Coal mining can cause significant local environmental damage while driving the regional economy of an area. The key index of net primary productivity (NPP) measures the amount of energy made available in an ecosystem and serves as a useful metric for understanding vegetation restoration in mining areas. This study used a CASA model to estimate the vegetation NPP of the Ordos area of the Shendong coal fields from 2000 to 2019. Model output, human factors, and regional meteorological data were subjected to trend analysis, significance testing, partial correlation analysis, and residual analysis. The NPP data generated by a CASA model inversion approximated measured data to a reasonable degree. The average annual NPP of the vegetation in the study area from 2000 to 2019 was 44.51 g C/m² a. In general, NPP showed a fluctuating upward trend, with slower increases before 2011 and more rapid increases after 2011. The trend exhibited considerable spatial heterogeneity. Areas with increasing NPP accounted for 21.54% of the study area and occurred mainly in the Dongsheng District, the Kangbashi District, and areas bordering the Ejin Horo Banner. Analysis detected consistent spatial variation between NPP and each factor in the study area. NPP is positively correlated with precipitation and human activities and negatively correlated with air temperature. The change in vegetation cover depended on both human activity and meteorological conditions. In terms of the strength of influence on vegetation NPP, human activity exceeded climate, followed by temperature and precipitation. Although the NPP of vegetation in the region directly affected by coal mining shows a trend of improvement, it is still lower than that in the natural growing region. In the next step, the ecological restoration of vegetation should be further strengthened to achieve regional ecological balance.

Wind erosion poses a major threat to ecosystem stability and land productivity in arid and semi-arid regions. Accurate identification of its spatiotemporal dynamics and underlying driving mechanisms is a critical prerequisite for effective risk forecasting and targeted erosion control. This study applied the Revised Wind Erosion Equation (RWEQ) model to assess the spatial distribution, interannual variation, and seasonal dynamics of the Soil Wind Erosion Modulus (SWEM) across Inner Mongolia from 1990 to 2022. The GeoDetector model was further employed to quantify dominant drivers, key interactions, and high-risk zones via factor, interaction, and risk detection. The results showed that the average SWEM across the study period was 35.65 t·ha⁻¹·yr⁻¹ and showed a decreasing trend over time. However, localised increases were observed in the Horqin and Hulun Buir sandy lands and central grasslands. Wind erosion was most intense in spring (17.64 t·ha⁻¹·yr⁻¹) and weakest in summer (5.57 t·ha⁻¹·yr⁻¹). Gale days, NDVI, precipitation, and wind speed were identified as dominant drivers. Interaction detection revealed non-linear synergies between gale days and temperature (q = 0.40) and wind speed and temperature (q = 0.36), alongside a two-factor interaction between NDVI and precipitation (q = 0.19). Risk detection indicated that areas with gale days > 58, wind speed > 3.01 m/s, NDVI < 0.2, precipitation of 30.17–135.59 mm, and temperatures of 3.01–4.23 °C are highly erosion-prone. Management should prioritise these sensitive and intensifying areas by implementing site-specific strategies to enhance ecosystem resilience.

Protection of the environment by returning farmland to forest and grassland through enclosing areas of land to permit regeneration of native flora is being implemented in the Loess hill region of China. Soil physical properties are important components of ecological systems, as comparisons between cultivated and enclosed areas demonstrate. The results showed: the soil moisture content in the enclosed area was 14.6% and that in the cultivated area was 14%; the soil bulk density and soil porosity were respectively 1.45 g/cm³ and 45.28% in the enclosed areas, and respectively 1.46 g/cm³ and 44.79% in cultivated land. The alteration of soil physical properties was not big between cultivated areas and enclosed areas in the short term.

Wind erosion is a multidimensional, dynamic process driven by natural and anthropogenic factors, but existing statistical methods struggle to capture its complex nonlinear relationships, resulting in incomplete quantification of drivers and their spatial variability. To address this, we integrate the Revised Wind Erosion Equation (RWEQ)model with explainable artificial intelligence to disentangle the spatiotemporal positive and negative effects of dominant drivers and their synergistic interactions in Inner Mongolia. Results show that, from 2000–2022, wind erosion has been decreasing on average by 1.1 t·ha⁻¹·yr⁻¹, mainly in the western deserts and locally in Hulunbuir sandy land. Severe erosion is mostly due to nature (78.7%) rather than anthropogenic (21.3%). Normalized difference vegetation index (NDVI), clay content (CL), windy days (WD), precipitation (PRE), temperature (TEM), and sand content (SA) were found to be the most important drivers of wind erosion. Critical threshold conditions for severe wind erosion are NDVI < 0.14, CL < 12%, GD > 26, PRE < 73.15 mm, and SA > 66%. When there is a certain combination of variables, wind erosion risk is greatly increased, which mainly happens in the western part of Alxa, Bayannur, and the area near the desert edge. Wind erosion control should shift toward region-specific precision management, including engineering protection, optimized grazing management, and vegetation restoration.

Against the backdrop of global warming, grassland degradation (GD) in arid and semi-arid regions has become a critical issue constraining ecosystem stability and socio-ecological resilience. This study aims to reveal the spatial differentiation of drivers of GD across four functional zones in Inner Mongolia—resource-oriented (RO), center-service-oriented (CSO), agro-pastoral-oriented (APO), and ecological-oriented (EO)—at the county level, using grassland conversion processes as a structural proxy. The results show that land cover changed in 19.29%, 18.69%, 17.28%, and 4.67% of the RO, CSO, APO, and EO regions, respectively, with GR mainly occurring in the RO region, while GD was more prevalent in the other zones. Drivers of GD exhibit significant variations across functional zones. In the RO zone, land use change is primarily associated with human disturbance. In the CSO and APO zones, it is associated with human activities, climatic factors, and urbanization. In the EO zone, the identified drivers show strong spatial heterogeneity, with urbanization, grazing intensity, and climate change emerging as key associated factors in the Hulunbuir, Xilingol, and Arxar regions. Overall, the results reveal a spatial gradient in the relative importance of anthropogenic pressure and climatic stress, with broader implications for adaptive and place-specific dryland governance under ongoing warming and increasing aridification.