch-in-2018/
Da Li, Xinbo Chen, Michela Becchi, and Ziliang Zong. 2016. Evaluating the energy efficiency of deep convolutional neural networks on cpus and gpus. 2016 IEEE International Conferences on Big Data and Cloud Computing (BDCloud), Social Computing and Networking (SocialCom), Sustainable Computing and Communications (SustainCom) (BDCloudSocialCom-SustainCom), pages 477?484.
Dean, J. [JeffDean]. (2024, October 7). However that single number is still clouding people's assessment of the environmental costs (e.g. see how this thread started), and @strubell has flatly refused to amend the Arxiv or ACL paper with correct data, and has even co-authored papers after knowing about these errors (which we informed her of as part of our discussions in our writing of the 2021 paper https://arxiv.org/abs/2104.10350). To them further spread this erroneous information (e.g. the first sentence of the related work section of the 2022 work in https://arxiv.org/pdf/2206.05229, which reiterates the wildly inaccurate "lifetime of five cars" meme) after being informed about the incorrect numbers is not honest and shouldn't be how science or transparency is done. [Post]. X. https://x.com/JeffDean/status/1843101133310714176
Dimock, W. C. (2022, April 5). What AI can do for climate change, and what climate change can do for AI. Scientific American. Retrieved July 10, 2025, from https://www.scientificamerican.com/article/what-ai-can-do-for-climate-change-and-what-climate-change-can-do-for-ai/
Di Placido, D. (2025, March 27). The AI-Generated Studio Ghibli Trend, Explained. Forbes. Retrieved July 4, 2025, from https://www.forbes.com/sites/danidiplacido/2025/03/27/the-ai-generated-studio-ghibli-trend-explained/
Dodge, J., Prewitt, T., Combes, R.T.D., Odmark, E., Schwartz, R., Strubell, E., Luccioni, A.S., Smith, N.A., DeCario, N., Buchanan, W. (2022). Measuring the Carbon Intensity of AI in Cloud Instances. arXiv preprint arXiv:2206.05229.
Driving global progress with our partners. (n.d.). Google Sustainability. Retrieved July 7, 2025, from https://sustainability.google/research/
Eklund, A., Dufort, P., Forsberg, D., & LaConte, S. M. (2013). Medical image processing on the GPU ? past, present and future. Medical Image Analysis, 17(8), 1073-1094. https://doi.org/10.1016/j.media.2013.05.008
ESG News. (2025, May 5). Google Backs 15 AI Startups to Tackle Nature Loss and Climate Risk. ESG News. Retrieved July 10, 2025, from https://esgnews.com/google-backs-15-ai-startups-to-tackle-nature-loss-and-climate-risk/
Expert insights on observation-based data of the climate system. (n.d.). Climate Data Guide. Retrieved July 12, 2025, from https://climatedataguide.ucar.edu/about
Faghmous, J. H., & Kumar, V. (2014). A big data guide to understanding climate change: The case for theory-guided data science. Big Data, 2(3), 155-163. https://doi.org/10.1089/big.2014.0026
Global news services. (n.d.). BBC. Retrieved July 7, 2025, from https://www.bbc.com/aboutthebbc/whatwedo/worldservice
Google. (2016). Google environmental report 2016. https://www.gstatic.com/gumdrop/sustainability/environmental-report-2016.pdf
Google Sustainability. (2023). Google 2023 Sustainability Report. Retrieved July 7, 2025, from https://www.gstatic.com/gumdrop/sustainability/google-2023-environmental-report.pdf
Google Sustainability. (2024). Google 2024 Environmental Report. Retrieved July 7, 2025, from https://www.gstatic.com/gumdrop/sustainability/google-2024-environmental-report.pdf
Growing the internet while reducing energy consumption. (n.d.). Google. Retrieved July 12, 2025, from https://datacenters.google/efficiency/#measuring-efficiency
Han, X., Zhang, Z., Ding, N., Gu, Y., Liu, X., Huo, Y., Qiu, J., Yao, Y., Zhang, A., Zhang, L., Han, W., Huang, M., Jin, Q., Lan, Y., Liu, Y., Liu, Z., Lu, Z., Qiu, X., Song, R., . . . Zhu, J. (2021). Pre-trained models: Past, present and future. AI Open, 2, 225-250. https://doi.org/10.1016/j.aiopen.2021.08.002
Hao, K. (2019, June 6). Training a single AI model can emit as much carbon as five cars in their lifetimes. MIT Technology Review. Retrieved July 5, 2025, from https://www.technologyreview.com/2019/06/06/239031/training-a-single-ai-model-can-emit-as-much-carbon-as-five-cars-in-their-lifetimes/
Huang, A., Chao, Y., de la Mora Velasco, E., Bilgihan, A., & Wei, W. (2021). When artificial intelligence meets the hospitality and tourism industry: An assessment framework to inform theory and management. Journal of Hospitality and Tourism Insights, 5(5), 1080-1100. https://doi.org/10.1108/jhti-01-2021-0021
IBM. (2017). IBM corporate responsibility report 2017. https://www.responsibilityreports.com/HostedData/ResponsibilityReportArchive/i/NYSE_IBM_2017.pdf
IBM and KONE - Watson IoT gives lift to innovation in smart buildings [Video]. (n.d.). IBM. https://mediacenter.ibm.com/media/IBM+and+KONE+-+Watson+IoT+Gives+Lift+To+Innovation+In+Smart+Buildings/1_pk8871cw
Khurana, S., Saxena, S., Jain, S., & Dixit, A. (2021). Predictive modeling of engine emissions using machine learning: A review. Materials Today: Proceedings, 38, 280-284. https://doi.org/10.1016/j.matpr.2020.07.204
Knight, W. (2020, January 21). AI can do great things–if it doesn't burn the planet. Wired. Retrieved July 10, 2025, from https://www.wired.com/story/ai-great-things-burn-planet/
Landram, K. (2025, February 11). Slashing AI data center cooling cost, GPU/CPU power use. Carnegie Mellon University. Retrieved August 10, 2025, from https://engineering.cmu.edu/news-events/news/2025/02/11-ai-data-center-cooling-breakthrough.html
Leal Filho, W., Wall, T., Rui Mucova, S. A., Nagy, G. J., Balogun, A.-L., Luetz, J. M., Ng, A. W., Kovaleva, M., Safiul Azam, F. M., Alves, F., Guevara, Z., Matandirotya, N. R., Skouloudis, A., Tzachor, A., Malakar, K., & Gandhi, O. (2022). Deploying artificial intelligence for climate change adaptation. Technological Forecasting and Social Change, 180, 121662. https://doi.org/10.1016/j.techfore.2022.121662
Lewis-Kraus, G. (2016, December 14). The great A.I. awakening. The New York Times. https://www.nytimes.com/2016/12/14/magazine/the-great-ai-awakening.html
Liu, V., Yin, Y. (2024). Green AI: Exploring Carbon Footprints, Mitigation Strategies, and Trade Offs in Large Language Model Training. arXiv preprint arXiv:2404.01157.
Lohr, S. (2024, August 26). Will A.I. ruin the planet or save the planet? The New York Times. https://www.nytimes.com/2024/08/26/climate/ai-planet-climate-change.html
Luccioni, A.S., Hernandez-Garcia, A. (2023). Counting Carbon: A Survey of Factors Influencing the Emissions of Machine Learning. arXiv preprint arXiv:2302.08476.
Luccioni, A.S., Hernandez-Garcia, A. (2023). Counting Carbon: A Survey of Factors Influencing the Emissions of Machine Learning. arXiv preprint arXiv:2302.08476.
Mata v. Avianca, No. 1:22-cv-01461 (S.D.N.Y. 2022). https://law.justia.com/cases/federal/district-courts/new-york/nysdce/1:2022cv01461/575368/54/
McClanahan, C. (2011, March). History and evolution of GPU architecture. Mcclanahoochie. Retrieved July 6, 2025, from https://mcclanahoochie.com/blog/wp-content/uploads/2011/03/gpu-hist-paper.pdf
McGuffie, K., Newhouse, A. (2020). The Radicalization Risks of GPT-3 and Advanced Neural Language Models. arXiv preprint arXiv:2009.06807.
Mittal, S., & Vetter, J. S. (2014). A Survey of Methods for Analyzing and Improving GPU Energy Efficiency. ACM Computing Surveys (CSUR), 47(2), 1-21. https://doi.org/10.1145/2636342
Mulkey, S. K., & Stevens, H. (2025, June 19). Can you choose an A.I. model that harms the planet less? The New York Times. https://www.nytimes.com/2025/06/19/climate/ai-emissions-chatbot-accuracy.html
Muller, B. (2022, March 2). BERT 101 ? state of the art NLP model explained. Hugging face. https://huggingface.co/blog/bert-101
NOAA. (2015). Monthly Global Climate Report for Annual 2014. https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ncdc:C00672
NOAA. (2018). Monthly Global Climate Report for Annual 2017. https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ncdc:C00672
NOAA. (2019). Monthly Global Climate Report for Annual 2018. https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ncdc:C00672
NOAA. (2025). Monthly Global Climate Report for January 2025. https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ncdc:C00672
O'Donnell, J., & Crownhart, C. (2025, May 20). Everything you need to know about estimating AI's energy and emissions burden. MIT Technology Review. Retrieved July 7, 2025, from https://www.technologyreview.com/2025/05/20/1116331/ai-energy-demand-methodology/
Patterson, D., Gonzalez, J., Le, Q., Liang, C., Munguia, L., Rothchild, D., So, D., Texier, M., Dean, J. (2021). Carbon Emissions and Large Neural Network Training. arXiv preprint arXiv:2104.10350.
Paul, K., & Robins-Early, N. (2024, June 10). Apple brings ChatGPT to Siri as it debuts 'Apple Intelligence' at WWDC 2024. The Guardian. Retrieved July 16, 2025, from https://www.theguardian.com/technology/article/2024/jun/10/apple-ai-product-launch
Polacco, A., & Backes, K. (2018). The amazon go concept: Implications, applications, and sustainability. Journal of Business and Management, 24(1), 79-92. https://doi.org/10.6347/JBM.201803_24(1).0004
Poulose, A., Kim, J. H., & Han, D. S. (2022). HIT HAR: Human Image Threshing Machine for Human Activity Recognition Using Deep Learning Models. Computational Intelligence and Neuroscience, 2022(1). https://doi.org/10.1155/2022/1808990
Press Gazette. (2025, July 14). Indian news websites are fastest growers in global English language top 50. Press Gazette. Retrieved July 7, 2025, from https://pressgazette.co.uk/media-audience-and-business-data/media_metrics/most-popular-websites-news-world-monthly-2/
Reports and case studies charting our progress and ambitions. (n.d.). Google Sustainability. Retrieved July 8, 2025, from https://sustainability.google/reports/
Salehin, I., Islam, S., Saha, P., Noman, S.M., Tuni, A., Hasan, M., & Baten, A. (2024). AutoML: A systematic review on automated machine learning with neural architecture search. Journal of Information and Intelligence, 2(1), 52-81. https://doi.org/10.1016/j.jiixd.2023.10.002
Samsi, S., Zhao, D., McDonald, J., Li, B., Michaleas, A., Jones, M., Bergeron, W., Kepner, J., Tiwari, D., & Gadepally, V. (2023). From words to watts: Benchmarking the energy costs of large language model inference. 2023 IEEE High Performance Extreme Computing Conference (HPEC), 1-9. https://doi.org/10.1109/hpec58863.2023.10363447
Schmitt, M. (2022). Automated machine learning: AI-driven decision making in business analytics. arXiv preprint arXiv:2205.10538.
Scientific American. (2025, March 18). AI needs to be more energy-efficient. Scientific American. Retrieved July 24, 2025, from https://www.scientificamerican.com/article/ai-needs-to-be-more-energy-efficient/
Shi, W., Alawieh, M. B., Li, X., & Yu, H. (2017). Algorithm and hardware implementation for visual perception system in autonomous vehicle: A survey. Integration, 59, 148-156. https://doi.org/10.1016/j.vlsi.2017.07.007
Slimani, I., Slimani, N., Achchab, S., Saber, M., Farissi, I., Sbiti, N., & Amghar, M. (2022). Automated machine learning: the new data science challenge. International Journal of Electrical and Computer Engineering (IJECE), 12, 42-43. https://doi.org/10.11591/ijece.v12i4.pp4243-4252
Sorkin, A. R., Mattu, R., Warner, B., Kessler, S., Merced, M. J. de la, Hirsch, L., & Livni, E. (2024, May 6). How bad is A.I. for the climate? The New York Times. https://www.nytimes.com/2024/05/06/business/dealbook/ai-power-energy-climate.html
Spencer, G. (2018, September 4). AI for earth: Helping save the planet with data science. Microsoft. Retrieved July 7, 2025, from https://news.microsoft.com/apac/features/ai-for-earth-helping-save-the-planet-with-data-science/
Stackpole, B. (2025, February 10). Climate change and machine learning ? the good, bad, and unknown. MIT Sloan School of Management. Retrieved July 6, 2025, from https://mitsloan.mit.edu/ideas-made-to-matter/climate-change-and-machine-learning-good-bad-and-unknown
Stix, G. (2015, December 1). Deep Learning Is the A.I. Breakthrough We've Been Waiting For. Scientific American. Retrieved July 7, 2025, from https://www.scientificamerican.com/article/deep-learning-is-the-a-i-breakthrough-we-ve-been-waiting-for/
Strubell, E., Ganesh, A., McCallum, A. (2019). Energy and Policy Considerations for Deep Learning in NLP. arXiv preprint arXiv:1906.02243.
Sutskever, I., Vinyals, O., Le, Q.V. (2014). Sequence to Sequence Learning with Neural Networks. arXiv preprint arXiv:1409.3215.
Torres, J., Vaca, C., Terán, L., & Abad, C. L. (2020). Seq2Seq models for recommending short text conversations. Expert Systems with Applications, 150. https://doi.org/10.1016/j.eswa.2020.113270
Torres, J., Vaca, C., Terán, L., & Abad, C. L. (2020). Seq2Seq models for recommending short text conversations. Expert Systems With Applications, 150, 113270. https://doi.org/10.1016/j.eswa.2020.113270
Trok, J. T., Barnes, E. A., Davenport, F. V., & Diffenbaugh, N. S. (2024). Machine learning?based extreme event attribution. Science Advances, 10(34). https://doi.org/10.1126/sciadv.adl3242
Tugend, A. (2019, May 12). How A.I. can help handle severe weather. The New York Times. https://www.nytimes.com/2019/05/12/climate/artificial-intelligence-climate-change.html
Ukoba, K., Onisuru, O. R., & Jen, T.-C. (2024). Harnessing machine learning for sustainable futures: Advancements in renewable energy and climate change mitigation. Bulletin of the National Research Centre, 48(1), 99. https://doi.org/10.1186/s42269-024-01254-7
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, ?., & Polosukhin, I. (2017). Attention is all you need. In NIPS'17: Proceedings of the 31st International Conference on Neural Information Processing Systems (pp. 6000-6010).
Wankhede, K., Wukkadada, B., & Nadar, V. (2018). Just walk-out technology and its challenges: A case of amazon go. 2018 International Conference on Inventive Research in Computing Applications (ICIRCA), 254-257. https://doi.org/10.1109/icirca.2018.8597403
What Is Climate Change? (n.d.). United Nations. Retrieved July 7, 2025, from https://www.un.org/en/climatechange/what-is-climate-change
Yang, T., Asanjan, A. A., Welles, E., Gao, X., Sorooshian, S., & Liu, X. (2017). Developing reservoir monthly inflow forecasts using artificial intelligence and climate phenomenon information. Water Resources Research, 53(4), 2786-2812. https://doi.org/10.1002/2017wr020482
Yin, X., & Wan, X. (2022). How Do Seq2Seq Models Perform on End-to-End Data-to-Text Generation? Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics Volume 1: Long Papers, 7701-7710.
Yuventi, J., & Mehdizadeh, R. (2013). A critical analysis of power usage effectiveness and its use in communicating data center energy consumption. Energy and Buildings, 64, 90-94. https://doi.org/10.1016/j.enbuild.2013.04.015
Zhang, M., & Li, J. (2021). A commentary of GPT-3 in MIT Technology Review 2021. Fundamental Research, 1(6), 831-833. https://doi.org/10.1016/j.fmre.2021.11.011
Zong, M., Krishnamachari, B. (2022). a survey on GPT-3. arXiv preprint arXiv:2212.00857.
Downloads
Posted
Categories
License
Copyright (c) 2025 Emily Han
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.