COVID-19 has created long-lasting yet unprecedented challenges worldwide. In addition to scientific efforts, political efforts and public administration are also crucial to contain the disease. Therefore, understanding how multi-level governance systems respond to this public health crisis is vital to combat COVID-19. This study focuses on China and applies social network analysis to illustrate interactive governance between and within levels and functions of government, confirming and extending the existing Type I and Type II definition of multi-level governance theory. We characterize four interaction patterns—vertical, inter-functional, intra-functional, and hybrid—with the dominant pattern differing across governmental functions and evolving as the pandemic progressed. Empirical results reveal that financial departments of different levels of government interact through the vertical pattern. At the same time, intra-functional interaction also exists in provincial financial departments. The supervision departments typically adopt the inter-functional pattern at all levels. At the cross-level and cross-function aspects, the hybrid interaction pattern prevails in the medical function and plays a fair part in the security, welfare, and economic function. This study is one of the first to summarize the interaction patterns in a multi-level setting, providing practical implications for which pattern should be applied to which governmental levels/functions under what pandemic condition.
Using social network analysis to identify key stakeholders in agricultural biodiversity governance and related land-use decisions at regional and local level