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

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  • Dou, Jie
  • Chen, Dongjing
  • Zhang, Yuchen

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.

Suggested Citation

  • Dou, Jie & Chen, Dongjing & Zhang, Yuchen, 2025. "Towards energy transition: Accessing the significance of artificial intelligence in ESG performance," Energy Economics, Elsevier, vol. 146(C).
    • Handle: RePEc:eee:eneeco:v:146:y:2025:i:c:s0140988325003391
    DOI: 10.1016/j.eneco.2025.108515
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    2. Wang, Qiang & Zhang, Siqi & Li, Rongrong, 2026. "Artificial intelligence in the renewable energy transition: The critical role of financial development," Renewable and Sustainable Energy Reviews, Elsevier, vol. 226(PA).
    3. Yiping Sun & Dengyi Luo & Longyu Tao & Xiaocong Xu, 2025. "The asymmetric influence of ESG performance on corporate innovation: understanding the role of supply chain transmission," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 12(1), pages 1-14, December.
    4. He, Qiong & Geng, Yunjiang & Yu, Kexiu & Chen, Peng, 2025. "How does multinational enterprises’ digital transformation affect ESG performance? Empirical evidence based on Chinese A-share multinationals," Finance Research Letters, Elsevier, vol. 86(PG).
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    JEL classification:

    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • Q55 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environmental Economics: Technological Innovation
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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