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Assessing the AI-ESG Nexus Through a Quantile-Wavelet Analysis

Author

Listed:
  • Aslan Aydoğdu
  • Umut Uyar

Abstract

This study investigates the impact of artificial intelligence (AI) on environmental, social, and governance (ESG) performance and explores how this relationship evolves across the quantile and time-frequency domains. Using daily data from 2018 to 2025, the analysis applies the Quantile-on-Quantile Regression (QQR), Wavelet Quantile-on-Quantile Regression (WQQR), and Quantile Causality (QC) approaches to examine the influence of the Artificial Intelligence Enabler Index (AII) on the Global ESG Index (ESGI). The results based on raw data reveal that the influence of AI on ESG performance is heterogeneous across quantiles, with both positive and negative effects. In the short, medium, and long term, AII positively affects ESGI at the lower and upper quantiles, while exerting negative effects around the median quantiles. The QC findings, consistent with the raw and wavelet-based analyses, indicate that AI has predictive power for ESG performance over the medium and long horizons, but not in the short run. These results underscore that the strategic and holistic integration of AI technologies can play a pivotal role in overcoming ESG challenges and fostering sustainable development. The study provides novel insights for investors and policymakers aiming to understand the dynamic and multi-scale linkages between AI adoption and ESG performance.

Suggested Citation

  • Aslan Aydoğdu & Umut Uyar, 2026. "Assessing the AI-ESG Nexus Through a Quantile-Wavelet Analysis," Journal of Research in Economics, Politics & Finance, Ersan ERSOY, vol. 11(2), pages 694-720.
    • Handle: RePEc:ahs:journl:v:11:y:2026:i:2:p:694-720
    DOI: 10.30784/epfad.1936077
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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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