What Will the Future Human–Environment Relationship in the Northeastern Qinghai–Xizang Plateau Be by 2030?

The human–environment interaction on the Qinghai–Xizang Plateau determines the direction of global human sustainable development, making it necessary to propose a refined prediction for this relationship. Currently, there is a lack of a predictive method for human–environment relationships, especially at the grid scale. This study focuses on Qinghai Province and proposes a human–environment relationship simulation method based on cellular automata (CA), utilizing land-use data and a remote sensing-based ecological (RSEI) index. The method enables grid-scale explicit predictions of human–environment relationships. The results show that by 2030, the human–environment relationship in Qinghai Province will become more diverse, with the coordination ratio rising to 11% and the degradation ratio to 7%. The ecological protection scenario serves a defensive role, preventing 3835 km² of land from degradation. In contrast, the urban development scenario plays a revitalizing role, achieving a coordinated area 2% larger than the business-as-usual scenario. By 2030, about 8956 km² of land in Qinghai will be suitable for agricultural revitalization, and 54,340 km² must be reserved for ecological protection. Due to the high-altitude environment, the human–environment relationship aligns only with the right half of the Environmental Kuznets Curve, namely, development brings greater harmony. We further discover the lag in the natural system’s response, for artificially increasing vegetation cover will not quickly improve habitat quality. Likewise, leapfrogging expansion in the urban development scenario may conceal long-term ecological risks behind short-term coordination. For stakeholders and policymakers, this study provides refined and differentiated governance measures at the grid scale, while highlighting the need to focus on underdeveloped regions and remain vigilant about the lag in human–environment relationship responses.