A Study of the Application of AI & ML to Climate Variation, with Particular Attention to Legal & Ethical Concerns





Artificial Intelligence, Machine Learning, United Nations Framework Convention on Climate Change, European Union, Greenhouse gas, International Covenant on Economic Social and Cultural Rights, International Association for Artificial Intelligence and Law


INTRODUCTION: This research investigates the utilization of artificial intelligence and machine learning in comprehending various climatic variations, emphasizing the associated use of legal and ethical considerations. This escalating impact of climatic change necessitates innovative approaches and the potential of AI/ML to offer tools for analysis and prediction.

OBJECTIVES: The primary objective here, was to assess the effectiveness of AI/ML in the deciphering of varying climatic patterns and projecting the future trends. Concurrently, this study aims for the identification and analysis of legal and ethical challenges that may arise from the integration of these technologies in climatic research and policy.

METHODS: Here, the literature review forms the basis for understanding various AI/ML applications related to climate science. This study employs various case analyses to examine the existing models to gauge the accuracy and efficiency of predictions. Legal frameworks and ethical principles need to be scrutinized through the qualitative analysis of relevant policies and guidelines.

RESULTS: This extensive research reveals the various significant contributions of AI/ML in the enhancement of climatic modeling precision and the prediction of extreme events. However legal and ethical considerations such as data privacy, accountability, and transparency also emerged as crucial challenges which required careful attention.

CONCLUSION: While AI/ML exhibited great potential in the advancement of climate research, a balanced approach is imperative to navigate the associated legal and ethical concerns. Striking this equilibrium will be pivotal for ensuring responsible and effective deployment of these technologies in the pursuit of best understanding and mitigating varying climatic variations.


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Gupta, T., & Roy, S. (2020, September). A hybrid model based on fused features for detection of natural disasters from satellite images. In IGARSS 2020-2020 IEEE International Geoscience and Remote Sensing Symposium (pp. 1699-1702). IEEE DOI: https://doi.org/10.1109/IGARSS39084.2020.9324611

Guha-Sapir D., Hoyois Ph., Below. R. (2016). Annual Disaster Statistical Review 2016: The Numbers and Trends. Brussels: CRED; 2016. p.91.

Masson-Delmotte, V., Zhai, P., Pörtner, H. O., Roberts, D., Skea, J., & Shukla, P. R. (2022). Global Warming of 1.5 C: IPCC special report on impacts of global warming of 1.5 C above pre-industrial levels in context of strengthening response to climate change, sustainable development, and efforts to eradicate poverty. Cambridge University Press.

Arfanuzzaman, M. (2021). Harnessing artificial intelligence and big data for SDGs and prosperous urban future in South Asia. Environmental and sustainability indicators, 11, 100127. DOI: https://doi.org/10.1016/j.indic.2021.100127

Dhar, P. (2020). The carbon impact of artificial intelligence. Nat. Mach. Intell., 2(8), 423-425. DOI: https://doi.org/10.1038/s42256-020-0219-9

Snezhana, D. (2023). Applying Artificial Intelligence (AI) for Mitigation Climate Change Consequences of the Natural Disasters. Dineva, S.(2023). Applying Artificial Intelligence (AI) for Mitigation Climate Change Consequences of the Natural Disasters. Research Journal of Ecology and Environmental Sciences, 3(1), 1-8.

Lesk, C., Rowhani, P., & Ramankutty, N. (2016). Influence of extreme weather disasters on global crop production. Nature, 529(7584), 84-87. DOI: https://doi.org/10.1038/nature16467

Chen, L., Chen, Z., Zhang, Y., Liu, Y., Osman, A. I., Farghali, M., ... & Yap, P. S. (2023). Artificial intelligence-based solutions for climate change: a review. Environmental Chemistry Letters, 1-33. DOI: https://doi.org/10.1007/s10311-023-01617-y

Ripple, W. J., Wolf, C., Newsome, T. M., Barnard, P., Moomaw, W. R., & Grandcolas, P. (2019). World scientists' w arning of a climate emergency. BioScience. DOI: https://doi.org/10.1093/biosci/biz088

Arias, P., Bellouin, N., Coppola, E., Jones, R., Krinner, G., Marotzke, J., ... & Zickfeld, K. (2021). Climate Change 2021: the physical science basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change; technical summary.

Masson-Delmotte, V. P., Zhai, P., Pirani, S. L., Connors, C., Péan, S., Berger, N., ... & Scheel Monteiro, P. M. (2021). Ipcc, 2021: Summary for policymakers. in: Climate change 2021: The physical science basis. contribution of working group i to the sixth assessment report of the intergovernmental panel on climate change.

Hausfather, Z., & Peters, G. P. (2020). Emissions–the ‘business as usual’story is misleading. Nature, 577(7792), 618-620. DOI: https://doi.org/10.1038/d41586-020-00177-3

Coeckelbergh, M. (2021). AI for climate: freedom, justice, and other ethical and political challenges. AI and Ethics, 1(1), 67-72. DOI: https://doi.org/10.1007/s43681-020-00007-2

Cowls, J., Tsamados, A., Taddeo, M., & Floridi, L. (2021). The AI gambit: leveraging artificial intelligence to combat climate change—opportunities, challenges, and recommendations. Ai & Society, 1-25. DOI: https://doi.org/10.2139/ssrn.3804983

Snezhana, D. (2023). Applying Artificial Intelligence (AI) for Mitigation Climate Change Consequences of the Natural Disasters. Dineva, S.(2023). Applying Artificial Intelligence (AI) for Mitigation Climate Change Consequences of the Natural Disasters. Research Journal of Ecology and Environmental Sciences, 3(1), 1-8.

Laface, V. L. A., Musarella, C. M., Tavilla, G., Sorgonà, A., Cano-Ortiz, A., Quinto Canas, R., & Spampinato, G. (2023). Current and Potential Future Distribution of Endemic Salvia ceratophylloides Ard.(Lamiaceae). Land, 12(1), 247. DOI: https://doi.org/10.3390/land12010247

Clutton-Brock, P., Rolnick, D., Donti, P. L., & Kaack, L. (2021). Climate change and AI. recommendations for government action. GPAI, Climate Change AI, Centre for AI & Climate.

Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., ... & Liang, P. (2021). On the opportunities and risks of foundation models. arXiv preprint arXiv:2108.07258.

Joulin, A., Grave, E., Bojanowski, P., & Mikolov, T. (2016). Bag of tricks for efficient text classification. arXiv preprint arXiv:1607.01759. DOI: https://doi.org/10.18653/v1/E17-2068

Hazelwood, K., Bird, S., Brooks, D., Chintala, S., Diril, U., Dzhulgakov, D., ... & Wang, X. (2018, February). Applied machine learning at Facebook: A datacenter infrastructure perspective. In 2018 IEEE International Symposium on High Performance Computer Architecture (HPCA) (pp. 620-629). IEEE. DOI: https://doi.org/10.1109/HPCA.2018.00059

Kaack, L. H., Donti, P. L., Strubell, E., Kamiya, G., Creutzig, F., & Rolnick, D. (2022). Aligning artificial intelligence with climate variation mitigation. Nature Climate Variation, 12(6), 518-527. DOI: https://doi.org/10.1038/s41558-022-01377-7

Strubell, E., Ganesh, A., & McCallum, A. (2019). Energy and policy considerations for deep learning in NLP. arXiv preprint arXiv:1906.02243. DOI: https://doi.org/10.18653/v1/P19-1355

Desislavov, R., Martínez-Plumed, F., & Hernández-Orallo, J. (2023). Trends in AI inference energy consumption: Beyond the performance-vs-parameter laws of deep learning. Sustainable Computing: Informatics and Systems, 38, 100857. DOI: https://doi.org/10.1016/j.suscom.2023.100857

Kurth, T., Treichler, S., Romero, J., Mudigonda, M., Luehr, N., Phillips, E., ... & Houston, M. (2018, November). Exascale deep learning for climate analytics. In SC18: International conference for high performance computing, networking, storage and analysis (pp. 649-660). IEEE. DOI: https://doi.org/10.1109/SC.2018.00054

Reddi, V. J., Cheng, C., Kanter, D., Mattson, P., Schmuelling, G., Wu, C. J., ... & Zhou, Y. (2020, May). Mlperf inference benchmark. In 2020 ACM/IEEE 47th Annual International Symposium on Computer Architecture (ISCA) (pp. 446-459). IEEE.

Gardner, W. A. (1984). Learning characteristics of stochastic-gradient-descent algorithms: A general study, analysis, and critique. Signal processing, 6(2), 113-133. DOI: https://doi.org/10.1016/0165-1684(84)90013-6

Prakken, H., & Sartor, G. (2015). Law and logic: A review from an argumentation perspective. Artificial intelligence, 227, 214-245. DOI: https://doi.org/10.1016/j.artint.2015.06.005

Lozo, O., & Onishchenko, O. (2021). The Potential Role of the Artificial Intelligence in Combating Climate Change and Natural Resources Management: Political, Legal and Ethical Challenges. J. Nat. Resour, 4(3), 111-131. DOI: https://doi.org/10.33002/nr2581.6853.040310

McInerney-Lankford, S. (2009). Climate Change and human rights: An introduction to legal issues. Harv. Envtl. L. Rev., 33, 431.

Knox, J. H. (2009). Linking human rights and climate change at the United Nations. Harv. Envtl. L. Rev., 33, 477.




How to Cite

M. N. Joshi, A. K. Dixit, S. Saxena, M. Memoria, T. Choudhury, and A. Sar, “A Study of the Application of AI & ML to Climate Variation, with Particular Attention to Legal & Ethical Concerns”, EAI Endorsed Trans IoT, vol. 10, Mar. 2024.