With the increase in global carbon dioxide emissions, China has put forward the goals of a carbon peak and carbon neutrality (double carbon) and formulated an action plan to consolidate and enhance the carbon sink capacity of the ecosystem. The Chinese grassland ecosystem (CGE) is widely distributed and is the key link for China to achieve the double carbon objectives. However, there is a relative lack of research on carbon sources and sinks in the CGE, so it is urgent to integrate and analyze the carbon sources and sinks in the grassland ecosystem on the national scale. Based on the refined grid data, the net ecosystem productivity (NEP) of the CGE was estimated by coupling the vegetation production model and soil respiration model. The results showed that the cumulative carbon sequestration of the CGE was 14.46 PgC from 2010 to 2020. In terms of spatial distribution, this shows that the differentiation characteristics are high in the northwest of China and low in the southeast of China, which strongly corresponds with the 400 mm isohyet and 0 °C isotherm of China. The results of the correlation analysis showed that the NEP of the CGE was positively correlated with precipitation and negatively correlated with temperature; that is, precipitation mainly promotes the accumulation of NEP, and temperature mainly inhibits it. The coupling effect of temperature and precipitation jointly affects the spatial change of carbon sources and sinks of the CGE. This study can provide a scientific basis for government departments to formulate targeted policies to deal with climate change, which is of great significance for China to improve ecosystem management, ensure ecological security and promote the realization of China’s double carbon goal.

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.

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.