“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.

In recent years, increasing urbanization has profoundly impacted the quality of regional habitats, presenting a severe risk to the ability of a region to develop in a high-quality manner. Therefore, the scientific assessment of the features of habitat quality (HQ) evolution over time and space and the prediction of future trends in changes in the HQ are of great significance for the formulation of effective ecological protection policies. Based on five periods of land use and land cover (LULC) data from 2000 to 2020, InVEST model was used to estimate both geographical and chronological trends in the HQ in the Tuha region, China. Spatial autocorrelation analysis methods were used to assess HQ and spatial aggregation of habitat degradation, and ecological zoning was delineated in conjunction with the Human Footprint Index (HFI). Based on the results of ecological zoning, the study predicted changes in habitat quality (HQ) in 2040 under three scenarios: natural development (ND), ecological preservation (EP), and urban development (UD) by applying the Patch-Generating Land Use Simulation (PLUS) model. The results demonstrated that (1) from 2000 to 2020, the habitat quality in the Tuha region exhibited a downward trend, with the proportion of low HQ increasing from 83.63% to 84.24%. Spatially, high habitat quality (HQ) is mainly concentrated in the Tianshan Mountains. From 2000 to 2020, the Moran index for habitat quality (HQ) decreased from 0.967 to 0.959, while the Moran index for habitat degradation declined from 0.805 to 0.780. The habitat quality (HQ) and degradation exhibit significant spatial aggregation, and the degree of degradation has increased incrementally. (2) From 2000 to 2020, human activities in the Tuha area increased continuously and were mainly concentrated in Tuha district and counties. The proportion of high Human Footprint Index (HFI) increased from 0.66% to 1.32%, while the proportion of medium HFI increased from 3.13% to 7.46%. (3) The expansion of urbanized land has exacerbated habitat degradation. The proportion of high HQ in the EP scenario is higher than that in ND and UD scenario. The results show that the ecological protection scenario is more conducive to the sustainable development of habitat quality in the Tuha region. The results can provide a scientific basis for ecological management and protection in the Tuha area.

Due to the rapid urbanization and global climate change, arid and semi-arid regions are becoming more vulnerable to the growing disparity between the supply and demand for ecosystem services. This study focuses on the Xinjiang Uygur Autonomous Region (XUAR) and examines four key ecosystem services: water yield (WY), soil retention (SR), carbon sequestration (CS), and food production (FP). Using the InVEST model, geographic information system (GIS) spatial analysis, and statistical methods, we quantify the supply-demand dynamics of these ecosystem services and identify the risk classification of ecosystem service supply-demand (ESSD) using the Self-Organizing Feature Map (SOFM) method. The results reveal significant spatial differentiation in the ESSD across Xinjiang. High-supply areas are primarily located in river valleys and along water systems, while high-demand regions are concentrated in the central cities of oases. WY and SR exhibit large deficit areas that are gradually expanding, whereas CS and FP have smaller deficit areas that are contracting. By integrating supply-demand ratios and trend indices, we identify four distinct clusters in the study area: B1 (WY-CS high-risk cluster), B2 (comprehensive high-risk cluster), B3 (comprehensive low-risk cluster), and B4 (WY high-risk cluster), with B4 being the dominant cluster, accounting for 46.2% of the total area. Based on the ESSD risk classification, we propose tailored ecological management recommendations for each cluster. This study provides a comprehensive ecological risk assessment grounded in ESSD, offering important perspectives for ecological management in arid and semi-arid regions.