Assessing the impact of artificial intelligence on the transition to renewable energy? Analysis of U.S. states under policy uncertainty

Yuzhu Fang, Chi-Chuan Lee and Xinghao Li

Renewable Energy, 2025, vol. 246, issue C

Abstract: In the context of the increasingly severe global climate change, technological innovation and energy transition have formed a critical path to achieving sustainable development. This research employs panel data from 51 states/district in the United States for 2018–2021 to explore the impact of artificial intelligence (AI) development on energy transition and discusses the moderating effect of economic policy uncertainty. The results indicate that AI has a significantly positive impact on energy transition. Economic policy uncertainty at the state level has a significantly positive moderating effect on AI's impact on energy transition, but national-level policy uncertainty does not affect their nexus. AI has a significantly positive correlation with energy transition in states with high electricity prices, low electricity consumption, and those states that are net electricity consumers, while its impact is also significant in regions with frequent extreme weather events. Heterogeneity analysis based on different geographical locations and economic characteristics shows that AI significantly promotes energy transition in the West and Midwest regions. Furthermore, AI significantly promotes the consumption of wind, solar, and hydropower, but inhibits biomass consumption. From the above results, this paper offers relevant policy recommendations.

Keywords: Technological innovation; Sustainable development goals; Economic policy uncertainty; Carbon neutrality; Regional heterogeneity (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:246:y:2025:i:c:s0960148125006317

DOI: 10.1016/j.renene.2025.122969

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