Research on the impact of extreme high temperature on the spatial correlation network of local government debt scales

Based on the spatial correlation of Chinese prefecture-level local government debt scales from 2010-2022, this paper employs complex network models, including Quadratic Assignment Procedure (QAP), Randomization Test of Autocorrelation (Joint-Count) and Exponential Random Graph Model (ERGM) to investigate the impact of extreme high temperature on the spatial correlation network of local government debt scales. The research findings reveal a strengthening relationship between extreme high temperature and the spatial correlation network of debt scales. Specifically, extreme high temperature positively affects the spatial centrality of debt scales and promotes their spatial spillover effect, mediated through the spatial distribution characteristics of extreme high temperature and public concerns regarding these temperature extremes. Furthermore, extreme high temperature emerges as the driving force for the evolution of spatial correlation network of debt scales by intensifying the Matthew effect’s divergence dynamics. It leads high-debt cities to attract disproportionately more debt scale correlations. Further investigation reveals that its impact on debt network evolution demonstrates a nonlinear pattern of Matthew effect intensification followed by saturation, while consistently serving as a positive driving factor. Simultaneously, an assortative mixing effect is observed, as the debt scale correlations of cities with similar climate conditions are enhanced. This research provides valuable insights for the formulation of spatial layout and management strategies of local government debt to cope with climate change such as extreme high temperature, and contributes to enhancing the climate finance resilience of debt management system.