Climate-Resilient City Pilot Programs and New-Quality Productivity: Causal Identification Based on Dual Machine Learning

Climate change is a critical constraint on the development of new-quality productive forces (NQPFs), making it essential to clarify its relationship with urban development strategies to enhance productivity. Using panel data from 284 Chinese cities during 2010–2022, this study leverages the climate-resilient city pilot policy as a quasi-natural experiment and applies a double machine learning approach to estimate both the causal impact and underlying mechanisms of this policy on NQPFs. We further examine heterogeneous effects across geographic regions and city types. Our findings show that first, climate-resilient urban development significantly boosts NQPFs, with results remaining robust across multiple sensitivity tests. Second, this effect operates through three key channels—talent agglomeration, data flow enhancement, and infrastructure-related industrial upgrading. Third, the policy’s impact is stronger in western and coastal cities; resource-based cities and non-environmentally protected cities exhibit greater responsiveness, amplifying the positive outcomes. This study provides systematic empirical evidence on the nexus between climate resilience and high-quality development, offering actionable insights for designing localized strategies to advance climate-resilient urbanization and foster high-quality productive forces.