To reveal the cross-scale trade-offs and synergies of ecosystem services (ESs) in resource-based cities, this study took Xingtai City, Hebei Province, as a case. Six ESs—water yield (WY), soil retention (SDR), habitat quality (HQ), urban cooling (UC), net primary productivity (NPP), and PM_2.5 removal—were quantified at the 1 km grid, township, and county scales. Using Spearman correlation, geographically weighted regression (GWR), and the XGBoost-SHAP framework, we analyzed the spatiotemporal evolution of the ecosystem service supply–demand ratio (ESDR) from 2000 to 2020 and identified the dominant driving mechanisms. The results indicate the following: (1) The mean ESDR in Xingtai decreased sharply from 0.14 in 2000 to 0.008 in 2020, a decline of 94.3%, showing a pronounced “high in the western mountains–low in the eastern plains” gradient pattern and an increasingly severe supply–demand imbalance. (2) Synergistic relationships dominated among the six ESs, accounting for over 80%. Strong synergies were observed between supply-related services such as WY–SDR and HQ–NPP, with correlation coefficients ranging from 0.65 to 0.88, whereas weak trade-offs (<20%) occurred between UC and PM₂.₅ removal in urbanized areas, which diminished with coarser spatial scales. (3) Population density (Pop), elevation (DEM), cropland proportion (Crop), and vegetation index (NDVI) were identified as the key driving factors, with a combined contribution of 71.4%. NDVI exhibited the strongest positive effect on ecosystem service supply (mean SHAP value = 0.24), while Pop and built-up land proportion showed significant negative effects once exceeding the thresholds of 400 persons/km² and 35%, respectively, indicating nonlinear and threshold-dependent responses. This study quantitatively reveals the spatiotemporal synergy patterns and complex driving mechanisms of ecosystem services in resource-based cities, providing scientific evidence for differentiated ecological restoration and multi-scale governance, and offering essential insights for enhancing regional sustainability.

As critical ecosystems, coastal zones necessitate the identification of their ecosystem service values, trade-off/synergy patterns, spatiotemporal evolution, and driving factors to inform scientific decision-making for sustainable ecosystem management. This study selected the coastal zone of the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) as the research region. By incorporating land-use types such as mangroves, tidal flats, and aquaculture areas, we analyzed land-use changes in 1990, 2000, 2010, and 2020. The InVEST model was employed to quantify six key ecosystem services (ESs): annual water yield, urban stormwater retention, urban flood risk mitigation, soil conservation, coastal blue carbon storage, and habitat quality, while spatial correlations among them were examined. Furthermore, Spearman’s rank correlation coefficient was used to assess trade-offs and synergies between ecosystem services, and redundancy analysis (RDA) combined with the geographically and temporally weighted regression (GTWR) model were applied to identify driving factors and their spatial heterogeneity. The results indicate that: (1) Cultivated land, forest land, impervious surfaces, and water bodies exhibited the most significant changes over the 30-year period; (2) Synergies predominated among most ecosystem services, whereas habitat quality showed trade-offs with others; (3) Among natural drivers, the normalized difference vegetation index (NDVI, positive effect) and evapotranspiration were critical factors. The proportion of impervious surfaces served as a key land-use change driver, and the nighttime light index emerged as a primary socioeconomic factor (negative effect). The impacts of drivers on ecosystem services displayed notable spatial heterogeneity. These findings provide scientific support for managing the supply-demand balance of coastal ecosystem services, rational land development, and sustainable development.

The synergy between carbon neutrality and urbanization is essential for effective climate governance and socio-ecological intelligent transition. From the perspective of coupled urban dynamic evolution and carbon metabolism systems, this study integrates the Sen-MK trend test and the geographical detector model to explore the spatial–temporal differentiation patterns and driving mechanisms of carbon balance across 337 prefecture-level cities in China from 2012 to 2022. The results reveal a spatial–temporal mismatch between carbon emissions and carbon storage, forming an asymmetric carbon metabolism pattern characterized by “expansion-dominated and shrinkage-dissipative” dynamics. Carbon compensation rates exhibit a west–high to east–low gradient distribution, with hotspots of expansionary cities clustered in the southwest, while shrinking cities display a dispersed pattern from the northwest to the northeast. Based on the four-quadrant carbon balance classification, expansionary cities are mainly located in the “high economic–low ecological” quadrant, whereas shrinking cities concentrate in the “low economic–high ecological” quadrant. Industrial structure and population scale serve as the dual-core drivers of carbon compensation. Expansionary cities are positively regulated by urbanization rates, while shrinking cities are negatively constrained by energy intensity. These findings suggest that differentiated regulation strategies can help optimize carbon governance within national territorial space.

The sustainable development of resource-based cities faces challenges due to the imbalance between ecosystem service supply and demand. This study examines Xingtai, a typical resource-based city located in northern China, using ecosystem service bundle theory to analyze the supply–demand relationships of six ecosystem services—water yield, soil retention, habitat quality, urban cooling, PM_2.5 removal, and carbon sequestration—from 2000 to 2020. Based on the ratio of supply–demand, we identify ecosystem service bundles and explore their driving factors using redundancy analysis (RDA) and the geographically and temporally weighted regression (GTWR) model. Results show a clear “mountain–plain” supply gradient, with high supply in the western Taihang Mountains and low supply in urbanized eastern plains. Demand follows a “center-high, periphery-low” pattern, with urban centers showing higher demand for urban cooling and PM_2.5 removal. A severe supply–demand imbalance exists: soil retention, PM_2.5 removal, habitat quality, and carbon sequestration are undersupplied in urbanized areas, while water yield and urban cooling are oversupplied in the western mountains. Natural factors (precipitation and temperature) shape western mountain supply, while human activities (GDP and nighttime light) drive demand polarization in the east. GTWR results reveal that urban GDP growth and land expansion intensify demand, while stable supply in mountain areas relies on precipitation and forest cover. This study provides scientific support for the sustainable development of resource-based cities.