Can green financial policy drive urban carbon unlocking efficiency? A causal inference approach based on double machine learning
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 f...
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Frontiers Media S.A.
2025-06-01
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| Series: | Frontiers in Environmental Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fenvs.2025.1608475/full |
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| author | Weixin Tang Qihao Zhou |
| author_facet | Weixin Tang Qihao Zhou |
| author_sort | Weixin Tang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-6bcb7657343c4139852457adea747e5f |
| institution | OA Journals |
| issn | 2296-665X |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Environmental Science |
| spelling | doaj-art-6bcb7657343c4139852457adea747e5f2025-08-20T02:36:53ZengFrontiers Media S.A.Frontiers in Environmental Science2296-665X2025-06-011310.3389/fenvs.2025.16084751608475Can green financial policy drive urban carbon unlocking efficiency? A causal inference approach based on double machine learningWeixin Tang0Qihao Zhou1School of Economics and Management, Fuzhou Institute of Technology, Fuzhou, ChinaSchool of Economics and Management, Fuzhou University, Fuzhou, ChinaThe 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.https://www.frontiersin.org/articles/10.3389/fenvs.2025.1608475/fulldouble machine learninggreen finance policycarbon unlocking efficiencymechanism validationspatial spillover effects |
| spellingShingle | Weixin Tang Qihao Zhou Can green financial policy drive urban carbon unlocking efficiency? A causal inference approach based on double machine learning Frontiers in Environmental Science double machine learning green finance policy carbon unlocking efficiency mechanism validation spatial spillover effects |
| title | Can green financial policy drive urban carbon unlocking efficiency? A causal inference approach based on double machine learning |
| title_full | Can green financial policy drive urban carbon unlocking efficiency? A causal inference approach based on double machine learning |
| title_fullStr | Can green financial policy drive urban carbon unlocking efficiency? A causal inference approach based on double machine learning |
| title_full_unstemmed | Can green financial policy drive urban carbon unlocking efficiency? A causal inference approach based on double machine learning |
| title_short | Can green financial policy drive urban carbon unlocking efficiency? A causal inference approach based on double machine learning |
| title_sort | can green financial policy drive urban carbon unlocking efficiency a causal inference approach based on double machine learning |
| topic | double machine learning green finance policy carbon unlocking efficiency mechanism validation spatial spillover effects |
| url | https://www.frontiersin.org/articles/10.3389/fenvs.2025.1608475/full |
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