The quantitative evaluation and prediction of ecosystem service value (ESV) in the Lhasa River Basin can provide a basis for ecological environment assessment and land-use optimization and adjustment in the future. Previous studies on the ESV in the Lhasa River Basin have focused mainly on static assessment and evolution analysis based on historical data, and have not considered future development trends. Moreover, most of the current driving factors selected in land use and ESV prediction studies are homogeneous, and do not reflect the geographical and cultural characteristics of the study area well. With the Lhasa River Basin as the research focus, 20 driving factors were selected according to the characteristics of the plateau alpine area, and the land-use changes under three developmental orientations, namely, natural evolution, ecological protection, and agricultural development, were predicted for the year 2030 with the FLUS-Markov model. Based on these predictions, the values of ecosystem services were calculated, and their spatiotemporal dynamic characteristics were analyzed. The results show that the model has high accuracy in simulating land-use change in the Lhasa River Basin, with a kappa coefficient of 0.989 and an overall accuracy of 99.33%, indicating a high applicability. The types of land use in the Lhasa River basin are dominated by the existence of grassland, unused land, and forest, with a combined proportion of 94.3%. The change trends of each land-use type in the basin under the three scenarios differ significantly, with grassland, cropland, and building land showing the most significant changes. The area of cropland increased only in the agricultural development scenario; the areas of forest and grassland increased only in the ecological protection scenario; and the expansion of building land was most effectively controlled in the ecological protection scenario. The ESV increased in all three scenarios, and the spatial distribution of the ESV per unit area in the middle and lower reaches was greater than that in the upper reaches. The ESV was the greatest in the ecological protection scenario, with grasslands, forests, and water bodies contributing more to the ESV of the basin. This study provides decision-making references for the effective utilization of land resources, ecological environmental protection planning, and urban construction in the Lhasa River Basin in the future.

Urban–rural fringe areas are critical transition zones where ecological functions and human activities interact intensely, often leading to complex spatial patterns and trade-offs among ecosystem services (ESs). Understanding these patterns and their socio-ecological drivers across multiple spatial scales is essential for sustainable land-use planning and ecosystem management. This study, using the urban–rural fringe (URF) of Beijing as an example, quantified eight representative ecosystem services at the 1 km grid, 3 km grid, and township scales. It employed hotspot analysis, Moran’s Index, and the Spearman correlation to analyze trade-offs and synergies (TOSs) among ESs. The study also applied a self-organizing map and the NbClust function to identify and determine the optimal number of ecosystem service bundles (ESBs) for ecological functional zoning. Redundancy analysis was used to explore the impacts of six socio-ecological drivers on the spatial distribution of ESs. The results revealed the following: (1) The spatial distribution of ESs in Beijing’s URF exhibits clustering and cross-scale variations, with spatial clustering intensifying as the scale expands. (2) TOSs among ESs vary in strength and direction across the three spatial scales. (3) The primary drivers of TOSs at all three scales are the normalized vegetation index and annual precipitation. (4) Based on the supply intensity of various ESs, the study area was classified into four types of ESBs across the three scales: ecologically restricted areas, food production areas, ecologically balanced areas, and high-quality ecological areas. The township scale is more conducive to planning and management, while the 1 km and 3 km grid scales are more helpful for understanding the relationship between land use and ESs.