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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

has been cited by the following article:

• TITLE: Artificial Intelligence in Climate Change Mitigation: A Socio-Technical Framework for Evaluating Implementation Effectiveness and Systemic Impact

  AUTHORS: Ibrahim Alhassan, Ayisha Maiga

  KEYWORDS: Artificial Intelligence Climate Change Mitigation, Socio-Technical Framework, Greenhouse Gas Emissions, Governance

  JOURNAL NAME: Voice of the Publisher, Vol.11 No.1, March 25, 2025

  ABSTRACT: 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.