A multi-dimensional risk point identification and governance framework: evidence from marine food safety incidents

Abstract

Marine food safety incidents occur frequently because of the uncertainty of marine ecosystems and the complexity of supply chains, posing challenges to global food safety. To address the challenges, this study constructs a Multi-dimensional Risk Point Identification and Governance Framework based on the HFACS-TER model. By analyzing risk points across 456 marine food safety incidents in China from 2017 to 2022, the research employs Fuzzy-set Qualitative Comparative Analysis (fsQCA) to explore the causal configurations. The results indicate that eight high-risk points mainly concentrate in the production, logistics, and sales processes. Notably, 54.3% of incidents were directly triggered by explicit unsafe behaviors, while 32.9% resulted from the interaction of multiple implicit factors, including Preconditions for Unsafe Acts, Unsafe Supervision, Organizational Influences, and Result Control. The fsQCA results identify eight causal configurations, which are categorized into three patterns: behavior-driven, supervision-failure, and systemic-vulnerability patterns. Based on these findings, three distinct governance pathways are proposed: (1) implementing credit constraints and strict penalties for behavior-driven configurations; (2) adopting blockchain-based traceability and multi-party collaboration for systemic-vulnerability configurations; and (3) using intelligent monitoring and public participation for supervision-failure and mixed-failure configurations. This framework addresses gaps in existing research and provides a systematic reference for marine food safety governance.