With the expansion of the social economy and adjustment of environmental policies, particularly with the onset of development policies for the western region, ecosystems in the arid areas of Northwest China have undergone profound changes. This study collected soil, topographical, climate, and nighttime light data to develop a set of ecological vulnerability assessment indexes based on the background ecological characteristics of the arid areas of Northwest China. The spatiotemporal evolution of ecological carrying capacity was analyzed by our team using Spatial Principal Component Analysis (SPCA) in 2000, 2007, 2012, and 2018 to construct an ecological security pattern. The results revealed that the ecological carrying capacities of the arid areas in the northwest were primarily weak, albeit decreasing, while those areas with strong carrying capacities were increasing. In terms of spatial distribution, the ecological carrying capacities of the Hexi, Northern Xinjiang, and Western Inner Mongolia regions were on the rise, while those of the Southern Xinjiang region were declining. The Minimum Cumulative Resistance (MCR) model was used to extract 51 road-type, river-type, and green corridors with a total length of 7285.43 km. A total of 71 nodes representing important patches, wet rivers, and ecologically fragile areas were extracted. According to the calculated results, the arid region of the northwest was divided into 16 ecological security patterns, which were optimized according to changes in their ecological carrying capacities.

Rapid urbanization significantly influences vegetation dynamics in ecologically fragile arid regions, but comprehensive quantification of its net effects remains limited. This study aims to analyze the spatiotemporal patterns and underlying driving mechanisms of urbanization’s net impact on vegetation growth, quantified as the Urban Background Difference (UBD = EVI_urban − EVI_rural), across China’s arid regions (Aridity Index < 0.5; precipitation < 500 mm/year) from 2000 to 2020.We integrated Landsat-derived Enhanced Vegetation Index (EVI), Global Artificial Impervious Area (GAIA) data, and multiple auxiliary factors including climate, socioeconomic variables, urban form, and topography. An urban–rural gradient framework was established to assess spatial variation. Driving factors were identified using an XGBoost machine learning model combined with SHapley Additive exPlanations (SHAP) for interpretability.Vegetation exhibited a consistent “core greening, peripheral browning” spatial pattern with significant heterogeneity: UBD was positive in western oasis cities (e.g., Kashi_West: 0.25), attributed to effective water management and economic growth, but negative in eastern grassland areas (e.g., Hulun_Buir: −0.16), constrained by conservation policies. UBD increased significantly between 2000 and 2010 (from 0.05 to 0.12), then diverged post-2010 with sharp declines linked to ecological zoning policies. SHAP analysis identified third-quarter precipitation and annual temperature as major negative drivers, while gross regional product, population density, built-up area proportion, and land-use diversity had positive effects. The barren land ratio accounted for 39.78% of UBD variance.Urbanization’s net impact on vegetation in arid regions is spatially heterogeneous and sensitive to policy interventions. Sustainable urban expansion in drylands requires tailored strategies that balance water resource management, economic development, and optimized urban form.