Optimization and regulation of “production-living-ecological” space on the northern slope of the Tianshan Mountains

This study takes the northern slope of the Tianshan Mountains as the research area and focuses on the evolution patterns, driving factors, and optimal regulation strategies of its “production-living-ecological” (PLE) space. By constructing a multiscale evaluation index system and applying the Analytic Hierarchy Process (AHP), the Coupling Coordination Degree Model, and the Future Land Use Simulation model, integrated with multisource data—including land use, topography, meteorology, and social economy—a systematic analysis of the dynamic change characteristics and coordination of PLE spaces from 2010 to 2020 was conducted. The results demonstrate that, mainly influenced by urbanization, industrialization, and agricultural development, production space and living space expanded by 8.3% and 6.5% respectively, while ecological space decreased by 7.2%. Regarding spatial distribution, agricultural production exhibited a trend of “retreating southward and advancing northward,” industrial production clustered along major transportation corridors, living space expanded outward with the Urumqi Urban Agglomeration as the core, and ecological space remained stable in high-altitude mountainous areas but degraded significantly in mid- to low-altitude regions. Multiscale integrated analysis demonstrated typical spatial conflict types, such as urban expansion vs. cultivated land protection and agricultural development vs. ecological conservation. Based on simulation and optimization results, a regulation strategy characterized by “core agglomeration, corridor connection, and zonal management” is proposed, offering a scientific basis for regional territorial spatial planning and sustainable development. This study introduces innovations in the sustainability evaluation of PLE space functions and multi-scale integration methods. However, future research should further quantify the impacts of climate change and policy interventions to optimize the model’s dynamism and accuracy.