Interactive effects of climate physical risks, climate policy uncertainty, and sustainable energy transition in China

This paper uses panel data from 30 Chinese provinces over the period 2003–2022 to construct a Panel Vector Autoregression (PVAR) model combined with System Generalized Method of Moments (GMM) estimation to investigate the dynamic interactions among changes in climate physical risks, adjustments in climate policy uncertainty, and the development of the low-carbon energy transition. Through Granger causality tests, impulse response analysis, and variance decomposition, the study reveals the causal relationships and shock transmission mechanisms among these variables. The results show that at the national level, there are bidirectional Granger causal relationships among exist among changes in climate physical risks, adjustments in policy uncertainty, and the development of the low-carbon energy transition. The low-carbon transition of energy structures significantly mitigates climate physical risks but increases climate policy uncertainty in the short term. An increase in climate policy uncertainty exacerbates physical risks in the short term but helps to digest risks in the long term. The response of energy transition to climate physical risks and policy uncertainty exhibits alternating positive and negative dynamic feedback. Regional analysis indicates that the interactions are strongest in the central region, while the eastern and western regions are more sensitive to policy fluctuations, and the northeastern region shows the weakest interactive effects. Based on these findings, the study recommends establishing a stable and predictable climate policy framework, enhancing investments in clean energy infrastructure, and improving climate risk early warning systems to synergistically promote China’s energy transition and climate risk governance.