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

A-Tier
Journal: Energy Economics
Year: 2025
Volume: 149
Issue: C

Authors (5)

Li, Zhengzheng (not in RePEc) Xing, Youze (not in RePEc) Shao, Xuefeng (not in RePEc) Zhong, Yifan (University of Western Australi...) Su, Yun Hsuan (not in RePEc)

Score contribution per author:

0.804 = (α=2.01 / 5 authors) × 2.0x A-tier

α: calibrated so average coauthorship-adjusted count equals average raw count

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.

Technical Details

RePEc Handle
repec:eee:eneeco:v:149:y:2025:i:c:s0140988325005560
Journal Field
Energy
Author Count
5
Added to Database
2026-01-29