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.

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.

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.

Ecosystem services are the benefits obtained from an ecosystem that have great significance in sustainable development. Urbanization has triggered significant changes on urban spatial patterns, which have had a great impact on the ecosystem services. However, studies on the spatiotemporally varying relationship between urban spatial patterns and ecosystem services are lacking. Taking as a case study, the Nansihu Lake Basin in China, this study aimed to explore the spatiotemporally varying relationship between urban spatial patterns and ecosystem services. Urban spatial patterns were derived by integrating remote sensing and spatial metrics. Ecosystem service values were calculated using ecosystem service models. The spatiotemporally varying impact of urban spatial patterns on ecosystem services was quantified using the Geographically Weighted Regression (GWR) model. The findings indicate that urban spatial patterns and ecosystem services have dramatically varied with the urbanization process. The estimated parameters indicate that urban spatial patterns have significant impacts on ecosystem services. The GWR revealed a spatiotemporally varying correlation and improved the explanatory ability in comparison with the Ordinary Least Squares (OLS) model. The investigation of the impact of urban spatial patterns on ecosystem services can provide more practical support for effective urban planning and ecosystem management.

Investigating the spatiotemporal trends and trade-off/synergy relationships among ecosystem services can provide effective support for urban planning and decision making toward sustainable development. With Nanjing city in China as a case study, this study assessed the spatiotemporal dynamics of six key ecosystem services from 2005 to 2030. Integration of Markov-cellular automata and ecosystem services models was realized to analyze the potential impacts of future urbanization on ecosystem services by simulating business-as-usual (BAU), cropland protection (CP), and ecological restoration (ER) scenarios. Furthermore, an innovative trade-off/synergy degree was developed to quantify the magnitude of the complex relationship among the multiple ecosystem services under the different scenarios. Due to the rapid expansion of built-up land, carbon storage, habitat quality, and air purification decreased 2.92%, 5.80%, and 7.91%, respectively. The CP scenario exhibited the highest crop production values, and the ER scenario was a better urban development strategy that enhanced the regulating ecosystem services at the expense of crop production. To promote urban ecosystem services and minimize trade-offs, we proposed certain future urban development strategies, including ecological corridor construction and compact development. The study could provide a scientific reference for the effective ecosystem management of Nanjing and other rapidly urbanized regions.

Urban land use patterns are increasingly recognized as significant contributors to energy consumption. However, few studies have quantified the impacts of urban land use patterns on energy consumption. In this study, we analyzed the impacts of urban land use patterns on energy consumption for 20 provincial capital cities in China from 2000 to 2010. Landsat data and spatial metrics were first used to quantify the urban land use patterns, and then city-level energy consumption was estimated based on nighttime light (NTL) data and statistical provincial energy consumption data. Finally, a panel data analysis was applied to investigate the impacts of urban land use patterns on energy consumption. Our results showed that NTL data were effective for estimating energy consumption at the city level and indicated that accelerated energy consumption was caused by increases in the irregularity of urban land forms and the expansion of urban land. Moreover, significant regional differences in the impacts of urban land use patterns on energy consumptions were identified. Our results provide insights into the relationship between urban growth and energy consumption and may support effective planning towards sustainable development.