Pilot climate resilient cities and ecological resilience: evidence from double machine learning method - Zhang, Tian; Wang, Chao; Duan, Dingkang (Frontiers in Environmental Science, 2025)
As climate change poses an escalating threat to the global ecological environment, strengthening urban ecological resilience has become a pressing priority. Our research leverages China’s “Pilot Climate Resilient Cities” (PCRC) initiative as a quasi-natural experiment, employing the Double Machine Learning approach to assess its impact on ecological resilience. The findings reveal that the PCRC significantly enhances pilot cities’ ecological resilience. Mechanism analysis indicates that reducing resource dependence and fostering green innovation are the two primary channels through which the PCRC improves ecological resilience. Heterogeneity analysis indicates that the PCRC’s effects are particularly pronounced in resource-based cities, as well as in ecologically fragile regions and central and western areas of China. This study not only provides empirical support for the formulation and optimization of climate adaptation policies, but also offers crucial theoretical insights for designing differentiated policies across various types of cities.