Artificial intelligence-driven energy technology innovation: Dynamic impact and mechanism exploration

Renbo Shi, Wei Shan, Richard Evans and Qingjin Wang

Energy Economics, 2025, vol. 147, issue C

Abstract: Energy Technology Innovation (ETI) has received considerable attention in recent decades as firms strive to comply with pollution regulations and advance green transformation. This study investigates the dynamic impact of Artificial Intelligence (AI) on firm-level ETI and the mechanisms driving this relationship. The results show that AI positively contributes to ETI, although its effects exhibit a time lag, with the long-term impact being more significant. Specifically, AI exerts a more pronounced positive impact on renewable ETI compared to traditional ETI, guiding firms' innovation towards cleaner energy sources. The promotion of ETI by AI is found to be stronger in heavily polluted industries and geographical regions with greater openness. In addition, mechanism analysis demonstrates that AI primarily promotes ETI through enhanced Research and Development (R&D) activities, factor allocation effects, and increased financial opportunities. Furthermore, these AI-driven advancements in ETI contribute to improved energy efficiency. This study provides valuable guidance for firms seeking to effectively integrate AI into their ETI strategies and holds significant theoretical and practical implications for accelerating energy transformation.

Keywords: Artificial intelligence; Energy technology innovation; R&D effects; Factor allocation effects; Financing effects (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:147:y:2025:i:c:s0140988325003652

DOI: 10.1016/j.eneco.2025.108541

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