DATA-DRIVEN MULTI-CRITERIA DECISION ANALYSIS FOR SOCIO-ENVIRONMENTAL RISK ASSESSMENT: A CASE STUDY OF ECOSYSTEM ENGINEERING IN POYANG LAKE, CHINA

This study assesses socio-environmental risks in the Poyang Lake Ecological Zone, China's largest freshwater lake system, under increasing pressures from large-scale ecosystem engineering and rapid land use change.A data-driven framework integrates Multi-Criteria Decision Analysis with Coupling Coordination Degree Modeling, using geospatial indicators such as NDVI degradation, land use change intensity, population density, and infrastructure proximity to construct a composite spatial risk index.Between 2012 and 2024, high-risk zones expanded across urban fringes and infrastructure corridors, where over 60 percent of grid cells exhibited poor coordination between environmental exposure and social vulnerability.While overall coordination improved modestly during the study period, significant regional disparities remain.Tobit regression analysis identifies ecological compensation coverage, government investment, and infrastructure accessibility as positive contributors to coordination, while vegetation degradation and land conversion exert negative effects.Dagum Gini decomposition shows that interregional variation accounts for 46 percent of coordination inequality, underscoring the influence of functional zoning.Results highlight the need for spatially adaptive planning, enhanced ecological incentives, and integrated monitoring systems to mitigate uneven risk distributions.This approach offers a scalable framework for socio-environmental risk assessment in ecologically sensitive development regions.