Climate risk spatio-temporal evolution, spatial network structure and consequence analysis in China

With the intensification of global warming, climate risk has become a crucial factor influencing global sustainable development. This study conducts a multi-dimensional assessment of climate risk in China, measuring both climate physical risk and climate transition risk at the provincial level from 2000 to 2022. The spatio-temporal evolution analysis reveals that while climate physical risk exhibits a fluctuating trend, climate transition risk shows a fluctuating upward trajectory. Kernel density estimation indicates that the peaks of both risks demonstrate an overall increasing trend, highlighting their intensification over time. The Dagum Gini coefficient and its decomposition uncover significant regional disparities in climate risk, with inter-regional differences being the primary contributor. To further explore the structural characteristics of climate risk propagation, a spatial network model based on the modified gravity model is constructed. The analysis reveals complex inter-provincial linkages, stable centrality rankings, and significant contagion effects, as evidenced by block model analysis. Moreover, the economic consequence analysis employing impulse response functions in a panel vector autoregressive model demonstrates that climate risks significantly affect economic development, social consumption, research and development, government intervention, energy structure, and green finance, underlining their far-reaching socio-economic implications. This study provides a novel integrated framework combining spatio-temporal analysis, network structure modeling, and economic consequence evaluation to enhance climate risk management. The findings offer valuable insights for policymakers in optimizing resource allocation and strengthening risk mitigation strategies.