Ripple Effects of Climate Policy Uncertainty: Spatial Structural Evolution and Heterogeneous Spillovers of Low-Carbon Energy Transition in China
Description
In the pursuit of global climate change mitigation and carbon neutrality objectives, climate policy uncertainty (CPU) has emerged as a significant constraining factor. Against this backdrop, gaining a deep understanding of how different regions respond to climate risks and achieve low-carbon energy transition (LET) becomes particularly crucial. Drawing on panel data from China's 31 provinces spanning 2000–2024, this study employs kernel density estimation and spatial econometric methods to examine the spatiotemporal evolution of CPU and LET, as well as the underlying mechanisms of influencing factors. Research findings indicate that: (1) CPU and LET exhibit an overall upward trend, with regional disparities showing signs of convergence. However, coordination levels in certain regions are becoming increasingly polarized. (2) Spatially, the evolution pattern shows Western regions > Eastern regions > Central regions, with spatial agglomeration primarily characterized by H-H-type and L-L-type agglomerations. (3) CPU can significantly accelerate the LET and possesses pronounced spatial spillover effects. (4) Both government environmental regulation and public environmental awareness amplify the role of CPU in promoting LET development, with government environmental regulation having a greater amplifying effect than public environmental awareness. Therefore, the findings of this study provide important theoretical foundations and practical guidance for local governments to develop climate incentive policies tailored to their specific conditions, advance regional energy strategy transformations, and contribute to achieving the dual carbon goals.