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.

The “West-to-East Gas Transmission” project has accelerated economic development in Xinjiang and the central-western regions along the pipeline. However, as the pipeline traverses multiple cities and counties in the Tianshan region, it has significantly impacted the local ecology, necessitating a comprehensive assessment. This study employs the Sensitivity-Resilience-Pressure (SRP) model to construct an ecological vulnerability assessment system for the Tianshan region, aiming to analyze changes in ecological vulnerability and evaluate the environmental impact of the “West-to-East Gas Transmission” project. The results indicate that, spatially, ecological vulnerability in the Tianshan region increases progressively from northwest to southeast. Temporally, from 2000 to 2010, the mean Ecological Vulnerability Index (EVI) exhibited a decreasing trend, with values of 0.476, 0.464, and 0.462, primarily shifting to lower vulnerability levels. From 2010 to 2020, the EVI showed an increasing trend, with values of 0.462, 0.466, and 0.468, predominantly transitioning from heavy to very heavy vulnerability. The key influencing factors of ecological vulnerability in the Tianshan region, ranked by importance, are NDVI, NPP, land use type, annual precipitation, and aridity. Furthermore, the “West-to-East Gas Transmission” project consists of three main pipelines (Lines 1, 2, and 3), for which buffer zone analyses were conducted at radii of 1 km, 3 km, and 5 km. The results indicate that ecological vulnerability patterns remained consistent across different buffer zone sizes, and larger buffer radii were associated with lower mean EVI values along the pipeline. After pipeline construction, the mean EVI along Line 1 decreased from 0.566 to 0.550, while the EVI along Line 2 remained nearly unchanged. In contrast, the mean EVI along Line 3 increased from 0.434 to 0.447. Regarding changes in ecological vulnerability levels, along Line 1, the area of improvement (18.83%) exceeded the area of deterioration (1.09%), primarily due to the high proportion of very heavy vulnerability zones (>80%), which are more likely to transition to lower vulnerability levels. Along Line 2, ecological vulnerability remained relatively stable, indicating minimal environmental impact. However, along Line 3, the improvement area (3.81%) was significantly smaller than the deterioration area (20.52%), suggesting that construction of Line 3 had a more pronounced ecological impact, leading to greater degradation of the ecological vulnerability along its route.

Exploring the main controlling factors of soil organic carbon (SOC) in farmland is crucial for evaluating soil quality, optimizing agricultural management, and ensuring food security. Current studies used machine learning methods to determine the relative importance of environmental variables, but fail to visualize nonlinear relationships between SOC and covariates. Moreover, the main controlling factors of SOC in riparian farmland may be particularly complex because of the dual factors of lakes and agricultural activities. [Materials and methods] Peixian County in the North China Plain is a typical agricultural area, adjacent to Weishan Lake. This study aims to explore the main control factors of SOC in Peixian by using gradient boosting decision tree (GBDT) model. Results show that 16 variables can explain 68% of SOC variation. Annual average precipitation, annual minimum temperature, the distance to rural settlements, and the distance to the lake (Dis_lake) emerge as the main influencing factors of SOC. Particularly, the R² of the two-way interaction effect between Dis_lake and of any variable exceeds 0.4. When some of the environmental variable values are extremely high or extremely low, SOC no longer changes accordingly. For instance, when Dis_lake surpasses 9000 m, SOC no longer decreases. These findings highlight the impact of lake-related factors in the spatial variation of riparian farmland. The results from the GBDT analysis provide valuable guidance for agricultural management in the region.