(1) Background: The issue of equity in the layout of urban green park spaces is an essential dimension of urban public resource allocation. (2) Objective: To analyze the equity of the distribution of parkland in the core area of Beijing from a quantitative and spatial perspective. By measuring both vehicular and pedestrian transport modes, the study identifies areas with low levels of green space provision and provides strategies for optimization. It is hoped that this study can provide a basis for future green space construction in the core area of Beijing. (3) Methods: In this paper, the Gauss Two-step Floating Catchment Area Method (Ga2SFCA) is used to study the green park space layout in the core area of Beijing. The two modes of 30min-walk and 10min-car-journey were used to measure the fair values of the residential unit scale, the street district scale, and the overall scale, respectively. (4) Results: The study results show that the fair values based on the 30-min walk and the 10-min car journey differ significantly. For the 30-min walk-based travel mode, the proportion of fair (Class IV) and fairer (Class V) areas is approximately 20%, while for the 10 min car-based travel mode, the corresponding class is over 90%. (5) Conclusions: The overall equity of urban parkland in Beijing core area is better for car-based travel modes, while for walking modes, the supply is still insufficient, and the distribution of parkland is polarized.

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.

As part of urban green space and emergency shelters, refuge green spaces (RGS) contribute significantly to the resilience of cities to natural disasters. In contrast, few studies have been conducted to assess the equity of RGS in relation to their planning layout. The presented research aims to quantitatively evaluate the equity of RGS within Chengdu’s Third Ring Road, and to propose corresponding optimization measures in conjunction with future green space planning. The rapid evacuation capacity of the RGS was evaluated by calculating the equity of the RGS in walking modes of 5, 10 and 15 min using an improved three-step floating catchment area method (3SFCA). Based on the results, RGS had an average equity in the study area. The total number of RGS within the Third Ring Road of Chengdu was insufficient, with an uneven spatial distribution and a structure to be optimized. The rapid evacuation capacity of RGS in 5 and 10 min needs to be further improved. The short-term resettlement capacity of RGS after 15 min was relatively good. Using Moran’s I index, the RGS equity and house price results were analyzed and no significant aggregation and polarization were observed. Following the evaluation, recommendations are made for optimizing and adding future RGS in accordance with Chengdu Green Space System Planning. The equity of RGS has been significantly improved after optimization, which can meet the needs of over 90% of residents for 15 min. This study provided feasible suggestions for the layout and structural optimization of the future RGS within the Third Ring Road of Chengdu, which aimed to create a RGS network with complex functions, to meet the multiple needs of citizens.

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.