Impact of climate resilience policy on urban carbon intensity: Evidence from econometrics and machine learning

Urban carbon intensity (CI) is a key concern for improving climate conditions and promoting sustainable development. The climate resilient city pilot policy (CRCPP) implemented in China aims to actively address global climate change and foster sustainable development. The paper uses data from 278 Chinese cities between 2010 and 2021, employing the difference-in-differences model and machine learning to analyze the relationship between CRCPP and urban CI. The finding reveals that CRCPP effectively reduces urban CI. Meanwhile, the causal forest model, an advanced machine learning approach, corroborates this finding. Notably, the implementation of CRCPP in high economic development, high urbanization, and high CI cities is particularly effective in reducing CI. Concurrently, economic benefit analysis indicates that CRCPP not only lowers urban CI but also stimulates economic development. Furthermore, the policy synergistic effect of CRCPP when implemented in conjunction with energy saving and emission reduction fiscal policy enhances the effect of reducing urban CI. The outcomes of the paper offer useful insights for cities to formulate climate resilience plans and decrease urban CI. • The CRCPP effectively reduces urban CI. • The causal forest model, a novel machine learning method, is employed for policy effect analysis in this study. • Economic benefit analysis indicates that CRCPP not only lowers urban CI but also stimulates economic development. • There are policy synergies in implementing CRCPP together with the energy saving and emission reduction fiscal policy.