Urban parks beautify the environment and promote urban public health, and their spatial allocation is significant in maintaining environmental justice. However, the current allocation of urban parks focuses on quantity fairness and pays insufficient attention to accessibility and quality fairness. This study investigated the core area of Beijing and analyzed the fairness of urban park allocation based on park accessibility, area, and quality. We used big data crawling, the two-step floating catchment area method, comprehensive equity evaluation of parks, spatial autocorrelation, and non-parametric tests. The results showed inequality in terms of accessibility, area, and quality, with high spatial distribution in the north and low spatial distribution in the south. The accessibility, shortest distance, and total area of urban parks in high-income residential areas were 3.0, 2.1, and 1.8 times higher, respectively, than those of the low-income residential areas. This indicates that high-income groups have better accessibility, live closer to, and have access to larger urban parks. Middle-income and above groups had access to green space, whereas medium-to-low-income residential areas had poor access to parks, particularly high-quality parks. These findings provide decision-making and planning references for the optimal allocation and rational planning of urban parks.

Urban green spaces (UGS) are crucial urban elements that serve as direct carbon sequestration and contribute to indirect carbon emission reduction. Accurately calculating the carbon density of urban green spaces allows for scientific planning and design, thereby advancing efforts toward achieving carbon neutrality. This study has developed a workflow for estimating carbon density in urban green spaces through point cloud measurements and model simulations, using the UGS in the Beijing Main District as a case study. From the sample level, a calculation methodology was constructed based on the point cloud technology-model simulation method, which can obtain the carbon density at the plant level and the sample level. At the UGS level, remote sensing inversion was utilized to map the carbon density of urban green spaces. Ultimately, the research calculated and compared carbon density at different scales, including the carbon density of individual plants, the carbon density of sample plots, and the carbon density of various types of urban green spaces. It was found that the carbon density of trees in UGS was 9.87 kg/m², while those of shrubs and herbaceous plants were 13.20 kg/m² and 0.11 kg/m². In urban green spaces, the carbon densities of the tree and herb layers were slightly lower than those in natural ecosystems, whereas the carbon density of the shrub layer was significantly higher. This highlights the substantial potential and value of shrubs in carbon sequestration and carbon storage. The average carbon density of all UGS types was 9.76 kg/m², with the following descending order: Neighborhood Parks (10.31 kg/m²) > Attached Green Spaces (7.22 kg/m²) > Regional Parks (5.75 kg/m²). Based on these findings, the study proposed optimization strategies for different UGS types, focusing on high carbon-density plant community optimization. The goal is to provide a theoretical foundation for carbon storage calculations and plant arrangements in future UGS construction.

Refuge Green Space (RGS) plays roles in both rapid evacuation and short-term resettlement in response to sudden natural hazards, especially geological disasters. As a result, its evaluation and location optimization are crucial for the development of sustainable cities. In this study, the Three-Step Floating Catchment Area Method (3SFCA) and Moran’s I index were used as measurement models. The rapid evacuation and short-term resettlement capacity of RGS were evaluated by calculating accessibility with 5, 10, 15 and 30 min of walking. The purpose of this study was to quantitatively evaluate the supply capacity of RGS in the central area of Tianjin and to provide recommendations for future additions to RGS. The results show that over 80% of the research area exhibited relatively poor service capacity within a 5–10 min walking distance, while less than 20% exhibited relatively good service capacity. Within a 15–30 min walking distance, approximately 60% of the study area was not well served by the RGS, while approximately 40% was well served. In conclusion, the layout of RGS in the central area of Tianjin exhibits unevenness and polarization of phenomena, and the supply of different categories of RGS varies widely. There is an insufficient supply of Emergency Sheltered Green Space and Temporary Sheltered Green Space, and that which is available is difficult to reach within 5–10 min. The coverage capacity of Disaster Prevention Park Space is relatively good, essentially covering the entire study area within a 15–30 min walking distance; however, the service capacity is average. Based on these conclusions, areas in which the RGS supply was weak were identified, and future potential RGS additions were identified in conjunction with the Green Space System Plan.

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.

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.