Research on the economic loss prediction and vulnerability risk zoning of coastal erosion disaster: a case study from Liaoning, China

Introduction Coastal erosion is widely distributed globally, with sandy coasts being particularly prominent, causing significant economic losses to coastal areas. This study focuses on the coastal areas of Liaoning Province, China, which represent typical sandy beach erosion.Methods Based on the assessment of coastal erosion disaster losses, a multivariate variable-weight combination prediction model is utilized to predict the losses caused by coastal erosion disasters, providing an effective mathematical modeling approach for predicting such losses. Furthermore, the cluster analysis method is employed to delineate vulnerability risk zones for coastal erosion along the Liaoning Province coast.Results and discussion The research findings indicate the following: (1) From 2023 to 2025, the losses due to coastal erosion disasters in Liaoning Province are projected to decrease significantly from a scale of 36 million RMB yuan to 5 million RMB yuan; (2) Based on the magnitude of disaster vulnerability risk, the coastal areas of Liaoning Province are categorized into four risk zones: high-risk zones (Jinzhou District), moderately high-risk zones (Zhuanghe City, Wafangdian City, Ganjingzi District), medium-risk zones (Lvshunkou District, Bayuquan), and low-risk zones (Gaizhou City, Suizhong County, Xingcheng City). Recommendations are proposed accordingly: (1) Effective measures should be implemented to mitigate the losses incurred by coastal erosion on land, buildings, infrastructure, agricultural output; (2) In the selection of disaster prevention and control measures,economic factors should be carefully considered in line with the specific circumstances of the losses. Specifically, the economic efficiency of disaster prevention and mitigation measures should be taken into account; (3) Precise efforts should be made to implement coastal erosion disaster prevention and control measures in different regions.