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The relationship between artificial intelligence, geopolitical risk, and green growth: Exploring the moderating role of green finance and energy technology

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  • Yang, Xiyue
  • Chen, Hui
  • Li, Baoxi
  • Jia, Danning
  • Ahmad, Mahmood

Abstract

Artificial intelligence (AI), viewed as a driver of progress in modern societies, is accompanied by uncertainties regarding its impact on green growth within the intricate background of geopolitical risk (GR). This study examines the relationship between AI, geopolitical risk and green growth, further exploring the moderating roles of green finance and energy technology on geopolitical risks affecting green growth. Using panel data from 20 OECD countries spanning from 1993 to 2021 and advanced econometric techniques, we find that the impact of AI on green growth exhibits a “U”-shaped trajectory, initially inhibiting but ultimately fostering growth. Additionally, GR exerts a negative influence on green growth, however, this adverse effect can be mitigated by green finance and energy technology, which drive green development. We also applied a suite of methodologies, including Two Stage Least Squares (2SLS), Fully Modified Ordinary Least Squares (FMOLS), quantile regression, and Artificial Neural Networks (ANN), to assess the robustness of the results. The findings of this study underscore the critical importance of incorporating artificial intelligence into the green growth agenda of OECD nations. Moreover, OECD countries should proactively address geopolitical risks by expanding green finance initiatives and intensifying investments in energy technology to strengthen their green growth frameworks.

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  • Yang, Xiyue & Chen, Hui & Li, Baoxi & Jia, Danning & Ahmad, Mahmood, 2025. "The relationship between artificial intelligence, geopolitical risk, and green growth: Exploring the moderating role of green finance and energy technology," Technological Forecasting and Social Change, Elsevier, vol. 217(C).
    • Handle: RePEc:eee:tefoso:v:217:y:2025:i:c:s0040162525001660
    DOI: 10.1016/j.techfore.2025.124135
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