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

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

Energy Economics, 2025, vol. 149, issue C

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.

Keywords: Artificial intelligence; Wavelet analysis; Renewable energy; Fossil fuels (search for similar items in EconPapers)
JEL-codes: O33 Q41 Q42 (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:149:y:2025:i:c:s0140988325005560

DOI: 10.1016/j.eneco.2025.108729

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