The impact of artificial intelligence on the energy transition: The role of regulatory quality as a guardrail, not a wall

Zequn Dong, Chaodan Tan, Biao Ma and Zhaoshuo Ning

Energy Economics, 2024, vol. 140, issue C

Abstract: In recent years, the economic impact and environmental contribution of Artificial Intelligence (AI) have gradually become a new focus in academia. This study uses a panel data sample of 50 countries to explore the impact of AI on energy transition (ET), aiming to fill an important research gap. The results highlight several critical insights. First, AI has had a significant positive impact on facilitating the ET. This conclusion still holds after a series of robustness tests. Second, AI positively affects ET by promoting renewable energy technology innovation and upgrading the electricity structure, resulting in both technological and structural effects. Third, the impact of AI on ET is non-linear. Threshold effect models show that AI impacts ET differently at various levels of regulation quality (RQ), exhibiting a double threshold effect. AI hinders ET when RQ is lower than the first threshold value. When RQ is in the second range, AI significantly facilitates ET. However, when RQ exceeds the second threshold value, AI hinders ET again. These findings provide insights into the mechanisms of AI's impact on ET and emphasize that an appropriate level of regulation is crucial for AI to facilitate ET. Finally, this study analyzes heterogeneity and offers targeted policy recommendations.

Keywords: Energy transition; Artificial intelligence; Regulatory quality; Threshold effects; Renewable energy (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:140:y:2024:i:c:s0140988324006960

DOI: 10.1016/j.eneco.2024.107988

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