Assessing Supply and Demand Discrepancies of Urban Green Space in High-Density Built-Up Areas Based on Vitality Impacts: Evidence from Beijing’s Central Districts, China

Abstract

In rapidly urbanizing areas, there is a notable aggregation of vitality in high-density urban environments, accompanied by an increasing discrepancy between the supply and demand of urban green space (UGS). This study presented an integrated framework comprising a model for UGS supply-demand coupling coordination and a measure of urban vitality. Using downtown Beijing as a case study, the Gini coefficient assessed UGS supply-demand disparities across different vitality types. The study examined how UGS supply and demand factors interact with urban vitality, revealing the impact of UGS supply-demand imbalances on various dimensions of vitality and the UGS mismatches experienced by different vitality groups. The study showed that: (1) 63.29% of central Beijing’s areas had low UGS supply-demand coordination, with 39.23% experiencing UGS mismatches; (2) UGS supply and demand were significantly correlated with urban vitality spatial distribution; (3) these factors significantly impacted urban comprehensive vitality; (4) and there were notable UGS distribution disparities among vitality groups, with economic vitality group perceiving the greatest inequity (Gini = 0.311), followed by social vitality (Gini = 0.289) and cultural vitality group (Gini = 0.247). These findings offer valuable insights for a more refined assessment and enhancement of UGS, aiming to achieve balanced, high-quality, and sustainable urban development.

1. Introduction

As an important indicator for assessing the sustainable development and competitiveness of cities, urban vitality comprehensively reflects multiple dimensions such as population mobility, economic diversity, and public participation [1]. It contributes to talent attraction, economic growth, and improvements in residents’ quality of life, and has thus attracted considerable attention from scholars and policymakers. Jacobs introduced the concept of urban vitality in his monograph The Death and Life of Great American Cities, linking urban vitality to diversity, and emphasizing its dependence on the diverse demands of residents for urban space and the provision of mixed-use areas. Based on Jacobs’ theory of diversity and the behavioral spatiotemporal framework, vitality derives from the sustained activities of diverse populations within dense and complementary public spaces. Therefore, urban vitality is not governed by a single factor, but is shaped by multiple variables spanning dimensions such as form, function, transportation, population, and economy [2]. Under these dimensions, specific indicators such as street configuration, land use diversity, population density, open space ratios, amenity density, and park distribution are employed to examine the role of urban vitality [3].

Measurement and evaluation of urban vitality have evolved from a single indicator to a comprehensive assessment system that reflects the socioeconomic context, physical environment, and their interactions [4]. In recent years, multidimensional approaches to measuring urban vitality have gained prominence. Several studies have conducted comprehensive assessments of urban vitality across various dimensions, including economy, culture, society, and ecology. For example, Lan et al. analyzed 35 large and medium-sized Chinese cities, categorizing urban vitality into five dimensions: economic, social, cultural, innovation, and environmental vitality [5]. Wang et al. proposed a framework for urban vitality that encompassed social, economic, cyberspace, and cultural-tourism vitality, and applied it in a comprehensive identification of urban vitality in Nanjing, China [2]. In selecting indicators and data sources for vitality measurement, scholars often use multi-source big data such as point-of-interest (POI) density [6,7], nighttime light intensity [8], mobile signaling data [9], location-based services (LBS) [10], social media check-ins [11], etc., to quantify different types of vitality. Studies have shown that cities tend to exhibit high levels of population density and social interaction when the type, quantity, and accessibility of public spaces fulfill the composite needs of daily life, work, and leisure [12].

In this context, urban green spaces (UGSs) are widely acknowledged as essential public infrastructure capable of stimulating and sustaining urban vitality due to their ecological, social, and cultural functions [13,14]. As a vital component of urban green infrastructure, UGS encompasses natural, semi-natural, and artificial ecological networks linking cities and their suburbs, including ecological cores and corridors such as parks, greenways, forests, and wetlands [15]. Numerous studies have shown that UGS not only mitigates the heat island effect [16], enhances urban carbon sequestration [17], reduces air and water pollution [18], minimizes noise [19], and preserves biodiversity [20], but also improves the quality of life [21], provides cultural and aesthetic values, offers recreational and social venues, and contributes to urban residents’ spiritual well-being [22]. The ecological and social benefits of UGS have a significant impact on residents’ quality of life, social cohesion, and even the overall urban vitality, thereby warranting considerable attention [23].

However, with the expansion of urban population and land development, cities—particularly in developing countries such as China—are encountering challenges, including overcrowded high-density zones and deteriorating environmental quality [24,25]. China’s urbanization rate increased from 49.7% in 2010 to 59.6% in 2018, and is projected to reach 70% by 2050 [26]. This trend has intensified the imbalance between UGS supply and demand, particularly in high-density areas [27]. On the one hand, many cities suffer from a lack of total UGS and uneven spatial distribution; on the other hand, socio-spatial disparities in green space demand among various groups pose challenges to environmental equity [28]. Existing UGS evaluation systems, often based on single geographic perspectives, can no longer support the nuanced governance required in rapidly evolving urban contexts [29]. Balancing UGS supply and demand to address rising environmental pressures and promote sustainable urban development has emerged as a critical concern in contemporary urban planning and governance [30,31].

Research on the supply-demand balance of UGS has grown steadily in recent decades. Many studies have examined the supply-demand balance of UGS by evaluating its spatial distribution and the efficiency of its alignment with urban population needs. Related studies focus on supply indicators such as area, type, quality, and accessibility of UGS, and demand indicators including population density, social structure, public health, and environmental awareness [32]. Coupled coordination models and matching-degree models are commonly employed to reflect the alignment between regional UGS capacity and overall population demand. However, focusing solely on the total quantity of UGS fails to capture spatial allocation efficiency, prompting a growing emphasis on equity as a key research dimension. UGS equity emphasizes fair access to UGS across different social groups, considering availability, accessibility, quality, and benefits. It is commonly conceptualized in terms of three dimensions: spatial equity, social equity, and social justice. Most studies emphasize the accessibility and utilization efficiency of UGS to achieve spatial equity [33,34]. Additionally, scholars mostly used the Lorenz curve and Gini coefficient to assess spatial equity [33]. Social equity concerns residents’ equitable access to UGS resources and ecosystem services [35]. Social justice highlights the needs of vulnerable groups to ensure equal access across diverse communities [36]. The imbalance between UGS supply and demand can result in significant equity challenges. Sufficiently accessible UGS resources not only enhance the urban microenvironment [37], but also serve as venues for social interaction, cultural exchange, and physical activity [36], which in turn may directly or indirectly energize commercial and cultural activities. Conversely, insufficient supply, imbalanced layout or inequitable distribution of UGS can undermine urban ecosystem services and limit the potential for urban development and quality of life [38].

Parallel to this, research on the impact mechanisms of UGS and built environments on vitality has gained momentum. Numerous studies have examined the influence of park elements, UGS characteristics, and street-level features—such as green–blue space ratio inside the UGS [39], number and availability of public service facilities [40], UGS accessibility via transportation [39], and visibility of street greenery [41]—on site-level vitality, using these variables as common evaluation metrics. These studies have demonstrated that the accessibility, size, and quality of UGS—serving both ecological and social functions—are key drivers of residents’ activity patterns. More convenient and multifunctional UGS tend to extend dwell time, foster neighborhood interaction, and generate positive externalities for urban vitality [42]. Although studies have addressed the impact of UGS distribution and internal morphology on localized vitality at micro and meso scales, systematic and quantitative evidence remains limited regarding whether—and through which mechanisms—the broader supply-demand dynamics of UGS affect overall urban vitality. This theoretical linkage requires further empirically investigation.

This study proposes an analytical framework to investigate the relationship between UGS supply-demand coordination and urban vitality, focusing on vitality-driven spatial mismatches. Using Beijing’s central districts as a case study, we aim to (1) model UGS supply-demand coordination from both supply and demand perspectives, mapping their spatial patterns; (2) assess urban vitality across social, economic, and cultural dimensions, categorize populations by vitality type, and evaluate UGS distribution disparities; (3) and analyze the relationship between UGS supply-demand dynamics and urban vitality, identify UGS imbalances, and uncover the underlying mechanisms. This research aims to optimize UGS provision in high-density cities, guiding local governments in refined management strategies to maximize social benefits, and ultimately enhance urban vitality and the well-being of residents.

2. Materials and Methods

2.1. Methodological Framework

The framework of this study draws upon existing theories on multidimensional urban vitality assessment as well as coupled coordination theory, aiming to provide a more nuanced understanding for exploring the equity of UGS supply and demand in high-density areas and its relationship with urban vitality. The framework consists of the following components (Figure 1): (1) Define indicators for UGS supply and demand, as well as urban vitality. (2) Assess the degree of matching and coordination between UGS supply and high-density built-up areas demand using supply-demand ratio and coupling-coordination degree. (3) Use the Lorenz curve and Gini coefficient to evaluate the equity of UGS distribution under different types of urban vitality. (4) Investigate the spatial differentiation and mechanism of UGS supply-demand coordination and its impact on multiple urban vitality dimensions, using spatial autocorrelation analysis and the Geodetector model.

2.2. Study Area

As a political and cultural hub, Beijing’s central urban districts (including Haidian, Chaoyang, Xicheng, Dongcheng, Shijingshan, and Fengtai) generally exhibit a high-density built environment with a population density reaching 7568 persons/km² in 2023, peaking at 23,407 persons/km² in Dongcheng and Xicheng districts (Figure 2). In the face of continuous population growth, there is a lack of UGS resources in the city, with obvious heterogeneous characteristics, and the social, economic, and cultural vitality of urban areas is strong. In this situation of both challenges and opportunities, Beijing faces ongoing progress and issues in UGS development. Over the past two decades, urban park green spaces in the central area of Beijing have continued to grow [43]. The “Beijing Garden City Special Plan (2023–2035)” established dual objectives for urban green space (UGS) enhancement: ≥8.2% improvement in 500 m park service coverage and ≥0.11 m² per capita green space increment. Therefore, the current UGS supply remains limited and needs improvements in both quantity and quality.

This study focuses on the UGS in the central urban area of Beijing, including completed urban parks, greenways, riverside green spaces, and micro-green spaces in the community. Most of these are concentrated within the Fifth Ring Road and mainly serve the daily recreational and leisure needs of local residents. Large-scale ecological green spaces are mostly situated outside the Fifth Ring Road, near the periphery of the study area. These areas function mainly as ecological buffer zones and weekend recreational destinations, with a longer passage time and a wider range of services.

2.3. Data Source and Pre-Processing

Administrative boundaries were sourced from China’s National Geographic Information Catalog Service (https://www.webmap.cn/, accessed on 4 January 2024), with 133 validated subdistricts in Beijing’s central districts. The total population data were obtained from the Beijing Municipal Bureau of Statistics’ 2020 Census (http://tjj.beijing.gov.cn/, accessed on 1 December 2023).

Urban green space (UGS) data integrated 594 parks from municipal records (https://yllhj.beijing.gov.cn/ggfw/bjsggml/, accessed on 10 January 2024), POI integration, OpenStreetMap, and the 1 m resolution dataset of the Beijing local green space dataset [44], classified into five categories per national green space standards (CJJ/T 85-2017; GB/T 51346-2019) (

Table 1. Classification Statistics of UGS in Beijing’s central districts.

UGS Category	Numbers	Area (hm2)	Area Ratio (%)	Suitable Size (hm2)	Service Radius (m)
Micro-scale green spaces	237	110.75	0.75	0.04~1.0	300
Small-scale green spaces	310	772.59	5.27	1.0~5.0	500
Community-level green Spaces	97	688.86	4.69	5.0~10.0	1000
District-level green Spaces	95	1548.06	10.55	10.0~25.0	2000
City-level green spaces	110	11,553.11	78.74	≥25.0	3000
Total	849	14,673.37	100	—	—

).

Python 3.8. was used to extract and process park entrance data, building data, transportation network data, POI data, and heatmap data from Baidu Maps (https://lbsyun.baidu.com/products/map, accessed on 12 January 2024) and OpenStreetMap (https://www.openstreetmap.org/, accessed on 15 January 2024). These datasets were subsequently cleaned, validated, and corrected, and were integrated with official statistical data (https://www.beijing.gov.cn/, accessed on 21 January 2024) to minimize biases caused by data limitations.

The POI data were categorized into 6 major categories and 18 medium categories in conjunction with the “Classification of Land Use and Sea Use for Territorial Spatial Survey, Planning, and Use Control” (https://www.gov.cn/zhengce/zhengceku/202311/content_6917279.htm, accessed on 22 January 2024). Information regarding POI classification is provided in Appendix B (

Table A1. POI classification (based on Baidu map POI classification).

Major	Medium	Minor
Residence	Business residential	Commercial office buildings, residential areas, industrial parks, commercial and residential buildings, etc.
Business service	Catering services	Chinese restaurant, foreign restaurant, snack fast food store, cake and dessert store, coffee shop, tea house, bar
	Life services	Communication business hall, post office, logistics company, ticket office, laundromat, graphic and printing store, photo studio, real estate agency, public utility, maintenance point, housekeeping service, funeral service, lottery ticket sales outlet, pet service, newsstand, public restroom
	Shopping services	Shopping center, department store, supermarket, convenience store, home building material, home appliance and digital appliance, store and bazaar
	Financial and insurance services	Bank, ATM, credit unions, investment banking, pawn shops
	Motorcycle services	—
	Automotive services	Automobile sale, automobile repair, automobile beauty, automobile parts, automobile leasing, automobile testing field
	Sports and leisure services	Stadium, extreme sports venue, fitness center, resort, farm house, movie theater, KTV, theater, dance hall, Internet cafe, game venue, baths and massage, leisure plaza
	Accommodation services	Hotel Guest House, Star Hotel, Express Hotel, Apartment Hotel
Public administration and public service	Public facilities	Public restroom, newsstand, public telephone, emergency shelter
	Science, education, and cultural services	Higher education institution, middle school, elementary school, kindergarten, adult education, parent-child education, special education school, study abroad agency, scientific research institute, training institution, library, science and technology museum, museum, science education and cultural venue, arts and cultural organization, media organization, cultural palace, exhibition center, exhibition hall, planetarium, archive
	Health care services	General hospital, specialty hospital, clinic, pharmacy, medical checkup, nursing home, emergency center, CDC
	Government agencies and social organizations	Central agency, government at all levels, administrative unit, public prosecutor and law enforcement agency, foreign-related agency, political party and organization, welfare agency, and political education institution.
Industries	Company enterprise	Company, park, agriculture, forestry and horticulture, factory and min
Green space and open space	Scenic spots	Park, square, zoo, botanical garden, amusement park, aquarium, heritage estimate, seaside bathing beach, church, scenic spot
Transportation	Transportation facilities services	Airport, railway station, subway station, long-distance bus station, bus stop, port, parking lot, gas station, service area, toll station, bridge, charging station, on-street parking space, road accessory facility, etc.

).

A multidimensional urban vitality dataset was developed using ArcGIS 10.4, as detailed in Appendix B (

Table A3. Data overview of urban vitality.

Dimension	Data Sources	Description
Social Vitality	Baidu (https://lbsyun.baidu.com/, accessed on 20–21 May 2023)	Location-based service data. Contains numerical points with an accuracy of 500 m. Describes real-time population distribution.
Economic Vitality	Earth Observation Group (https://eogdata.mines.edu/nighttime_light/monthly/v10/, accessed on 4 May 2024)	Remote sensing data. Raster data with a resolution of 500 m, used to describe the light intensity in the area.
Cultural Vitality	Gaode (https://lbs.amap.com/, accessed on 22 March 2024)	Point of Interest (POI), point data containing geographic location, category, and other information.

). The dataset comprises three core dimensions—social, economic, and cultural vitality—each represented by distinct proxies based on established literature and data availability. The composite index derived from standardized economic, social, and cultural vitality metrics represents comprehensive urban vitality.

2.4. Methods

2.4.1. Construction of UGS Supply and Demand Indicators System

This study developed a comprehensive indicator system for evaluating UGS supply and demand in high-density built-up areas, grounded in coupling-coordination theory. The coupling-coordination theory suggests that complex systems comprise multiple interacting subsystems. In urban studies, this model provides a robust framework for quantitatively assessing dynamic interactions and equilibrium development among multiple urban subsystems, making it particularly well-suited for analyzing supply-demand relationships [45]. Specifically, the UGS supply level was evaluated through two dimensions—quantity configuration and spatial uniformity—comprising a total of 6 indicators. Drawing on Ewing et al.’s “5D” framework for evaluating urban built environments [46], and taking into account the existing studies and the situation of this study area, urban demand evaluation in high-density settings was categorized into 6 dimensions: crowd, density, design, diversity, transportation accessibility, and destination accessibility—totaling 11 indicators [47]. Most indicators were selected and calculated with reference to previous studies. The evaluation model incorporated multi-scale UGS service radii and a multi-source data approach to enhance its comprehensiveness and objectivity in high-density urban settings (

Table A2. System of supply and demand evaluation indicators.

Goal Level	Criteria Level	Indicator	Description of Indicator	Nature of Indicator	Weight
Indicators of supply	Quantity Configuration	UGS distribution density	Number of UGS per subdistrict unit area	+	0.2789
		UGS coverage	UGS area per subdistrict unit area	+	0.2969
		UGS Recreation Opportunity Index	UGS service radius coverage area (with overlapping areas counted multiple times) per subdistrict unit area	+	0.1960
	Spatial uniformity	UGS Service coverage overlap ratio	[UGS service radius coverage area (with overlapping areas counted multiple times)—the total UGS service area (with overlapping areas counted only once)] per subdistrict unit area	+	0.0236
		Location entropy of UGS service per capita	Per capita effective UGS service area within the subdistrict unit/Per capita effective UGS service area in the study area	+	0.2046
		Location entropy of UGS per capita	Per capita UGS area within the subdistrict unit/Per capita UGS area in the study area
Indicators of demand	Crowd	population density	Total population per subdistrict unit area	+	0.0159
	Density	POI density	Total number of POI per subdistrict unit area	+	0.0418
		building density	Building footprint area per subdistrict unit area	+	0.62468
	Design	Average number of building stories	Total number of building floors per subdistrict unit area/Total number of buildings per subdistrict unit area	+	0.02521
		Intersection density	Number of intersections per subdistrict unit area	+	0.03885
	Diversity	land use mix degree	Shannon entropy index of POIs within the subdistrict unit	+	0.02284
	Transportation accessibility	road density	Total street network length per subdistrict unit area	+	0.02573
		Bus stop density	Total number of bus stops per subdistrict unit area	+	0.03099
		Metro station density	Total number of metro stations per subdistrict unit area	+	0.05902
	Destination accessibility	Distances to administrative centers	The shortest distance from the center point of the subdistrict unit to the subdistrict government	−	0.05534
		Distance to CBD	The shortest distance from the center point of the subdistrict unit to the CBD	−	0.05964

Note: “+” is a positive indicator, the larger the value of the indicator, the higher the corresponding index value; “−” is a negative indicator; the larger the value of the indicator, the smaller the corresponding index value.

). The rationale for indicator selection is provided in the Appendix A.

The data were standardized, and the entropy weighting method was used to assign weights to various indicators of UGS supply and demand layers, to calculate the comprehensive UGS supply index and the comprehensive demand index for high-density built-up areas (z_i). The equations are as follows:

$$e_j=-{\displaystyle \frac{1}{\mathit{ln}n}}{\sum }_{i=1}^n\left(y_{ij}\cdot \mathit{ln}{y}_{ij}\right)$$

(1)

$$y_{ij}=x_{ij}∕{\sum }_{i=1}^n{x}_{ij}$$

(2)

$$W_j=\left(1-e_j\right)∕{\sum }_{j=1}^m\left(1-e_j\right)$$

(3)

$$z_i={\sum }_{j=1}^m\left(W_j\cdot x_{ij}\right)$$

(4)

In the equation, e_j represents the entropy value of the j-th indicator; W_j represents the entropy weight of the j-th indicator; x_ij stands for the standardized value of the i-th indicator; y_ij represents the proportion of the i-th block in the j-th indicator; m represents the number of indicators, and n represents the number of blocks.

2.4.2. UGS Supply-Demand Matching and Coupling-Coordination Degree

The coupling-coordination model describes the degree of interaction and coordinated development between two systems, as well as the overall balance of regional development. The model consists of coupling degree, comprehensive evaluation index, and coupling-coordination degree. The coupling degree reflects how strongly the two subsystems interact during development, while the comprehensive evaluation index captures their overall weighted contribution to regional UGS status. Together, these two indices determine the coupling-coordination degree, which serves as an integrated measure of whether the subsystems are both interconnected and developing in a coordinated, high-quality manner. The equations are as follows:

$$C=\sqrt{\left[1-\sqrt{{\left(U_2-U_1\right)}^2}\right]}\times {\displaystyle \frac{U_1}{U_2}}=\sqrt{\left[1-\left(U_1-U_2\right)\right]\times {\displaystyle \frac{U_1}{U_2}}}$$

(5)

$$T=w_1{U}_1+w_2{U}_2, w_1+w_2=1$$

(6)

$$D=\sqrt{C\times T}$$

(7)

where C represents the degree of coupling; U₁ and U₂ represent the integrated supply index and integrated demand index, respectively, both ranging from [0, 1]; T represents the comprehensive evaluation index of supply and demand; w_i is the weight of each subsystem, with this study assuming that the two systems are equally important, hence w₁ = w₂ = 0.5; and D indicates the degree of coupling coordination, ranging between [0, 1], with higher values indicating stronger coordination and system balance, and lower values reflecting weaker, less balanced development.

The z-score method is used to standardize the comprehensive supply index and comprehensive demand index, and quadrant matching to classify the UGS supply-demand matching types within the study area. The formula is as follows:

$$a={\displaystyle \frac{a_i-\overline{x}}{s}}$$

(8)

where α is the standardized composite supply/composite demand; α_i is the supply index/demand index of the i-th subdistrict; $\overline{x}$ is the mean value of the study area; and s is the standard deviation of the study area.

2.4.3. The Lorenz Curve and the Gini Coefficient

The Lorenz curve and the Gini coefficient are widely used to measure the equity of resource distribution, and they are also applicable to the measurement of the equity of UGS [48]. According to the actual conditions of the study area, four types of vitality, namely urban comprehensive vitality, urban social vitality, economic vitality, and cultural vitality, are selected to represent different types of demand groups, supplemented by comparisons with the resident population. The Gini coefficient is used to quantify the degree of distributional equity: a lower value indicates a more equitable distribution of UGS resources, while a higher value reflects greater inequality.

2.4.4. Spatial Autocorrelation Analysis

Spatial autocorrelation analysis is commonly used to explore the spatial distribution characteristics, in which the global spatial autocorrelation tool is primarily used to analyze the overall system distribution pattern, typically represented by Moran’s I [49]. When I > 0, the system exhibits positive spatial correlation; when I = 0, there is no significant spatial correlation; and when I < 0, the system shows negative spatial correlation [50]. Local indicators of spatial association (LISA) are used to measure the state of local clustering status of each element within the system, often represented by local Moran’s I [51]. GeoDa1.22 software and ArcGIS10.4 were used to calculate Moran’s I of the coupling-coordination degree.

2.4.5. The Geodetector

The Geodetector is a statistical tool used to analyze spatial heterogeneity and is widely applied in geography. It assumes that if an independent variable significantly affects the dependent variable, their spatial distributions will be similar [52]. Unlike traditional regression analysis, Geodetector does not rely on linearity assumptions and is not affected by multivariate covariance [53].

First, the factor detector is used to identify the spatial variation in urban vitality and quantify the explanatory power (q-value) of each supply and demand indicator. The value of q ranges from 0 to 1, with values closer to 1 indicating stronger explanatory power. Statistical significance (p-value) is also tested. Second, the interactive detector assesses the combined influence of pairs of indicators on urban vitality by comparing their joint q-value with the individual q-values. This reveals the nature of the interaction between two factors, which can fall into one of five types: nonlinearly weakening, univariate nonlinear weakening, bivariate enhancement, independent, and nonlinear enhancement.

3. Results

3.1. Spatial Patterns of UGS Supply and High-Density Urban Demand

The spatial distribution of the UGS comprehensive supply indicator in the study area showed a decreasing trend from the center to the periphery, with higher levels in the west, north, and central zones, and lower levels in the remaining peripheral areas (Figure 3a). The highest level of UGS supply was found in the west and north, with 25% concentrated in Haidian, such as Xiangshan, Qinglongqiao, and Wanliu subdistricts; and 20% in Chaoyang’s Olympic Village and Maizidian subdistricts. Dongcheng District accounted for 25%. These subdistricts are densely populated with urban parks, themed gardens, greenways, green corridors, and other high-quality UGS, offering strong comprehensive service capabilities. In the east and south, the supply level of UGS was low and the layout was scattered, such as Shangzhuang, Wulituo, Fengtai, and Nanyuan subdistricts, etc. The UGS in these areas are mostly community parks, amusement parks, roadside green spaces, etc., which are small and have low park services. The eastern part of the study area has fewer UGS as a whole, and the spatial layout is seriously unbalanced. Detailed spatial distribution patterns and quantitative indicators of UGS provision are provided in Appendix A (Figure A1).

Comprehensive demand characteristics based on 11 high-density city indicators revealed a concentrated high-demand zone in the central areas, with a decreasing trend towards the periphery, mirroring the high-density development in central Beijing (Figure 3b). In Chaoyang District, subdistricts like Xiaoguan, Xiangheyuan, Zuojiazhuang, Sanlitun, Jianwai, and Hujialou exhibit strong demand, driven by their roles as administrative, diplomatic, and international residential hubs. Parts of Dongcheng and Xicheng Districts also show high demand, serving as core areas for political, cultural, and international interactions, and designated for historical and cultural preservation. Subdistricts of Zhongguancun, Yanyuan, Xinjiekou, and Financial Street in Haidian District are also at a high level of comprehensive demand due to carrying higher education institutions, commercial hubs, and platforms for cultural and technological construction. The northwest, west, southwest, and east regions feature extensive parks and ecological areas, serving as Beijing’s primary green belt regions with relatively underdeveloped infrastructure and lower comprehensive demand levels. Detailed spatial distribution patterns and quantitative indicators of high-density urban demand are provided in Appendix A (Figure A2).

3.2. Coupling Coordination and Spatial Matching Characteristics of UGS Supply and Demand

Based on the preceding analysis, the spatial coupling and matching of UGS supply and high-density urban demand were assessed using the quadrant matching method and the coupling-coordination degree model. A significant imbalance was observed in supply-demand matching (

Table 2. UGS supply and demand matching types.

Supply and Demand Matching Type	Quadrant	Percentage
High-high balance	First quadrant	12.42%
High-low override	Second quadrant	28.42%
Low-low balance	Third quadrant	48.35%
Low-high lag	Forth quadrant	10.81%

, Figure 4a). The low-high type accounted for 10.81% of areas, primarily in central and northwestern subdistricts with higher population, building, and road densities, diverse land use, and insufficient UGS supply. High-low areas comprised 28.42% of the population, mainly in northern and western subdistricts, featuring large natural parks such as Olympic Forest Park, the Summer Palace, Yuanmingyuan, Fragrant Hills Park, and Beijing West Mountain National Forest Park, which provide abundant UGS resources. The low-low type covered 48.35% of the region, mainly in the southern, eastern, and northwestern peripheries, characterized by limited UGS supply and low urban development, including dense hutong areas within core urban zones. The high-high type accounted for 12.42%, concentrated in the core urban area, with developed urban parks and greenway systems that ensure high UGS accessibility in high-density built-up areas.

The coupling-coordination degree analysis revealed (

Table 3. Classification types of coordination.

Coupling-Coordination Types	Coupling-Coordination Degree	Numerical Interval	Percentage
			Single	Total
Imbalance degradation	Extreme Disorder	[0.0, 0.1)	0	63.29%
	Severe Disorder	[0.1, 0.2)	3.09%
	Moderate Disorder	[0.2, 0.3)	12.07%
	Mild Disorder	[0.3, 0.4)	48.13%
Balance transition	Near Disorder	[0.4, 0.5)	28.18%	36.57%
	Reluctant Coordination	[0.5, 0.6)	8.39%
Coordinated development	Primary Coordination	[0.6, 0.7)	0	0.14%
	Moderate Coordination	[0.7, 0.8)	0.14%
	Good Coordination	[0.8, 0.9)	0
	Quality Coordination	[0.9, 1.0)	0

) a general imbalance between UGS supply and demand in the study area, with no regions achieving full coordination (Figure 4b). As many as 63.29% of the areas were in the imbalance degradation category, distributed in the peripheral regions of the study area. Severely imbalanced areas were mainly clustered in the eastern and northwestern parts, with a few scattered on the edge of the core urban area. Mild areas occupied the largest portion, at 48.13%, respectively. Moreover, only one neighborhood showed moderate coordination, located in the Xiaoguan subdistrict, Chaoyang District. Overall, the spatial distribution of coordination showed the highest values in the central part, with higher values in the north than in the south.

3.3. Distribution of Vitality in High-Density Built-Up Areas

The comprehensive urban vitality in the study area exhibited a pattern of high centrality and low peripherality (Figure 5). While all vitality dimensions generally declined from the center outward, notable differences emerged in the spatial distribution of high and low values across specific dimensions.

High values of social vitality were concentrated in the central areas of the study area, mainly including Haidian, Chaoyang, and Fengtai Districts. At the subdistrict level, numerous core business districts offer extensive commerce, employment, and leisure spaces, attracting large crowds. Subdistricts like Beixinqiao, Sanlitun, Dongzhimen, Hujialou, and Panjiayuan, etc. have high population density and well-supported life-service facilities, exerting strong radiating effects on residents of surrounding subdistricts in terms of life consumption services, thus have stronger social vitality. In the central areas of Dongcheng and Xicheng Districts, subdistricts such as Shichahai, Jianguomen, and Xichang’an Street are political and cultural core areas, with a high proportion of government and public service facilities. However, they lack municipal or district-level supporting facilities for commerce, entertainment and recreation, and living services, making it difficult for crowds to gather, resulting in a few areas of low vitality.

The highest values of economic vitality were concentrated in Xichang’an Street and Donghuamen subdistricts in the center, Olympic Village and Asian Games Village subdistricts in the north, Sanlitun and Hujialou subdistricts in the east, and Huaxiang subdistrict in the south. These areas are dotted with the core business districts which have generated significant consumption and financial attractiveness through traditional businesses, large-scale shopping centers, integration with trendy culture and technology industries, and proximity to famous attractions like Tiananmen Square and the Forbidden City. The sub-high-value areas were concentrated in the parts of Haidian, Fengtai, and Chaoyang Districts that are close to the core urban areas. In addition, in the central study area, some subdistricts in Dongcheng, Xicheng, and Fengtai Districts, are mainly dotted with cultural service facilities and residential communities of a certain scale, showing relatively lower economic vitality.

The spatial distribution of cultural vitality decreased from the center to the surroundings, with a clear concentration along the east-west central axis, particularly in areas rich in cultural facilities in Haidian and Chaoyang Districts. College Road, Yanyuan, Tsinghua Park and Garden Road subdistricts in Haidian District are home to numerous colleges and universities, libraries, and universities for the elderly, while Jiuxianqiao, Xiaoguan, and Hepingjie subdistricts in Chaoyang District are clustered with contemporary art galleries, art museums, exhibition halls, and museums, showcasing strong cultural appeal. In contrast, the peripheral areas of the study area suffer from less cultural facility support, which correlates with a comparatively lower level of cultural vitality.

3.4. Balance-of-Differences Analysis of UGS Supply

We plotted the Lorenz curves of groups with different vitality types’ access to UGS integrated supply (Figure 6). In general, the Lorenz curves for all vitality types and the resident population showed clear deviation from the absolute equality curve, indicating unequal and highly polarized UGS distribution. Among them, the Gini coefficient of the comprehensive vitality group (0.277) was lower than that of the resident population (0.296), suggesting an improvement in the fairness of UGS resource allocation under the influence of vitality. Among the three single vitality types, the economic vitality group has the highest Gini coefficient (0.311), with the most severe inequality in UGS distribution; followed by social vitality (0.289), and cultural vitality was the lowest (0.247), indicating that culturally influenced groups may enjoy relatively fairer UGS access compared to social or economic vitality groups.

3.5. Spatial Autocorrelation Analysis of the UGS Supply-Demand Coupling-Coordination Degree

The Moran’s I of the UGS supply-demand coupling coordination in the study area was 0.259, indicating that the coordination was non-random. The local Moran’s I spatial autocorrelation (LISA) clustering map was generated using ArcGIS10.4 software. Significant spatial variation in coupling coordination existed among various districts within the study area. The high supply-high demand clustering pattern was particularly prominent in parts adjacent to the core areas of Haidian, Chaoyang, and Fengtai Districts (Figure 7).

3.6. Spatial Autocorrelation Characteristics of UGS Supply-Demand Coupling Coordination and Urban Vitality

The bivariate LISA plot illustrated the spatial correlation between the UGS supply-demand coupling-coordination index and the urban comprehensive vitality. Strong spatial associations were mainly concentrated on the east and west sides of the study area, with fewer clusters in the central region (Figure 8a). The “High-High” and “High-Low” cluster of areas corresponded to “mild imbalance” and “approaching imbalance” degrees of UGS supply and demand coupling coordination, accounting for 52.27% and 47.73%, respectively. While the degree of UGS supply-demand coupling coordination in the “Low-Low” and “Low-High” cluster of regions was mainly “severe disorder”, “moderate disorder”, “mild disorder”, and “near disorder”, accounting for 7.05%, 31.70%, 55.26%, and 5.99%, respectively (Figure 8b,c).

(1)

The “High-High” clusters accounted for a relatively small proportion and were mainly distributed in the central areas of the study region. In Haidian District, they were found in subdistricts like Xueyuan Road, Zhongguancun, Huayuan Road, and Beixiaguan, characterized by a dense and diverse UGS layout. These areas, focused on ecological protection and quality improvement, also feature a high level of UGS supply-demand coordination, with abundant educational and cultural facilities, such as universities and science parks. In Chaoyang District, “High-High” clusters were found in subdistricts like Dongsi, Chaowai, and Hujialou, where UGS are well-distributed and economic activities are active. These areas prioritize street-level greening and quality improvement of UGS.

(2)

The “Low-Low” clusters comprised the largest proportion and were located on the east and west sides of the study area, including some subdistricts in Chaoyang, Haidian, Shijingshan, and Fengtai Districts. On the western side, although large-scale forest and waterfront parks exist, their limited accessibility and proximity to the ecological control zone result in lower urban development and vitality. On the eastern side, UGSs are fewer and mostly located within restricted or ecological control zones, contributing to lower urban infrastructure development and vitality compared to the central areas.

(3)

The “Low-High” clusters represented the smallest proportion, only found in the central part adjacent to the study area in the Sanlitun and Hujialou subdistricts of Chaoyang District. This is due to their location within the centers of important commercial and economic activities, with strong economic activities within the subdistricts, resulting in an imbalance between supply and demand of UGS in the area, and the supply level of UGS is extremely low, while the high-density urban demand is extremely high.

(4)

The “High-Low” clusters were characterized by a concentration in the northwest of the study area, including Sujiatuo and Xiangshan subdistricts in Haidian District, Sunhe subdistrict in Chaoyang District, and Jinding Road subdistrict in Shijingshan District. These areas offer abundant high-quality forest parks, scenic spots, and suburban parks, offering a good UGS supply. Their proximity to major science and technology and industrial zones contributes to moderate urban demand, resulting in a relatively balanced supply-demand situation. However, their distance from central urban areas leads to lower overall vitality, forming the “High-Low” aggregation pattern.

We also performed bivariate local spatial autocorrelation analysis of the three single dimensions of social, economic, and cultural vitality with the UGS coupling-coordination degree, respectively (Figure 9). The results showed that social and cultural vitality exhibited spatial clustering patterns similar to those of integrated vitality, with Moran’s I of 0.422 and 0.381, respectively, for a significant positive correlation. The Moran’s I of economic vitality and coupling-coordination degree was −0.149, and the spatial correlation was less significant.

3.7. Factor Detection of UGS Supply and Demand Indicators for Urban Vitality

According to the factor detector (

Table 4. Factor detection results of urban vitality. *** denotes significance at p < 0.001 level.

Supply and Demand Indicator Layer Factors	Social Vitality	Economic Vitality	Cultural Vitality	Integrated Vitality
UGS distribution density	0.45 ***	0.12 ***	0.28 ***	0.34 ***
UGS coverage	0.06 ***	0.15 ***	0.08 ***	0.05 ***
UGS Recreation Opportunity Index	0.34 ***	0.04 ***	0.27 ***	0.40 ***
UGS Service coverage overlap ratio	0.29 ***	0.03 ***	0.17 ***	0.31 ***
Location entropy of UGS service per capita	0.03 ***	0.05 ***	0.03 ***	0.04 ***
Entropy of UGS location per capita	0.47 ***	0.15 ***	0.39 ***	0.32 ***
population density	0.84 ***	0.21 ***	0.62 ***	0.58 ***
POI density	0.81 ***	0.22 ***	0.77 ***	0.63 ***
Building density	0.74 ***	0.16 ***	0.56 ***	0.55 ***
Average number of building stories	0.48 ***	0.13 ***	0.41 ***	0.29 ***
Intersection density	0.25 ***	0.02 ***	0.23 ***	0.29 ***
Land use mix degree	0.79 ***	0.13 ***	0.76 ***	0.54 ***
Road density	0.41 ***	0.05 ***	0.39 ***	0.35 ***
Bus stop density	0.74 ***	0.31 ***	0.63 ***	0.47 ***
Metro station density	0.60 ***	0.14 ***	0.50 ***	0.51 ***
Distance to administrative centers	0.46 ***	0.07 ***	0.26 ***	0.36 ***
Distance to CBD	0.38 ***	0.02 ***	0.18 ***	0.37 ***

), the vitality of the three dimensions and the comprehensive vitality were significantly correlated with each indicator of the UGS supply and demand layer (p < 0.001). The explanatory power of the demand layer factors was generally stronger than that of the supply layer factors. In the demand layer, the explanatory power of population density, building density, poi density, bus stop density, metro station density, and land use mix degree were stronger. In the supply layer, the explanatory power of UGS distribution density, UGS recreation opportunity index, UGS service coverage overlap ratio, and location entropy of UGS per capita was stronger. The impact strengths of these factors varied across different dimensions of vitality. In the demand layer, population density has the greatest impact on social vitality, POI density has the greatest impact on both cultural vitality and integrated vitality, and bus station density has the strongest impact on economic vitality; while in the supply layer, the influence of location entropy of UGS per capita was the strongest among all single vitality dimensions. UGS Recreation Opportunity Index has the strongest influence in the integrated vitality dimension.

The detection of urban vitality on the supply and demand criterion layer showed the same trend as the indicator layer, with the explanatory power of the demand layer higher than that of the supply layer as a whole (Figure 10a). Among them, the diversity factor X6, density factor X4, and crowd factor X3 exhibited greater explanatory power, indicating that characteristics of high-density urban demand are the main influencing factors on urban vitality. Within the supply layer, the UGS uniformity X2 had higher explanatory power than that of the UGS quantity factor X1, suggesting that the equitable distribution of UGS contributes more to urban vitality than the aggregation of density and quantity. An exception was observed in the detection of economic vitality, where the explanatory power of the UGS quantity factor was stronger than that of the UGS uniformity factor. This may be due to a preference in economic activities for commercial districts with well-maintained green environments [54].

According to the analysis of urban vitality within the framework of supply-demand criteria layer detection, the results showed two types: bivariate enhancement and nonlinear enhancement (Figure 10b–e), the interaction of the effects of each factor was enhanced. Interactions among criteria layer factors demonstrated varying impacts on different facets of urban vitality. Specifically, “X population ∩ X diversity” showed the strongest explanatory power for social vitality, “X number of UGS ∩ X accessibility” for economic vitality, “X design ∩ X diversity” for cultural vitality, and “X number of UGS ∩ X density” for comprehensive vitality. The above results indicated that dominant interactions are mainly the result of the interaction between the dominant factor detected by the criterion layer factors and other factors.

It is noteworthy that certain factors only exhibited effectiveness when combined with other factors. In this study, UGS quantity and UGS uniformity emerge as typical factors, with their explanatory power in composite vitality detection relatively lower for UGS quantity (q = 12.77%) and UGS uniformity (q = 31.30%). In contrast, X UGS quantity and X UGS uniformity showed a linear bivariate enhancement in the interaction probes of X diversity, respectively, and showed a stronger explanatory power for comprehensive vitality (q = 71.99% and 70.42%). In the interaction detection of the three single dimensions of vitality, such as “X density” and “X design” with “X UGS quantity” and “X UGS uniformity”, the interactions of “X density” and “X design” with “XUGS quantity” and “XUGS uniformity” showed nonlinear enhanced effects, respectively, indicating that “density” and “design” enhance the impact strength of these two UGS single factors. These findings suggested that UGS quantity and UGS uniformity have relatively significant effects only in conjunction with factors such as diversity, density, and design of the built environment. Therefore, a comprehensive consideration of various dimensions of UGS supply and demand is necessary to leverage synergistic gains for urban vitality, requiring tailored strategies for optimizing UGS and enhancing urban vitality based on specific contexts.

4. Discussion

4.1. Selection of Model and Indicators

This study integrated a novel framework linking UGS supply and demand with urban vitality, offering fresh insights for UGS planning in high-density cities.

Previous UGS studies have typically emphasized either the supply of UGS or its use by resident groups [55,56]. Few have addressed the alignment and synergy between UGS services and public demand in high-density urban areas. This study develops a comprehensive evaluation framework to measure UGS supply and demand, selecting 17 relevant indicators for high-density cities. We used the entropy weight method to reduce subjectivity in weighting. The UGS supply-demand coordination was assessed through the coupling-coordination model. For urban vitality, we integrated multi-source data (e.g., POI data, heat maps, and night lighting data) to quantify social, economic, cultural, and overall vitality.

We further investigated how UGS supply-demand dynamics interact with urban vitality, assessing equity differences in UGS enjoyment across vitality types. We also examined the spatial clustering and divergence of UGS supply-demand factors and vitality, along with their independent and combined effects on vitality. The findings support the robustness of our analytical framework.

4.2. UGS Supply and Demand and Its Association with Urban Vitality

The findings confirm the inequitable distribution of UGS in high-density urban environments and demonstrate its impact and association with vitality:

(1)

In central Beijing, UGS supply showed a spatial pattern with higher availability in the central, western, and northern regions, while demand was highest in the central area, decreasing towards the periphery. This finding aligns with previous studies [57].

(2)

The coordinated development of UGS in the study area is concerning, with 63.29% of zones identified as imbalanced and degraded. This imbalance can be attributed to urban density and functional distribution, which exacerbate inequitable UGS allocation [58,59].

(3)

Specifically, 60.77% of areas achieved balanced UGS supply-demand coordination, while 28.42% experienced oversupply and 10.81% faced undersupply. Central areas, with higher construction density, featured smaller and more fragmented UGS, while northern areas tended to feature larger and more contiguous UGS, leading to oversupply in those zones.

(4)

Compared to groups defined by static demographic data, those identified through composite vitality metrics—reflecting dynamic behavior patterns—tended to experience more equitable access to UGS resources. Among the single-dimension vitality groups, cultural vitality showed the greatest equity in UGS access, followed by social vitality, with economic vitality exhibiting the least access. These groups, influenced by vitality, were better able to select spatial environments suited to their needs [60]. Moreover, disparities in UGS quality highlight the need to consider the specific modes of access and usage preferences of different vitality groups [61,62].

(5)

The UGS supply-demand system exhibited a positive agglomeration effect, with high-high clusters concentrated in subdistricts in central Haidian, Chaoyang, Dongcheng, Xicheng, and Fengtai Districts. These subdistricts and adjacent areas exhibited high coupling-coordination values, underscoring the influence of spatial proximity.

(6)

A significant correlation was found between UGS supply-demand coupling coordination and urban vitality. In central districts like Haidian, Chaoyang, and Fengtai, high-high clusters of UGS coordination were associated with comprehensive vitality, while low-low clusters predominated in peripheral areas. Social and cultural vitality appeared to increase with better UGS supply-demand coordination, whereas economic vitality showed a potential negative correlation [63]. Despite the indication of this possibility, further discussion is warranted regarding the balance between UGS supply and demand and economic vitality [64]. Economically prosperous areas often prioritize economic gains over the provision of green spaces. In such areas, urban green spaces (UGS) typically face a trade-off between economic returns and social equity, leading to disparities in availability and accessibility [65]. Despite better access to parks for high-income groups, the overall quality and equitable distribution of UGS in these areas may still lag, exacerbating spatial inequality [66]. These findings highlight the need for urban policies that balance economic development with equitable UGS provision to promote both environmental and social sustainability.

(7)

Concerning the strength of the influence of UGS supply and demand on urban vitality, the demand for high-density built-up areas was higher than the UGS supply. Factors such as diversity, density, population, and UGS uniformity exert strong influences on the spatial differentiation of urban comprehensive vitality at both the supply and demand levels. At the same time, UGS quantity and UGS uniformity demonstrated significant effects on vitality when combined with factors such as the diversity of the built environment, whereas their impacts as single factors were weak. This highlights the synergistic gain effect of UGS supply-demand has a more obvious impact on the role of urban vitality.

4.3. Optimization Strategy of UGS Supply and Demand Based on Vitality Guidance

In recent years, the balanced and equitable distribution of UGS has become a priority in high-density urban planning. Understanding the coordination between UGS supply and demand is essential for urban sustainable development [67]. Well-connected green networks generate a positive feedback loop: healthier ecosystems and improved microclimates foster vibrant communities, economic prosperity, and cultural development, thereby enhancing urban adaptive capacity [68]. This synergy between ecological and socioeconomic systems has gained global recognition. Within our supply-demand vitality framework, effective UGS management promises to significantly contribute to urban sustainability while strengthening the resilience of high-density cities like Beijing to address both current and future challenges.

Based on the findings, we offer several recommendations for planning strategies.

(1)

Expanding the coverage of UGS services to the public from both UGS quantity and UGS uniformity. The study showed that the comprehensive supply capacity of UGS in most subdistricts in downtown Beijing is lower than the urban comprehensive demand, indicating insufficient UGS service supply (Figure 4). In the Xueyuan Road subdistrict, Haidian District, for example, a UGS coverage increase of 5% would increase the coupling-coordination degree by 0.4%, assuming the other indicators remain unchanged. The Beijing Major Infrastructure Development Plan for the 14th Five-Year Plan (2016–2035) proposes the establishment of a more comprehensive infrastructure system, which includes advancements in both green ecology and urban public transportation networks.

According to the evaluation indicators, selectively increasing the amount or improving the quality of UGS can enhance service coverage and spatial equity [69]. For example, in core subdistricts such as Sanlitun, Hujialou, Jianguomen, and Dashila (Dongcheng and Xicheng), where demand outpaces supply or slight mismatches persist despite high-level matching, refined greening strategies are needed. These include establishing pocket parks and upgrading existing green spaces, which is particularly vital in high-density areas with limited land availability [70]. Evenly distributed small green patches offer critical ecosystem services, including microclimate regulation, stormwater retention, biodiversity enhancement, and social interaction [71,72]. Moreover, Prioritizing greenway development to link fragmented green spaces can further improve accessibility and promote public health and well-being.

In outer-core subdistricts such as Zizhuyuan, Taiyanggong, Hepingli, and Desheng—where UGS quantity exceeds demand yet spatial imbalance persists, efforts should focus on green space quality, connectivity, and user equity, rather than simply meeting quantitative targets. Improving accessibility often leads to higher environmental satisfaction [73]. Strategies such as adding entry points, enhancing pedestrian and cycling networks, constructing multifunctional greenways, and encouraging participatory design—guided by community feedback—can improve the openness and sustainability of communities [74].

In peripheral subdistricts outside the Fifth Ring Road in Fengtai, Chaoyang, and Haidian, where both supply-demand coordination and balance remain low, planning should focus on building large-scale ecological parks in tandem with urban renewal and spatial expansion. This approach reinforces ecological foundations while guiding future urban growth. Such investments can remedy historical deficiencies, boost environmental carrying capacity, and reserve ecological space for sustainable urban expansion. Newly constructed or enhanced large UGS can address the anticipated ecological needs associated with population growth and densification. They also serve as ecological anchors, providing water conservation, biodiversity preservation, local climate regulation, and improved recreational experiences. To strengthen ecological continuity, the development of green corridors and connected greenway networks is essential. These linkages can integrate existing and planned green spaces into cohesive ecosystems, helping bridge environmental and service disparities between central and peripheral areas, and supporting balanced urban sustainability.

Additionally, the level of UGS supply is also affected by the road network. For instance, in the southern part of the study area, the accessibility of UGS is primarily constrained by the efficiency of public transportation and the layout of transportation routes, thus improving the public transportation system could enhance connectivity and accessibility of UGS in these regions [75].

(2)

Urban planners should balance UGS supply and demand by focusing on supply and demand layer factors that affect different dimensions of vitality. In areas where both UGS coordination and overall vitality are low, or where UGS coordination is relatively high but vitality remains weak, the core challenge lies in effectively stimulating local vibrancy and enhancing the actual utility of green spaces. One key strategy is to encourage population mobility. Planners and policymakers should integrate population scale, demographic structure, and activity needs of potential UGS users into spatial planning. For instance, in the Jinzhan subdistrict, an increase in population by 50,000 can improve the coupling-coordination level by 0.01%, assuming other indicators remain unchanged. Furthermore, regional development strategies should drive population concentration and economic prosperity by attracting employment, optimizing land functions, and introducing green facilities. For example, research has shown that installing rooftop gardens can raise nearby property values by approximately 11% [76]. Such improvements can enhance land value, expand the local tax base, and support small business development. In addition, UGS projects generate employment opportunities in the design, construction, and maintenance sectors. Therefore, local governments are encouraged to integrate parks with commercial and tourism functions, leveraging the synergy between ecological investment and urban vitality to strengthen regional attractiveness [77].

In areas where UGS coordination is weak but vitality remains high, planning efforts should focus on supply-side improvements, including increasing the UGS quantity and improving the UGS uniformity to expand service coverage. However, it is essential that UGS development pay close attention to social equity and proactively address the risk of green gentrification [78]. Broader studies in environmental sustainability have shown that environmental stress tends to have more detrimental effects in socioeconomically disadvantaged areas [79]. This suggests that when greening initiatives aim to boost vitality without accounting for existing disparities in human capital or policy fairness, the benefits of green spaces may fail to reach all residents—and may, in fact, exacerbate existing inequalities. Consequently, vitality-oriented UGS strategies should prioritize underserved communities, enhance community organizational capacity and environmental literacy, and ensure that marginalized groups have a meaningful voice in planning and management processes. Aligning green space development with progressive social policy can promote inclusion and shared benefits. Furthermore, it is important to recognize the connection between social, economic, and cultural development and natural conditions, and to proactively enhance land-use diversity by integrating elements of culture, leisure, economy, sports, and recreation with ecology, which in turn enhances the living experience and vitality benefits for local residents [80].

(3)

The spatial layout of UGS should follow a multi-scalar strategy to enhance adaptability and align with the specific usage demands of different vitality zones. Existing studies have shown that variations in socioeconomic status, cultural background, life stage, and activity patterns across urban regions shape residents’ preferences for UGS types and functions, which in turn influence the form and quality of UGS provision [81,82].

In areas with a high distribution of cultural vitality, where schools, educational institutions, and cultural media organizations are concentrated, UGS tends to be used more frequently by cultural professionals. In these contexts, incorporating cultural elements into green space design can help optimize supply-demand coordination. Examples include outdoor art exhibition areas, small open-air theaters, culturally themed trails, or quiet zones for reading and creative work [83].

In economically vibrant areas with limited UGS availability, greater emphasis should be placed on integrating UGS with adjacent commercial spaces. This may involve the creation of convenient pedestrian connections, the introduction of small commercial amenities such as coffee kiosks, or embedding green elements—like pocket parks, green walls, and rooftop gardens—into business districts to enhance environmental appeal and employee well-being. In land-constrained zones, neighborhood-scale greening should be actively pursued to maximize UGS coverage within limited space [84]. From a sustainability perspective, such interventions can improve the microclimate and air quality of commercial districts, while creating more pleasant environments that attract investment and consumption, thereby supporting the local economy.

In areas with high social vitality, UGS should prioritize public service functions that meet residents’ needs for daily recreation, social interaction, and health. This includes providing fitness facilities, entertainment areas, seating, and barrier-free pathways [85]. Furthermore, encouraging community participation in the planning, construction, and maintenance of UGS—such as through community gardening projects—can improve local responsiveness, promote environmental awareness, and strengthen social cohesion. From a resilience perspective, these spaces can also serve as safe outdoor areas during public health emergencies and act as temporary shelters in times of disaster.

4.4. Contributions, Limitations, and Future Perspectives

This study makes four key contributions to the existing literature. First, we provide a basis for fine-grained UGS management. By setting the study unit at the subdistrict level, we closely align with actual living spaces, addressing the common omission of smaller UGS types, such as roadside greenery and pocket parks [59]. We incorporated them through POI-based classification methods that are both objective and practical. Second, we constructed a high-density urban demand indicator system, using multi-source data, which forms a comprehensive evaluation system for UGS supply-demand coordination. Third, we proposed a quantitative framework for urban vitality across social, economic, and cultural dimensions, assessing the equity of UGS access for different vitality groups. Fourth, we analyzed the impact of UGS supply-demand coordination on vitality and provided targeted strategies for optimizing UGS layouts in under-served areas.

This study also has certain limitations. First, while focusing on spatial heterogeneity in Beijing’s central urban area, the generalizability of the findings to peripheral areas of Beijing or other cities warrants further investigation. Large-scale ecological green spaces in suburban areas may differ significantly in both function and impact on urban vitality compared to the smaller-scale green patches commonly found in central districts [86]. Second, the quality of different UGS types should be considered in the evaluation system. Green belts and corridors differ from other green spaces in qualities such as water scale and vegetation coverage, which may influence their service provision quality [87]. These distinctions were not examined due to data constraints. Additionally, social vitality was represented by short-term mobile phone signaling data, and cultural vitality relied on POI data, which may not fully reflect real-time or group-specific behavioral patterns, including user preferences for different types of UGS [88]. Lastly, although current analyses reveal systematic relationships between UGS supply–demand dynamics and urban vitality, these relationships have not been examined over time. Longitudinal analyses could provide deeper insight into how vitality patterns respond to policy changes or major events.

Future research should extend this study across diverse geographic contexts, including urban–rural gradients and multiple cities. To better capture user needs, more detailed data on UGS attractiveness and the usage preferences of individual vitality groups could be gathered through surveys and social media analytics, with greater emphasis on quality-related indicators. For regions or UGS types identified as particularly sensitive to supply–demand imbalances and vitality dynamics, long-term monitoring is recommended to examine how changes in vitality influence planning and adaptive management. We also plan to incorporate temporal dynamics into future analysis, exploring UGS–vitality relationships across diurnal, weekly, and seasonal timescales.

5. Conclusions

This study presents a framework to assess the relationship between urban green space (UGS) supply-demand coordination and urban vitality. In Beijing’s central districts, significant spatial disparities in UGS supply and demand were identified, with low coordination levels failing to meet the needs of high-density areas. Urban vitality exhibited similar spatial heterogeneity, resulting in unequal UGS access across vitality groups, with economically vital areas experiencing the least equitable provision. The combined influence of UGS supply and high-density demand on urban vitality was more impactful than individual factors. Notably, UGS uniformity had a stronger positive effect on vitality than UGS quantity. Based on these findings, we propose optimization strategies for aligning UGS supply with urban vitality types. These strategies offer innovative solutions for refining UGS management in high-density cities, contributing to the sustainable development goal of improving residents’ health and well-being while providing actionable insights for creating more equitable and vibrant urban environments.