Ecosystem services (ESs) are increasingly recognized as critical to sustainable development and human well-being and are frequently used as indicators in environmental governance policies. However, existing studies mostly assess the performance of isolated single ESs, ignoring the management data needs of local governments for comprehensive gate-keeping and the easy monitoring of regional ecosystems, and lacking holistic gate-keeping indicators for local ESs. To address these shortcomings, this study assessed the spatial changes in five main ESs in the Yangtze River basin (YTRB) in China by creating a comprehensive ESs indicator (CESI) using multi-source data, and introduced the hotspot analyses and spatial econometric models to explore the driving forces of CESI. Results showed that during the study period, the CESI in the YTRB increased from 0.44 in 2000 to 0.47 in 2020. High-value areas were mainly concentrated in the hilly and mountainous regions, whereas the low-value areas were predominantly situated in the plain areas. From 2000 to 2020, the hot spots of CESI were primarily located in the middle and the lower reaches of the YTRB. Conversely, the cold spots were situated in the upper reaches of the YTRB. The regression analysis revealed a significant negative association between socioeconomic factors and CESI, while a significant positive association between natural background factors and CESI. Of the natural background factors, average precipitation has the largest positive effect on CESI, with each 1% increase resulting in up to 0.369% increase in CESI. In contrast, GDP density had the greatest negative impact on CESI, with each 1% increase triggering a reduction in CESI of up to 6.210%. The findings suggest that CESI, which integrates multiple ESs, can effectively simplify the difficulty of regional ecological regulation. The driving mechanism indicates that environmental protection policies, when combined with the natural conditions and intensity of human activities in the region, would be more coherent with varying regulatory intensities.

Urban agglomerations (UAs) are the main battlefield of urbanization and the most concentrated areas of carbon emissions (CEs). Nevertheless, limited studies have examined the impact of urbanization level (UL) on CEs in UAs in China. This study aimed to identify the spatial relationship between UL and CEs in Chinese UAs and to conduct a comprehensive analysis of the differences in CEs caused by urbanization. The findings would provide scientific support for the China’s dual-carbon goals and the achievement of green and low-carbon urban development. Spatial variations in UL and CEs in 19 Chinese UAs were assessed in 2000, 2010, and 2020 using distribution dynamics and spatial regression models. The results indicated that the UL of UAs in China evidently increased over time, and UAs contributed approximately 80% of the national CEs. Significant spatial dependence was identified between urbanization factors and CEs. The regression results indicated that an increase in UL promoted the growth of CEs, and the form of the urban land had a significant and highly variable impact on CEs. Our findings provide a valuable case study for exploring relationships between UL and CEs in other UAs worldwide.

Urbanization is inevitably accompanied by drastic changes in regional land use and therefore presents an evident influence on ecosystems. Habitat quality (HQ) reflects the ability of a habitat to provide suitable conditions for the survival of an individual or population, and clarifying the interrelationships between urbanization level (UL) and HQ provides insights into sustainable urbanzation and ecosystems conservation. Much attention has focused on how urbanization related to HQ at multi-scales, but few studies have analyzed the interactive coercive relationships between UL and HQ on the global county level. To address this gap, we adopted the bivariate spatial autocorrelation and coupling coordination degree (CCD) model to determine their interactive coercive relationships at the county level globally. Results showed that the global average UL was 0.0807, 0.0838, and 0.0857 in 2000, 2010, and 2020, respectively, with a continuously increasing trend. The global average HQ was 0.6186, 0.6133, and 0.6111 for 2000, 2010, and 2020, reflecting opposite declining trends. The Moran’s I of population urbanization and HQ in 2000, 2010, and 2020 globally remained negative but showed an increasing trend, with values of −0.189, −0.228, and −0.254, respectively, while those of economic urbanization and HQ and land urbanization and HQ also remained negative and exhibited a similar increasing trend. The spatial autocorrelations for UL and HQ in different dimensions indicated that deserts and plateaus functioned as catchment areas with low UL and HQ. The CCD between UL and HQ in each county globally ranged from 0 to 0.70, while CCD between land urbanization and HQ was the most optimal at 0–0.90. The CCD between population urbanization and HQ was similar to that of economic urbanization in terms of spatial distribution. Nevertheless, the CCD of land urbanization in HQ exhibited the greatest degree of coordination among the three urbanization dimensions during the study period. These findings provided important support for sustainable urbanization and ecosystem protection globally.

Urbanization, as an important engine of modernization, plays an important role in promoting regional economy and improving living standards. Nevertheless, unchecked urban expansion over recent decades has strained natural resources and the environment, leading to crises, especially in densely populated urban areas that act as ecological barriers within river basins. The investigation of the interactive coercive relationship between the urbanization level (UL) and eco-environmental quality (EEQ) can facilitate the identification of sustainable pathways towards regional sustainability. Therefore, this study employed a set of multidisciplinary approaches, integrating simple linear regression, bivariate spatial autocorrelation, and coupling coordination degree (CCD) models, alongside multi-source remote sensing data to analyze the interactive coercive relationship between UL and EEQ in the Yangtze and Yellow River basins (YYRBs) in China. Key findings included a 6.97% improvement in EEQ in the Yellow River basin (YLRB) from 2001 to 2020, with higher values in the southeastern and southwestern regions and lower values in the central region, while the Yangtze River basin (YTRB) saw only a 1.28% increase, characterized by a lower EEQ in the west and higher levels in the middle and east, although the Yangtze River Delta showed a decline and significant variation among tributaries. UL rose steadily in both basins, especially in the middle reaches of the YLRB. Spatial autocorrelation analysis revealed a positive correlation between UL and EEQ in the YLRB, whereas a negative correlation was found in the YTRB. The CCD between UL and EEQ in the YYRBs improved, particularly in the middle and lower reaches, indicating the need for integrated urban development strategies that consider regional ecological capacities. These findings provided a scientific basis for ecological protection and sustainable urban development at a large river basin scale.

Urban agglomerations (UAs), which play a significant role in socioeconomic development and urbanization, are confronted with mounting ecological stress and a profound global imbalance in ecosystem services (ES). Understanding the conflict and coordination of knowledge about ES supply and demand (ESSD) can facilitate more efficacious guidance on the ecological sustainable development of UAs. Nevertheless, the characteristics of the conflict and coordination relationship between ESSD in Chinese UAs remain unclear, and further investigation into the interactive coercive relationship between ESSD is warranted. Consequently, we employed spatial regression and coupled coordination models to elucidate the conflict and coordination relationship between ESSD, utilizing multi-source data on Chinese UAs from 2000 to 2020. We found that ES supply in the UAs decreased, while ES demand increased. Furthermore, the coupling coordination degree between ESSD demonstrated an increase trend. The overall coupling coordination degrees between ESSD in UAs were 0.260, 0.285, and 0.311 in 2000, 2010, and 2020, respectively. The central UAs were identified as stress areas, whereas the peripheral areas were classified as non-stress areas. This study offered valuable insights into the interactive relationship between ESSD in UAs and provided a basis for formulating differentiated policies for the sustainable development of ecosystems and human activities.

The rapid urban expansion (UE) in the Yangtze River Economic Belt (YREB) in China has profoundly reshaped landscape patterns and ecosystem functions. Understanding the impact of UE on ecosystem health (EH) across different urban agglomerations is crucial for informing effective ecological governance and sustainability strategies. However, whether UE ultimately promotes or constrains EH across urban agglomerations under multi-scenario remains unclear. This study aims to address this gap by employing the Patch-generating Land Use Simulation model and the Vigor–Organization–Resilience–Service framework to simulate UE and EH in three major urban agglomerations of the YREB, while also examining the mechanisms through which UE influences EH. The results revealed substantial UE under all scenarios, with the Yangtze River Delta urban agglomerations exhibiting the most pronounced growth. The EH index showed a downward trend, from 0.621 in 2010 to 0.613 in 2020. Bivariate spatial autocorrelation and spatial regression analyses revealed a significant negative correlation between UE and EH. The study identified land fragmentation and occupation due to UE as the primary factors contributing to the deterioration of EH. The findings indicated the necessity of strategic urban planning to mitigate potential ecosystem risks while promoting sustainable urban development. Furthermore, regional cooperation is critical for addressing transboundary ecological challenges and ensuring the long-term sustainability and resilience of the YREB ecosystem.