Taking the northern slope of the Tianshan Mountains (NSTM) in Xinjiang as the research area, this study analyzes the changes in the Production–Living–Ecological (P-L-E) Spaces and their Ecological Environmental Effects, providing a basis for optimizing the spatial pattern of the P-L-E Spaces and regional ecological environment protection in this area. Based on land use data and DEM data, various methods were used for analysis. These methods include the land use transfer matrix, ecological environment quality index, hot spot analysis, ecological contribution rate, and Terrain Position Index. The analysis focused on changes in the spatial pattern of the P-L-E Spaces from 1980 to 2020. It also examined the spatiotemporal distribution of ecological environment quality (EEQ). Furthermore, it explored the differentiation characteristics of EEQ in terrain gradients. The conclusions are as follows: (1) On the NSTM, Ecological Space decreased while Production and Living Space expanded. From 1980 to 2020, Agricultural Production Space increased rapidly. Industrial Production Space also saw rapid growth during this period. Urban Living Space expanded significantly from 1980 to 2020. Rural Living Space experienced steady growth over the same period. Forest Ecological Space initially increased but later decreased. Water Ecological Space showed an initial increase followed by a decrease from 1980 to 2020. (2) The EEQ first remained stable, declined slightly from 2000 to 2010, improved significantly, and then deteriorated from 2010 to 2020. The distribution of EEQ exhibits a “high in the northwest, low in the southeast” pattern. EEQ hot spots on the NSTM are concentrated in the Tianshan Mountains, with clustering increasing in both northern and southern areas. Cold spots are found in the southern, eastern, and northern NSTMs, with aggregation strengthening in the south and north and slightly weakening in the east. Hot spots of EEQ changes on the NSTM show stable distribution, with stronger aggregation from 2000 to 2020. However, aggregation of cold spots has gradually weakened, yet noticeable aggregation persists throughout the study period. (3) There is a significant gradient difference in EEQ distribution. Higher terrain gradients have a higher EEQ. From 1980 to 2020, lower terrain gradients saw improvement, while higher gradients experienced deterioration. The EEQ on the NSTM has declined, showing significant spatial differences, with better quality on the northern side than the southern side. Future efforts should focus on restoring the environment at lower gradients, mitigating deterioration at higher gradients, and enhancing water conservation in the Tianshan Mountains.

The assessment of ecological resilience in arid regions is crucial for understanding and mitigating the impacts of climate change and human activities, ensuring the sustainable management of these vulnerable ecosystems. Taking the Economic Belt on the Northern Slope of the Tianshan Mountains (EBNSTM) as the research area, a multi-dimensional evaluation model coupling vulnerability, health, and connectivity was used to explore the spatiotemporal variation and driving forces of ecological resilience. Firstly, a sub-item evaluation of ecological resilience was conducted from three aspects, including ecological vulnerability evaluation based on the CRITIC and AHP models, ecological health evaluation based on the InVEST model, and landscape connectivity evaluation based on the MSPA method. Then, the sequence polygon method was utilized to conduct a comprehensive multi-dimensional assessment of ecological resilience based on the aforementioned three evaluation results. Finally, the geographical detector model was utilized to identify the driving factors behind the spatial heterogeneity of ecological resilience. The results show the following: (1) From 2000 to 2020, the overall ecological resilience showed an upward trend and significant spatial heterogeneity. The overall distribution pattern exhibited a spatial feature of south higher, north lower, where the southern region displayed a clear high-high clustering characteristic, exerting a positive and radiating influence on surrounding areas. (2) The main driving factors of the spatial heterogeneity are DEM, precipitation, NPP, GDP, and PM2.5. And among different factors, the dual-factor enhancement effect is greater than the nonlinear enhancement of a single factor. (3) Human activities are important influencing factor, and the impact of urban expansion and economic growth on ecological resilience is becoming increasingly significant. Therefore, in the process of economic development, full consideration should be given to the self-repairing and adaptive capabilities of the ecosystem.

Exploring future habitat quality changes in the Mid-Section of the Tianshan Mountains (MSTM) is crucial for regional biodiversity conservation. This study utilizes climate projection data from CMIP6 and integrates the SD-PLUS-InVEST analytical framework to simulate future LULC and habitat quality under three distinct future scenarios. Additionally, the XGBoost-SHAP model is applied to identify and interpret the key regulatory factors within the modeling framework that influence habitat quality spatial heterogeneity. The results show the following: (1) the projections under the three 2035 scenarios generally follow the development trend of 2020, with continued spread of dry land and construction land, but general reduction in the ecological land, reflecting an intensifying conflict between land development and ecological preservation. (2) Habitat quality varies significantly across scenarios, generally exhibiting a “U-shaped” distribution pattern characterized by larger areas of high and low quality and smaller areas of moderate quality. Within the SSP5–8.5 scenario, habitat quality is relatively poor, accompanied by pronounced spatial heterogeneity and imbalance. (3) NDVI is identified as the dominant factor influencing habitat quality spatial heterogeneity, followed by GDP, TEM, and DEM. Although the influence of these factors varies slightly across scenarios, their relative importance remains generally consistent, reflecting the structural stability and response coherence of the ecosystem.