Next Article in Journal
Land-Use Evolution and Driving Forces in Urban Fringe Archaeological Sites: A Case Study of the Western Han Imperial Mausoleums
Previous Article in Journal
Fusion of airborne, SLAM-based, and iPhone LiDAR for Accurate Forest Road Mapping in Harvesting Areas
Previous Article in Special Issue
Investigating the Serviceability of Urban Green Spaces from a Spatial Perspective: A Comparative Study Across 368 Cities on the Chinese Mainland
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Beyond a Single Story: The Complex and Varied Patterns of Park Accessibility Across China’s Emerging Cities

1
GPSS, Graduate School of Frontier Science, The University of Tokyo, Chiba 277-8563, Japan
2
Department of Natural Environmental Studies, Graduate School of Frontier Sciences, The University of Tokyo, Chiba 277-8563, Japan
*
Author to whom correspondence should be addressed.
Land 2025, 14(8), 1552; https://doi.org/10.3390/land14081552
Submission received: 6 June 2025 / Revised: 17 July 2025 / Accepted: 26 July 2025 / Published: 28 July 2025
(This article belongs to the Special Issue Spatial Justice in Urban Planning (Second Edition))

Abstract

China’s rapid urbanization has driven tremendous socioeconomic development while posing new forms of social–spatial inequalities that challenge environmental sustainability and spatial justice. This study investigates urban park-accessibility patterns across 10 s-tier provincial capital cities in China, examining how these patterns relate to housing-price dynamics to reveal diverse manifestations of social–spatial (in)justice. Using comprehensive spatial analysis grounded in distributive justice principles, we measure park accessibility through multiple metrics: distance to the nearest park, park size, and the number of parks within a 15 min walk from residential communities. Our findings reveal significant variation in park accessibility across these cities, with distinctive patterns emerging in the relationship between housing prices and park access that reflect different forms of social–spatial exclusion and inclusion. While most cities demonstrate an unbalanced spatial distribution of parks, they exhibit different forms of this disparity. Some cities show consistent park access across housing-price categories, while others display correlations between high housing prices and superior park accessibility. We argue that these divergent patterns reflect each city’s unique combination of economic development trajectory, politically strategic positioning within national urban hierarchies, and geographical constraints. Through this comparative analysis of second-tier cities, this study contributes to broader understandings of social–spatial (in)justice and urban environmental inequalities within China’s urbanization process, highlighting the need for place-specific approaches to achieving equitable access to urban amenities.

1. Introduction

As urban populations continue to grow, addressing socio-spatial disparities and ensuring equitable access to fundamental amenities becomes imperative for achieving what Fainstein [1] conceptualizes as the “just city.” From a social–spatial justice perspective, all urban dwellers, regardless of their socioeconomic background, should have equal rights and opportunities to access, use, and transform urban environmental amenities according to their needs and preferences [2]. This urgency is especially evident when considering urban green spaces, where the unequal distribution of environmental benefits among different social groups represents a critical dimension of social–spatial (in)justice that requires thorough investigation through the lens of environmental justice theory. From an environmental justice perspective, these disparities can be understood through examining how environmental amenities are spatially distributed across different socioeconomic groups, with a particular focus on the accessibility of such resources. This distributive approach to environmental justice is especially relevant for analyzing park-accessibility patterns, as it focuses on whether all residents have equitable spatial access to environmental benefits regardless of their status.
Among urban green spaces, urban parks are crucial components of urban green infrastructure, embodying this principle by providing environmental benefits to cities, improving physical and mental well-being, and enhancing the appeal of local communities [3,4,5,6,7,8]. However, the spatial distribution of these benefits often reflects broader patterns of social–spatial inequality, where access to environmental amenities is differentiated by class, income, and other social markers. This creates various forms of exclusion that disproportionately burden disadvantaged urban inhabitants. Research consistently demonstrates that park-access disparities manifest through multiple pathways: wealthy communities often receive parks with superior design and equipment, while marginalized populations face involuntary resettlement to areas with limited green space access, creating what scholars term “green gentrification” paradoxes [9,10].
China has experienced unprecedented urban growth while implementing extensive greening projects, making notable achievements in forestation and the reversal of land degradation; urban greening policies have also proven to be effective [11]. These policies have typically emphasized increasing green space per capita, with China’s Ordinance for Urban Greening mandating the installation of more parks to guarantee sufficient green spaces. As a result, a considerable number of parks have been established in the urban landscape. However, the disparity in the distribution of these green spaces relative to housing prices has the potential to raise issues related to environmental justice and spatial equity. The Chinese urban context presents unique challenges for environmental justice analysis, as the country’s authoritarian governance structure, power dynamics, and development practices create distinct patterns of environmental inequality that differ from those in Western contexts [10].
A growing body of research suggests that disparities in urban park accessibility, when correlated with housing prices, can exacerbate socioeconomic inequalities and contribute to broader environmental injustices and social equity concerns [12,13,14]. Previous research has identified positive correlations between urban landscape infrastructures and amenities (e.g., parks, open spaces, and forests) and housing prices [15,16]. While research has demonstrated that residents in higher-priced communities often enjoy greater proximity to urban parks, these findings have been predominantly limited to major metropolitan areas [17,18,19]. Consequently, a significant knowledge gap remains regarding park accessibility in China’s rapidly evolving second-tier cities. This research bias is particularly concerning given the growing importance of these cities as residential destinations and regional economic centers.
Understanding the geographical and political dynamics of second-tier cities is crucial for studying environmental equity, as these factors significantly impact both social–spatial disparities and park-accessibility patterns through various mechanisms. Second-tier provincial capitals in China exemplify the evolving urban landscape and developmental trajectory of the nation, yet each follows its own distinct path of development, creating diverse urban experiences rather than a uniform pattern. Ranging from 3 to 9 million inhabitants [20], these cities are primarily situated in economically vibrant regions of the eastern and central parts of the country and are key drivers of economic growth and demographic change [21]. As newly emerging centers of commerce and innovation, they attract both opportunities and elite residents [22]. While these cities attract new urban residents, several challenges persist, including the demand for resources in urban contexts, the possibility of increased pollution, and strained city budgets [23]. Each city navigates these challenges through distinct paths, shaped by regional economics, geography, and governance. Our focus on second-tier provincial capitals is strategically important for understanding environmental justice in China’s urban hierarchy. As Wei [24] demonstrates, China’s hierarchical urban system creates resource allocation disparities, with provincial capitals receiving preferential treatment in governmental investment. These cities function as policy bellwethers within their provinces, establishing guidelines and investment priorities that other cities subsequently adopt. Understanding park-accessibility patterns in these cities is therefore crucial for identifying environmental justice issues and developing solutions that can be scaled across China’s urban system to improve future equity outcomes.
Rapid economic development in these cities creates uneven spatial patterns where affluent areas attract more investments and planning attention. Physical geography, including topography, natural vegetation, water bodies, and climate conditions, could lead to varied opportunities and constraints for urban green spaces planning. Governance structures and institutional arrangements, including government funding and implementation priorities, further shape park-accessibility patterns. Among these varied factors, the equitable distribution and access to urban green spaces emerge as a particularly significant challenge. Hence, examining the accessibility and distribution of urban parks across second-tier cities is crucial for understanding the complexity of livability and urban development quality in China’s emerging cities.
Current research on park accessibility in China predominantly focuses on single-city case studies, with limited comparative analysis of how park-accessibility patterns vary across urban centers. Most studies have focused on first-tier metropolitan areas, creating a dominant narrative that may not capture the reality of second-tier cities. This research bias is particularly concerning given that second-tier cities are now attracting both rural migrant workers and well-educated, skilled groups who prefer to stay in cities closer to their hometowns. However, even when second-tier cities are studied, they are typically examined in isolation. For instance, recent research by He et al. [21] highlighted the spatial inequality of parks in Taiyuan, finding that park access was stratified by demographic and socioeconomic characteristics, with some groups facing barriers to these vital community resources. Such single-city narratives cannot be assumed to be representative of all second-tier provincial capitals, and generalizing from individual case studies risks oversimplifying the complex reality of urban development across China’s diverse regional context.
In response to these gaps, this study aims to present a comprehensive comparative analysis of urban park accessibility across second-tier provincial capitals in China and concurrently investigate its relationship with local housing prices. This research applies environmental justice theory’s distributive framework to analyze park accessibility, examining how spatial access varies across different housing-price categories within China’s governance context. By measuring park accessibility for each residential community across these cities, we first document the varied patterns of park accessibility. A price-categorized accessibility comparison is then conducted to reveal how park access relates differently to housing prices across these urban areas. Finally, we analyze these descriptive results through geographical, socioeconomic, and politically strategic lenses to understand the factors underlying observed variations. This approach offers a holistic evaluation of park-accessibility variations within and among cities based on their unique characteristics. By moving beyond the single story of Chinese urban development, this research explores the following questions:
(1)
How does urban park accessibility vary across 10 s-tier provincial capitals in China?
(2)
How do contextual characteristics influence the spatial patterns of park accessibility observed in different cities?
(3)
How do the relationships between housing prices and park accessibility differ among these second-tier cities, and what insights do these patterns offer regarding potential issues of social equity?

2. Methods

2.1. Study Area

This research is a multi-city study focusing on 10 s-tier provincial capitals in China (Table 1). These cities have significant political and socioeconomic importance in the province and local region and are the main cities that undertake the policies from the central government [25]. Due to the lack of available data on Changchun (Jilin Province), the study area comprised the other 10 provincial capitals, which are Shijiazhuang, Taiyuan, Shenyang, Harbin, Fuzhou, Jinan, Nanchang, Nanning, Guiyang, and Lanzhou. These 10 cities are distributed across China, including the north, northeast, east, west, and southwest areas (Figure 1). While we acknowledge that the geographic distribution appears a bit unbalanced with fewer cities in the west and limited representation along the Yangtze River, this constraint reflects the availability of comprehensive data for our analytical framework. Considering the population distribution and economic focus in these cities, only city districts are included in this study. The study areas have similar socioeconomic and political characteristics but are different in terms of geographical location, which could be one of the factors influencing the later analysis.

2.2. Data Description

This study used mainly park and housing data to analyze the accessibility to urban parks from the residential communities. The comprehensive dataset employed in this research is summarized in Table 2. The park data (point of interest (POI) and area of interest (AOI)) were extracted from one of the leading map services in China (AMAP). With its web services application programming interface, spatial data from a total of 1538 parks were obtained for 10 cities. The location and average property price for each residential community in the 10 cities were retrieved from Lianjia, a popular real estate service platform in China. A total of 31,458 residential communities from 10 cities’ city districts were extracted for analysis. For park data validation, we employed a multi-step verification process: first, cross-referencing all park information, including names, locations, construction years, and official webpages; second, visual verification using satellite imagery to confirm park boundaries. Then, in the data-processing stage, the park data were refined by removing the wrong points and merging the polygons of the same park. Similarly, for the housing data, those residential communities with empty information were removed. Other data, including road network data from OpenStreetMap (https://www.openstreetmap.org, accessed on 28 August 2023) and statistical data from the Chinese Statistical Yearbook [20], were used in this study. Data for this study were collected from various online sources, offering an invaluable and broad perspective for the research. While extensive efforts were made to clean and refine the data, it is important to acknowledge that online-sourced data may have limitations in terms of accuracy and representativeness because of the variability of sources and the potential for outdated or incomplete information. We acknowledge that housing-price data were not standardized for temporal variations, thus representing a study limitation. Given that our housing-price data represent a snapshot from a specific time period, and our analysis focuses on spatial patterns rather than temporal trends, we believe this approach is acceptable for our research objectives.

2.3. Data Analysis

The accessibility to urban parks for each residential community was computed and assigned to the communities’ attributes; this research first listed the general city-level results for overall comparisons. Integrating city-specific characteristics, such as the level of socioeconomic development and geographical conditions, helped us examine the underlying factors contributing to the similarities and disparities in urban park accessibility across cities. This city-level comparative analysis contributed to revealing patterns of urban park accessibility. Second, an investigation of park accessibility and housing prices within each city was conducted. Residential communities in each city were classified into five distinct categories (low, mid-low, mid, mid-high, and high) based on the average price per square meter, employing the Jenks natural breaks classification method. The accessibility to urban parks for each category was used to help identify whether disparities exist between different housing-price categories. By examining the interplay between housing prices and park accessibility, this study could contribute to a deeper understanding of urban inequality and environmental justice issues. Figure 2 illustrates the overall workflow of this study.
After grasping the park accessibility for each residential community in these cities, the one-way ANOVA test is performed to robustly determine whether there are significant differences in park accessibility among the ten cities. This is appropriate since the index is a continuous variable, and the cities represent independent groups with sufficient sample sizes. After finding the significant differences, the Tukey HSD test was applied to conduct pairwise comparisons between the cities. This allows for a more targeted analysis of the disparities in urban park accessibility, which can then be interpreted within a broader urban context to inform data-driven urban-planning decisions. Dendrograms and PCA scatter plots were employed to reflect the clustering and relationships among the cities regarding accessibility and variability. Figure 2 illustrates the overall workflow of this study.

2.4. Accessibility Index

The concept of accessibility, which refers to the ease of reaching a desired destination, has been extensively examined in different fields, and various concerns and objectives have been addressed. Park accessibility has been discussed in relation to urban development, landscape planning, and spatial- and social-equity issues [26,27,28,29]. However, no concrete method has been established for measuring the accessibility to urban green spaces and parks. Geographic information system methods are often adopted to calculate accessibility using sets of different indicators [19,30,31,32]. A literature review focusing on park accessibility with proximity, acres, and quality as parameters [12] found that privileged groups of residents were more likely to have better access to bigger and more comprehensive parks.
Building upon the conceptual framework outlined by Biernacka and Kronenberg [33], this study assessed urban park accessibility across three key dimensions: availability, distance (which they referred to as accessibility), and attractiveness. Our three-indicator (Table 3) framework is theoretically grounded in spatial justice theory and established accessibility research. Drawing from environmental justice principles, we conceptualize park accessibility as a multidimensional construct that encompasses spatial proximity, resource adequacy, and service availability.
To address these three aspects, the present research employed three indicators to assess urban park accessibility for each residential community (Table 3). The first is the proximity to the nearest park, which is the travel distance along the route; here, higher scores are assigned to residential communities closer to the parks. The definition of accessibility primarily deals with the ease of people reaching their desired activities, where the distance between locations is the primary factor. Most of the available research considers the distance to parks when measuring access to parks [34,35,36], and thus, the proximity to the nearest park is listed as one of the indicators of the accessibility index, and setting the 1500 threshold is well-established in park-accessibility literature as representing a reasonable walking distance to a park [37]. Thus, the proximity to the nearest park is listed as one of the indicators of the accessibility index.
The second indicator is the size of the nearest park. Based on the Standard for Classification of Urban Green Space published by the Ministry of Housing and Urban–Rural Development of the People’s Republic of China [38] parks are categorized into comprehensive parks and community parks. Comprehensive parks, in the document, are defined as those with an area exceeding 10 ha; that usually encompass diverse recreational opportunities, well-equipped facilities, and effective management; and that cater to various outdoor activities. By contrast, community parks, which should be bigger than 1 ha, feature essential recreational and service amenities, primarily serving residents within a specific community area for their daily leisure demand. Hence, parks with greater sizes would offer more diverse recreational opportunities, which are more attractive to users seeking various activities, and this would lead to a higher score on the accessibility index [39]. Our park-size classifications directly correspond to China’s national standards for urban green space, which are distinguished based on their intended service functions and recreational capacity.
The third indicator takes the number of parks within a 15 min walking distance (approximately 1500 to 1600 m), where a greater number of parks contributes to a better score. A dense and effective 15 min neighborhood network could raise urban resilience and work toward a more inclusive society [40,41,42]. China is one of the countries that promotes a 15 min walkable neighborhood where residents should have access to various services and resources [42]. Accordingly, the number of accessible parks stands as a reasonable indicator for measuring general urban park accessibility. These three indicators could tackle the three aspects raised by Biernacka and Kronenberg [33]. Availability is captured by the proximity to the nearest park and the number of parks within a 15 min walking distance. The distance to the closest park measures the accessibility. Park size serves as a quantifiable measure of attractiveness because it directly relates to the kinds of recreational activities. By assigning equal weight to these three indicators, the overall accessibility to urban parks is computed by normalizing the sum of the three indicators from 0 to 1. Figure 3 provides a detailed sample map that demonstrates our accessibility analysis methodology, illustrating how the three indicators are applied in calculating accessibility for a representative residential community.
Equal weighing is theoretically justified as each dimension represents an independent barrier to park access—inadequate proximity, insufficient availability, or limited attractiveness can independently constrain accessibility regardless of performance in other dimensions. By assigning equal weight to these three indicators, the overall accessibility to urban parks is computed by normalizing the index to a range between 0 and 1; the resulting values represent the degree of accessibility to parks for each residential community. To establish a practical threshold for categorizing accessibility, we consider a scenario where a residential community has at least one accessible community park located within 1500 m from the resident’s home. In this context, the accessibility score of such a community, which is 0.5, serves as the boundary. This threshold establishes a baseline that aligns with spatial justice principles, requiring all residents to have minimum access to essential urban amenities regardless of their socioeconomic status. Communities with an accessibility index equal to or greater than 0.5 are believed to have “good” accessibility, whereas those with lower scores may experience “limited” accessibility. However, the threshold is primarily used for analytical purposes, and it is acknowledged that communities falling below the score might still have varying accessibility to urban parks or other alternatives.

2.5. Home-Price-to-Disposable-Income Ratio

Based on data from the residential community, which includes the average price (CNY/m2), the home-price-to-disposable-income ratio is calculated to measure the general affordability of the cities. Similarly, this ratio is also calculated for a 90 m2 home, which could be suitable for a family household. By considering the home-price-to-disposable-income ratio, it becomes possible to assess whether households are burdened when purchasing a home—a common practice in China, not reserved for the elite. Furthermore, it allows for comparisons of economic pressures across different cities, which could help determine whether cities with better affordability also have more equitable park distribution by aligning the ratio with park accessibility.

3. Results

In response to the first two research questions, among cities, the results showed that urban park accessibility (defined as availability, distance, and attractiveness) varied between the 10 cities studied, and could be clustered relatively to the regional GDP (Section 3.1). Within each city, disparities in park accessibility were related to housing-price categories as follows (Section 3.2). For each result, an example of a city was developed.

3.1. Urban Park Accessibilities Among Cities

By analyzing the normalized accessibility of each city, this research identified the disparities between cities and the general trends of the associations with better park accessibility and higher regional GDP. The one-way ANOVA test was conducted to compare the differences in urban park accessibility across the study cities. The results revealed a significant difference between cities, F(9, 34796) = 200.26, p < 0.001, indicating that the accessibility index varies significantly among the ten cities. A post hoc analysis of Tukey’s HSD test was applied after, and it showed that most city pairs had statistically significant differences, while a few pairs did not (Figure 4). As depicted in Figure 5, Shijiazhuang and Fuzhou have the highest average accessibility, whereas Shijiazhuang has a higher median than the mean. Most of the cities have a lower median than the mean value, which could be a better representation of the overall tendency. A closer examination of the specific values of the accessibility distribution for each city in Table 4 reveals that Shijiazhuang and Fuzhou (with Jinan’s value being close to 0 and therefore neglected) exhibit negative skewness values. This suggests that more values concentrate toward higher accessibility, indicating that more residential communities have relatively better accessibility to urban parks. Conversely, other cities with positive skewness values indicate a leftward leaning of accessibility, with more communities tending to have relatively limited access to urban parks.
Among the study area, more than one-third of the residential communities in half the cities have enhanced accessibility (greater than or equal to 0.5), indicating the ongoing development of urban greening. In these cities, Fuzhou has the highest mean value (0.45), and Shijiazhuang has the highest median value (0.5). Both cities enjoy good accessibility to urban parks, with approximately 49.78% and 52.51% of communities having good accessibility, respectively. Another city with almost 50% of communities enjoying good accessibility to parks is Harbin; however, the average value is 0.37, indicating that many communities may have extremely low park-accessibility values. Meanwhile, half the cities still have accessibility values in the 20 s percentage, with a majority of residential communities experiencing limited access to urban parks. For kurtosis, which measures the peakedness and flatness of the distribution, all cities have negative values, indicating a flatter and wavier distribution of accessibility. This suggests that there are few extreme results of accessibility values, and the distribution is less peaked compared with a normal distribution.
To further understand the relationships among cities, cluster analysis was conducted using hierarchical clustering methods based on the mean accessibility and standard deviation of accessibility for each city, with results represented in a dendrogram and PCA scatter plot (Figure 6). The analysis revealed four distinct clusters of cities based on their accessibility characteristics. The first cluster comprises Taiyuan, Lanzhou, and Nanchang, which are characterized by relatively lower mean accessibility and higher standard deviations, indicating more unequal park accessibility. Shijiazhuang stands alone in the second cluster with relatively high accessibility but substantial standard deviation, reflecting high average accessibility with considerable internal variability. The third cluster includes Harbin, Shenyang, Guiyang, Nanning, and Jinan, representing cities with moderate and relatively balanced accessibility. Finally, Fuzhou forms the fourth cluster with high mean accessibility and lower standard deviation, representing a uniquely favorable accessibility profile with low variability. This clustering analysis provides additional insights into the grouping of cities beyond simple ranking by accessibility scores, revealing underlying patterns in how accessibility varies both in magnitude and distribution within these urban areas.
Figure 7 illustrates the mean accessibility and regional GDP across the study area. Through visual inspection of the plot, Harbin and Shijiazhuang are identified as potential outliers, as they deviated from the overall distribution of the data points. When all observations are included, no apparent relationship can be observed between the GDP and accessibility. However, after removing these outliers, a moderate positive correlation emerged (R2 = 0.60; p = 0.024), suggesting that higher GDP is associated with increased accessibility. This relationship is statistically significant, though it is based on a small sample. The outliers of Harbin and Shijiazhuang have a strong influence on the overall trend, which suggests a need for detailed examinations of individual data points.

3.2. Accessibility Distribution Within the Cities Based on Housing Prices

To address the differences between housing prices, it was essential to examine the distribution of accessibility from the residential community in each price category across cities. The findings emphasized a clear contrast across housing-price categories and park accessibility, where higher property values were positively correlated with better park accessibility. Meanwhile, the challenge of housing unaffordability remained significant, as evidenced by the price-to-income ratio. It is crucial to ensure that parks are equitably distributed and accessible to every resident. To take a closer look, first, a distribution graph of the cities’ housing prices is presented in Figure 8. It can be observed that all cities have positive skewness with a long tail on the right side. This suggests that most of the housing prices are concentrated toward the left, with some extremely high property values on the right. While most cities have a wider bell-shaped graph, Shenyang displays a narrow distribution that is highly peaked and clustered around the lower end of the graph, as reflected by the high kurtosis.
In most of the cities, there is a lack of uniformity in accessibility between different housing-price categories, as illustrated in Figure 9. In most instances, either lower-priced communities have notably lower accessibility than other categories, or higher-priced communities enjoy superior access to urban parks. For Shijiazhuang, Taiyuan, Nanning, and Lanzhou, based on the analysis, the lower-priced housing prices are often associated with limited accessibility to urban parks. Similarly, the results for Shenyang, Harbin, and Jinan demonstrated that higher-priced housing correlates with enhanced accessibility to urban parks. By contrast, some cities, such as Guiyang and Nanchang, showed relatively uniform accessibility across price categories. Although the plot reveals some differences within cities, the spread is similar for each category in these two cities. One unique case is Fuzhou, which has comparatively better accessibility for all price categories than the other cities, despite the fact that there is a slight difference between each. These findings indicate that there are disparities within the city that need to be addressed to ensure equitable access to public spaces.
Table 5 shows the price-to-income ratio, which represents how many years it would take for a household with an average disposable income to purchase a single square meter or 90 m2 home (a typical size for a family residence). A lower ratio indicates greater affordability, whereas a higher ratio suggests less affordability. As shown in the table, while Harbin and Guiyang have the lowest price-to-income ratios, Fuzhou is the least affordable city in the study area, where the years needed to buy a home are double that of most other cities. Although Fuzhou has the highest disposable income and regional GDP, the economic pressure on residents in terms of housing affordability is significant.

4. Discussion

4.1. Diverse Pathways of Park Accessibility Across Cities

As demonstrated in the previous section, significant disparities in urban park accessibility exist among the 10 studied cities (Figure 4). To explain these differences, this section examines broader contextual factors contributing to park-accessibility outcomes. Specifically, it considers how economic performance, strategic importance within the national urban context, and geographical opportunities and constraints may influence the park accessibility across cities.

4.1.1. Economic Performance

The economic performance of each city represents a crucial factor influencing the park-accessibility narratives. Although the 10 provincial capitals are all categorized as second-tier cities, their stages of development and resulting distributions of environmental amenities reveal multiple stories. As shown in Figure 7, the plot of park accessibility and regional GDP showed divergent patterns: (1) cities following the expected correlation between economic development and urban parks provision (Fuzhou, Guiyang, Shenyang, Taiyuan, Nanchang, Jinan, Nanning, and Lanzhou); and (2) outliers where Shijiazhuang and Harbin demonstrate higher accessibility despite relatively lower GDP.
For instance, Fuzhou has the highest regional GDP and is ranked at the top in accessibility among the study areas. Fuzhou serves as one of the major ports in China, facilitating international trade activities. The implementation of free trade policies has stimulated local economic development in the city [43]. Because Fuzhou has a relatively more affluent economic status than the other study areas, it is possible that Fuzhou possesses more resources that can be allocated toward the development and maintenance of urban parks and green spaces. Fuzhou enjoys a more advanced level of economic development, which potentially translates into greater financial resources that can be allocated toward urban green space initiatives. As another example, Fuzhou has developed the 14th Five-Year Plan, which specifically focuses on urban landscaping and greening [44], and this is not shared by all cities. However, the ratio between housing prices and disposable income is the highest among all study areas, indicating that achieving inclusive growth in urban areas is challenging, even with significant economic development.

4.1.2. Strategical Position

Besides economic performance, strategic position could be another factor influencing the differences between cities. For instance, Shijiazhuang presents a unique case where the regional GDP is relatively low, yet the average urban park accessibility is considerably advanced (Figure 7). One possible explanation for this phenomenon lies in Shijiazhuang’s strategic location within the Coordinated Development of the Beijing–Tianjin–Hebei Region, which is subject to coordinated development efforts with the direct-administered municipalities of Beijing and Tianjin. Given that this special metropolitan area operates under a shared urban green development plan [45], Shijiazhuang is likely to implement greening initiatives more efficiently. Similarly, the document “Regarding Hebei Province’s Development of the Beijing–Tianjin–Hebei Ecological Environment Support Zone based on the 14th Five-Year Plan” [46] mentions that Hebei Province should be the moat for Beijing and prioritize ecological and green development. This plan could play a significant role in enhancing Shijiazhuang’s efforts toward greening and sustainability.

4.1.3. Geographical Context

A final factor that could explain the differences in park accessibility between cities is the geographical context. As mentioned earlier, the second-tier provincial capitals are distributed broadly in China. Hence, the geographical conditions vary significantly. For example, in Southern China, Guiyang enjoys exceptional natural advantages and is surrounded by vast mountains and forests. In Guiyang’s case, local greening projects and policies are established in the natural environment, such as creating a “Forest City” and building “Green Corridors” [47,48]. Moreover, the humid subtropical climate with abundant rainfall has fostered diverse vegetation in the area. On the contrary, Lanzhou, which is situated in the northwest region, is another example. Due to its proximity to the Gobi Desert, Lanzhou is prone to sandstorms, especially in the spring and autumn. A solution would be establishing windbreak forests before prioritizing other urban green spaces, including parks. Moreover, Lanzhou’s semi-arid climate is usually characterized by shrubbery as the dominant vegetation, and this results in an additional task of shielding the city from dust. Geographical conditions can be a significant factor influencing urban park accessibility, as cities with varying topographies and natural features have distinct challenges and opportunities. While some cities may rely on existing natural resources, others might need to tackle issues such as land scarcity and environmental degradation. This is stated in the “14th Five-Year Plan for Forestry, Grassland, and Urban Landscape Greening Development Plan of Lanzhou” [49], where Preservation, Restoration, and Governance, are the primary keywords. The document states the importance of preserving grassland and forestry resources; ecological system restoration projects; and the comprehensive governance of mountains, waters, forests, fields, lakes, and grasses.

4.2. Explaining Varying Patterns of Accessibility–Housing-Price Patterns

In addition to differences in overall park accessibility, cities also exhibit varied patterns in the relationship between park accessibility and housing prices (Figure 9). While some cities experience high price–high accessibility or low price–low accessibility, there are cities that display a more balanced distribution of accessibility across housing-price categories. This section explores the reasoning behind these different patterns by focusing on two contrasting cities. Through comparison, it highlights how factors such as socioeconomic development and natural environment contribute to the formation of distinct accessibility–price dynamics for different cities.

4.2.1. Socioeconomic Development and Disparities

Socioeconomic development is the first aspect that needs to be considered to examine why the disparities in park accessibility exist. Take Lanzhou as an example, as illustrated in Figure 10a. Lanzhou exhibits significant disparities in park accessibility between lower- and higher-priced communities. This disparity indicates potential social equity issues, with disadvantaged groups benefiting less from urban parks. Lanzhou’s unique long and narrow shape contributes to its uneven urban development, with the city center in the eastern region being more developed than the western areas [50]. This results in a concentration of urban economic and geographical elements in the center, as well as the phenomenon known as “single-center urban sprawl” [50,51]. Figure 10b depicts the location of urban parks and the distribution of residential communities categorized by housing price. The figure shows that most parks and higher-priced communities are clustered on the eastern side of Lanzhou, which aligns with the uneven urban development. While Lanzhou developed some green infrastructure, such as the green belt along the Yellow River in the urban area, Tong and Shi [50] mentioned that the fragile natural environment makes maintaining these projects costly in terms of both human and financial resources. To develop Western Lanzhou, the researchers have suggested constructing a new city center to help balance urban and economic development.

4.2.2. Natural Environment and Uniformity

Among the cities analyzed, Guiyang stands out for its similar park accessibility across housing-price categories. As shown in Figure 11, the park distribution corresponds to the residential community’s location regardless of the property price. To uncover the reason that underlies this phenomenon, it is worth noticing that Guiyang is covered by karst, which is an irregular limestone region characterized by sinkholes, underground streams, and caverns. Although this landscape is fragile, it offers abundant, productive forests that can be utilized for urban greening projects. These urban forests serve multiple functions, including enhancing the city’s environmental resilience while providing essential social benefits and contributing to the aesthetic enjoyment of the urban landscape. To address the challenges posed by the delicate karst terrain, the local government of Guiyang launched the Special Construction Plan for Sponge City Center (2019–2025), where one of the focuses is to rely on the rich forests, wetlands, rivers, and other natural characteristics to build more “Sponge Parks” [52]. From 2019 to 2020, Guiyang invested more than CNY 10 billion in Sponge City construction, aiming to develop and renovate 65 parks within 2 years. Guiyang’s greening policy trajectory is building on its unique geomorphological characteristics to ensure balanced park accessibility across all residential communities, thereby mitigating potential social inequities.

4.3. Social Equity, Urban Change, and the Reproduction of Inequality

The findings above address the broader question of spatial justice and the socio-spatial implications of urban development, as the park accessibility and housing price take different forms across these urban landscapes. For instance, while Fuzhou has higher overall accessibility, it combines with housing unaffordability, resulting in the accessibility–affordability dilemma, and indicating the coexistence of improved livability and intensified special justice concerns. This reflects tensions between environmental upgrading and the risk of gentrification, especially in rapidly transforming second-tier provincial capitals.
The varied patterns observed among these second-tier cities reflect diverse processes of socio-spatial differentiation that should receive closer attention. In cities where park accessibility is more closely related to housing prices, urban parks potentially aggravate the gentrification process, which reinforces existing social hierarchies. Even in cities with more evenly distributed parks, the size and quality of these spaces could possibly reveal patterns of urban inequality. Considering future practice, the results in this research emphasize the importance of place-specific strategies that address each city’s unique characteristics, while addressing the potential concerns related to environmental justice and social sustainability. As these cities continue to grow, it is important for them to navigate the tension between expanding green space and mitigating the risks of gentrification and deepening social inequality [53].

4.4. Study Limitations

While this study offers a robust spatial analysis of park accessibility, some limitations remain. The use of cross-sectional data restricts causal interpretation, and park quality or amenity details were beyond the scope of this analysis. Geographic representation was shaped by data availability, resulting in limited inclusion of western and riverine cities. Additionally, more visual presentation and analysis could be developed with access to more detailed data; such visual extensions remain a valuable direction for future work. Despite these constraints, the findings provide meaningful insights into spatial equity across China’s second-tier provincial capitals.

5. Conclusions

The patterns observed in park accessibility across second-tier provincial capitals in China highlight how each city represents a unique configuration of park distribution. The results demonstrated significant variations in park accessibility shaped by economic performance, strategic positioning, and geographical contexts. These variations act differently in each urban setting, producing distinctive accessibility–housing-price relationships reflecting the Chinese cities’ varied development trajectories.
When affluent residential communities are positioned to enjoy superior accessibility to urban parks, it becomes evident that wealthier groups tend to enjoy a greater share of the physical and environmental benefits that these parks bring. This disparity in park accessibility raises concerns regarding social inequality and the potential for exclusivity. Specifically, the results of this study suggest that higher-priced residential communities tend to enjoy better accessibility to parks. This imbalance in park accessibility across different housing-price categories may exacerbate the gentrification processes, which may lead to the displacement of vulnerable communities—in this case, being distanced from green amenities, which could raise critical questions of social justice and equity. To address these disparities, it is necessary to consider a more equitable distribution of park planning and development, ensuring that everyone, regardless of socioeconomic status, has more equal access to urban green spaces. Such an approach could establish a more inclusive urban society where the benefits of parks are accessible to all residents, thus promoting social cohesion and environmental justice.
This study offers valuable insights into environmental inequalities across second-tier Chinese cities by revealing the multiplicity of stories that unfold beyond simplified urbanization narratives. While the methodological approach provides a comprehensive spatial analysis, future research could incorporate more specific socioeconomic factors and various types of green space to enhance the analysis. This study emphasizes that each city’s unique development trajectory shapes distinct environmental outcomes, requiring tailored approaches to urban environmental governance. By highlighting these diverse patterns, this research contributes to further understanding of spatial justice and social equity in China’s emerging urban landscapes, where common challenges intersect with the city’s individual characteristics to create complex park distribution and environmental outcomes.

Author Contributions

Conceptualization, M.L. and T.T.; methodology, M.L. and T.T.; software, M.L.; validation, M.L.; formal analysis, M.L.; investigation, M.L.; data curation, M.L.; writing—original draft preparation, M.L.; writing—review and editing, M.L. and T.T.; visualization, M.L.; supervision, T.T. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

The data presented in this study are available in Zenodo at https://doi.org/10.5281/zenodo.15644974. These data were derived from the following resources available in the public domain: OpenStreetMap (https://www.openstreetmap.org, accessed on 28 August 2023), Amap API (https://lbs.amap.com, accessed on 29 August 2023), and Lianjia (https://www.lianjia.com, accessed on 25 September 2023).

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Fainstein, S.S. The Just City; Cornell University Press: Ithaca, NY, USA, 2010; ISBN 978-0-8014-4655-9. [Google Scholar]
  2. Israel, E.; Frenkel, A. Social Justice and Spatial Inequality: Toward a Conceptual Framework. Prog. Hum. Geogr. 2018, 42, 647–665. [Google Scholar] [CrossRef]
  3. Bedimo-Rung, A.L.; Mowen, A.J.; Cohen, D.A. The Significance of Parks to Physical Activity and Public Health: A Conceptual Model. Am. J. Prev. Med. 2005, 28, 159–168. [Google Scholar] [CrossRef] [PubMed]
  4. Crompton, J.L. The Impact of Parks on Property Values: A Review of the Empirical Evidence. J. Leis. Res. 2001, 33, 1–31. [Google Scholar] [CrossRef]
  5. Duan, Y.; Wagner, P.; Zhang, R.; Wulff, H.; Brehm, W. Physical Activity Areas in Urban Parks and Their Use by the Elderly from Two Cities in China and Germany. Landsc. Urban Plan. 2018, 178, 261–269. [Google Scholar] [CrossRef]
  6. Maas, J.; Verheij, R.A.; Groenewegen, P.P.; de Vries, S.; Spreeuwenberg, P. Green Space, Urbanity, and Health: How Strong Is the Relation? J. Epidemiol. Community Health 2006, 60, 587–592. [Google Scholar] [CrossRef]
  7. Mitchell, R.; Popham, F. Greenspace, Urbanity and Health: Relationships in England. J. Epidemiol. Community Health 2007, 61, 681–683. [Google Scholar] [CrossRef]
  8. Yao, X.; Yu, K.; Zeng, X.; Lin, Y.; Ye, B.; Shen, X.; Liu, J. How Can Urban Parks Be Planned to Mitigate Urban Heat Island Effect in “Furnace Cities”? An Accumulation Perspective. J. Clean. Prod. 2022, 330, 129852. [Google Scholar] [CrossRef]
  9. Wolch, J.R.; Byrne, J.; Newell, J.P. Urban Green Space, Public Health, and Environmental Justice: The Challenge of Making Cities ‘Just Green Enough’. Landsc. Urban Plan. 2014, 125, 234–244. [Google Scholar] [CrossRef]
  10. Wang, C.; Li, C.; Wang, M.; Yang, S.; Wang, L. Environmental Justice and Park Accessibility in Urban China: Evidence from Shanghai. Asia Pac. Viewp. 2022, 63, 236–249. [Google Scholar] [CrossRef]
  11. Feng, D.; Bao, W.; Yang, Y.; Fu, M. How Do Government Policies Promote Greening? Evidence from China. Land Use Policy 2021, 104, 105389. [Google Scholar] [CrossRef]
  12. Rigolon, A. A Complex Landscape of Inequity in Access to Urban Parks: A Literature Review. Landsc. Urban Plan. 2016, 153, 160–169. [Google Scholar] [CrossRef]
  13. Xiao, Y.; Wang, Z.; Li, Z.; Tang, Z. An Assessment of Urban Park Access in Shanghai–Implications for the Social Equity in Urban China. Landsc. Urban Plan. 2017, 157, 383–393. [Google Scholar] [CrossRef]
  14. Zhang, R.; Sun, F.; Shen, Y.; Peng, S.; Che, Y. Accessibility of Urban Park Benefits with Different Spatial Coverage: Spatial and Social Inequity. Appl. Geogr. 2021, 135, 102555. [Google Scholar] [CrossRef]
  15. Schläpfer, F.; Waltert, F.; Segura, L.; Kienast, F. Valuation of Landscape Amenities: A Hedonic Pricing Analysis of Housing Rents in Urban, Suburban and Periurban Switzerland. Landsc. Urban Plan. 2015, 141, 24–40. [Google Scholar] [CrossRef]
  16. Zhang, Y.; Dong, R. Impacts of Street-Visible Greenery on Housing Prices: Evidence from a Hedonic Price Model and a Massive Street View Image Dataset in Beijing. ISPRS Int. J. Geo-Inf. 2018, 7, 104. [Google Scholar] [CrossRef]
  17. Wu, C.; Ye, X.; Du, Q.; Luo, P. Spatial Effects of Accessibility to Parks on Housing Prices in Shenzhen, China. Habitat Int. 2017, 63, 45–54. [Google Scholar] [CrossRef]
  18. Wu, J.; He, Q.; Chen, Y.; Lin, J.; Wang, S. Dismantling the Fence for Social Justice? Evidence Based on the Inequity of Urban Green Space Accessibility in the Central Urban Area of Beijing. Environ. Plan. B Urban Anal. City Sci. 2020, 47, 626–644. [Google Scholar] [CrossRef]
  19. Yu, S.; Zhu, X.; He, Q. An Assessment of Urban Park Access Using House-Level Data in Urban China: Through the Lens of Social Equity. Int. J. Environ. Res. Public. Health 2020, 17, 2349. [Google Scholar] [CrossRef]
  20. Li, X.; Wang, Y.; Ju, C.; Huang, B.; Wang, X.; Ma, L.; Wang, P.; Wang, H.; Wang, X.; Fang, X.; et al. China City Statistical Yearbook 2021; National Bureau of Statistics of China, Ed.; National Bureau of Statistics of China: Beijing, China, 2022. [Google Scholar]
  21. He, J.; Ren, F.; Dong, J.; Zhang, H.; Yan, W.; Liu, J. Social Inequity of Park Accessibility in Taiyuan: Highlighting the Unfair Layout of Parks in Second-Tier Cities of China and the Relative Role of Contributors. Geo-Spat. Inf. Sci. 2022, 27, 1118–1140. [Google Scholar] [CrossRef]
  22. Chen, J.; Hu, M.; Lin, Z. Does Housing Unaffordability Crowd out Elites in Chinese Superstar Cities? J. Hous. Econ. 2019, 45, 101571. [Google Scholar] [CrossRef]
  23. Devan, J.; Negri, S.; Woetzel, J.R. Meeting the Challenges of China’s Growing Cities; McKinsey Global Institute: New York, NY, USA, 2008. [Google Scholar]
  24. Wei, H. The Administrative Hierarchy and Growth of Urban Scale in China. Chin. J. Urban Environ. Stud. 2015, 03, 1550001. [Google Scholar] [CrossRef]
  25. Yin, K.; Wang, R.; An, Q.; Yao, L.; Liang, J. Using Eco-Efficiency as an Indicator for Sustainable Urban Development: A Case Study of Chinese Provincial Capital Cities. Ecol. Indic. 2014, 36, 665–671. [Google Scholar] [CrossRef]
  26. Loures, L.; Santos, R.; Panagopoulos, T. Urban Parks and Sustainable City Planning–The Case of Portimão, Portugal. Environ. Dev. 2007, 15, 171–180. [Google Scholar]
  27. Nesbitt, L.; Meitner, M.J.; Girling, C.; Sheppard, S.R.J.; Lu, Y. Who Has Access to Urban Vegetation? A Spatial Analysis of Distributional Green Equity in 10 US Cities. Landsc. Urban Plan. 2019, 181, 51–79. [Google Scholar] [CrossRef]
  28. Rao, Y.; Zhong, Y.; He, Q.; Dai, J. Assessing the Equity of Accessibility to Urban Green Space: A Study of 254 Cities in China. Int. J. Environ. Res. Public. Health 2022, 19, 4855. [Google Scholar] [CrossRef]
  29. Rigolon, A.; Browning, M.; Jennings, V. Inequities in the Quality of Urban Park Systems: An Environmental Justice Investigation of Cities in the United States. Landsc. Urban Plan. 2018, 178, 156–169. [Google Scholar] [CrossRef]
  30. Comber, A.; Brunsdon, C.; Green, E. Using a GIS-Based Network Analysis to Determine Urban Greenspace Accessibility for Different Ethnic and Religious Groups. Landsc. Urban Plan. 2008, 86, 103–114. [Google Scholar] [CrossRef]
  31. Higgs, G.; Fry, R.; Langford, M. Investigating the Implications of Using Alternative GIS-Based Techniques to Measure Accessibility to Green Space. Environ. Plan. B Plan. Des. 2012, 39, 326–343. [Google Scholar] [CrossRef]
  32. Kabisch, N.; Strohbach, M.; Haase, D.; Kronenberg, J. Urban Green Space Availability in European Cities. Ecol. Indic. 2016, 70, 586–596. [Google Scholar] [CrossRef]
  33. Biernacka, M.; Kronenberg, J. Classification of Institutional Barriers Affecting the Availability, Accessibility and Attractiveness of Urban Green Spaces. Urban For. Urban Green. 2018, 36, 22–33. [Google Scholar] [CrossRef]
  34. Guo, S.; Song, C.; Pei, T.; Liu, Y.; Ma, T.; Du, Y.; Chen, J.; Fan, Z.; Tang, X.; Peng, Y.; et al. Accessibility to Urban Parks for Elderly Residents: Perspectives from Mobile Phone Data. Landsc. Urban Plan. 2019, 191, 103642. [Google Scholar] [CrossRef]
  35. Wang, P.; Zhou, B.; Han, L.; Mei, R. The Motivation and Factors Influencing Visits to Small Urban Parks in Shanghai, China. Urban For. Urban Green. 2021, 60, 127086. [Google Scholar] [CrossRef]
  36. Wüstemann, H.; Kalisch, D.; Kolbe, J. Access to Urban Green Space and Environmental Inequalities in Germany. Landsc. Urban Plan. 2017, 164, 124–131. [Google Scholar] [CrossRef]
  37. McCormack, G.R.; Giles-Corti, B.; Bulsara, M. The Relationship between Destination Proximity, Destination Mix and Physical Activity Behaviors. Prev. Med. 2008, 46, 33–40. [Google Scholar] [CrossRef]
  38. Standard for Classification of Urban Green Space. Available online: https://www.mohurd.gov.cn/gongkai/zc/wjk/art/2018/art_17339_236545.html (accessed on 24 August 2024).
  39. Giles-Corti, B.; Broomhall, M.H.; Knuiman, M.; Collins, C.; Douglas, K.; Ng, K.; Lange, A.; Donovan, R.J. Increasing Walking: How Important Is Distance to, Attractiveness, and Size of Public Open Space? Am. J. Prev. Med. 2005, 28, 169–176. [Google Scholar] [CrossRef]
  40. Abdelfattah, L.; Deponte, D.; Fossa, G. The 15-Minute City: Interpreting the Model to Bring out Urban Resiliencies. Transp. Res. Procedia 2022, 60, 330–337. [Google Scholar] [CrossRef]
  41. Ma, W.; Wang, N.; Li, Y.; Sun, D. 15-Min Pedestrian Distance Life Circle and Sustainable Community Governance in Chinese Metropolitan Cities: A Diagnosis. Humanit. Soc. Sci. Commun. 2023, 10, 364. [Google Scholar] [CrossRef]
  42. Weng, M.; Ding, N.; Li, J.; Jin, X.; Xiao, H.; He, Z.; Su, S. The 15-Minute Walkable Neighborhoods: Measurement, Social Inequalities and Implications for Building Healthy Communities in Urban China. J. Transp. Health 2019, 13, 259–273. [Google Scholar] [CrossRef]
  43. Fan, G.; Xie, X.; Chen, J.; Wan, Z.; Yu, M.; Shi, J. Has China’s Free Trade Zone Policy Expedited Port Production and Development? Mar. Policy 2022, 137, 104951. [Google Scholar] [CrossRef]
  44. Fuzhou Municipal People’s Government Fuzhou City’s 14th Five-Year Plan for Ecological Civilization Construction (2021–2025). Available online: https://www.fuzhou.gov.cn/zgfzzt/sswgh/fzssswghzswj/202303/P020230313380045030001.pdf (accessed on 20 July 2024).
  45. Cui, H.; Liu, Z. Spatial-Temporal Pattern and Influencing Factors of the Urban Green Development Efficiency in Jing-Jin-Ji Region of China. Pol. J. Environ. Stud. 2021, 30, 1079–1093. [Google Scholar] [CrossRef]
  46. Office of the People’s Government of Hebei Province Notice of the General Office of the People’s Government of Hebei Province on Issuing the 14th Five-Year Plan for the Construction of the Beijing-Tianjin-Hebei Ecological Environment Support Zone in Hebei. Available online: https://hbepb.hebei.gov.cn/hbhjt/zwgk/fdzdgknr/guihuazongjie/guihua/101633000446818.html (accessed on 16 August 2024).
  47. General Office of Guiyang Municipal People’s Government Notice of the General Office of the Municipal People’s Government on Issuing the Implementation Plan for Consolidating and Enhancing the Construction of National Forest Cities in Guiyang (2023–2025). Available online: https://www.guiyang.gov.cn/zwgk/zfxxgks/fdzdgknr/lzyj/gfxwj/szfgfxwj/qfbh/202310/t20231018_82796233.html (accessed on 16 August 2024).
  48. General Office of Guiyang Municipal People’s Government Notice of the General Office of the Municipal People’s Government on Issuing the Implementation Plan for Promoting the Construction of the Western Land-Sea New Channel in Guiyang City. Available online: https://www.guiyang.gov.cn/zwgk/zwgkzfgb/zwgkzfgb2020/202004/t20200421_56136369.html (accessed on 16 August 2024).
  49. Lanzhou Municipal People’s Government Lanzhou City’s 14th Five-Year Plan for Forestry, Grassland, and Urban Landscape Development. Available online: https://www.lanzhou.gov.cn/module/download/downfile.jsp?classid=0&filename=2fa714430de24833af4aa5a8c3a6faa3.pdf (accessed on 16 August 2024).
  50. Tong, H.; Shi, P. City Profile Lanzhou. Cities 2015, 45, 51–59. [Google Scholar] [CrossRef]
  51. Lu, C.; Pang, M.; Zhang, Y.; Li, H.; Lu, C.; Tang, X.; Cheng, W. Mapping Urban Spatial Structure Based on POI (Point of Interest) Data: A Case Study of the Central City of Lanzhou, China. ISPRS Int. J. Geo-Inf. 2020, 9, 92. [Google Scholar] [CrossRef]
  52. General Office of Guiyang Municipal People’s Government Guiyang City Center Urban Area Sponge City Construction Plan (2019–2025). Available online: https://www.guiyang.gov.cn/ztzl/rdzt/ghgj/qygh_5888935/202111/t20211115_71669740.html (accessed on 16 August 2014).
  53. Krings, A.; Schusler, T.M. Equity in Sustainable Development: Community Responses to Environmental Gentrification. Int. J. Soc. Welf. 2020, 29, 321–334. [Google Scholar] [CrossRef]
Figure 1. (a) Situation maps of cities included in this study. (b) Map of (1) Shijiazhuang, (2) Taiyuan, (3) Shenyang, (4) Harbin, (5) Fuzhou, (6) Jinan, (7) Nanchang, (8) Nanning, (9) Guiyang, and (10) Lanzhou.
Figure 1. (a) Situation maps of cities included in this study. (b) Map of (1) Shijiazhuang, (2) Taiyuan, (3) Shenyang, (4) Harbin, (5) Fuzhou, (6) Jinan, (7) Nanchang, (8) Nanning, (9) Guiyang, and (10) Lanzhou.
Land 14 01552 g001
Figure 2. The overall workflow of the study.
Figure 2. The overall workflow of the study.
Land 14 01552 g002
Figure 3. Sample accessibility calculation illustration.
Figure 3. Sample accessibility calculation illustration.
Land 14 01552 g003
Figure 4. Tukey’s HSD test of pairwise significance p-value.
Figure 4. Tukey’s HSD test of pairwise significance p-value.
Land 14 01552 g004
Figure 5. Boxplot of the accessibility to urban parks for 10 cities (green triangle: mean, pink line: median, red dashed line: 0.5 threshold for good accessibility).
Figure 5. Boxplot of the accessibility to urban parks for 10 cities (green triangle: mean, pink line: median, red dashed line: 0.5 threshold for good accessibility).
Land 14 01552 g005
Figure 6. (a) Hierarchical cluster analysis dendrogram of cities based on mean accessibility and variability. (b) PCA scatter plot.
Figure 6. (a) Hierarchical cluster analysis dendrogram of cities based on mean accessibility and variability. (b) PCA scatter plot.
Land 14 01552 g006
Figure 7. City’s regional GDP and normalized park accessibility (median, mean, and IQR).
Figure 7. City’s regional GDP and normalized park accessibility (median, mean, and IQR).
Land 14 01552 g007
Figure 8. Distribution of housing prices in the study area.
Figure 8. Distribution of housing prices in the study area.
Land 14 01552 g008
Figure 9. Housing-price categories and accessibility to urban parks in 10 cities (x-axis: normalized accessibility to urban parks; y-axis: housing-price category; green triangle: mean; red dashed line: 0.5 threshold for good accessibility).
Figure 9. Housing-price categories and accessibility to urban parks in 10 cities (x-axis: normalized accessibility to urban parks; y-axis: housing-price category; green triangle: mean; red dashed line: 0.5 threshold for good accessibility).
Land 14 01552 g009
Figure 10. Housing-price interpolation (a) and park distribution in Lanzhou (b).
Figure 10. Housing-price interpolation (a) and park distribution in Lanzhou (b).
Land 14 01552 g010
Figure 11. Housing-price interpolation (a) and park distribution in Guiyang (b).
Figure 11. Housing-price interpolation (a) and park distribution in Guiyang (b).
Land 14 01552 g011
Table 1. List of the cities in this study and general statistical information [20].
Table 1. List of the cities in this study and general statistical information [20].
AreaCityProvinceAdministrative Areas (km2)Park and Green Land Area (ha)Green Covered Areas as % of Completed Areas (%)
Total CityCity DistrictsBuilt-Up Area
NorthShijiazhuangHebei15,8482240312500342.85
TaiyuanShanxi69881500340471646.59
NortheastShenyangLiaoning12,8605116563811540.82
HarbinHeilongjiang53,07610,193446515334.22
EastFuzhouFujian12,2551756301527345.40
JinanShandong10,2448376716783340.85
NanchangJiangxi719527772973814-
Central SouthNanningGuangxi Zhuang AR22,2459947320464041.14
SouthwestGuiyangGuizhou80432525369526941.80
NorthwestLanzhouGansu13,1921574235291240.03
Table 2. List of the cities in this study and general statistical information [20].
Table 2. List of the cities in this study and general statistical information [20].
Data TypeSourcesPurpose of Utilization
Park POI and AOIAMAPSpatial location and boundary data for accessibility calculations
Housing prices and residential community locationsLianjia PlatformReal estate pricing and geocoded residential communities
Road network dataOpenStreetMapNetwork analysis and routing analysis to determine the accessibility indicators
Statistical dataChinese Statistical YearbookCity-level socioeconomic indicators
Table 3. Indicators for computing the accessibility to urban parks.
Table 3. Indicators for computing the accessibility to urban parks.
IndicatorsClassificationScore
P—Proximity, distance to the nearest park, meterP > 15001
1500 ≥ P > 10002
1000 ≥ P > 5003
500 ≥ P4
S—Size, size of the nearest park, ha1 > S1
5 > S ≥ 12
10 > S ≥ 53
S ≥ 104
D—Density, number of parks within 15 min walking distanceD = 01
D = 12
1 > D ≥ 33
D > 34
Table 4. Values for analyzing the normalized accessibility for 10 provincial capitals.
Table 4. Values for analyzing the normalized accessibility for 10 provincial capitals.
CityMeanMedianSDSkewnessKurtosis% of Good Accessibility
Fuzhou0.450.440.20–0.13–0.4242.33
Shijiazhuang0.440.500.23–0.10–0.7252.51
Guiyang0.380.380.200.01–0.4639.60
Harbin0.370.370.210.26–0.4849.78
Taiyuan0.360.370.220.19–0.8537.49
Nanchang0.360.330.220.23–0.3826.50
Shenyang0.350.330.200.15–0.5624.63
Jinan0.340.330.19–0.02–0.4620.54
Nanning0.300.370.200.30–0.3020.65
Lanzhou0.290.250.220.57–0.4722.45
Table 5. Home-price-to-disposable-income ratio in the second-tier provincial capitals.
Table 5. Home-price-to-disposable-income ratio in the second-tier provincial capitals.
CityAverage Price (CNY/m2)Disposable Income 2022 (CNY)Regional GDPPrice-to-Income Ratio (/m2)Price-to-Income Ratio (/90 m2)
Shijiazhuang12,585.6544,74552,5890.2825.31
Taiyuan9132.4343,69490,9680.2118.81
Shenyang9013.0344,00377,7770.2018.43
Harbin7888.4443,98155,1750.1816.14
Fuzhou26,312.9553,817120,8790.4944.00
Jinan16,358.459,459106,4690.2824.76
Nanchang11,185.5752,622100,4150.2119.13
Nanning11,335.7342,63661,7380.2723.93
Guiyang8490.9946,24281,9950.1816.53
Lanzhou12,142.745,27775,2170.2724.14
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Liu, M.; Terada, T. Beyond a Single Story: The Complex and Varied Patterns of Park Accessibility Across China’s Emerging Cities. Land 2025, 14, 1552. https://doi.org/10.3390/land14081552

AMA Style

Liu M, Terada T. Beyond a Single Story: The Complex and Varied Patterns of Park Accessibility Across China’s Emerging Cities. Land. 2025; 14(8):1552. https://doi.org/10.3390/land14081552

Chicago/Turabian Style

Liu, Mengqi, and Toru Terada. 2025. "Beyond a Single Story: The Complex and Varied Patterns of Park Accessibility Across China’s Emerging Cities" Land 14, no. 8: 1552. https://doi.org/10.3390/land14081552

APA Style

Liu, M., & Terada, T. (2025). Beyond a Single Story: The Complex and Varied Patterns of Park Accessibility Across China’s Emerging Cities. Land, 14(8), 1552. https://doi.org/10.3390/land14081552

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop