Purpose: This study aimed to develop a socio-technical framework for evaluating the effectiveness and systemic impact of Artificial Intelligence (AI) in climate change mitigation. This study addressed the need to integrate technical performance metrics with social, ethical, and environmental considerations to assess AI-driven climate solutions. Design/Methodology/Approach: This study adopts a case study approach to examine AI applications in energy optimisation, carbon sequestration, climate risk modelling, and agriculture. The socio-technical framework was applied to these sectors to evaluate AI’s role of AI in reducing greenhouse gas emissions and improving climate resilience. Key evaluation metrics include emission reduction potential, energy efficiency gains, equity and inclusivity, and the sustainability of AI systems. Findings: The findings demonstrate that AI can significantly enhance climate action by optimising energy systems, improving carbon capture processes, and providing accurate climate risk predictions. However, challenges such as algorithmic bias, unequal access to technology, and the environmental footprint of AI systems must be addressed using robust governance frameworks. Originality/Value: This study contributes original insights into how AI can be harnessed effectively for climate change mitigation, while addressing broader societal impacts. The proposed socio-technical framework provides a comprehensive tool for policymakers and stakeholders to responsibly evaluate the implementation of AI-driven climate solutions.
References
[1]
BCG & Google Report (2023). Accelerating Climate Action with Artificial Intelligence, Google Sustainability Report. https://sustainability.google/reports/
[2]
BMZ Digital Global (2024). UN Climate Change Conference Initiates Digitalisation Day. https://www.bmz-digital.global/en/cop-digitalisation-day/
[3]
Brandt, K., & Lesser, R. (2024). AI Could Accelerate Progress toward the World’s Climate Goals. Fortune.
[4]
Chen, L., Chen, Z., Zhang, Y., Liu, Y., Osman, A. I., Farghali, M. et al. (2023). Artificial Intelligence-Based Solutions for Climate Change: A Review. Environmental Chemistry Letters, 21, 2525-2557. https://doi.org/10.1007/s10311-023-01617-y
[5]
Comunale, M., & Manera, A. (2024). The Economic Impacts and the Regulation of AI: A Review of the Academic Literature and Policy Actions. IMF Working Papers, 2024, 69. https://doi.org/10.5089/9798400268588.001
[6]
del Rey, S., Martínez-Fernández, S., Cruz, L., & Franch, X. (2023). Do DL Models and Training Environments Have an Impact on Energy Consumption? In 2023 49th EuroMicro Conference on Software Engineering and Advanced Applications (SEAA) (pp. 150-158). IEEE. https://doi.org/10.1109/seaa60479.2023.00031
[7]
Ding, Y., Zhang, H., & Li, J. (2022). Advances in Mineral Carbonation for CO2 Sequestration: A Review. Journal of Environmental Management, 302, 113-129.
[8]
EcoAct (2024). AI: Helpful or Harmful to Climate Change? EcoAct. https://eco-act.com/blog/ai-helpful-or-harmful-climate-change
[9]
Eli-Chukwu, N. C., Gulzar, S., Abbas, A., & Waqas, M. M. (2019). Artificial Intelligence Applications in Precision Agriculture: A Review. Journal of Agricultural Science, 11, 123-145.
[10]
Evans, R., & Gao, J. (2016). DeepMind AI reduces Google Data Centre Cooling Bill by 40%. DeepMind. https://deepmind.com/blog/article/deepmind-ai-reduces-google-data-centre-cooling-bill-40
[11]
Farghali, S. M., Lyu, H. T., & Liu, Z. P. (2023). Artificial Intelligence-Based Solutions for Mitigating Climate Change Impacts. Environmental Science & Technology, 57, 123-134.
[12]
Galaz, V. R., Kaack, L. H., & Gupta, R. S. (2021). Ethical Implications of Artificial Intelligence in Addressing Global Warming. Global Environmental Change, 29, 245-258.
[13]
Harvard Business Review (2024). Climate Change. https://hbr.org/topic/climate-change
[14]
Information Technology Industry Council (2024). Sustainable Technology Policy Guide: Artificial Intelligence. https://www.itic.org/documents/artificial-intelligence/ITI_Sustainable-Tech-Policy-Guide_AI_FINAL_Sept2024.pdf
[15]
Intergovernmental Panel on Climate Change (IPCC) (2021). Summary for Policymakers. In V. Masson-Delmotte, P. Zhai, A. Pirani, S. L. Connors, C. Péan, S. Berger, et al., (Eds.), Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. IPCC. https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf
[16]
International Telecommunication Union (ITU) (2024). Measuring Digital Development: Facts and Figures 2024. ITU.
[17]
Islam, M. A., Lomax, S., Doughty, A., Islam, M. R., Jay, O., Thomson, P. et al. (2021). Automated Monitoring of Cattle Heat Stress and Its Mitigation. Frontiers in Animal Science, 2, Article 737213. https://doi.org/10.3389/fanim.2021.737213
[18]
Kaack, L. H., Maher, R. G., & Cheong, S. M. (2022). Governance Frameworks for Artificial Intelligence in Global Warming Mitigation. Climate Policy, 22, 678-695.
[19]
Kurgat, B. K., Lamanna, C., Kimaro, A., Namoi, N., Manda, L., & Rosenstock, T. S. (2020). Adoption of Climate-Smart Agriculture Technologies in Tanzania. Frontiers in Sustainable Food Systems, 4, Article 55. https://doi.org/10.3389/fsufs.2020.00055
[20]
Li, Z. Y., McGovern, A. L., & Yan, H. (2021). Carbon Sequestration Using Artificial Intelligence-Based Geological Assessment Tools. Energy Systems, 45, 345-367.
[21]
Lyu, H. T., & Liu, Z. P. (2021). Artificial Intelligence-Based Solutions for Mitigating Climate Change Impacts. Environmental Science & Technology, 57, 123-134.
[22]
McGovern, A., Elmore, K. L., Gagne, D. J., Haupt, S. E., Karstens, C. D., Lagerquist, R. et al. (2017). Using Artificial Intelligence to Improve Real-Time Decision-Making for High-Impact Weather. Bulletin of the American Meteorological Society, 98, 2073-2090. https://doi.org/10.1175/bams-d-16-0123.1
[23]
Microsoft (2024). How Microsoft Kept Its Underwater Datacenter Connected While Retrieving It from the Ocean. Microsoft Inside Track Blog. https://www.microsoft.com/insidetrack/blog/how-microsoft-kept-its-underwater-datacenter-connected-while-retrieving-it-from-the-ocean/
[24]
Monitor Deloitte (2024). Digital as a Key Enabler for Climate Action: The Latin America Perspective. Deloitte. https://www2.deloitte.com/content/dam/Deloitte/il/Documents/digital-sprinters-2024/MonitorDeloitte_DigitalSprinters_LATAM.pdf
[25]
Murino, T., Monaco, R., Nielsen, P. S., Liu, X., Esposito, G., & Scognamiglio, C. (2023). Sustainable Energy Data Centres: A Holistic Conceptual Framework for Design and Operations. Energies, 16, Article 5764. https://doi.org/10.3390/en16155764
[26]
Mutuku, L. (2022). Using Artificial Intelligence to Help Smallholder Farmers Combat Climate Change. Local Development Research Institute. https://www.developlocal.org/using-artificial-intelligence-to-help-smallholder-farmers-combat-climate-change/
[27]
Ren, S., & Wierman, A. (2024). The Uneven Distribution of AI’s Environmental Impacts: How Companies Can Responsibly Manage the Growing Water and Energy Demands of Their Data Centers across the World. Harvard Business Review. https://hbr.org/2024/07/the-uneven-distribution-of-ais-environmental-impacts
[28]
Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., Luccioni, A., Maharaj, T., Sherwin, E. D., Mukkavilli, S. K., Körding, K. P., Gomes, C., Ng, A. Y., Hassabis, D., Platt, J. C., Creutzig, F., Chayes, J., & Bengio, Y. (2019). Tackling Climate Change with Machine Learning. arXiv: 1906.05433. https://doi.org/10.48550/arXiv.1906.05433
[29]
Sandalow, D., Fan, Z., Friedmann, J., Halff, A., Kucukelbir, A., Leal, E. M., McCormick, C., & Nagrani, T. (2023). Artificial Intelligence for Climate Change Mitigation Roadmap. Columbia University Center on Global Energy Policy.
[30]
SDU Center for Energy Informatics (2024). Nordic Energy Informatics Summit 2024: Harnessing Informatics for the Green Energy Transition. University of Southern Denmark. https://www.sdu.dk/en/forskning/centreforenergyinformatics/news/20240828-nordic-energy-informatics-summit
[31]
Stanford University & Colorado State University Research Team (Stanford & CSU) (2024). Using AI to Link Heat Waves to Global Warming. Science Advances. https://advances.sciencemag.org/content/early/recent
[32]
United Nations Framework Convention on Climate Change (UNFCCC) (2024). Report of the Conference of the Parties on Its Twenty-Eighth Session, Held in the United Arab Emirates from 30 November to 13 December 2023 (Addendum). FCCC/CP/2023/11/Add.2. https://unfccc.int/documents/267207
[33]
UNU-INWEH Report (2023). Harnessing the Power of Artificial Intelligence for Climate Change Impact Assessment. United Nations University Institute for Water Environment & Health.
[34]
van de Poel, I. (2020). Embedding Values in Artificial Intelligence (AI) Systems. Minds and Machines, 30, 385-409. https://doi.org/10.1007/s11023-020-09537-4
[35]
World Bank (2024). Climate Change. https://www.worldbank.org/en/topic/climatechange
[36]
World Economic Forum (WEF) (2024). 9 Ways Artificial Intelligence Is Helping Tackle Climate Change.
