Artificial Intelligence-driven regional energy transition:Evidence from China

Zuoxiang Zhao, Qiuyun Zhao, Siqi Li and Jiajia Yan

Economic Analysis and Policy, 2025, vol. 85, issue C, 48-60

Abstract: As Artificial Intelligence (AI) technology advances rapidly, its role in promoting regional energy transformation is becoming increasingly apparent. This paper utilizes regional-level panel data from China, covering the period from 2011 to 2021, to systematically assess the impact of AI development on energy transformation. Using econometric models, including fixed-effects and instrumental variable regressions, the study reveals that an increase in AI enterprises within a region significantly reduces energy consumption per unit of GDP, thereby accelerating regional energy transition. The analysis identifies two primary channels through which AI exerts this effect: by facilitating the upgrading of the regional industrial structure and by promoting the growth of the digital economy. The findings also show that the impact of AI is more pronounced in highly urbanized regions, particularly in the Yangtze River Economic Belt. Additionally, the results highlight that AI-driven reductions in energy consumption are largely achieved through improved efficiency in coal and electricity usage, addressing key structural issues in China's energy landscape.

Keywords: Artificial Intelligence; Energy consumption; Structural change; Digital economy; Regional disparity (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecanpo:v:85:y:2025:i:c:p:48-60

DOI: 10.1016/j.eap.2024.10.004

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