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DOI 10.1038/s41598-026-44479-4

Медицинская библиотека · Scientific reports · 2026

Spatial early-warning assessment of ecological security in the Poyang Lake Basin based on PSR and Spatial Durbin Modeling.

Guo HaiXiang, Hu CongLe, Xin MeiYi, Wu Qi

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Аннотация

Constructing a basin-level ecological security early-warning (ESEW) system is critical for overcoming the fragmentation of administrative management and achieving holistic watershed governance. Focusing on the Poyang Lake Basin, this study develops a spatial ESEW assessment framework based on the Pressure-State-Response (PSR) model. By integrating 1 km grid-scale data with the Spatial Durbin Model (SDM), we investigate the spatiotemporal evolution of ecological security and quantify the cross-regional spillover effects of key drivers from 2000 to 2020. The results reveal a structural "deterioration-then-recovery" trajectory: the area of "Moderate Warning" expanded from 52.40% in 2000 to a peak of 64.53% in 2010 due to rapid industrialization, before dropping to 37.93% in 2020 as "Light Warning" areas became dominant (62.00%). Spatial analysis confirms significant clustering of ecological risks. Crucially, the SDM identifies a strong spatial spillover effect, indicating that ecological degradation is highly contagious across county borders. Specifically, industrial sulfur dioxide emissions and urbanization intensity (NUADI) in neighboring areas exert significant negative spillovers on local ecological security, whereas farmland-to-forest conversion generates positive externalities. Based on these findings, we propose a differentiated governance strategy, including "Source Control" for high-risk pollution clusters and market-oriented ecological compensation mechanisms to internalize the benefits of upstream protection, providing a scientific basis for cross-regional collaborative governance.

Для цитирования: Guo HaiXiang, Hu CongLe, Xin MeiYi, Wu Qi. Spatial early-warning assessment of ecological security in the Poyang Lake Basin based on PSR and Spatial Durbin Modeling.. Scientific reports. 2026. DOI: 10.1038/s41598-026-44479-4.
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