Global aging is getting worse, especially in China, a country with a large population. It is urgently needed to plan the site of new urban elderly care facilities for an aging society. Based on point of interest data and machine learning algorithms, we established a site selection model of urban elderly care facilities for Wuhan in China and selected potential optimal sites for new urban elderly care facilities. We found that 2059 of the 31,390 grids with a resolution of 500 m × 500 m of Wuhan are priority layout grids for new urban elderly care facilities. A total of 635 priority grids were further selected based on the agglomeration degree of the aging population in each street. They are mainly distributed in the areas with a concentrated aging population within the Second Ring Road around the urban centers. Additionally, some outer suburban streets with a relatively high aging degree also require immediate facility construction. The point of interest data and machine learning algorithms to select the location of urban elderly care facilities can optimize their overall configuration and avoid the subjectivity of site selection to some degree, provide empirical support for how to achieve a good configuration of “population–facilities” in space, and continuously improve the science of the spatial allocation of elderly care facilities.

Continued urban expansion (UE) has long been regarded as a huge challenge for climate change mitigation. However, much less is known about how UE affects carbon emissions (CEs), especially in the urban agglomerations of the Yellow River Basin (UAYRB), China. In this regard, this study introduced kernel density analysis, the Gini coefficient, and Markov chains to reveal the UE patterns and carbon emissions intensity (CEI) in the UAYRB at the county level, and explored the spatial heterogeneity of the impact of UE on CEI with the geographically and temporally weighted regression model. The results show that both CEI and UE in the UAYRB showed a steady growing trend during the study period. The kernel density of CEI and UE revealed that CEI in the UAYRB was weakening, while the UE rate continuously slowed down. The Gini coefficients of both CEI and UE in the UAYRB region were at high levels, indicating obvious spatial imbalance. The Markov transfer probability matrix for CEI with a time span of five years showed that CEI growth will still occur over the next five years, while that of UE was more obvious. Meanwhile, counties with a regression coefficient of UE on CEI higher than 0 covered the majority, and the distribution pattern remained quite stable. The regression coefficients of different urban landscape metrics on CEI in the UAYRB varied greatly; except for the landscape shape index, the regression coefficients of the aggregation index, interspersion and juxtaposition index, and patch density overall remained positive. These findings can advance the policy enlightenment of the high-quality development of the Yellow River Basin.

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.

Within the context of the “30·60 dual carbon” goal, China’s low-carbon sustainable development is affected by a series of environmental problems caused by rapid urbanization. Revealing the impacts of urbanization on carbon emissions (CEs) is conducive to low-carbon city construction and green transformation, attracting the attention of scholars worldwide. The research is rich concerning the impacts of urbanization on CEs but lacking in studies on their spatial dependence and heterogeneity at multiple different scales, especially in areas with important ecological statuses, such as the Han River Ecological Economic Belt (HREEB) in China. To address these gaps, this study first constructed an urbanization level (UL) measurement method. Then, using a bivariate spatial autocorrelation analysis and geographically weighted regression model, the spatial relationships between UL and CEs from 2000 to 2020 were investigated from a multiscale perspective. The results were shown as follows. The total CEs in the HREEB witnessed an upsurge in the past two decades, which was mainly dispersed in the central urban areas of the HREEB. The ULs in different regions of the HREEB varied evidently, with high levels in the east and low levels in the central and western regions, while the overall UL in 2020 was higher than that in 2000, regardless of the research scale. During the study period, there was a significant, positive spatial autocorrelation between UL and CEs, and similar spatial distribution characteristics of the bivariate spatial autocorrelation between CEs and UL at different times, and different scales were observed. UL impacted CEs positively, but the impacts varied at different grid scales during the study period. The regression coefficients in 2020 were higher than those in 2000, but the spatial distribution was more scattered, and more detailed information was provided at the 5 km grid scale than at the 10 km grid scale. The findings of this research can advance policy enlightenment for low-carbon city construction and green transformation in HREEB and provide a reference for CE reduction in other similar regions of the world.

Land-use change is a primary driver of changes in ecosystem services, and different development scenarios imply markedly different ecological responses, with consequences for regional economic development and ecological protection. However, existing studies have mostly focused on historical and current conditions, and quantitative assessments of future ecosystem-service changes under multiple land-use scenarios remain limited. In this study, the patch-generating land-use simulation (PLUS) model was used to simulate land-use patterns of Hubei Province under business-as-usual (BAU), economic development (ED), and ecological conservation (EC) scenarios for 2030 and 2040; five ecosystem services—habitat quality (HQ), soil conservation (SC), grain productivity (GP), water yield (WY), and water purification (WP, proxied by nitrogen export)—were then assessed for 2000–2020 and the future scenarios at a 30 m resolution using InVEST-based modules; Spearman correlation analysis was used to identify trade-offs and spatial associations among services, and the geographical detector model (GDM) was applied at 5 km and 10 km grid scales to quantify the explanatory power of driving factors. The results show that from 2000 to 2020, cultivated land, forest land, grassland, and unused land in Hubei Province decreased by 273,146.76 hm², 61,111.08 hm², 8712.99 hm², and 5392.26 hm², respectively, while construction land and water areas expanded. Over the same period, GP, SC, and WY increased, nitrogen export grew (indicating a weakening of water purification), and HQ declined continuously from 0.545 to 0.527; the comprehensive ecosystem-service index (CESI) followed a U-shaped trajectory (0.268 in 2000, 0.243 in 2010, and 0.267 in 2020). Under the future scenarios, GP increases markedly under BAU and ED, while HQ and SC deteriorate and nitrogen export rises; under EC, HQ and SC improve significantly, GP first decreases and then increases, and both WY and nitrogen export decline. Among the driving factors, elevation, slope, and NDVI dominate the spatial heterogeneity of ecosystem services, and their explanatory power varies between the 5 km and 10 km scales. These findings provide a reference for optimizing land-resource allocation and for reconciling economic development, food security, and ecological protection in Hubei Province.