A comprehensive understanding of the ecosystem services (ESs) trade-off/synergy relationships has become increasingly important for ecological management and sustainable development. This study employed the Yangtze River Delta (YRD) region in China as the study area and investigated the spatiotemporal changes in three ESs, namely, carbon storage (CS), water purification (WP), and habitat quality (HQ). A trade-off/synergy degree (TSD) indicator was developed that allowed for the quantification of the trade-off/synergy intensity, and the spatial pattern of the TSD between ESs in the YRD region to be analyzed. Furthermore, a geographically weighted regression (GWR) model was used to analyze the relationship between the influencing factors and trade-offs/synergies. The results revealed that CS, WP, and HQ decreased by 0.28%, 2.49%, and 3.38%, respectively, from 2005 to 2015. The TSD indicator showed that the trade-off/synergy relationships and their magnitudes were spatially heterogeneous throughout the YRD region. The coefficients of the natural and socioeconomic factors obtained from the GWR indicated that their impacts on the trade-offs/synergies vary spatiotemporally. The impact factors had both positive and negative effects on the trade-offs/synergies. The findings of this study could improve the understanding of the spatiotemporal dynamics of trade-offs/synergies and their spatially heterogeneous correlations with related factors.

The development of traditional resource-based cities requires drastic changes owing to the exhaustion of resources. During this transformation, the ecological environment of resource-based cities is threatened because of resource exploitation, in addition to the ecological risks caused by urban expansion. However, there is currently a lack of research on the evolution of ecological dangers in cities during this transformational period. Therefore, conducting relevant studies is essential to establishing a mechanism to mitigate these dangers. The present study analyzed Xuzhou, a typical resource-based city in China, as a case study. The main objective was to consider the dynamic changes in land use and ecological risks during the transformation of this resource-based city. The land-use changes in Xuzhou in 2000, 2010, and 2020 were analyzed, using the Markov model and landscape-pattern indices, allowing an ecological risk-assessment model of land-use changes to be constructed. Additionally, the spatial heterogeneity of ecological risks was evaluated by using spatial autocorrelation. The results showed that urban expansion influenced land use in Xuzhou significantly. Owing to the rapid urban expansion, the area of extremely high-risk regions increased significantly in 2010. Furthermore, the subsidence areas caused by mining had profound impacts on the region’s ecology, and early interventions for ecological restoration are needed to prevent further deterioration. During the transformation, Xuzhou’s overall ecological risks reduced gradually, which was conducive to its transition into an ecological city.

Exploring the complex effects of landscape patterns on ecosystem services (ESs) has become increasingly important in offering scientific support for effective spatial planning and ecosystem management. However, there is a particular lack of research on the nonlinear effects of landscape patterns on ESs and scale dependence. Taking Huainan City (in China) as a case study, this study adopted the InVEST model to estimate four key ESs including carbon storage (CS), habitat quality (HQ), nitrogen export (NE), and water yield (WY). Then, we calculated the selected landscape metrics at multiple spatial scales. Furthermore, the gradient boosting decision tree (GBDT) model was developed to investigate the relative importance of landscape metrics in explaining ESs and their nonlinear interrelation. The results indicated that most of the selected landscape metrics were significantly correlated with ESs. The GBDT model, which can explore nonlinear relationships, performed better than the linear regression model in explaining the variations in ESs. The landscape-level metrics of the Shannon’s diversity index (SHDI) and the contagion index (CONTAG) and the class-level metrics of the aggregation index (AI) and edge density (ED) were the most important variables that influenced ESs. The landscape metrics affected ESs within a certain range, and the nonlinear effects varied with scale.

Mining activities have contributed to the growth of the city, but also raised non-negligible eco-geological environmental issues that threaten ecological safety. Ecological security pattern (ESP), as an important grip on the ecological restoration and protection of national land space, helps to balance mining activities and ecological protection in coal resource-based cities. Taking Huaibei City as a study area, we applied the ESP research paradigm: an ecosystem “function-structure” conceptual framework was developed to identify ecological sources, the “coal mining subsidence—economic activities” framework was used to revise ecological resistance surface, and the circuit theory was used to extract ecological corridors. Then, key areas for ecological restoration and protection were identified, including ecological pinch points, barrier points, and fracture points. Finally, the pattern and strategies for ecological restoration and protection were proposed. Study results show that there were 51 ecological sources, covering an area of 152.75 km²; 111 ecological corridors were extracted with 6000 as truncation threshold; 17 pinch points, 75 barrier points, and 117 fracture points were identified. Ecological restoration and protection patterns of “one axis, two shields, four zones, eight belts and multiple corridors”, and strategies for key areas were proposed. The results of the study are important for the sustainable development of coal-resource-based cities.

The aim of this study was to reveal the spatiotemporal pattern of the supply and demand of ecosystem services (ESs), as well as the significant driving factors for understanding the impact of human activities on the natural ecosystem. To provide a scientific basis for formulating regional sustainable development strategies that enhance human well-being, resource-based cities in the Yellow River Basin (YRB) were selected as the case study. The supply and demand of ecosystem services in these cities from 2000 to 2020 were measured. The spatiotemporal evolution of the supply-demand relationship was illustrated by taking its coordination degree. In addition, geographical detector and geographically weighted regression (GWR) models were applied to quantify the spatiotemporally varying effects of natural and socioeconomic factors on the ES supply--demand relationship. The results showed that resource-based cities in the YRB were experiencing expansion in supply and demand overall, but the supply-demand relationship tended to be tense. The northwest YRB had higher coordination values of supply-demand, while lower values were found in the southeast YRB. Moreover, the relationship between supply and demand was significantly affected by natural and socioeconomic factors, such as elevation, slope, precipitation, land-use type, population density, and gross domestic product (GDP) per land. Furthermore, the GWR model suggested that the effects of driving factors on the supply-demand relationship had notable spatial heterogeneity. The coordination of ES supply-demand in the resource-based cities of southeast YRB was mainly influenced by socioeconomic factors, while that of the west YRB was mainly influenced by natural factors. Our study suggested that it is necessary to enhance the awareness of environmental protection, pay attention to ecological restoration, and avoid unreasonable human disturbance to the ecosystem.

Carbon storage is closely connected to the productivities and climate regulation capacities of ecosystems. Assessing the effects of urban expansion on carbon storage has become increasingly important for achieving urban sustainability. This study analyzed the effects of urban expansion on terrestrial carbon storage in Xuzhou City, China during 2000–2025. The cellular automata (CA) model was developed to simulate future urban expansion under three scenarios, namely, the business as usual (BAU), ecological protection (ECO), and planning strengthened (PLS) scenarios. The Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model was further applied to explore the consequences of urban expansion on carbon storage. The results show that urban expansion resulted in 6.099 Tg of carbon storage loss from 2000–2015. Moreover, significant differences in the effects of the urban expansion scenarios on carbon storage were identified in terms of both magnitude and spatial pattern from 2015–2025. Compared with the other scenarios, the PLS scenario could be considered as a good option that would allow future development to achieve the objectives of the lowest carbon storage losses. The findings improve the understanding of the effects of urban expansion on carbon storage and may be used to support urban planning and management.