%0 Journal Article
%T Artificial Intelligence in Climate Change Mitigation: A Socio-Technical Framework for Evaluating Implementation Effectiveness and Systemic Impact
%A Ibrahim Alhassan
%A Ayisha Maiga
%J Voice of the Publisher
%P 171-190
%@ 2380-7598
%D 2025
%I Scientific Research Publishing
%R 10.4236/vp.2025.111014
%X <b>Purpose: </b>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. <b>Design/Methodology/Approach:</b> 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&#8217;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. <b>Findings:</b> 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. <b>Originality/Value:</b> 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.
%K Artificial Intelligence Climate Change Mitigation
%K Socio-Technical Framework
%K Greenhouse Gas Emissions
%K Governance
%U http://www.scirp.org/journal/PaperInformation.aspx?PaperID=141493