Naijing Lu

Ecological sustainability quantifies the capacity of an ecological system to sustain its health while fulfilling human survival needs and supporting future development. An accurate projection of ecological dynamics for sustainability is crucial for decision-makers to comprehend potential risks. However, the intricate interplay between climate change and human activity has hindered comprehensive assessments of future ecological sustainability, leaving it inadequately investigated thus far. This study aimed to assess future ecological sustainability shaped by the Shared Socioeconomic Pathways (SSPs) using remote sensing data from a typical arid farming–pastoral zone located at the northern foot of Yinshan Mountain (NFYM), Inner Mongolia, China. Five machine learning models were employed to evaluate the relationship between ecological sustainability and its driving factors. The results indicate that (1) overall ecological sustainability initially decreased and then increased during 2003–2022; (2) the Geophysical Fluid Dynamics Laboratory Earth System Model version 4 (GFDL-ESM4) mode and random forest model demonstrated the best performance in climate and ecological sustainability simulations; and (3) the annual change rates of ecological sustainability from 2023 to 2099 are projected to be +0.45%, −0.05%, and −0.46% per year under the SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios, respectively, suggesting that stringent environmental policies can effectively enhance ecological sustainability. The proposed framework can assist decision-makers in understanding ecological changes under different SSPs and calls for strategies to enhance ecosystem resilience in the NFYM and similar regions.