Analysis of the Nonlinear Impact of Climate Policy Uncertainty on Total Factor Carbon Productivity in Chinese Cities

With the frequent introduction of climate policies in China, the uncertainty surrounding these policies has gradually increased. However, the relationship between climate policy uncertainty and total factor carbon productivity remains unclear. To address this gap, the total factor carbon productivity of 277 Chinese cities from 2007 to 2022 is assessed using the EBM-GML model. Subsequently, a panel smooth transition model is employed to investigate the nonlinear relationship between climate policy uncertainty and total factor carbon productivity, incorporating economic growth, energy consumption structure, green finance, green innovation, and extreme climate as transition variables. Empirical analysis reveals that the impact of climate policy uncertainty on total factor carbon productivity is not uniformly positive or negative. When influenced by the five transition variables, higher levels of economic growth, the development of green finance, and advancements in green technology can shift the impact of climate policy uncertainty on total factor carbon productivity into a positive direction. Conversely, a higher reliance on coal consumption and frequent extreme weather events impede this positive influence. The heterogeneity analysis confirms significant regional and resource endowment heterogeneity in the observed nonlinear effects among cities. Furthermore, in most regions, the values of the transition variables do not exceed the threshold. Notably, under the influence of economic growth and green technology innovation, the potential for an increase in the impact coefficient of climate policy uncertainty on carbon productivity is substantial. Therefore, it is imperative to further enhance economic growth and green technology innovation. Additionally, specific climate policy targets must be established to address energy consumption structure and extreme weather, thereby improving carbon productivity.