Habitat quality and ecosystem service value (ESV) are important foundations for sustainable development. Baoding, as the strategic hinterland of Beijing–Tianjin–Hebei, is of great significance to regional ecological conservation and sustainable urban development. Based on land-use data from 2000 to 2020, the land-use scenarios of natural development (ND), water protection (WP), forest rehabilitation (FR), and cultivated land protection (CP) in 2030 were predicted by the PLUS model and adopt the InVEST model and equivalent ESV table to assess ecological sustainability. The results show that: (1) From 2000 to 2020, the construction land in Baoding has increased by 812 km², and the cultivated land and forest land decreased by 708 km² and 154 km². Habitat quality is obviously deteriorating in 4.66% of the city. (2) Under different scenarios, the order of habitat quality is CP > FR > WP > ND. The habitat quality under each scenario is dominated by medium habitat quality. (3) Under different scenarios, the order of ESV is FR > CP> WP > ND. The fluctuation of forest land and cultivated land scale is affecting the ESV. (4) CP and FR will form a land-use pattern that has “high ecological quality and value”, which better balances the economic development and ecological protection of Baoding. This research study will provide a reference for the effective allocation of land resources and will guide the formulation of urban land space planning policy in Baoding.

The quantitative evaluation and prediction of ecosystem service value (ESV) in the Lhasa River Basin can provide a basis for ecological environment assessment and land-use optimization and adjustment in the future. Previous studies on the ESV in the Lhasa River Basin have focused mainly on static assessment and evolution analysis based on historical data, and have not considered future development trends. Moreover, most of the current driving factors selected in land use and ESV prediction studies are homogeneous, and do not reflect the geographical and cultural characteristics of the study area well. With the Lhasa River Basin as the research focus, 20 driving factors were selected according to the characteristics of the plateau alpine area, and the land-use changes under three developmental orientations, namely, natural evolution, ecological protection, and agricultural development, were predicted for the year 2030 with the FLUS-Markov model. Based on these predictions, the values of ecosystem services were calculated, and their spatiotemporal dynamic characteristics were analyzed. The results show that the model has high accuracy in simulating land-use change in the Lhasa River Basin, with a kappa coefficient of 0.989 and an overall accuracy of 99.33%, indicating a high applicability. The types of land use in the Lhasa River basin are dominated by the existence of grassland, unused land, and forest, with a combined proportion of 94.3%. The change trends of each land-use type in the basin under the three scenarios differ significantly, with grassland, cropland, and building land showing the most significant changes. The area of cropland increased only in the agricultural development scenario; the areas of forest and grassland increased only in the ecological protection scenario; and the expansion of building land was most effectively controlled in the ecological protection scenario. The ESV increased in all three scenarios, and the spatial distribution of the ESV per unit area in the middle and lower reaches was greater than that in the upper reaches. The ESV was the greatest in the ecological protection scenario, with grasslands, forests, and water bodies contributing more to the ESV of the basin. This study provides decision-making references for the effective utilization of land resources, ecological environmental protection planning, and urban construction in the Lhasa River Basin in the future.

Urban expansion, especially the construction of megacities, increases carbon emissions and adversely affects the carbon storage of terrestrial ecosystems. However, scientific land-use management policies can increase carbon storage. This study takes two megacities at different stages of development, Beijing and Tianjin, as examples to explore the impact of different ecological conservation scenarios on both urban land use and carbon storage to provide recommendations for the construction planning of large cities with low-carbon development as the goal. Furthermore, we coupled the patch-generating land use simulation (PLUS) model with the integrated valuation of ecosystem services and tradeoffs (InVEST) model to simulate land use and carbon storage under a natural development scenario, a planned ecological protection scenario (PEPS), and a policy-based ecological restoration scenario (PERS). From 2000 to 2020, both cities had different degrees of construction land expansion and carbon loss, and Tianjin's dynamic degree of construction land was 0.94% higher than Beijing's, with a carbon loss 183,536.19 Mg higher than Beijing's; this trend of reducing carbon reserves will continue under the natural development scenario (NDS). Under the PEPS and PERS, the carbon stock of both cities increases, and the impact on Tianjin is greater, with an increase of 4.51% and 8.04%, respectively. Under PERS, the carbon stock increases the most, but the dynamic degree of construction land use is negative for both cities. Beijing's carbon stock is 0.40% lower than Tianjin's, which deviates slightly from the trend of urban economic development. Megacities in the rapid development stage can refer to Tianjin, strictly following the ecological protection land planning scope and vigorously implementing ecological restoration policies to effectively increase regional carbon stock. Megacities in the mature stage of development can refer to Beijing, and flexibly implement ecological restoration policies to increase regional carbon stock without affecting the city's economic development.

Urban–rural fringe areas are critical transition zones where ecological functions and human activities interact intensely, often leading to complex spatial patterns and trade-offs among ecosystem services (ESs). Understanding these patterns and their socio-ecological drivers across multiple spatial scales is essential for sustainable land-use planning and ecosystem management. This study, using the urban–rural fringe (URF) of Beijing as an example, quantified eight representative ecosystem services at the 1 km grid, 3 km grid, and township scales. It employed hotspot analysis, Moran’s Index, and the Spearman correlation to analyze trade-offs and synergies (TOSs) among ESs. The study also applied a self-organizing map and the NbClust function to identify and determine the optimal number of ecosystem service bundles (ESBs) for ecological functional zoning. Redundancy analysis was used to explore the impacts of six socio-ecological drivers on the spatial distribution of ESs. The results revealed the following: (1) The spatial distribution of ESs in Beijing’s URF exhibits clustering and cross-scale variations, with spatial clustering intensifying as the scale expands. (2) TOSs among ESs vary in strength and direction across the three spatial scales. (3) The primary drivers of TOSs at all three scales are the normalized vegetation index and annual precipitation. (4) Based on the supply intensity of various ESs, the study area was classified into four types of ESBs across the three scales: ecologically restricted areas, food production areas, ecologically balanced areas, and high-quality ecological areas. The township scale is more conducive to planning and management, while the 1 km and 3 km grid scales are more helpful for understanding the relationship between land use and ESs.

Mountain cities with complex topographies have always been highly vulnerable areas to global environmental change, prone to geological hazards, climate change, and human activities. Exploring and analyzing the vulnerability of coupling systems in mountain cities is highly important for improving regional resilience and promoting sustainable regional development. Therefore, a comprehensive framework for assessing the spatial vulnerability of mountain cities is proposed. A vulnerability assessment index system is constructed using three functional systems, ecological protection, agricultural production, and urban construction. Subsequently, the BP neural network and the genetic algorithm (GA) are combined to establish a vulnerability assessment model, and geographically weighted regression (GWR) is introduced to analyze the spatial influence of one-dimensional systems on the coupling system. Linzhou, a typical mountain city at the boundary between China’s second- and third-step terrains, was selected as a case study to demonstrate the feasibility of the framework. The results showed that the vulnerability of the ecological protection system was highly aggregated in the east–central region, that of the agricultural production system was high in the west, and that of the urban construction system was low in the central region and high in the northwestern region. The coupling system vulnerability was characterized by multispatial distribution. The complex topography and geomorphology and the resulting natural hazards are the underlying causes of the vulnerability results. The impact of ecological and urban systems on the coupling system vulnerability is more prominent. The proposed framework can serve as a reference for vulnerability assessments of other similar mountain cities with stepped topographies to support the formulation of sustainable development strategies.

Against the backdrop of rapid development in megacities, urban park green spaces serve as essential public resources whose accessibility and equity directly affect residents’ quality of life and broader social justice. This study addresses the imbalance between the spatial distribution of green space resources and the socio-demographic characteristics of different population groups in megacities. It takes the six central districts of Beijing as the study area and integrates data from 457 urban parks. The research applies the Gaussian two-step floating catchment area (G2SFCA) method and bivariate spatial autocorrelation analysis (Moran’s I) to systematically evaluate the equity of urban park green space provision across multiple social dimensions, including economic status, educational attainment, and vulnerable groups. The results indicate that urban park green spaces in Beijing’s six central districts exhibit a pronounced central and northern advantage, with significant deficits in southern and peripheral areas. High accessibility and greater per capita green space are concentrated in core and high-housing-price districts, overlapping with high-income and highly educated populations. In contrast, vulnerable groups and migrant workers are more likely to reside in green-space-deficient areas, facing a structural “high population density–low green space provision” disadvantage, reflecting clear social inequities. In addition, inequity is more pronounced at the walking scale than at the cycling scale. The study reveals a dual mismatch in green space provision across both spatial and social dimensions within a megacity context. The findings suggest that future urban planning should shift from quantitative expansion to the optimization of existing green space resources. Planning strategies should prioritize vulnerable groups and adopt a people-oriented approach. Policymakers should allocate greater support to southern and peripheral areas, increase the provision of pocket parks, and improve slow-mobility systems. These measures can more precisely safeguard equitable access to green space for disadvantaged populations and promote the realization of spatial justice.