Characteristics and adaptive structure of urban flood governance network: the 'July 20th′ flooding event in Zhengzhou, China

With urbanization, the complexity and uncertainty of flood disasters have grown, rendering traditional emergency management approaches insufficient to fulfill expectations. There is an urgent need for creative governance models that improve emergency capabilities and governance effectiveness. This research investigates a novel adaptive governance model for urban flood management that combines Complex Adaptive System (CAS) theory with Social Network Analysis (SNA). The goal is to optimize the emergency organizational structure, increasing the adaptability of emergency reactions. Conducted using the SNA, this study examines the density and centrality of urban flood disaster governance network, as well as the importance of actors and how they can play a role. The Exponential Random Graph Model (ERGM) is used to simulate the urban flood adaptive network, illustrating how network endogeneity and interaction promote the generation of adaptive networks. A study is conducted on the 'July 20th′ flooding event in Zhengzhou to uncover the flexible adjustment trend and adaptive governance process. The results show that endogeneity and interactivity both influence the establishment of adaptive networks. Networks with significant endogeneity and high interactivity are more likely to build tight collaborative links and enable information flow, increasing the network's adaptive capability. Based on these findings, the study advocates the formation of network committees or collaborative alliances to optimize actor participation and assist cities in developing effective adaptive organizational response models. This study offers suggestions for clarifying organizational collaboration ties within emergency networks and strengthening adaptive linkage mechanisms.