<p>Understanding the spatial and temporal dynamics of ES trade-offs and synergies, along with their driving mechanism in RBC, is essential for effective resource management and ecosystem optimization in these cities. This study employs the InVEST model to quantify key ESs in Shanxi Province, China, from 2000 to 2020. We utilized Spearman correlation analysis and GWR to elucidate trade-off and synergistic relationships among ESs, and applied random forests to identify the primary drivers influencing these interactions. Results indicate that: (1) HQ remained stable over the period, whereas CS and WY exhibited fluctuations. Spatially, high WY values were concentrated in the province’s southeast, while HQ, SC, and CS displayed a “low in the middle, high in the periphery” pattern, with a similar distribution of topographic gradients and land types in the same region. (2) Correlations revealed a stable positive relationship between HQ and CS, while the associations of WY with SC and other ESs varied at the provincial scale. At the county level, positive correlations persisted among SC-HQ, WY-CS, and HQ-CS. Notably, the northern regions exhibited trade-offs, while the south showed synergies, with SC-CS relationships indicating multiple synergies. (3) Dominant drivers for WY, HQ, and SC were PRE, GDP, and SLO, respectively; CS was influenced by multiple factors. The interplay of trade-offs and synergies was shaped by various factors including DS, PRE, DEM, and TEM. To enhance sustainable development in Shanxi, tailored measures are needed for different regions—continuing afforestation in the west and mitigating anthropogenic impacts in the central-eastern and southern areas. Exploring the ESs of RBC has important practical value for optimizing regional land use planning and promoting the development of urban green economy, which provides reference for improving the living environment of residents and helping the construction of national ecological civilization.</p>