ORIGINAL RESEARCH article
Front. Environ. Sci.
Sec. Environmental Economics and Management
Volume 13 - 2025 | doi: 10.3389/fenvs.2025.1608475
Can green financial policy drive urban carbon unlocking efficiency? A causal inference approach based on double machine learning
Provisionally accepted
Weixin Tang1
Qihao Zhou2*- 1Fuzhou Institute of Technology, Fuzhou, China
- 2School of Economics and Management, Fuzhou University, Fuzhou, Fujian Province, China
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
The establishment of green finance reform and innovation pilot zones (GFRIPZ) is a crucial initiative in China's advancement of green finance development. Whether this policy can effectively enhance carbon unlocking efficiency (CUE) constitutes a significant research question. Utilizing panel data from 267 Chinese cities spanning 2011 to 2022 and treating the GFRIPZ policy as a quasi-natural experiment, this study employs a double machine learning (DML) model to empirically investigate the impact of green finance policy on urban carbon unlocking efficiency. The results show that: (1) GFRIPZ significantly enhances CUE, and this conclusion remains valid after undergoing a series of robustness checks. (2) Mechanism validation reveal that GFRIPZ enhances CUE through three pathways: optimizing industrial structure, reducing energy intensity, and strengthening public environmental concern. (3) Heterogeneity analysis indicates that the carbon unlocking effects of GFRIPZ are more pronounced in eastern regions, large cities, non-resource-based cities, cities with higher internet development levels, and cities with advanced financial development. Concurrently, the applicability of GFRIPZ also benefits from regional institutional contexts such as public data openness, carbon emissions trading, and green resource endowment.(4) Spatial spillover effects demonstrate that GFRIPZ significantly enhances CUE in surrounding areas. This research not only provides a novel analytical framework for regional carbon unlocking pathways but also offers policy recommendations for enhancing green finance systems and overcoming carbon lock-in dilemmas.
Keywords: Double machine learning, green finance policy, carbon unlocking efficiency, Mechanism validation, thereby significantly overlooking the importance of
Received: 09 Apr 2025; Accepted: 09 Jun 2025.
Copyright: © 2025 Tang and Zhou. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Qihao Zhou, School of Economics and Management, Fuzhou University, Fuzhou, 350108, Fujian Province, China
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.