Identification of priority conservation areas in Beijing-Tianjin-Hebei using multi-scenario trade-offs based on different spatial scales and their drivers

•We analyzed and compared spatial and temporal variations and trade-offs/synergies in ESs at different scales.•Priority conservation areas were identified based on ecosystem services trade-offs.•Multiple factors combined to influence changes in ecosystem services in priority conservation areas. Identifying priority conservation areas (PCAs) on the basis of ecosystem services (ESs) trade-offs is a crucial step in achieving optimal ecosystem management in the BTH, noting the complex spatio-temporal and scaling effects inherent in these ecosystems. A cross-scale quantification of spatial and temporal changes in ecosystem services and their correlations was conducted in the BTH. Furthermore, the Ordered Weighting Algorithm (OWA) was introduced to simulate a variety of scenarios in order to determine the PCAs and to explore the drivers of ES in the PCAs. The results indicated that Food supply (FS), Water yield (WY), and Soil retention (SR) in the BTH increased by 16.4 × 104t/hm2, 81.81 mm, and 37081.29 t/hm2, respectively. In contrast, Carbon storage (CS) and Habit quality (HQ) exhibited a decrease of 0.01 t/hm2 and 0.03. Spatially, the high-value areas of ESs at the county scale exhibited greater clustering. At the county scale, there were two trade-offs and eight synergies in 2000, and four trade-offs and six synergies by 2020. In contrast, at the raster scale, there were two trade-offs and eight synergies from 2000 to 2020. The mechanisms of change were similar and exhibited spatial heterogeneity. Scenario 6 represents the optimal priority conservation area, exhibiting the most balanced conservation efficiency of each ecosystem service. This scenario enables the simultaneous conservation of multiple ESs. The natural factors within the PCAs exert a stronger influence on ESs than socio-economic factors. The inclusion of interacting factor effects will enhance the explanatory power of the drivers. The findings of this study can serve as a theoretical foundation for cross-scale ecosystem service management decisions.