The impact of artificial intelligence on carbon market in China: Evidence from quantile-on-quantile regression approach

Wei Jiang, Yanhui Hu and Xiangyu Zhao

Technological Forecasting and Social Change, 2025, vol. 212, issue C

Abstract: As the core driving force of the industry, artificial intelligence (AI) has brought technological progress that has a profound impact on carbon emission reduction. In this paper, the quantile unit root test, quantile cointegration test, quantile Granger-causality test, and quantile-on-quantile (QQ) technique are used to study the relationship between the AI index and carbon price in nine carbon emission trading schemes (ETS) in China from the start-up to July 26, 2022. In general, our empirical results show that in all nine ETSs, the AI index is the Granger cause of carbon prices at all quantiles, except for the Chongqing ETS; its causal relationship only exists in the extremely high tail. The QQ approach shows that there is a nonlinear relationship between the AI index and carbon prices, especially in the extremely low and extremely high tails. However, there are differences between markets and between different quantiles of AI and carbon prices within each market. Our results have important practical significance for policymakers, carbon emission enterprises, and investors.

Keywords: Artificial intelligence; Carbon market; Quantile Granger-causality test; Quantile-on-quantile technique (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:212:y:2025:i:c:s0040162525000046

DOI: 10.1016/j.techfore.2025.123973

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