Assessing Urban Park Equity in China Through Supply and Demand Balance: A Case Study of Wuhan City, China

Abstract

In the context of rapid urbanization and increasing awareness of environmental justice, ensuring equitable access to high-quality park services is crucial for promoting healthy lifestyles and sustainable urban development. This study investigates park equity in Wuhan, China, by developing a comprehensive evaluation index system that incorporates park quality and considers the heterogeneity of park demand among different population groups, particularly older adults. Using multi-source big data and spatial analysis, this study examines the patterns of park supply and demand and explores the causes of mismatch between them. The results find that the further away from the city center one is, the lower the park supply and demand value. The difference is that the decrease in demand is more moderate relative to supply, and the demand is less affected by the natural environment and urban construction. The findings reveal that park accessibility is closely related to urban infrastructure development and natural resources, with central urban communities enjoying better park supply and quality than peripheral communities. Additionally, the study uncovers that the elderly comprise a vulnerable group that needs more park services in urban China. There are still 28.25% of communities with an unmet demand for parks. The overall goal of the paper is to come up with policy recommendations for optimizing city park equity. It is found that the relieving strategies of park equity are different and should be based on local context, such as enhancing existing park service capacity in urban centers and prioritizing new park development in underserved suburban areas.

1. Introduction

Environmental inequity has emerged as a critical issue in urban planning and development, with concerns about the unequal distribution of environmental benefits and burdens among different socio-economic groups [1,2,3]. Urban green space has been regarded as a solution to environmental inequity. Urban green space is an important bridge between the natural environment and human society. It can provide diverse ecological and economic benefits, as well as ecological, cultural, educational, and emergency functions [1,2,3]. Urban green space contains various types of road greening, urban parks, urban forests, and so on. Among them, urban parks are green spaces that are open to the public and have specific service facilities. Compared with other urban green spaces, the main function of parks is recreation, providing residents with places for social interaction, leisure, and relaxation [4]. Therefore, urban parks are closely connected with the daily life of urban residents and are one of the important measures of the quality-of-life level of residents, which is closely connected to local quality [5,6]. Universal access to parks is part of the United Nations sustainable development goals. In this regard, addressing environmental inequity in urban parks requires a comprehensive understanding of the equitable distribution and accessibility of parks and their services for various population groups.

To understand urban equity and inequity of park services, a large body of literature has begun to explore the concept of park equity. For a long time, the equality of regional park area or park area per capita has been regarded in terms of equity [7,8,9]. However, while residents are entitled to equal access to all park resources within a regional unit, factors such as quality and accessibility can affect the actual benefit they receive [2,10]. Later, the concept of accessibility was introduced to the study of the spatial equity of public service facilities. Scholars often characterize the spatial equity of parks in terms of how easy it is for residents to reach the park from their place of residence [11]. But studies based on distance and accessibility tend to ignore the differences in service capacity and demand side of public facilities [12]. However, equity in park provision does not fully represent equity in park services. For example, studies in Shanghai, Harbin, the Mediterranean islands, and the Netherlands have demonstrated the mismatch between the provision of park services and the needs of residents [13,14,15,16]. Studies in the U.S. have found that minority groups, such as African-Americans, visit parks less often, even when more accessible [17]. Similarly, African-American and Hispanic/Latino residents feel that access to local parks is more of a problem than white residents [14,18]. Thus, the equity of park services as a public good provided by urban infrastructure should be understood from a supply and demand perspective, which implies not only that all people have equal opportunities to acquire services but also that all people have the ability to access services that meet their demands [19,20,21,22,23]. However, current studies either focus only on the supply side, measuring equity with quantitative indicators such as the number of parks, total park area, and park area per capita [7,8,9], or only assesses demand through surveys or interviews, usually using participatory methods and economic assessment techniques (such as willingness to pay), which suffer from a lack of generalizability and reproducibility [24,25,26]. Therefore, it is necessary to develop a quantitative park equity evaluation method that considers both supply and demand perspectives.

Scholars have proposed measures of park supply and demand. Park supply is defined as the availability of park services to residents within a specific range at a specific time [27], a definition that usually relies on park accessibility and park service capacity [28,29,30]. Studies have identified park accessibility, which implies the barrier that residents have to overcome to get to parks, as an important factor influencing park supply [23,31]. In terms of park service capacity, it is well proven that parks with larger areas can provide more services [23]. Specifically, studies have found that different types of parks also have different service capacities [23,26]. For example, studies in Beijing have found that people are willing to spend more time on the road on their days off to visit parks with specific themes for recreational activities [26]. Meanwhile, high-quality parks can provide more benefits, such as compensating for limited accessibility, compared to low-quality parks [32]. A few studies have focused on quality as a scarce yet critical element in park equity research and measured park quality [33,34]. For example, Larson et al. used the subjective perceptions of residents to measure park quality in their study [18,35], and Hughey et al. argued that the amount and type of infrastructure in a park could have an impact on park quality [34]. The quality of parks in these studies is usually measured by questionnaires and audit scales with respect to residents’ subjective perceptions, park configuration, and maintenance of green/blue/grey facilities [35]. However, studies have found that residents are attracted to more natural environments, more diverse landscapes, better facilities, and richer water bodies, but these dimensions have rarely been measured comprehensively and quantitatively in previous studies [36,37,38,39,40,41,42]. Thus, a comprehensive and quantitative approach to evaluating park service capacity is urgently needed.

On the demand side, studies usually evaluate park demand by using social media or census demographic data to reflect actual or potential visits [21,29,43,44,45]. Some scholars also use socio-economic indicators such as land development intensity and commercial service density to reflect demand from the physical attributes [23,46]. Relying on these indicators, they have identified the existence of vulnerable urban groups. For example, studies in the United States have shown that opportunities for low-income groups and people of color to visit parks are disproportionate to their demand for parks. This mismatch further contributes to social inequity [18]. Among these vulnerable groups, older people show a higher demand for parks in order to exercise and socialize [47,48]. In China, the aging phenomenon is serious, and the strong demand of the elderly group cannot be ignored; otherwise, it will seriously affect social equity [49,50]. While these current studies reveal the higher demands of older people and the status of unmet needs, there is still a lack of park equity metrics that consider the needs of vulnerable groups in their measurement and provide substantive guidance for planning.

Moreover, governments and urban planners are concerned about park equity and want to use limited resources to build a more nature-friendly and equitable city. Although much progress has been made in the current research, the existing findings are still relatively poor in providing recommendations for policymaking, and the implications and assistance for urban and rural planning remain unclear [51]. Some relevant studies suffer from directly ignoring the demand side and oversimplifying park equity, which may partly be cause by the difficulty of obtaining research data. Some relevant studies use methods based on surveys or interviews to evaluate park supply and demand [7,28,52,53,54]. However, this method is usually limited to a single or small number of parks and does not apply to city-level surveys. The emergence of big data, such as remote sensing and social media, has recently provided perspectives to approach these issues [55,56]. From a park supply perspective, remote sensing image data can be used to analyze the vegetation cover of a park from a global perspective [57], social media data with accompanying text and image information can provide a large number of images of the interior landscape of the park for machine learning, and textual information can be used to identify visitors’ satisfaction with the park [58,59]. And from a demand perspective, a large amount of cell phone signaling data can reflect the real visits of residents to a park [60,61]. Combining big data, supply–demand theory, and spatial analysis methods contributes to quantitative park equity studies based on actual supply and demand relationships.

Wuhan, as the economic, political, and cultural center of Central China, is experiencing rapid urban expansion [62]. Wuhan has good natural resources and comprehensive green planning. But similar to many large cities that are growing rapidly, there is still a huge loss of green space in Wuhan [63,64]. Therefore, there is an urgent need to improve the equity of parks through urban planning methods and to reduce the waste of green resources caused by the mismatch between park supply and demand. We interpret park equity from the perspective of supply and demand balance. The main objectives of this paper are as follows: (1) interpreting park equity from the perspective of matching supply and demand and constructing a framework for evaluating park supply and demand based on that perspective; (2) estimating park supply and demand in the study area and revealing park supply and demand patterns; (3) exploring the spatial relationship between park supply and demand and identifying areas with supply–demand mismatch; and (4) proposing policy recommendations for optimizing park equity.

2. Data and Methods

2.1. Study Area

Wuhan (113°41′–115°05′ E, 22°29′–31°58′ N) is the capital city of Hubei Province and the central city of central China. Wuhan is known as “the city of a hundred lakes,“ with rivers and abundant natural scenery resources [62]. The construction of urban parks in Wuhan developed in an unorganized manner before the 1930s, and parks were mainly private gardens, which did not belong to public open space in the modern sense. In 1929, Wuhan established the first urban park in a real sense. The government began to intervene in the construction of green space. However, at this stage, although new parks and green spaces were added in Wuhan, their addition was not systematic. It was not until after the mid-1990s that urban green space system planning was seen as an important part of urban ecological development planning, moving formally into the government-dominated stage. In the planning of green space, the government determined that the completion of “500 m to see green, 1000 m to see the garden, 2000 m to see the water” should be the goal of a green landscape. In 2021, the green coverage rate of built-up areas in Wuhan reached 43.07%, and the per capita green space area reached 14.82 square meters/person. It is not difficult to see that the construction of parks in the study area has made great progress, but there are still problems such as insufficient area, unfair layout, varying quality, and weak relevance. Overcoming these problems plays an important role in creating a livable urban ecological environment, building an equitable ecological infrastructure layout, and enhancing urban competitiveness. Most modern parks in Wuhan are built based on the original private parks and scenic areas, and most are located in the main urban area. Therefore, this study chooses to take the main urban area delineated by the Wuhan City Master Plan 2010–2020 as the study area. The total area of the study is 734.83 km². According to the Wuhan Unified Planning and Management Map (2016), and with reference to 2018 Wuhan satellite image data and Wuhan Urban Green Space System Planning (2003–2020), a total of 225 parks in the study area were identified In addition, the study area can be divided into an inner ring area, an inner ring to the second ring area, a second ring to a third ring area, and beyond the third ring area, containing 1229 communities. Since the construction of urban ring roads is inseparable from urban development, this study will further discuss the spatial equity of urban parks in the context of urban circle development patterns (Figure 1a). A statistical assessment of the demographic distribution and socio-economic environment of the study area would be helpful for subsequent research discussions. Based on the population distribution in the study area (Figure 1b,c), it can be seen that the communities in the Hankou and Wuchang districts have larger populations than the Hanyang district. Communities with high total populations in Hankou District are concentrated between the second and third ring roads, but this part of the community is dominated by a non-elderly population and residential use. Communities in the older urban areas within the second ring road have a higher proportion of elderly people, with the proportion of elderly groups mostly in the range of 18–41%. The degree of land development and commercial density were used to reflect the socio-economic conditions of the community (Figure 1d,e). Socio-economic conditions are better in the central part of the study area than in the peripheral areas. The degree of land development is higher within the third ring road. Important high-value commercial service clusters appear in the northern part of the area at the confluence of the Yangzi River and the Han River.

2.2. Data Sources

This study used three different datasets, except for the dataset identifying parks. First, 16 m resolution Gaofen-1 data were downloaded from the National Space Agency Earth Observation and Data Center (https://data.cresda.cn/, accessed on 10 March 2022). The image was taken on 28 September 2019. Then, the images were pre-processed and NDVI (Normalized Vegetation Index) was used to identify the greenery and the classification of trees, shrubs, and grasses. All the above processing was performed using ENVI 5.3 software. In addition, urban census data were used for demand calculations, which can be used to distinguish age groups. This paper uses the population data for Wuhan updated in 2015, with the community as the basic statistical unit. Finally, the building data and poi data were obtained from Gaode Map (https://lbs.amap.com/, accessed on 19 March 2020).

2.3. Methods

Figure 2 summarizes the research framework of this paper, which essentially views equity as the level of match between residents’ demand for park use and park supply. Urban parks provide recreational services to urban residents, so the community, as the basic unit for organizing the life of urban residents, is an appropriate unit for the study of park supply and demand [65]. Accurate measurement of park supply and demand is the basis for equity inquiry. With reference to existing research, supply is determined by a combination of park service capacity and park accessibility [28,29,30]. Different from the traditional method of determining service capacity based on area size, this study focuses on the impact of park type and park quality on park service capacity. The diversity of the needs of the elderly population was further emphasized when calculating park demand.

This framework was followed by spatial analysis based on park supply and demand measures: Firstly, the spatial mapping method is used to identify the supply–demand matching pattern and the spatial coupling coordination degree to discover the coordination degree of the supply–demand system. Then, policy priority intervention areas are identified synthetically based on the results of spatial mapping and coupling coordination degree.

2.3.1. The Calculation Method of Park Supply and Demand

Park Supply Calculation

Park supply is measured by park service capacity and accessibility. This study uses path planning based on Gaode Map to measure park accessibility. In previous studies, park service capacity considered area, type, water body, and vegetation [23]. The capacity of park services is improved by considering park area, park type, and park quality, in which quality not only includes water bodies and vegetation but also considers the naturalness of park form and the quality of park facilities. A more complete park supply evaluation index system was established. The calculation method of park quality is shown in

Table 1. Comprehensive quality evaluation system based on park morphology, hydrophilicity, naturalness, and facilities.

Evaluation Projects	Secondary Indicators	Meaning	Calculation Method
Park quality B3	Morphological index C1	The larger the morphological index, the fewer the traces of human disturbance in the park and the more stable the internal ecosystem.	$C_1={\displaystyle \frac{0.25P_{\mathrm{p}\mathrm{a}\mathrm{r}\mathrm{k}}}{\sqrt{M_{\mathrm{p}\mathrm{a}\mathrm{r}\mathrm{k}}}}}$ $M_{\mathrm{p}\mathrm{a}\mathrm{r}\mathrm{k}}$—park area; $P_{\mathrm{p}\mathrm{a}\mathrm{r}\mathrm{k}}$—park perimeter
	Hydrophilic index C2	The higher the water coverage and the greater the percentage of waterfront, the higher the quality of the park.	$C_2=0.5\ast {\displaystyle \frac{M_{\mathrm{w}\mathrm{a}\mathrm{t}\mathrm{e}\mathrm{r}}}{M_{\mathrm{p}\mathrm{a}\mathrm{r}\mathrm{k}}}}+0.5\ast {\displaystyle \frac{P_{\mathrm{w}\mathrm{a}\mathrm{t}\mathrm{e}\mathrm{r}}}{P_{\mathrm{p}\mathrm{a}\mathrm{r}\mathrm{k}}}}$ $M_{\mathrm{w}\mathrm{a}\mathrm{t}\mathrm{e}\mathrm{r}}$—water area inside the park; $P_{\mathrm{w}\mathrm{a}\mathrm{t}\mathrm{e}\mathrm{r}}$—circumference of water
	Nature index C3	The higher the green coverage and the more balanced the vegetation species, the higher the naturalness of the park.	$C_3=0.5\ast {\displaystyle \frac{S_{grass}+S_{shrub}+S_{tree}}{M_{\mathrm{p}\mathrm{a}\mathrm{r}\mathrm{k}}-M_{\mathrm{w}\mathrm{a}\mathrm{t}\mathrm{e}\mathrm{r}}}}-0.5\ast {\mathrm{G}}_{\mathrm{v}}$ $S_{grass}$,  $S_{shrub}$,  $S_{tree}$—area of grass, shrubs, and trees in the park; $G_v$—standard deviation of the area of trees, shrubs, and grasses
	Comprehensive facility index C4	The greater the number and variety of recreational facilities, the better the quality of the park.	Points are given for measurable facilities such as badminton courts by number and for non-measurable facilities such as running tracks by whether they are equipped or not.

. Specifically, park morphology index C1 is calculated from the patch shape index in the landscape index. Hydrophilicity index C2 considers a variety of situations, such as the inclusion of water systems within the park and the park being built on water. The hydrophilicity index is measured by a combination of the water body coverage rate and the percentage of the water shoreline. Nature index C3 not only focuses on the green coverage of the park, but also on the balance of trees, shrubs, and grasses within the park. Park facilities are an important component of park quality, and comprehensive facilities index C4 uses the visual interpretation method of remote sensing images to obtain the number of facilities and their presence or absence in the park.

Finally, park supply is calculated as follows.

$$E_s={\sum }_{r=1}^n{A}_1^r{A}_2^r$$

(1)

$$A_1^r=s^{-1}$$

(2)

$$A_2^r=0.557\ast B_1^r+0.122\ast B_2^r+0.321\ast B_3^r$$

(3)

$$B_3^r=0.102\ast C_1^r+0.269\ast C_2^r+0.213\ast C_3^r+0.416\ast C_4^r$$

(4)

where $E_s$ presents the park supply; $A_1^r$ is the accessibility of the community to the park $r$; and $\mathrm{s}$ represents the distance of the walking path from the community to the park as planned by Gaode Map. $A_2^r$ is the service capacity of park $r$ and $r$ is the service area covering all parks in that community ($r$ = 0, 1, 2, … $n$, $n$ is a natural number). This paper argues that residents located outside a park’s service area will not visit the park, so determining a park’s service area is important in determining whether residents can visit the park [6]. The service capacity of park $r$ is calculated by Equation (3), where $B_1^r$ and $B_2^r$ represent the area and type scores of the parks; the quantification method refers to the study by Liu et al. [23]. $B_3^r$ represents the quality of the parks, and the specific indicators and quantification methods are determined by Table 1 and Equation (4). The indicator weights of both Equations (3) and (4) are assigned by the entropy weight method (EWM).

Park Demand Calculation

The demand side favors previous studies that link three socio-economic indicators—population density, degree of land development, and density of commercial services—to community beneficiaries [23]. Furthermore, attention is paid to the special needs of the elderly in the context of China’s aging population, differentiating the heterogeneity of park demand among different age groups.

Basic age information and frequency of park visits were obtained through questionnaires, as in Appendix A, and the frequency of visits was used to quantify the degree of population demand. The frequency of visits was divided into five classes: 1 for daily visits and above, 0.75 for 3–4 weekly visits, 0.5 for 2–3 monthly visits, 0.25 for 2–3 quarterly visits, and 0 for lower frequencies. A total of 89 questionnaires were distributed, including 27 within the inner ring road, 25 within the inner ring road to the second ring road, 20 within the second ring road to the third ring road, and 17 outside the third ring road; 77 were valid, of which 35.06% were answered by the elderly. The results showed that the frequency of park visits by the elderly population was 0.86, and the frequency of park visits by the non-elderly population was 0.58. The results of the questionnaire indicated that there is a stronger demand for park use among older age groups.

$$E_d=0.668\ast B_4+0.169\ast B_5+0.163B_6$$

(5)

$$B_4=0.86\ast N_{<60}+0.58N_{<60}$$

(6)

Park demand is calculated according to Equation (5), where $B_4$ represents rigid demand quantified by population data; and $B_5$ and $B_6$ represent the degree of land development and commercial service density, calculated with reference to existing studies [23]. $B_4$ is calculated as shown in Equation (6), where $N_{<60}$, $N_{<60}$ represents the number of elderly and non-elderly groups in the community. It is worth noting that the results of the indicators are normalized to participate in the final calculation, and the indicator weights are assigned by the entropy weight method (EWM).

Regional Disparities in Park Supply and Demand

The Gini coefficient is widely used to examine the degree of unequal distribution of wealth and public infrastructure services. The Gini coefficient ranges from 0 (indicating perfect equality) to 1 (indicating perfect inequality), with lower Gini coefficients indicating a more equal supply or demand for parks within the range.

$$Gini=1-{\sum }_{i=1}^n\left(P_i-P_{i-1}\right)\left(E_i+E_{i+1}\right)$$

(7)

where $n$ is the number of communities in the study area, and the value of $i$ ranges from 1 to $n$. $P_i$ is the cumulative proportion of the communities and $E_i$ is the cumulative proportion of supply or demand for parks in the community.

2.3.2. Analysis of Supply–Demand Spatial Relationships

Supply and Demand Space Matching Identification

Spatial mapping is based on community units and can identify the spatial heterogeneity of park supply and demand systems [66]. The principle of spatial mapping is to superimpose the layers of two spatial objects located in the same area so as to establish the spatial correspondence between the objects [67]. In this study, a 3 * 3 color matrix is constructed to represent the interaction of park supply $E_s$ and demand $E_d$ [68], where the columns represent park supply and the rows represent park demand. The final park supply and demand pattern was classified into five types: severe undersupply, undersupply, relatively matching, oversupply, and excessive oversupply.

Priority Intervention Area Identification

Coupling coordination degree (CCD) can represent the coordination quality of interacting systems and reflect the coordination of different systems or elements interacting with each other [69]. Supply and demand of urban parks are two systems that depend on each other and influence each other. In order to explore the integrated development level and development synchronization of these two systems, CCD is calculated using SPSS 25 software. The spatial coupling coordination degree model is as follows.

$$C_{s-d}=\sqrt{{\displaystyle \frac{E_s\times E_d}{{\left(\frac{E_s+E_d}{2}\right)}^2}}}$$

(8)

$$T_{s-d}=\sqrt{\theta E_s\times \eta E_d}$$

(9)

$$D_{s-d}=\sqrt{C_{s-d}\times T_{s-d}}$$

(10)

$\theta , \eta$ are the weights of supply and demand. In this study, supply and demand are considered equally important, so the weights are all taken as 0.5. $T_{\mathrm{s}-\mathrm{d}}$ is the comprehensive coordination index of supply and demand; $D_{s-d}$ indicates the coordination index; and ${\mathrm{C}}_{\mathrm{s}-\mathrm{d}}$ denotes the coupling degree index, which takes a value between 0 and 1. The higher value of the coupling degree represents a higher degree of coupling between two systems. According to existing studies, CCD can be divided into five levels of [0–0.2), [0.2–0.4), [0.4–0.6), [0.6–0.8), and [0.8–1], representing severe disorder, general disorder, basic coordination, general coordination, and high coordination between the two systems. Using the results of the coupling coordination degree to overlay the supply–demand matching map, priority intervention areas of the policy can be determined.

3. Analysis of Park Supply and Demand

The park services available to community residents depend on how many parks are located around the community, the service capacity of those parks, and how easily community residents can reach those parks. The study found that the park supply decreases with increasing distance (Figure 3a). The Gini coefficient reflects the park supply disparity in the study area. The Gini coefficient values for communities outside the third ring road are much larger than those within the inner ring road, revealing that earlier built-up urban areas enjoy higher quality and more equitable park services (

Table 2. Park supply and demand results within different circles and their Gini coefficients.

Scope	Average_Supply	Gini Coefficient_Supply	Average_Demand	Gini Coefficient_Demand
Inner ring road	0.4239	0.0920	0.5504	0.0971
Inner ring road to the second ring road	0.3893	0.1528	0.5244	0.1056
Second ring road to the third ring road	0.3468	0.1867	0.4815	0.1395
Outside the third ring road	0.2301	0.3854	0.2272	0.5580

). The overall demand in the study area is high (Figure 3b). Communities within the inner city ring have a high proportion of the elderly population and a high demand for park use. Meanwhile, high-intensity urban land development and high-density commercial activities gathering also bring a great demand for park use in the old city [6]. Therefore, the inner ring road communities have the highest demand for parks (Table 2). Similar to the spatial distribution characteristics of park supply, park demand also meets a gradual decrease from the inner city ring to the outside [70].

However, one difference between supply and demand is that the demand variation between regions within the third ring is more moderate relative to the supply, except for the steep increase in demand variability outside the third ring, which also implies a more balanced distribution of residents’ demand for parks. Distinct from park supply, park demand is less affected by the distribution of natural conditions and infrastructure construction. This may be one of the essential reasons for the mismatch between supply and demand.

4. Analysis of Park Supply–Demand Patterns

4.1. Identifying Regions of Mismatch Between Park Supply and Demand

This study used spatial matching to discover the matching pattern of park supply and demand (Figure 4). Based on the results of supply and demand calculations, park supply and demand were categorized into low, medium, and high grades, respectively, using the natural breakpoint method. The grades of park supply were categorized as [0, 0.212), [0.212, 0.431), and [0.431, 1], and the grades of park demand were categorized as [0, 0.301), [0.301, 0.556), and [0.556, 1]. We identified a community as a relative match if its park demand and supply ranks were the same; a community with a one-rank difference between demand and supply was defined as an undersupply or oversupply community; and a community with a difference of two ranks between demand and supply was defined as a severe undersupply or excessive oversupply community. It is intuitively clear from the spatial mapping that the pattern of park supply and demand is well matched, with only 30 communities experiencing significant undersupply. A total of 28.25% of communities have an unsatisfied demand for parks, and 21.86% of communities have opportunities to have more park services than they need (

Table 3. Patterns of matching park supply and demand within different circles.

Scope	Severe Undersupply/Ratio (%)	Undersupply/Ratio (%)	Relatively Matching/Ratio (%)	Oversupply/Ratio (%)	Excessive Oversupply/Ratio (%)
Inner ring road	0/0	73/30.42	112/46.67	51/21.25	4/1.67
Inner ring road to the second ring road	5/1.23	113/27.9	216/53.33	65/16.05	6/1.48
Second ring road to the third ring road	21/3.76	134/23.97	276/49.37	113/20.21	15/2.68
Outside the third ring road	4/4.21	17/17.89	44/46.32	28/29.47	2/2.11
Total	30	337	648	257	27

).

Moreover, the research finds that the proportion of park demand met by residents of outer urban communities is better than that of inner cities. This is because although there is an adequate supply of parks in the central city, residents in the central area also have a much stronger demand for parks. The proportion of undersupplied communities decreases with urban expansion. Riverfront parks are the main source of park supply for the communities along the river. The high population density and urban construction level of the northern Hanshui community have created a higher demand for parks that cannot be fulfilled by the services of the riverside park, creating a belt of undersupplied communities.

Conversely, some communities in the study area are able to enjoy redundant park services, but the reasons for the redundancy are different. The natural environment was damaged and ecological resources were limited in the urban center during construction, resulting in low park service capacity in the area. However, because the local government values natural habitats and the environment, the number of parks built in the central city is high. The dense distribution of road networks in urban centers leads to residents being able to reach more than one park in a short period of time and enjoy park services that are in close distance. The surplus of park services in the central area is mainly due to the convenience of transportation and the advantage of the number of parks. Distinct from this, some peripheral urban communities enjoy a surplus of park services partly because of the lower demand for parks, such as in the Zhuankou industrial zone near the third ring road, and partly because of the superior natural ecological conditions. Such communities are distributed centrally around several lakes that Wuhan focused on protecting and developing, such as East Lake and Ink Lake.

4.2. Discovering Priority Management Areas

The study identified areas of mismatched park supply and demand, which are scattered and widely distributed. To better guide urban planning and management, further grading of policy priority intervention areas is needed. Coupling coordination degree was used to assist in the analysis. The CCD of the park supply and demand system is 0.6698, and the interaction and dependence between the two systems is high. However, the spatial CCD of the park supply and demand system is clearly divided by the third ring road, and the average CCD inside the third ring road is higher than outside the third ring road, implying that the supply–demand system of parks in this region still needs to be transformed from a disorderly to an orderly state (Figure 5,

Table 4. Degree of coupled coordination with respect to park supply and demand.

Scope	Severe Disorder/Ratio (%)	General Disorder/Ratio (%)	Basic Coordination/Ratio (%)	General Coordination/Ratio (%)	High Coordination/Ratio (%)
Inner ring road	0/0	6/2.5	4/1.67	218/90.83	12/5
Inner ring road to the second ring road	0/0	10/2.47	16/3.95	348/85.93	31/7.65
Second ring road to the third ring road	3/0.54	20/3.58	74/13.24	447/79.96	15/2.68
Outside the third ring road	14/14.74	41/41.16	10/10.53	30/31.58	0/0
Total	17	77	104	1043	58

).

The coupling coordination degree is overlaid with the supply–demand matching pattern to discover priority intervention zones (Figure 6). Although they are all undersupplied communities, the coupling coordination degree of the supply and demand systems in the outer second-ring road communities is worse compared to the inner city (Figure 6a,b). Among them, most of the poorly coupled communities around the third ring road are industrial parks. Among the communities where park supply and demand are relatively matched, the supply and demand systems of Tianxing Island at the edge of the study area, the logistics park on the east side of Tianxing Island, and the Fuzuling area are in a state of disorder (Figure 6c). Notably, the fragile balance of low supply and low demand in these regions can easily be broken and cannot be easily ignored. In general, the balance of supply and demand in industrial parks in the study area, including logistics parks and industrial parks, needs further attention.

Table 5. Park management zoning statistics.

Scope			Park Supply and Demand Matching Patterns
			Severe Undersupply	Undersupply	Relatively Matching	Oversupply	Excessive Oversupply
Coupling coordination degree (CCD)	severe disorder	I	0	0	17	0	0
	general disorder	II	0	7	15	41	14
	basic coordination	III	16	40	19	22	7
	general coordination	IV	14	290	550	183	6
	high coordination	V	0	0	47	11	0

shows the statistics of zoning management, where the smaller the value in the vertical columns, the worse the coupling coordination degree of supply and demand systems. Interventions in parks prioritize undersupplied areas, and interventions in undersupplied communities prioritize areas with a poor coupling coordination degree.

5. Discussion and Suggestions

5.1. Discussion

Behind people pursuing healthier lives, the demand for parks is growing rapidly. Park equity has received widespread attention. This study combines multi-source big data to construct a park equity evaluation index system, adding park quality evaluation indexes to the index system and taking into account the heterogeneity of demand of older groups [55,56]. Taking Wuhan as a study area, the main findings from this study are as follows.

First, on the supply side, it is revealed that park accessibility is closely related to urban infrastructure development. Residents of central cities with dense road networks usually have reduced difficulty in accessing parks. On the other hand, parks serve as one of the bridges between natural resources and human society, and except for a few community parks, most of the parks built are based on natural ecological environments, such as forest parks and lakeside parks [71]. The influencing factors of park service capacity, such as park area and park quality, mainly depend on a park’s specific natural attributes. The earlier built-up urban areas enjoy higher-quality and more equitable park services. This may be due to the tendency of governments to allocate limited resources to central city areas, which are more likely to produce greater benefits. This tendency is not conducive to a reasonable and equitable distribution of public resources.

On the park demand side, a strong demand for parks by older adults is found to have reshaped park demand patterns. Traditional studies that focus only on population density or numbers assume that population demand for parks is uniform, but this assumption is not universally applicable. Taking the old industrial area of Wugang as an example, the demand for parks in this area is extremely low as measured by the population size or population density indicators that traditional studies focus on. However, this area has a high percentage of elderly people, who actually need more park services to meet their social and fitness needs. Ignoring the heterogeneity of the needs of different groups will further reduce the voice of disadvantaged groups in the distribution of public services, and the reasonable needs of disadvantaged groups will not be guaranteed.

Supply and demand patterns in the study area are well matched, and spatial coupling of the supply and demand systems is in the general coordination class. This indicates that the fairness of the park layout in the study area is still acceptable. In recent years, the Chinese government has actively promoted the construction of parks and green spaces. With the slogan of “300 m to see the green, 500 m to enter the park”, the construction of parks has been greatly promoted, and significant results have been achieved. Most communities in the study area can enjoy park services that match their needs, and the supply and demand system is relatively stable.

However, nearly one-third of the communities still have an unmet need for parks. While inner cities have a larger supply of parks, they still have the most severe undersupply due to their high aging level and dense urban development, which bring higher demand for parks. The finding that the central city is most severely suffering from an undersupply of parks is consistent with the findings of studies in cities such as Guangzhou and Shanghai [13,72]. In contrast, by benefiting from better natural conditions and lower population distribution, the contradiction between park supply and demand is relatively moderated in the peripheral urban communities. It is worth noting that the balance between the supply and demand system of community parks in the urban periphery is still fragile. Although, from a development perspective, the demand for parks in the urban suburbs is best met at this stage, the urban suburbs, as a reserve force for urban development, will experience dramatic changes in their park demand, with urban expansion bringing industries and population into the suburbs. The current supply of parks in the periphery of the city still has a high potential for improvement. Exploring parks’ potential further and improving park supply are necessary to cope with rising park demand. Moreover, similar to other waterfront cities, water systems provide development potential for urban park construction, which suggests that rivers and lakes positively impact urban development and environmental construction [6,13]. But while water systems promote park construction, they may also exacerbate the problem of inequitable urban park layout. In the early urban construction process, financial revenue prevailed in the profit game with respect to economic and ecological benefits. The construction of the lakefront was out of control, and the privatization of lakefront space was frequent. Some parks built on lakes in the study area, such as Ziyang Lake Park, have almost become private parks. In this situation, the public character of parks as public products is not reflected, and the fairness of parks is challenged. How to use urban blue–green space rationally and provide more equitable services is a topic that still needs to be explored.

5.2. Suggestions for Urban Planning and Policies

The geographical location of urban parks as natural resourced is one of the reasons for their unevenness. The notion of park equity emphasized in this paper is not intended to assimilate the variability of natural resources themselves, nor is it intended to give all areas equal park treatment. Rather, we hope to achieve a dynamic balance between the park services enjoyed by urban residents and the demand for parks by urban residents through planning and other means. To ensure equitable and effective park use by residents, there is a requirement to balance supply and demand with respect to urban parks [23]. Based on the results of this study, and through a focus on residents in communities with unsatisfied demand, suggestions are made for spatial planning of parks from two perspectives: enhancing the supply capacity of existing parks and adding new parks.

First, it is important to enhance the supply capacity of existing parks because of the compactness of urban land use [35,73]. According to the influencing factors and measurement results of park supply, for existing parks, park type and area are already unchangeable due to land use planning, so the existing park supply can be enhanced by increasing park quality and accessibility. Increasing the quality of a park requires an exploration of the characteristics of the park [74]. For parks built on natural scenery, such as East Lake Scenic Area Park, park service facilities can be appropriately increased to improve the quality of urban parks. For example, for Wangjiadun Park, Baibuting Park, and other community parks, artificial water systems can be added to increase the park’s attractiveness and improve the park’s quality. Meanwhile, for parks with poor accessibility, improving the road network’s density, setting up public transportation stops, setting reasonable park openings, and reducing walking distance can effectively improve supply capacity [75]. At the same time, such a plan should comprehensively consider the demand of users. In the context of global aging, the age-appropriate retrofitting of urban infrastructure has attracted the attention of scholars and policymakers. When considering disadvantaged groups such as the elderly and the disabled, the construction of parks should also pay attention to barrier-free facilities.

Second, from a global perspective, park equity should be improved based on the supply and demand of parks when adding new city parks, in conjunction with the fourth part of the article. Arbitrarily adding new parks by considering only the increase in index data may increase the problem of inequitable park layout. When adding new parks, the correct approach should be to consider the differences in park supply and demand and future development trends. Priority should be given to clusters of areas with extreme undersupply and oversupply, and in such clusters, priority should be given to areas with poor coupling coordination of supply and demand systems. Then, areas with a point distribution of undersupply are targeted. Government intervention is graded and phased in to ensure steady progress and equitable development of a park. Meanwhile, with the high levels of land development and increased population densities in metropolitan areas in recent years, pocket parks are increasingly being proposed as a practical alternative to large urban parks and a viable solution for high-density areas. Governments should take into account the actual situation when adding new parks in order to meet the short-distance demand for recreational activities, noting where smaller-scale parks such as small parks and community parks are dominant and arranging pocket parks at every opportunity [28,76].

Notably, local governments are both implementers of park planning and managers of park use. This requires local governments to move away from the narrow perspective of focusing on quantitative indicators, such as area, and focusing on matching human-use demand with supply when planning and managing parks. The quality of parks is quantitatively scored in the review of planning schemes, and local advantages such as abundant water resources are utilized to build high-quality parks with local characteristics. In addition, when managing park use, the demand for park use changes with the development of society and moving of the times. The actual demands of a population around a park should be investigated through questionnaires, interviews, and big data before the park is renovated, and local policies should be formulated, with one program for one park.

6. Conclusions

Most cities in China are experiencing rapid urbanization, and the lack of park equity has emerged as an important source of environmental inequity in China. Based upon this research background, this study provides a comprehensive analysis of park equity and environmental inequity in the context of rapidly urbanizing cities, with Wuhan as a case study. By developing a novel evaluation index system that incorporates park quality and accounts for the heterogeneity of park demand among various population groups, this research offers valuable insights into the challenges of park equity and proposes strategies to improve it.

First, this paper proposes a practical evaluation system by combining multi-source big data and an evaluation framework based on a qualitative and quantitative mixed method. Our findings indicate the spatial nature of park equity. Park supply always prioritizes central urban areas but does not necessarily produce higher levels of green equity. At the same time, such an evaluation reveals the unique needs of vulnerable populations, such as the elderly, who often experience disparities in park access and resources. By bringing attention to these overlooked groups, the study underscores the importance of incorporating demographic data into urban planning and policy decisions to create more inclusive and equitable public spaces.

Second, by identifying disparities between central and peripheral urban communities, our study emphasizes the need for targeted urban planning and policy interventions to address environmental inequity and ensure equitable access to high-quality park services. Specifically, 28.25% of communities still have an unmet demand for parks. In the context of urban renewal, for cities with tight land and limited green space resources, balancing the heterogeneous distribution of green space, optimizing the quality of green space, and setting the priority of supplementary green space are more effective intervention directions than arbitrarily adding new green space. Urban planning and policy should focus on enhancing existing parks’ service capacity and prioritizing new park development in underserved areas while maintaining a dynamic balance between park services and demand. On the other hand, although supply and demand for parks in the urban periphery are better matched at this stage, the balance of supply and demand is fragile and unstable and still requires extensive attention. Nearly 70% of communities in this region have average or below levels of coordination between park supply and demand. Overall, the park supply–demand matching pattern results contribute to the broader literature on environmental inequity by providing empirical evidence on the spatial distribution of park resources and its implications for different population groups.

Several limitations of this study deserve special attention and future improvement. First, the frequency of park use by different age groups in the demand estimation used a questionnaire survey, and the sample size obtained was limited. Big data has the advantage of reflecting characteristics more objectively and accurately. The subsequent study can use cell phone signaling data with age attributes to count the visit frequency of different age groups. Moreover, four objective indicators were added to evaluate park quality in this research. But the fact is that with parks as physical presences that are used by people, the quality perceived by users is also one of the important factors that influence whether users will visit again. Further attention should be paid to human perceptions in subsequent studies to explore park quality and spatial equity based on user perceptions.