Unpacking Policy–Instrument Mixes: An Exponential Random Graph Model Analysis of China’s Urban Public Safety Policy

Urban public safety (UPS) governance depends on policy–instrument mixes (PIMs), which combine instruments to address complex and systemically interdependent public safety problems. However, researchers have paid limited attention to their formation patterns. This study examines PIM formation in China’s UPS policy across two dimensions: policy–instrument characteristics and the structural properties of the mixes. Using 13,841 instrument-containing official policy texts from 2004 to 2023, this study identifies fine-grained policy instruments using a named entity recognition (NER) model. It then constructs annual policy–instrument co-occurrence networks and combines descriptive network analysis with exponential random graph models (ERGMs). The four main findings are as follows: (1) PIM networks expanded substantially, but observed combinations continued to comprise only a small share of all possible policy–instrument pairings. (2) Policy instruments participated unevenly in PIM formation, with connections tending to concentrate around highly connected instruments. (3) Usage frequency and category homophily showed stable positive associations with PIM formation, indicating stability and focus in policy–instrument use. (4) Annual changes in PIM formation broadly corresponded with shifts in China’s UPS policy agenda, suggesting timely adjustment to changing risks and governance demands. These findings offer systems-level insights into the relational organization of PIMs in China and comparable governance contexts facing cross-sectoral public safety risks.