Against the background of global climate change and the increasing frequency of extreme weather events, the Guangdong–Hong Kong–Macao Greater Bay Area—as a typical coastal highdensity urban agglomeration—has long been exposed to compound natural disaster risks (typhoons, heavy rainfall, floods, storm surges, and geological disasters) under the combined influence of land–sea interactions, rapid urbanization, and multiple overlapping hazards. Regional disaster governance has therefore become a critical interdisciplinary issue bridging geoscience and public governance. To systematically evaluate the structural characteristics and design quality of disaster governance policies in this region, this study analyses 115 policy texts and constructs an integrated analytical framework.
We combine text mining, semantic network analysis, and the Policy Modeling Consistency (PMC) index. First, word segmentation statistics and keyword cooccurrence analysis are employed to identify policy themes and core semantic structures. Second, a PMC evaluation index system is built to quantitatively assess the policy texts. Finally, grade classification and PMC surface plots are used to compare structural differences among various policies.
The results show that disaster governance policies in the Guangdong–Hong Kong–Macao Greater Bay Area have generally formed a relatively systematic institutional framework, with most sample policies rated as “excellent” or “good.” Excellent policies perform more evenly in terms of policy instruments, policy content, supporting measures, and target coverage, whereas average policies show deficiencies in the configuration of issuing bodies, temporal arrangements, crosslevel coordination, and evaluation and feedback mechanisms.
Accordingly, this study suggests strengthening multiactor collaboration, improving closedloop policy management, and enhancing the balance and synergy of policy structures, so as to improve the governance capacity and regional resilience of the Greater Bay Area in response to compound natural disaster risks. These findings also provide a reference for disaster risk governance and regional disaster prevention and reduction policy optimization in the field of geoscience.
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