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Can financial innovation mitigate carbon dependency in China? An advanced quantile and machine learning analysis

Author

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  • Yang Yu

    (Hainan University
    Hainan University)

  • Xin Jian

    (Hainan University)

  • Haitao Wu

    (Hainan University
    Hainan University)

Abstract

As the global challenge of climate change escalates, the issue of carbon dependency has garnered extensive attention. Financial innovation, being the nascent form of innovation, is posited as the potential determinant influencing the mitigation of carbon emissions. However, in the current intricate and dynamically evolving economic landscape, the precise influence of financial innovation on carbon dependency remains the subject that demands comprehensive examination. This study tackles the challenge by employing multivariate Quantile-on-Quantile Regression and machine learning to examine the effects of financial innovation on carbon dependency in China from 2005 to 2021. The investigation unveils that the influence of financial innovation on carbon dependency is subject to modulation by those variables, including GDP per capita, energy consumption intensity, population size, and the degree of openness. Notably, this impact exhibits dependency across various quantiles. The principal objective of this study is to discern more efficacious strategies and formulate policies that facilitate China’s attainment of its dual-carbon objectives. Additionally, the outcomes of the machine learning analysis suggest that the association between financial innovation and carbon dependency is complicated and nonlinear, characterized by the presence of one or more threshold effects between the variables. By delving into the intrinsic nexus between financial innovation and carbon dependency, this research endeavors to offer valuable insights and experiences to the broader global effort in addressing climate change.

Suggested Citation

  • Yang Yu & Xin Jian & Haitao Wu, 2025. "Can financial innovation mitigate carbon dependency in China? An advanced quantile and machine learning analysis," Economic Change and Restructuring, Springer, vol. 58(4), pages 1-30, August.
    • Handle: RePEc:kap:ecopln:v:58:y:2025:i:4:d:10.1007_s10644-025-09899-8
    DOI: 10.1007/s10644-025-09899-8
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