Аннотация
Addressing escalating land use conflicts (LUCs) is critical for sustainable development in resource-scarce, transboundary regions. The Aral Sea Basin (ASB), Central Asia’s largest transboundary basin characterized by arid conditions and vulnerable ecosystems, serves as a crucial case study. This research introduces an innovative framework, integrating multi-scale spatial assessments with interpretable machine learning (XGBoost-SHAP), to overcome limitations of previous fragmented analyses and provide deeper insights into LUCs dynamics. We systematically evaluated land suitability for ecological preservation, agriculture, and urban construction, quantified conflict intensity, and identified key drivers across the entire ASB, including its Amu Darya and Syr Darya sub-basins. Quantitative results reveal profound spatial heterogeneity in land use potential, with 56.29% of the basin suitable for ecological preservation, only 6.54% for agriculture, and 72.67% for urban construction—indicating dominant ecological value, limited agricultural suitability, and high urban development pressure. Conflicts were found to be pervasive and intense, driven by a complex interplay of natural factors and socio-economic pressures, with distinct upstream-downstream patterns across sub-basins. Crucially, this study provides spatially explicit evidence highlighting the urgent need for integrated, transboundary land management. The results offer actionable, data-driven insights essential for designing targeted strategies, fostering collaborative resource governance, and ultimately promoting sustainable development pathways that balance ecological integrity with human needs in the ASB and similar complex transboundary basins worldwide.