Development of an Eco-Environmental Evaluation System for Islands in Jiangsu, China, Based on the Time-Varying Entropy Weight Method and a Bayesian Network

This study developed an ecological environment evaluation framework tailored for islands in Jiangsu and validated its applicability using nine representative islands. The evaluation system encompasses 14 indicators across three dimensions: ecological, socio-economic, and policy-climate. By coupling the Time-varying Entropy Weight method with a Bayesian Network, the framework quantifies the dynamic impacts of policy interventions, extreme weather, and human activities. To enhance model accuracy under small-sample conditions, machine learning and deep learning techniques were integrated to construct a multi-layer ensemble evaluation model. The results indicate that this model improves prediction accuracy by 11.3% and reduces the root mean square error by 33.3%. The assessment results reveal significant differences in ecological quality among islands of different types. Natural-type inhabited islands maintain relatively high ecological quality through the synergy of ecological conservation and industrial activity, whereas artificial-type inhabited islands experience significant negative impacts from industrial development. Uninhabited islands generally score around 10, indicating relatively stable ecological conditions but high natural vulnerability. This framework provides a high-precision quantitative approach for dynamic evaluation of island ecological quality under small-sample constraints and offers a scientific basis for customized, island-specific conservation and development management strategies.