Identification of Production–Living–Ecological Spatial Conflicts and Multi-Scenario Simulations in Extreme Arid Areas

“Production–Living–Ecological” spatial conflicts (PLECs) are critical issues arising from regional land development, affecting economic, social, and ecological security. Identifying and analyzing these conflicts’ spatiotemporal characteristics is essential for sustainable development. This study focuses on the Tuha region, which experiences an extremely arid climate, classifying the region’s “Production–Living–Ecological” (PLE) spaces into four types: living–production, ecological–production, production–ecological, and ecological spaces. A spatial conflict measurement model based on landscape patterns was developed to analyze the evolution of PLECs from 2000 to 2020. Additionally, the PLUS model was used to simulate PLEC patterns in 2030 under different development scenarios. The results indicate that between 2000 and 2020, the area proportions in the Tuha region ranked from largest to smallest as follows: ecological space, ecological–production space, production–ecological space, and living–production space. The area of living–production space increased, while production–ecological space first increased and then stabilized, and the areas of ecological and ecological–production spaces decreased. From 2000 to 2020, spatial conflicts in the region were predominantly characterized by mild weak conflicts. High–high PLEC clusters were concentrated in urban and surrounding areas of Gaochang District, Toksun County, Shanshan County, and Yizhou District, while low–low clusters were found in the Eastern Tianshan Mountains and northern Barkol Kazakh Autonomous County. NDVI, GDP, population, and proximity to roads positively influenced PLECs, while elevation, slope, aspect, and precipitation had inhibitory effects. Under different development scenarios, the natural development scenario leads to the most severe spatial conflicts, while the cropland protection scenario reduces PLECs and enhances regional welfare, making it the optimal pathway for future development.