Over the past 40 years of reform and opening up, human activities in the Yangtze River Delta region have caused major changes in land use patterns and ecosystem functions. Clarifying the spatiotemporal change characteristics and future development trends of ecosystem service functions is the basis for rational land development and utilization. In this study, the InVEST model and the CASA model were used to calculate habitat quality, water conservation, carbon sequestration and oxygen release, and soil conservation ecosystem services in the Yangtze River Delta urban agglomeration from 1980 to 2020. The spatial pattern, change law, and future trend of these services were analyzed using the Theil–Sen median trend analysis, Mann–Kendall test, and Hurst index analysis. The results show that the four types of ecosystems in the Yangtze River Delta urban agglomeration (habitat quality, water conservation, carbon sequestration and oxygen release, and soil conservation) exhibited an overall spatial pattern of being high in the southwest mountainous area and low in the northeast plain, and the conversion from constructed to agriculture was the most frequent type of land conversion over the past 40 years. From 1980 to 2020, the average level of habitat quality showed a downward trend and is expected to continue to deteriorate in the future. Water conservation, carbon sequestration and oxygen release, and soil conservation showed a fluctuating upward trend, with the latter two primarily predicted to have a future trend of improvement. The changes in ecosystem services exhibit gradient effects and horizontal spatial differentiation. The decline in ecosystem service functions is more pronounced in the vicinity of large cities. It is thus necessary to accelerate the transformation of the economic development model, and abandon the extensive urbanization development model, and promote high-quality urbanization development on the basis of improving resource and environmental carrying capacities.

Exploring the constraint relationship between physical geographic features and urbanization on ecosystem services is important for managing and optimizing regional ecosystem services. Taking Anhui Province as an example, we assessed the spatial and temporal evolution characteristics of five types of ecosystem services (habitat support, water production services, soil conservation, NPP, and carbon fixation) and five types of urbanization levels (population, economic, social, ecological, and spatial) in 2000, 2010, and 2020, and integrated the constraint line method, bivariate spatial autocorrelation model, and spatial regression model to measure the relationship between ecosystem services. The spatial constraints between ecosystem services and urbanization level and natural topography in Anhui Province were measured using the constraint line method, bivariate spatial autocorrelation model and the spatial regression model. The results show that: (1) the spatial distribution of the five types of ecosystem services in Anhui Province is characterized as “low in the north and high in the south”. At the provincial level, the five ecosystem services in southern and central Anhui Province are synergistic, while the five ecosystem services in northern Anhui Province show a trade-off; (2) topography has different effects on the five ecosystem services with “exponential” effects on water production services and NPP, “positive convex” effects on habitat support, and “positive convex” effects on habitat support”; (3) the bivariate global autocorrelation Moran’s I index between ecosystem services and urbanization level in Anhui Province is significant, confirming that ecosystem services and urbanization are spatially related, where the development of population urbanization, spatial urbanization, economic urbanization, and social urbanization leads to the decrease in ecosystem services, and ecological urbanization promotes the increase in ecosystem services. In the spatial regression model, the Spatial Lag Model passed the significance test, indicating that there is a spatial spillover effect between ecosystem services and urbanization. That is, changes in ecosystem services are influenced not only by their own urbanization elements, but also by urbanization elements in neighboring units or more distant units. Exploring the constraints of ecosystem services and identifying their interaction with urbanization can provide a scientific basis for land-use optimization, adjusting management measures and achieving regional sustainability.

Intensive human activities and climate change have led to the degradation of regional ecosystem functions. Accurately assessing the dynamics of ecosystem services and their driving factors is essential for developing differentiated ecological management strategies and supporting regional sustainable development. However, understanding the responses of ESs’ drivers across different spatial scales in various geographic contexts remains limited. This study focuses on the Hefei Metropolitan Area, utilizing the InVEST model to evaluate changes in four ESs: water yield, soil retention, carbon storage, and habitat quality from 2000 to 2022. The Optimal Parameter Geodetector (OPGD) model was employed to quantify the driving factors at different spatial scales. The results reveal a general decline in water yield, soil retention, and carbon storage, while habitat quality has improved. The spatial distribution of ESs exhibits a pattern of "high in the west, low in the east; high in the south, low in the north." Natural factors predominantly influence the changes in water yield and soil retention, while human activities significantly impact the spatial variation of carbon storage and habitat quality. The optimal spatial scale for detecting driving factors is 7~8 km. The findings provide a theoretical basis for optimizing ecological space in rapidly urbanizing areas.

Urban heat islands (UHIs)—one of the most severe environmental side effects of urbanization—can be effectively alleviated by blue–green spaces, whose shading and evaporative cooling generate localized cool islands with well-documented effectiveness. Taking the central urban area of Hefei as the study area, this study identified cold island core sources, cold island corridors, and cooling and heating nodes. The results indicated that: (1) the central and southern parts of the study area exhibited the highest building density and the most pronounced UHI effect; (2) 133 cold island core sources, totaling 196.36 km² and accounting for 22% of the study area, were identified through morphological spatial pattern analysis (MSPA); and (3) 145 cold island corridors, 112 cooling nodes, and 23 heating nodes, together with the five areas of cooling-heating node overlap, were extracted using the Linkage Mapper toolbox to construct the cold island network. This network can provide a scientific basis for urban blue–green space planning and ventilation corridor regulation, and offers a transferable technical framework for UHI mitigation in comparable rapidly urbanizing regions.