Characterizing the spatiotemporal dynamics and driving mechanisms of ecological quality in spatially heterogeneous drylands remains a challenge due to the inability of global linear models to capture spatial non-stationarity and complex interaction effects. This study proposes an integrated modeling approach combining the Light Gradient Boosting Machine with Shapley Additive Explanations (LGBM-SHAP) and the Geographically Optimal Zones-based Heterogeneity (GOZH) model to explore the dynamics of the Remote Sensing Ecological Index (RSEI) in Inner Mongolia from 2000 to 2024. Results reveal an intensifying spatial polarization: 23.2% of the region, primarily in the arid west, exhibits a Persistent Degradation trajectory, while 56.1% in the northeast exhibits Persistent Improvement. While the global LGBM-SHAP analysis identified Precipitation, DEM, and Grazing Intensity as dominant drivers, it also exposed a Simpson’s Paradox where grazing intensity showed a misleading positive correlation with ecological quality globally due to resource tracking effects. The GOZH model resolved this by delineating 12 data-driven ecologically distinct zones, demonstrating that grazing acts as a strict stressor in high-quality zones once hydro-climatic confounding effects are removed. Furthermore, the study identified a critical precipitation threshold of 340 mm as a constraint for ecological potential; anthropogenic stressors, such as the percentage of barren land, were found to be conditional drivers, exerting substantial degradation pressure (11.0% relative contribution) only within specific water-limited zones. These findings demonstrate that global averaging effects mask critical local degradation mechanisms. The proposed approach provides a robust tool for decoupling natural and anthropogenic impacts and offers a scientific basis for zoning-based ecosystem management that operates independently of administrative boundaries.
Yang et al. (2026) studied this question.