Rapid urbanization and intensification of human activities increases the risk of disturbance of ecological systems via multiple sources, with consequences for regional ecological security and health. Landscape ecological risk assessment (LERA) is an effective way to identify and allocate risk to resources. We used the north and south Qinling Mountain area as a case study to analyze the spatial heterogeneity of landscape ecological risk using a potential- connectedness-resilience three-dimensional (PCR 3D) framework based on an integrated and dynamic risk assessment concept from adaptive cycle theory. We explored factors driving the risks with a spatial model GeoDetector. The results show that the comprehensive landscape ecological risk was north–south polarized and dominated by low and moderate risk levels (90.13% of total risk) across the whole study area. The high-risk area was centered on the Weihe plain north of the Qinling Mountains (NQL), while low-risk areas accounted for 86.87% of the total area and were prevalent across the south of the study area. The areas with high potential and connectedness risks were centered in the Xi’an–Xianyang urban agglomeration and those with high-resilience risk were in the upper reaches of the Hanjiang River. The vast majority of the area to the south of the Qinling Mountains (SQL) is at low risk. In terms of driving forces, population density and vegetation coverage (NDVI) are the primary factors affecting landscape ecological risk. Our findings suggest that anthropogenic activity is the primary cause of landscape ecological risks in the study area and regional socioeconomic exploitation and environmental conservation need to be rebalanced to achieve sustainability for the social ecosystem. The PCR 3D LERA framework employed in this study can be used to inform landscape ecological health and security and to optimize socioeconomic progress at regional scales.

Urban green spaces (UGSs) are indispensable for providing various ecosystem services (ESs) to society and city-dwellers. The equitable allocation of ESs provided by UGSs is a prerequisite for maintaining urban sustainability and human well-being. However, empirical studies have assessed the dynamics of UGS equity from ES facets, while neglecting to strengthen integrated evaluation indicators and coupled spatially explicit models. A framework was developed in this study to assess the spatio-temporal patterns of ESs provided by UGSs and their equity based on models of the Intelligent Urban Ecosystem Management System, location entropy, and the Gini coefficient. The results indicated that with urban spatial expansion, in each concentric circle, the values of three ES averages were generally the lowest in the megacity (Xi’an), and the values of carbon sequestration and the recreation averages in the medium-sized cities (Baoji and Xianyang) were also lower than in the small-sized city (Tongchuan). The inequity of ESs provided by UGSs was exacerbated from 2000 to 2020 in the Xi’an Metropolitan Area. The ES equity was the worst in Xi’an and the best in Tongchuan. Areas with extremely low CR location entropy occupied 67.01% in 2020. As cities spatially expanded, areas with extremely low ES location entropy generally increased in 2020. Additionally, the zero-value area of the ES Gini coefficient in Xi’an was the largest from 2000 to 2020. The ES Gini values mostly increased in the same concentric circle of each city in 2020, which demonstrates that equity declined. Spatial equity could be improved by diversifying UGS structures, promoting spatial matching between the ES supply and beneficiaries’ demands, and integrating UGSs into any available space in future UGS planning. The findings of the study could offer insights for optimizing the equitable provision of UGS resources and the targeted planning of urban greening.

With accelerated urbanization and rapid expansion of the urban population, there is an increasing demand among urban residents for urban green spaces (UGS) and the ecosystem services (ES) they provide. The existing research mostly focuses on the spatial pattern of UGS types and ES provided by different UGS but ignores that residents’ preferences for UGS types and their ecological and social services should also be incorporated into the spatial planning decision-making of UGS. A web-based questionnaire was distributed randomly in urban areas of China and 1050 valid samples were collected in this study. Descriptive statistical analysis methods, structural equation modeling, and Pearson correlation were used to parse the residents’ preference for both UGS types, ecological–social services, and the relevant impact factors. The results showed that: (1) the strongest preference of residents for UGS and their ecological and social services are the attached green space, “beautifying the city”, and “physical and mental relaxation”, respectively; (2) the leading factors for residents’ access to UGS are “age” and “sufficient time”, except for attached green space. The most significant effect on both ecological and social services is the “season” factor. Further, “social gathering” and “exercise” are the services most and least likely to be affected. (3) Future planning of UGS should reinforce construction of attached green space and improve the aesthetics-related ES they provide. Construction of park green space and plaza green space should be enhanced to deal with the aging trend in society. Further, maintaining the existing construction of attached green space and building regional green space are crucial to the sustainability of UGS and its ecological–social services.

Climate change can have critical impacts on ecosystem services (ESs) and their inter-relationships, especially for water-related services. However, there has been little work done on characterizing the current and future changes in these services and their inter-relationships under a changing climate. Based on the revised universal soil loss equation (RUSLE), the soil conservation service curve number model (SCS-CN), and the improved stochastic weather-generator-based statistical downscaled global climate models (GCMs), we examined two important water-related services, namely, the soil conservation (SC) service and the flood mitigation (FM) service, and their inter-relationship under baseline and future climate scenarios (Representative Concentration Pathways (RCPs) 4.5 and 8.5). We took the Upper Hanjiang River Basin (UHRB), which is the core water source area of the China’s South-to-North Water Diversion Project (S–NWDP), as an illustration. The findings revealed that (1) the SC and FM services will both decrease under the two climate scenarios examined; (2) the SC and FM services showed a significant synergistic inter-relationship and the synergy will be improved by 16.48% and 2.95% under RCP 4.5 and RCP 8.5, respectively, which provides an opportunity for management optimization; (3) the ecological degradation in the UHRB will likely have serious consequences for the middle and lower reaches of the Hanjiang river basin, and therefore impact the actual economic benefits of the S–NWDP. This study points to the necessity for understanding the dynamic changes and inter-relationships of ecosystem services under future climate change and provides information regarding the consequences of climate change, which is useful for policy and infrastructure investment.

As population growth and urbanization continue to accelerate, city dwellers are increasingly conscious of the demand for urban green space (UGS) and the ecosystem services (ESs) it provides. Great efforts are made for the supply of certain ESs in UGS. However, less is known about the residents’ preferences and the supply–demand matching of UGS types, as well as the various ESs it provides at different spatial scales. Given this, our research establishes a research framework to reveal the heterogeneity of USG types and the supply–demand matching degree (SDM) of ESs from municipal, provincial, and national spatial scales, and examines the correlation between the influencing factors and demands of residents for UGS. This study mainly used the Gini coefficient, the Lorenz curve, Z-scores, the Jenks natural breaks classification method, Pearson correlation analysis, and spatial analysis. The main findings are that (1) the Gini coefficients are 0.433 and 0.137 at the municipal and provincial scales, respectively, indicating that the supply of UGS is more unequal at the municipal scale than provincial scale; (2) the multi-scale demand for ESs between residents has no significant difference. At the provincial scale, the area with low demand is larger than that of high demand, while at the municipal scale, the contrary is the case; (3) the SDM was in a deficit at both the provincial and municipal scales. And as the scaling-up occurred, the spatial heterogeneity of the SDM decreased; (4) the number of influencing factors that significantly affected the UGS type and ESs grew as the scale increased. Among them, the impact of age and COVID-19 on three scales deserves attention. These results identify regions with deficits and surpluses in ESs provided by UGS in China at different scales. This research also advises that attention should be paid to the distribution of UGS between cities within provinces, and future UGS planning should focus on building regional green spaces to promote the well-being of an aging society. The findings in this study would offer insights for managers to improve UGS construction and urban forestry planning in the future.

Efficiently mitigating the severe air pollution resulting from rapid progress is crucial for the sustainable development of the socio-ecological system. Recently, concerns about nature-based solutions have emerged in the research on the treatment of air pollution. Studies on the purification of PM_2.5 using vegetation currently concentrate on the individual scale of tree species or urban vegetation, ignoring the regional scale, which could better assist ecological governance. Therefore, taking the Fenwei Plain of China as the study area, an assessment framework of the air purification service’s spatial distribution reflecting regional vegetation was constructed. The dry deposition model and GeoDetector were used to quantify the spatial-temporal pattern and explore natural driving factors on the removal of PM_2.5. The results showed that (1) the PM_2.5 purification services offered by various types of vegetation exhibit notable variations. The average removal rates of PM_2.5 by vegetation were 0.186%, 0.243%, and 0.435% in 2000, 2010, and 2021, respectively. (2) Meanwhile, a wide range of spatial mismatch exists between the PM_2.5 concentration and PM_2.5 removal. Insufficient supply regions of PM_2.5 purification services account for 50% of the Fenwei Plain. (3) PM_2.5 removal was strongly influenced by the types of vegetation and the Normalized Vegetation Index (NDVI), followed by the Digital Elevation Model (DEM), and less affected by meteorological factors; a strong joint effect was shown among the factors. The findings in this research provide a new perspective on regional air pollution management at the regional scale.