ORIGINAL RESEARCH article
Front. Environ. Sci.
Sec. Environmental Policy and Governance
Volume 13 - 2025 | doi: 10.3389/fenvs.2025.1620195
This article is part of the Research TopicIs All Politics Local When the Problem is National? The Role of Local Governments in Mitigating Climate ChangeView all articles
How to Promote Local Governments Collaboration? Evidence from Carbon Reduction in the Yangtze River Delta
Provisionally accepted- 1Nanjing Forestry University, Nanjing, China
- 2Dalian University of Technology, dalian, China
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China's central government has proposed a "dual carbon" goal to promote carbon peaking in every province. Collaboration on carbon reduction among local governments is considered an efficient approach to address the carbon emission issues in China. In response, the YRD region implemented the collaborative mechanism and achieved success in carbon reduction. However, there are still some factors that limit the effectiveness of collaboration, such as inconsistencies in priority sectors and goals for carbon reduction. Therefore, comprehensively identifying the factors that influence collaboration would contribute to understanding the reasons for inefficient collaboration, and exploring the relationships among these factors could provide guidance on promoting collaboration. This study presents a structural model and an impact mechanism model for collaboration through grounded theory, cluster analysis and variation coefficient analysis. The results suggest that there are five factors that influence collaboration: Equitable allocation and pressure from monitoring are pressure factors, governance cost and collaborative benefit are state factors, and governance responsibility is the individual factor. The pressure factors could affect collaboration by affecting state factors, while individual factor plays a moderating role between state factors and collaboration. The research findings provide new insights for promoting collaboration on carbon reduction.
Keywords: Collaboration on carbon reduction, local governments, grounded theory, K-means cluster, Variation coefficient analysis
Received: 29 Apr 2025; Accepted: 29 Jul 2025.
Copyright: © 2025 Liu and Cang. 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:
Yue Liu, Nanjing Forestry University, Nanjing, China
Yaodong Cang, Dalian University of Technology, dalian, China
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