A Thematic Review of AI and ML in Sustainable Energy Policies for Developing Nations

Hassan Qudrat-Ullah ()
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Hassan Qudrat-Ullah: School of Administrative Studies, York University, Toronto, ON M3J 1P3, Canada

Energies, 2025, vol. 18, issue 9, 1-26

Abstract: The growing global energy demand and the pursuit of sustainability highlight the transformative potential of artificial intelligence (AI) and machine learning (ML) in energy systems. This thematic review explores their applications in energy generation, transmission, and consumption, emphasizing their role in optimizing renewable integration, enhancing operational efficiency, and enabling data-driven decision-making. By employing a thematic approach, this study categorizes and analyzes key challenges and opportunities, including economic considerations, technological advancements, and social implications. While AI/ML technologies offer significant benefits, their adoption in developing nations faces challenges, such as high upfront costs, skill shortages, and infrastructure limitations. Addressing these barriers through capacity building, international collaboration, and adaptive policies is critical to realizing the equitable and sustainable integration of AI/ML in energy systems.

Keywords: artificial intelligence (AI); machine learning (ML); energy systems; renewable energy integration; sustainable energy policies; developing nations; energy transition (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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