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Supply chain challenges and energy insecurity: The role of AI in facilitating renewable energy transition

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  • Li, Lingxiao
  • Wen, Jun
  • Li, Yan
  • Mu, Zi

Abstract

The global energy industry has been undergoing a transition toward renewable energy due to high energy insecurity, disruption in the global supply chain, and industry 4.0 technologies. Given this, it is imperative to identify the factors influencing renewable energy transition by examining the impact of artificial intelligence, supply chain pressure, and energy insecurity in emerging countries. This study employs the method of moments quantile regression on monthly data of selected countries from 2010 to 2022. The findings show that supply chain pressure significantly reduces renewable energy transition, with the negative effects being most prominent at lower quantiles. However, artificial intelligence and energy insecurity stimulate renewable energy transition, with profound impacts observed at lower quantiles. The interaction term of supply chain pressure and artificial intelligence indicates that when nations integrate supply chains with artificial intelligence, it significantly promotes renewable energy transition by addressing supply chain disruptions, with positive effects being pronounced at lower quantiles. These regression parameters are validated using alternative estimators and offer valuable policy suggestions.

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  • Li, Lingxiao & Wen, Jun & Li, Yan & Mu, Zi, 2025. "Supply chain challenges and energy insecurity: The role of AI in facilitating renewable energy transition," Energy Economics, Elsevier, vol. 144(C).
    • Handle: RePEc:eee:eneeco:v:144:y:2025:i:c:s0140988325002026
    DOI: 10.1016/j.eneco.2025.108378
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    1. Hassan Qudrat-Ullah, 2025. "A Thematic Review of AI and ML in Sustainable Energy Policies for Developing Nations," Energies, MDPI, vol. 18(9), pages 1-26, April.

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