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Transitioning the energy landscape: AI's role in shifting from fossil fuels to renewable energy

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  • Li, Zhengzheng
  • Xing, Youze
  • Shao, Xuefeng
  • Zhong, Yifan
  • Su, Yun Hsuan

Abstract

This study examines the evolution of the energy market within the scope of artificial intelligence (AI). By employing wavelet analysis, we discern that AI has predominantly fostered the growth of renewable energy sectors, notably wind and solar energy, across short-, medium- and long-term horizons, except during 2016–2017. This deviation is mainly attributable to supply-side structural reforms. The positive correlation between AI and renewable energy has become increasingly pronounced after 2019, driven by the heightened demand for technological innovation and energy transformation after the pandemic. Conversely, the relationship between AI and fossil fuels fluctuates, exhibiting positive and negative correlations at various stages of AI's development. Our findings, therefore, offer valuable insights for policymakers seeking to design energy transition policies that leverage AI technology.

Suggested Citation

  • Li, Zhengzheng & Xing, Youze & Shao, Xuefeng & Zhong, Yifan & Su, Yun Hsuan, 2025. "Transitioning the energy landscape: AI's role in shifting from fossil fuels to renewable energy," Energy Economics, Elsevier, vol. 149(C).
    • Handle: RePEc:eee:eneeco:v:149:y:2025:i:c:s0140988325005560
    DOI: 10.1016/j.eneco.2025.108729
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    Keywords

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    JEL classification:

    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • Q42 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Alternative Energy Sources

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