Towards energy transition: Accessing the significance of artificial intelligence in ESG performance

Jie Dou, Dongjing Chen and Yuchen Zhang

Energy Economics, 2025, vol. 146, issue C

Abstract: Investigating the crucial role of artificial intelligence in enhancing ESG performance is of utmost importance, particularly for advancing the energy transition. In this study, we utilise the wavelet-based quantile on quantile regression (QQR) to capture the evolving impact of the Artificial Intelligence Enabler Index (AII) on the Global ESG Index (ESGI). Based on raw data, AII's impact on ESGI varies across different quantiles, indicating artificial intelligence doesn't always boost ESG performance. We use wavelet analysis to explore AII's effects on ESGI over various time horizons. AII has more negative impacts in the short term due to early technology stages, slow social adaptation, and limited ESG awareness. However, in the medium to long term, AII's positives may outweigh its negatives, fueled by technological progress, efficiency gains, wider ESG adoption, and artificial intelligence applications in ESG. Hence, our analysis reveals that artificial intelligence's inhibitory effect on ESG performance is more pronounced in the short run. Still, its enhancement effect becomes significant over the medium to long term. In the face of a new technological revolution and industrial transformation, we will offer practical advice to boost ESG progress using artificial intelligence while simultaneously fostering energy transition.

Keywords: Artificial intelligence; ESG performance; Energy transition; Quantile on quantile regression; Wavelet analysis (search for similar items in EconPapers)
JEL-codes: C32 O33 Q55 (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:146:y:2025:i:c:s0140988325003391

DOI: 10.1016/j.eneco.2025.108515

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