Supply–Demand Assessment of Cultural Ecosystem Services in Urban Parks of Plateau River Valley City: A Case Study of Lhasa

Abstract

Cultural ecosystem services (CES) in urban parks, as a vital component of urban ecosystem services (ES), are increasingly recognized as an important tool for advancing urban sustainability and implementing nature-based solutions (NbS). The supply–demand relationship of CES in urban parks is strongly shaped by sociocultural and spatial geographic factors, playing a crucial role in optimizing urban landscape structures and enhancing residents’ well-being. However, current research generally lacks adaptive evaluation frameworks and quantitative methods, particularly for cities with significant spatial and cultural diversity. To address this gap, this study examines the central district of Lhasa as a case study to develop a CES supply–demand evaluation framework suitable for plateau river valley cities. The study adopts the spatial integration analysis method to establish an indicator system centered on “recreational potential–recreational opportunities” and “social needs–material needs,” mapping the spatial distribution and matching characteristics of supply and demand at the community scale. The results reveal that: (1) in terms of supply–demand balance, 25.67% of communities experience undersupply, predominantly in the old city cluster, while 16.22% experience oversupply, mainly in key development zones, indicating a notable supply–demand imbalance; (2) in terms of supply–demand coupling coordination, 55.11% and 38.14% of communities are in declining and transitional stages, respectively. These communities are primarily distributed in near-mountainous and peripheral urban areas. Based on these findings, four urban landscape optimization strategies are proposed: culturally driven urban park development, demand-oriented park planning, expanding countryside parks along mountain ridges, and revitalizing existing parks. These results provide theoretical support and decision-making guidance for optimizing urban park green space systems in plateau river valley cities.

1. Introduction

Cultural ecosystem services (CES) in urban parks are a crucial component of urban ecosystem services (ES), playing a significant role in advancing urban sustainability and the implementation of nature-based solutions (NbS) [1,2,3,4]. As important green spaces in urban areas, parks play a key role in promoting residents’ well-being and physical and mental health [5,6], providing a variety of environmental, social, and cultural benefits. These benefits, whether direct or indirect, are recognized as ecosystem services [7,8]. The Millennium Ecosystem Assessment (2005) categorizes these services into provisioning, regulating, cultural, and supporting services [9,10]. Among them, cultural ecosystem services (CES) refer to the non-material benefits that people derive from natural or semi-natural ecosystems, and are described in dimensions such as aesthetics, recreation, inspiration, research and education, and cultural heritage value [11]. These services reflect the emotional connection and cultural identity that human societies have with the natural environment. In urban ecosystem services, CES have the characteristic of transcending the boundaries of green spaces. However, urban parks, as focal points for ecological, social, and cultural functions, also serve as accessible and frequent spaces where residents experience the benefits of CES in their daily lives, making research on CES particularly relevant and significant in practice. Such research is crucial for addressing and managing urban natural environments, contributing to the integration of multifunctional ecosystems and their services into urban landscapes. It also raises the awareness of urban residents and planners, ultimately positively influencing urban sustainability [3,4,12].

With the acceleration of urbanization [13], the expansion of urban parks in central districts struggles to meet the demand driven by population growth [14], particularly in areas characterized by high geographical and cultural heterogeneity. Issues such as uneven distribution of park green spaces and mismatched supply and demand are common, severely affecting urban ecological functions and residents’ well-being. As a key provider of “near nature” and major CES within urban ecosystems [15,16], the assessment of CES supply and demand in urban parks has become a critical reference for urban planning, ecological governance, and municipal services [17]. However, due to the complexity of urban park CES, including cultural diversity and complex spatial characteristics [3,8], this field of research has not yet been fully developed, and its practical applications remain promising [7]. Particularly, the quantification of park CES supply and demand faces significant challenges due to the intangible, subjective, and often ambiguous boundaries of CES, making the identification and quantification of these services especially complex [8]. Many studies currently focus only on the supply side of park CES, with a lack of comprehensive research on the matching of supply and demand, especially in terms of spatial explicitness [18,19,20].

In CES assessment, non-monetary qualitative evaluation is considered one of the primary methods [7]. Among these, the observational method [7] and social media-based method [21] are associated with revealed preferences, where the value of CES is assessed by observing behaviors or analyzing texts, photos, and other documents that represent preferences. Meanwhile, stated preference methods are also widely used to directly obtain CES evaluations from the public, including interviews [22], questionnaires [23], and participatory mapping [24]. Since CES assessments for all types of parks in central urban areas are subjective processes, a single method is insufficient to fully capture the supply and demand characteristics [19]. Therefore, a hybrid evaluation framework combining revealed and stated preferences should be adopted, integrating remote sensing imagery, field surveys, POI platform data, and official statistics, developing multidimensional indicators to quantify the structural and cultural functions of parks. This approach retains the objectivity of spatial analysis while incorporating cultural perceptions into the model through operational metrics. By bridging qualitative non-monetary evaluation with quantitative methods, the framework can enhances the scientific rigor and practical applicability of CES assessments [7,25].

According to the Common International Classification of Ecosystem Services (CICES), most current studies on the value of CES in urban parks focus on recreational and aesthetic benefits, followed by cultural heritage value, while educational and inspirational values are seldom examined [26]. In supply-side evaluations, indicators such as park area and degree of naturalness are commonly used to represent aesthetic value [27,28]. Recreational value is primarily associated with the proportion of activity-friendly areas, such as plazas and water bodies. Cultural heritage is reflected in landscape elements that embody local historical characteristics and contribute to the park’s regional service advantages [29]. Educational and inspirational values enhance human–nature connections, fostering interest and empathy toward parks [30]. However, there remains a lack of multidimensional indicator systems capable of capturing the differences in park CES value across urban areas, especially in the assessment of cultural heritage, education, and inspiration [31]. In addition, accessibility is a prerequisite for converting the potential supply of park green spaces into actual services perceived by residents, as it reflects the ease with which people can obtain CES [19,26]. While some studies have included accessibility in supply–demand frameworks, most lack assessments of spatial coverage at the community scale. Such assessment is crucial for measuring the proportion of different accessibility zones relative to total residential area and for assessing the efficiency of service flow between supply and demand. This enables a more realistic measurement of CES supply aligned with actual use, helping to overcome limitations in locational analysis and spatial expression of service potential [32,33]. Regarding demand evaluation, social demand focuses on reflecting the demand for CES from communities and resident groups. Material demand emphasizes the demand for parks driven by commercialization and development processes across different regions. By combining both aspects, these indicators provide a comprehensive understanding of the demand structure for park CES, accurately capturing regional demand differences across multiple dimensions, thus informing evidence-based urban park planning [26,34].

Current research on the supply and demand of CES in parks mainly focuses on metropolitan areas in plains, mountain, or coastal regions [19,24,35,36], without considering the connection between the assessment frameworks and the characteristics of the urban area. However, existing studies have shown that sociocultural diversity and geographic environment significantly influence park CES, subtly guiding park use and residents’ demand [37]. As a result, the applicability of existing assessment frameworks and indicator systems is limited for cities with high spatial heterogeneity and cultural specificity [38], such as Lhasa. In this context, developing regionally adaptive assessment methods, especially for plateau river valley cities, will contribute to deeper exploration of park CES and provide valuable insights for other cities.

As the capital of the Tibetan Plateau and a national historical and cultural city, Lhasa has a unique geographical, cultural, and ecological background. The city’s narrow, strip-shaped multi-cluster spatial structure is constrained by mountains on both sides of the river valley. Due to the limited urban construction land in the central urban area [39], the quantity of parks is relatively insufficient and unevenly distributed. With the increasing demand for park services by residents, the phenomenon of supply–demand mismatch in CES has become more pronounced. This study examines the central urban area of Lhasa, a plateau river valley city, as the research area, using street communities as the research unit. The study integrates spatial mapping, network analysis, and spatial overlay analysis to quantify the CES supply–demand levels in the central urban area of Lhasa, selecting both objective environmental and socio-economic indicators for analysis, establish an indicator system centered on “recreational potential—recreational opportunities” and “social needs—material needs”. Based on this, the study assesses the spatial distribution and matching characteristics of CES supply and demand at the community scale and proposes CES optimization strategies. The results can provide valuable reference for the rational allocation and planning of parks in the central urban area of Lhasa. The objectives of this study are: (1) to develop an index system adapted to plateau river valley cities for quantifying urban park CES supply capacity, providing a more accurate tool for assessing park CES; (2) to conduct urban park CES spatial mapping at the community scale, improving the readability and interpretability of CES spatial distribution characteristics; (3) to identify CES supply–demand matching characteristics and propose CES-guided urban park layout optimization strategies, ensuring the resolution of identified mismatches. Through this research, the significance of the study lies in providing theoretical support and practical guidance for improving the supply–demand matching of park CES and optimizing urban landscape planning in plateau river valley cities.

2. Study Area and Data Processing

2.1. Study Area

Lhasa, located in the heart of the Qinghai–Tibet Plateau at coordinates 91°06′ to 94°00′ east longitude and 29°36′ to 31°00′ north latitude, has an average elevation of 3650 m. As a representative plateau river valley city, Lhasa is traversed by the Lhasa River, with the terrain gradually descending from the north to the south. The city’s valley exhibits a distinctive spatial pattern of “two mountains surrounding a valley, blending natural features with urban areas.” To the north lie the Nyenchen Tanglha Mountains, while the south is bordered by the Guo Kara Riju Mountain Range. The central and southern areas consist of the flat Lhasa River valley plain. Influenced by its mountainous terrain and river courses, Lhasa’s central urban area has developed a spatial structure featuring “one core, two wings, and multiple centers and clusters.” This area is home to key natural landscapes, such as the Northern and Southern Mountains, Lalu Wetland, and the Lhasa River Scenic Area. It is also a crucial hub for preserving Tibetan culture and integrating the diverse cultures of various ethnic groups. Landmark historical sites, including the Potala Palace, Jokhang Temple, and Norbulingka, are located within the city. This study focuses on the central urban area of Lhasa, as defined in the “Lhasa City Territorial Spatial Master Plan (2021-2035).” The study area covers 360.08 square kilometers and includes one central urban core (Chengguan District), two sub-centers (Duilong District and Dazi District), 20 distinct clusters, and 75 communities (Figure 1).

Figure 1. Location of the central urban area of Lhasa. (a) The location of Lhasa’s central urban area within Lhasa City and the Tibet Autonomous Region. (b) Communities in the map are labeled with numerical codes; detailed names are provided in Appendix A.2.

2.2. Data Sources

The data used in this study primarily consists of multi-source spatial vector data and socio-economic data: (1) Park data were sourced from the park directory in the “Lhasa Green Space System Plan (2021–2035).” These data were combined with high-resolution Google imagery and field surveys. After conducting geometric corrections, removing duplicates, and merging the data, 113 parks built before 2022 were identified. Based on the “Standard for classification of urban green space (CJJ/T85-2017) [40]”, these parks were categorized into four types: comprehensive parks, specialized parks, recreational parks, and community parks (Figure 2). (2) Land use data came from China’s 30 m resolution remote sensing monitoring data (https://www.gis5g.com, accessed on 2 February 2025). (3) DEM data (30 m resolution) were obtained from NASA Earthdata (https://search.earthdata.nasa.gov/search, accessed on 2 February 2025). (4) NDVI data (30 m spatial resolution) were sourced from the Chinese Academy of Sciences Ecological Data Center (https://www.nesdc.org.cn, accessed on 2 February 2025). (5) Transportation network data were obtained from the OpenStreetMap website (https://www.openstreetmap.org, accessed on 2 February 2025). (6) Population data (1 km resolution) were sourced from the WorldPop website (https://www.worldpop.org, accessed on 2 February 2025). (7) Data on commercial services and park entrance points of interest were extracted from Amap using Python 3.13.

Figure 2. Distribution of parks in the central urban area of Lhasa.

3. Methods

This study focused on the geographical and cultural characteristics of plateau river valley cities to construct an evaluation system for the supply and demand of urban parks CES in central urban area in Lhasa. The study analyzed the spatial distribution and matching characteristics of supply and demand at the community level. This study, guided by the goals of indicator quantification, spatial mapping, and supply–demand assessment, follows three methodological steps: (1) From the aspects of recreational potential and recreational opportunities, a total of eight and two core indicators were selected, respectively, based on five categories of cultural service values and residents’ travel modes to quantify CES supply levels. Two core indicators from each of the social and material demand dimensions were selected to quantify CES demand levels. (2) At the community scale, the spatial distribution of both individual and composite indicators of CES supply and demand was mapped using spatial overlay analysis. (3) The supply–demand ratio method and the coupling coordination degree model [41,42] were employed to assess supply–demand balance and coordination, identify problematic communities, and propose optimization strategies (Figure 3).

Figure 3. Analysis and optimization framework for the cultural ecosystem services (CES) supply–demand match.

3.1. Quantification of CES Supply and Demand Indicators

3.1.1. Quantification of CES Supply Indicators

The potential supply of CES from urban parks represents the total CES these spaces can provide. The supply level depends on both the potential for service provision and the accessibility of recreational opportunities [43]. Building on previous studies, this research constructed a CES supply quantification framework based on two key dimensions: recreational potential and recreational opportunities [44] (Table 1).

Table 1. Indicator system for evaluating the supply of cultural ecosystem services (CES) in parks.

Category	Primary Indicator	Secondary Indicator	Indicator Description
Supply	Entertainment potential	Park area	Larger parks often exhibit higher spatial complexity and environmental connectivity, which enhances the visual layering and aesthetic quality of the landscape.
		Visible scenic area ratio	Based on the geographical characteristics and natural worship concepts of Lhasa, mountains and water are the main objects of scenic area calculation.
		NDVI index	The NDVI index reflects vegetation productivity, indicating the quality of park vegetation, and is positively correlated with the aesthetic value of cultural services and the sense of well-being of the population.
		Recreational land area ratio	Urban parks provide spatial carriers for CES, and the larger the available recreational area, the more tourists it can accommodate, and the higher the recreational value.
		Park road area ratio	The larger the park road area ratio, the greater the possibility of activities such as walking and running, and the higher the recreational value.
		Number of art works per unit area	Urban parks inspire artists to create, and through park-related art sculptures, inscriptions, and poetry, they showcase their inspirational value.
		Number of scientific research papers published per unit area	Buildings, vegetation, water, and other essential elements in parks, as well as cultural connotations, can provide research objects for scientific education.
		Number of cultural relics and events per unit area	Cultural relics are products accumulated over historical periods, and their value is deeply connected with the service environment of the area.
	Entertainment opportunities	Walkability effective service area ratio	The proportion of residential areas covered by walking within 5, 10, and 15 min.
		Non-motor vehicle accessibility effective service area ratio	The proportion of residential areas covered by non-motor vehicle travel within 5, 10, and 15 min.

1. In terms of recreational potential, this study was based on the cultural service environment of Lhasa, referencing the MEA report. From five service categories—aesthetic value, recreational value, inspirational value, scientific education value, and cultural heritage value—eight indicators were selected for evaluation. Consistent with previous studies, the focus remains on aesthetic and recreational values [25]. Considering the geographical characteristics of plateau river valley cities and the local residents’ cultural relationship with nature, the visibility and proximity of mountains and water were incorporated into the evaluation framework. Moreover, previous studies on inspirational, scientific education, and cultural heritage values often relied on broad data that were inadequate for capturing specific urban elements, such as cultural relics, monuments, and historic buildings [45]. To improve the precision of this evaluation, data was collected from government documents, field research, and publicly available information from websites (Figure 4).

Figure 4. Score of 113 City Park Indicators. Park IDs (x-axis), 8 recreational indicators (y-axis); colors/sizes show potential levels. Park names and values in Appendix A.1.

• Large parks have more complex landscape compositions (such as woodlands, grasslands, water bodies, and built facilities), thereby enhancing landscape element diversity [12]; a higher proportion of visible landscape area allows visitors to have a broader visual exposure to the scenery [46]. High NDVI values typically indicate lush vegetation, which is considered by people to have greater visual appeal [47,48]. These three indicators serve as key measures for assessing the aesthetic value of urban parks. Among them, the proportion of visible landscape area is determined using the landscape visibility function in ArcGIS. The analysis utilized the 2022 DEM of Lhasa to calculate the ratio of each park’s visible mountain and water landscape area to its total green space area.
• Recreational land in park includes lawns, sand areas, squares, and water bodies. The larger the proportion of these areas, the more space is available for recreation, making the park more accessible [45]. Additionally, the greater the proportion of road area within the park, the more abundant the open spaces for activities such as walking and running. These two indicators were selected to evaluate the recreation value of parks. Using the “Lhasa Green Space System Plan (2021–2035)” as a reference, high-resolution satellite imagery was analyzed to obtain relevant data and assess the park recreational service capacity.
• The inspirational value of urban park is primarily reflected in artworks within the parks, such as sculptures and inscriptions. Data for this was gathered through field surveys and records of park green spaces, with the number of artworks per unit area used to assess this value.
• The scientific education value is primarily reflected in the number of research papers focusing on park green spaces. Published Chinese papers are retrieved from the China Journal Full-text Database (https://www.cnki.net, accessed on 5 January 2025), while English-language publications are sourced from Web of Science (https://webofscience.clarivate.cn, accessed on 5 January 2025). The number of research papers published per unit area was used for evaluation.
• Lhasa is home to many historically significant urban parks with rich cultural heritage, offering substantial cultural heritage services. This value is reflected by the number of cultural relics and events per unit area. Data were collected from the Lhasa City Historical and Cultural City Protection Plan outlined in the “Lhasa City Land and Space Overall Plan (2021–2035),” the “National Representative List of Intangible Cultural Heritage” (https://www.ihchina.cn, accessed on 5 January 2025), and park introductions.

2. In terms of recreational opportunities, considering the travel habits of local residents, the accessibility of urban parks was measured by the ratio of effective service area for walking and non-motorized vehicles. The calculation involves both accessibility and effective service area ratio. Accessibility is a key factor in converting the potential recreational opportunities of urban green spaces into actual CES provision, reflecting the ease with which residents can access CES, measured by absolute time values [44]. This study used a network analysis model to calculate urban parks accessibility based on two modes of transportation: walking and non-motorized vehicles, reflecting the most commonly used travel modes by local residents. Following the “Standard for planning of urban green spaces (GB/T51346-2019)” [47] and the concept of living circles, accessibility was categorized into three levels based on time costs: 5 min, 10 min, and 15 min, with values of 1, 0.5, and 0.33, respectively [49,50]. The calculated accessibility data were combined with residential area data in Lhasa’s central urban area. The accessibility calculation results were overlaid with the residential area of Lhasa’s central urban district. By summing up the effective service areas corresponding to different travel times for residents, the final walking and non-motorized vehicle effective service area ratio can be obtained. The calculation formula is as follows:

$$WA=(S_5^W\ast 1+S_{10}^W\ast 0.5+S_{15}^W\ast 0.33)/S$$

(1)

$$BA=(S_5^B\ast 1+S_{10}^B\ast 0.5+S_{15}^B\ast 0.33)/S$$

(2)

where $WA$ and $WA$ represent the effective service area ratios for walking and non-motorized vehicles, and $S_5^W,S_{10}^W,S_{15}^W$, $S_5^B,S_{10}^B,S_{15}^B$ represent the effective service areas for residents with different time costs. S is the community unit area.

3. The data for each indicator was normalized using the range method. Assuming that recreational potential and recreational opportunities were of equal importance, the Entropy Weight Method (EWM) was used to calculate the weight of each indicator. The calculation formula for the CES supply of parks was as follows:

$$S=\left(R+A\right)\ast 0.5$$

(3)

$$\begin{gathered} R=0.086PA+0.088PN+0.055RV+0.135RW+0.017RR+0.1RI+0.31RA \\ +0.21RH \end{gathered}$$

(4)

$$A=0.874WA+0.126BA$$

(5)

where S represents the CES supply of parks, and R and A represent the recreational potential and recreational opportunities of the parks. The variables are defined as follows:

PA: Park area index.

RV: Visibility landscape area ratio index.

PN: NDVI index.

RW: Park recreational land area index.

RR: Park road area ratio index.

RI: Artworks per unit area index.

RA: Research papers per unit area index.

RH: Cultural relics and events per unit area index.

WA: Walking accessibility effective service area ratio index.

BA: Non-motorized vehicle accessibility effective service area ratio index.

To calculate the CES provided by parks to the community, the radius delivery method was used to quantify the value of S provided to t the community [19], as follows:

$$S_d=\left\{\begin{array}{c}S, 0\le d\le {\displaystyle \frac{1}{3}}r\\ {\displaystyle \frac{2}{3}}S, {\displaystyle \frac{1}{3}}r<d\le {\displaystyle \frac{2}{3}}r\\ {\displaystyle \frac{1}{3}}S, {\displaystyle \frac{2}{3}}r<d\le r\end{array}\right.$$

(6)

where $S_d$ is the supply value at a distance d (0 ≤ d ≤ r), S is the park’s supply value, and r is the service radius. The park service radius, r, used in this study is based on the service radius standards for different park types (comprehensive parks, specialized parks, recreational parks, and community parks) as outlined in the “Lhasa Green Space System Plan (2021-2035)” and the “Standard for classification of urban green space (CJJT85-2017) [40].” These standards are shown in Table 2.

Table 2. Service radius standards for different park types.

Type of Parks	Service Radius (m)	Appropriate Scale (hm2)	Functional Characteristic	Number of Parks	Average Area
Comprehensive park	3000	20–50	Green spaces with rich contents suitable for various types of outdoor activities and with comprehensive recreational and ancillary management and service facilities	8	11.67
	2000	10–20
Specialised park	3000	20–50	Green space with specific content or form with appropriate recreational and service facilities, including botanical gardens, historical gardens, heritage parks, play parks, children’s parks, sports parks, waterfront parks, etc.	22	20.14
	2000	10–20
	1000	5–10
	500	2–5
Community park	1000	5–10	Green spaces with independent sites and basic recreational and service facilities, mainly serving residents within a certain community area to carry out daily recreational activities nearby	33	2.96
	500	1–5
Recreation park	300	0.2–1	Green spaces with independent sites, small scale or diversified shapes, convenient for residents to enter in the vicinity and with certain open space functions	50	2.17

3.1.2. Quantification of CES Demand Indicators

The CES demand measurement system was based on the relevant research framework, from the two dimensions of social demand and material demand [34], four specific indicators are selected. (1) The social demand dimension included two basic indicators: population density and population activity intensity. (2) The material demand dimension included two indicators: commercial service capacity and development and construction intensity (Table 3). The specific formula is as follows.

$$D=PD+HAILS+SD+BD$$

(7)

where D represents the demand for CES in urban parks, PD is the population density, calculated using population raster data; SD is the density of commercial services, based on POI data, obtained by applying kernel density analysis; BD is the intensity of development and construction, calculated by the ratio of all construction land within the region; HAILS is the intensity of human activities in the surface layer of the land, with reference to Xu Y., who divided land use into cropland, forest land, and forest land; and D is the intensity of human activities in the surface layer of the land. It is divided into six categories: arable land, forest land, grassland, water, construction land and unused land, and its equivalent conversion coefficients of construction land are 0.2, 0, 0.067, 0.6 [51], 1 and 0, respectively, and the magnitude of the coefficients reflects the magnitude of the intensity of human activities in each land use type.

Table 3. Indicator system for evaluating the demand of CES in parks.

Category	Primary Indicator	Secondary Indicator	Indicator Description
Demand	Social Demand	Human Activity Intensity	Reflects the degree of human impact and activity on the land surface, the size of population demand, and serves as a comprehensive indicator of human impact and activity on the land surface.
		Population Density	The number of people per unit area, reflecting the spatial distribution of the population. The larger the value, the greater the residents’ demand for parks; conversely, the smaller the value, the smaller the residents’ demand.
	Material Demand	Commercial Service Capability	Reflects the intensity of regional economic activities. The larger the value, the more concentrated the development area, and the greater the demand for open spaces such as park green spaces.
		Development and Construction Intensity	Measured by the proportion of construction land within the community. The larger the value, the higher the degree of urban construction, and the greater the demand for open spaces such as park green spaces.

3.2. Identification of CES Supply–Demand Matching Characteristics

The supply–demand ratio method was used to classify CES supply–demand matching types [52], reflecting the supply–demand equilibrium relationship of communities. Communities were categorized as supply excess, supply–demand balance, and supply lag, where the supply–demand balance was divided into high-level balance and low-level balance.

The supply–demand coupling coordination degree (CCD) was analyzed using a coupling coordination model [53], which was commonly used to measure the mutual influence and coordination consistency between two or more variables. In this study, it was employed to indicate the degree and trend of synergistic development between urban parks supply and demand, reflecting the coupling of development speed and direction. Based on the calculation results, the coupling coordination degree was divided into three categories to indicate the development status of CES supply and demand at the community level: coordinated development, transitional development, and uncoordinated and declining. The formula is as follows [54].

$$C=n{\left\{{\displaystyle \frac{\left\{u_1\times u_2\times u_3\dots \dots u_n\right\}}{\prod {\left[ui+uj\right]}^k}}\right\}}^{{\displaystyle \frac{1}{k}}}={\displaystyle \frac{2\sqrt{u_1{u}_2}}{u_1+u_2}}$$

(8)

$$T=\alpha u_1+\beta u_2$$

(9)

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

(10)

In the formula, C is for the coupling of supply and demand, u₁, u₂, respectively, said for the supply and demand index, the distribution of both intervals are [0, 1], T is for the coordination index, taking the value of the range of [0, 1]; $\alpha$, $\beta$ are for the coefficient to be determined. According to the previous relevant research, the parks supply and demand for the two types of indicators are considered to be equally important, and $\alpha$ and $\beta$ are taken as the value of 0.5; D is for the coupling coordination degree of supply and demand, with the range in value of [0, 1]; a D that is bigger indicates that the coupling of supply and demand co-ordination is higher, and with reference to the relevant literature will be divided into 10 levels and 3 types of coupling and coordinated development.

To determine the classification thresholds for the CES supply–demand ratio and the coupling coordination degree, this study applied four data classification methods based on the characteristics of the dataset: K-means clustering, Z-score normalization, Jenks natural breaks, and the interquartile range (IQR) method. The optimal classification thresholds were identified through the following steps: (1) Sensitivity analysis was conducted to assess the robustness of each classification method. Four sets of classification data were subjected to perturbations ranging from ±1% to ±16%, and the classification procedures were repeated using the four methods. (2) The Kappa coefficient was used as the sensitivity analysis metric. By plotting the trend of Kappa values under different levels of perturbation, the classification method demonstrated the highest stability and lowest volatility was selected.

4. Results

4.1. Spatial Distribution of Urban Park CES in Central Urban Areas

4.1.1. Spatial Distribution of CES Supply

The spatial distribution of CES supply for urban park in central urban area of Lhasa is shown in Figure 5 and Figure 6, presenting a spatial pattern of high in the middle, low in the east and west wings, and distributed along the river. High-supply communities are primarily concentrated in the western part of the Old City Cluster, the southern part of the Nyange Cluster, the Two Island Street Cluster, and the West City Cluster. Medium-supply communities are predominantly located on the periphery of the city center and in the key development area at the confluence of the three forks, which includes the Eastern New City Cluster, the southern part of the Duodi Cluster, the Cijueling Culture Cluster, the northern part of the Lhasa National High-tech Zone (LNHZ), and the Lhasa Economic and Technological Development Zone (LETDZ). Low-supply communities are primarily located in peripheral zones and underdeveloped new urban areas distant from the city center, including the Dulong New City Cluster, Dulong Industrial Cluster, Comprehensive Bonded Area Cluster, LNHZ (central and south) Cluster, Education New City Cluster, Dazi Central City Cluster, Dazi New City Cluster, Yeba Cluster, Puxiong Cluster, Gangdui Cluster, and others. It is worth noting that extremely high values appear in the Potala Palace and Norbulingka areas in the core area of the old city. These areas are affected by historical development, retaining more historical and cultural gardens with perfect integrated functions, providing greater recreational potential and more opportunities, and the level of supply is significantly higher than that of other areas. The extremely low values appear in the near-mountain valley areas far away from the city center, affected by the terrain, with a lower intensity of development and construction, and the level of supply is generally lower than downtown.

Figure 5. CES supply of parks for each indicator (a) Recreational Potential Indicators; (b) Recreational Opportunities Indicators.

Figure 6. The CES supply of parks in central urban area of Lhasa at the community scale.

4.1.2. Spatial Distribution of CES Demand

The spatial distribution of CES demand for urban park in central urban area of Lhasa presents the distribution characteristics of high in the center, low in the surrounding area, and decreasing in a stepwise manner along the east and west directions. As shown in Figure 7 and Figure 8, the four types of service demand, namely, human activity intensity, population density, commercial service capacity, and development and construction intensity, show a tendency to gather towards the main and sub-centers of the city. High-demand communities are distributed in the population gathering area of the main center of the city, with the largest value appearing in the Barkhor Street area of the core group of the old city. Medium-demand communities are mainly distributed in the population gathering area around the Barkhor Street area, as well as in the eastern new city group and western city group, which are the focus of the development of the city of Lhasa. Low-demand communities are widely distributed in the suburbs of north and south mountain where the density of the population is relatively low, as well as in some of the new city areas.

Figure 7. Demand for CES of parks by each indicator: (a) Social Demand (b) Material Demand.

Figure 8. The demand for parks in central urban area of Lhasa at the community scale.

4.2. Supply–Demand Matching Analysis of Urban Park CES in Central Urban Areas

Based on the above research, this study analyzes the spatial coupling between park supply and demand using the supply–demand balance relationship and the CCD model. Following sensitivity analysis and Kappa coefficient comparison, the Jenks natural breaks method was determined to be the most stable and was thus adopted for classification [19,52]. Considering both the supply–demand ratio and the supply level, the study area is categorized into four supply–demand matching types: undersupply, oversupply, high-level balance, and low-level balance. Additionally, three levels of coordinated development status are identified: coordinated development, transitional development, and uncoordinated and decline.

The analysis of supply–demand matching types reveals a pronounced imbalance in the CES supply and demand of urban parks in Lhasa’s city center. Approximately 25.67% of communities experience undersupply, predominantly located in the eastern core of the old city and adjacent near-mountain areas. In contrast, 16.22% of communities exhibit oversupply, concentrated in the western part of the old city, the central urban core, and several key development zones. Although 58.11% of communities maintain a generally balanced supply–demand relationship, only 3.73% achieve a high level of equilibrium. (Figure 9). It is worth noting that in the undersupplied communities of Barkhor Street, Jibanggang Street, Gamagongsang Street, Raidi Street, and Najin Street, located in the downtown area, there is high demand but low supply. This imbalance is due to the intensive movement of the population, ongoing construction and development, and the over-compression of urban open space caused by high-density development. The population in the near-mountain river valley areas located at the eastern and western wings, as well as the northern and southern ends of the city, is relatively small. However, the mountains and river valleys in these regions have not been fully utilized to provide CES, resulting in a phenomenon where supply lags behind demand. Among the oversupplied communities, the Gongdelin and Jipenggang Street areas located in the core cluster of the old city are relatively densely populated, but due to the historical development of the area, there are numerous parks retained that rank high in terms of their cultural service capacity, presenting the phenomenon of supply overrun and high-level balance. Located at the mouth of the three forks river as well as along the banks of the river, the LNHZ Cluster, the Eastern New Town, and the Cijueling Cluster are the key construction areas in Lhasa in recent years and are relatively less populated, with lower demand for parks, presenting the phenomenon of oversupply.

Figure 9. CES supply and demand balance distribution.

The spatial distribution of the CCD of parks in the central urban area of Lhasa shows a “center-periphery” concentric diffusion pattern (Figure 10). Specifically, 55.11% of the area exhibits a state of disorganization and decline, while only 6.75% of the area shows coordinated development. Additionally, 38.14% of the area is in a transitional development phase, characterized by a tendency towards coordination but still approaching a disorganized state (Table 4).

Figure 10. The spatial distribution of the coupling coordination degree (CCD).

Table 4. Classification and coordination development degree of supply and demand matching types.

	Area Proportion	Supply and Demand Matching Type	Area Proportion	Community Quantity
Coordinated Development	6.75	Oversupply	1.02	1
		High-Level Balance	3.73	11
		Low-Level Balance	1.79	9
		Undersupply	0.21	7
Transition Development	38.14	Oversupply	15.19	5
		High-Level Balance	0.00	0
		Low-Level Balance	22.95	19
		Undersupply	0.00	0
Uncoordinated and Decline	55.11	Oversupply	0.00	0
		High-Level Balance	0.00	0
		Low-Level Balance	29.63	11
		Undersupply	25.48	12

Comprehensive analysis of the relationship between supply and demand balance and coordinated development of parks CES in central urban area of Lhasa, the areas in a coordinated development state account for a relatively small proportion. Among these, sseven communities, predominantly characterized by a lag in supply, are mainly concentrated in the Barkhor Street area of the Old City Core Cluster. Notably, five oversupplied communities are identified within the transitional development areas, primarily distributed across the Cijueling Culture Cluster, the northern part of the LNHZ, and the West City Cluster. The communities exhibiting an uncoordinated and declining state are primarily supply-lag and low-level balanced communities (23 in total), mainly located in the peripheral mountainous districts of the city. This phenomenon can be attributed to the relatively late initiation of urban green space planning in Lhasa, the lack of countryside park types, and the absence of a systematic framework for the conservation and utilization of the city’s extensive mountainous and river valley areas.

5. Discussion

5.1. Supply–Demand Assessment of Urban Park CES in the Central Urban Area

This study reveals a significant spatial supply–demand mismatch in the CES of urban parks in the central urban area of Lhasa. There are notable disparities in the supply–demand balance and coordination levels between different urban clusters, and this finding aligns with research results from several cities worldwide. Existing studies have shown that the degree of supply–demand matching for park CES is influenced by the spatiotemporal coupling of geographic, cultural, and developmental factors [17,37,55,56]. In the central urban area of Lhasa, the supply–demand imbalance is more pronounced in the old city cluster compared to the new city cluster. Specifically, in the Barkhor Street area and its surrounding communities, high-demand communities remain widespread due to difficulties in implementing historical preservation and population relocation policies [26], aggravating the supply–demand imbalance. In contrast, new city clusters such as the Northern LNHZ and the Cijuelin Cultural Cluster, following a “plan first, build later” development model, have seen an oversupply of park CES. However, due to relatively slow population aggregation and economic vitality, and insufficient service demand, a typical transitional development pattern in urban expansion process has emerged [57]. This spatial pattern indicates that urban park development is not aligned with population growth, emphasizing the importance of urban population dispersal and the identification of resident demand in urban park planning [32].

In contrast to studies in Europe, the United States, and India, which focus on aesthetic and recreational functions of urban CES [8,20,58], this study further reveals cultural differences in the CES supply structure in Lhasa. In the old city cluster, the historical cultural gardens in the Potala Palace and Norbulingka areas provide services far exceeding those in other regions. The prominent cultural heritage value of these areas constitutes a critical support for local CES supply. These parks not only represent high-potential service points but also strengthen residents’ cultural identity and emotional connection through symbolic cultural landscapes [59]. This phenomenon reflects the deep driving influence of urban culture and geographic history on CES supply. The long-standing recognition of “Linka” (meaning garden in Tibetan) by the Tibetan people, coupled with urban historical preservation practices, has jointly shaped the unique advantages of CES supply in the old city cluster of Lhasa. Moreover, the suburban clusters in the near-mountain area of Lhasa’s central urban area have lower urban development intensity and sparse population, and due to natural mountainous terrain, a systematic suburban park system has not yet formed. This area presents a development pattern with uncoordinated and decline development, and significant supply lag, differing markedly from cities like Chongqing, where parks follow a high-value belt distribution along the “Four Mountains” [36]. This research highlights the significant impact of natural landforms, urban development strategies, and green space planning levels on CES spatial supply–demand patterns.

China is undergoing rapid urbanization, and to ensure the sustainable use of parks in future landscape management [19], this study explores the urban typology perspective to develop a CES evaluation system for parks in plateau river valley cities. The study focuses on the factors of various indicators on supply and demand levels, combining clear geographic spatial data with field survey data. It quantifies the landscape attributes of water bodies, roads, plazas, art pieces, and cultural heritage sites in the park green spaces of the central urban area of Lhasa while considering the spiritual needs of local residents for mountains and water. The visibility of mountain and water features and the selection of holiday gathering places are incorporated into the assessment system. A relatively comprehensive assessment of park recreational potential is achieved from five aspects: aesthetic value, recreational value, heritage value, research value, and inspirational value. Additionally, to intuitively reflect the conversion of the “potential service volume” provided by urban parks into the “actual service volume” used by residents, the effective service area ratio based on walkability and non-motorized accessibility is used to measure recreational opportunities, effectively reflecting residents’ ability to access park services [19,26]. Moreover, the study analyzes CES supply–demand matching at the community scale in terms of supply–demand balance and coordination levels, facilitating the analysis of neighborhood spatial characteristics of supply–demand mismatch. This enables an adaptive assessment of supply-demand capacity and temporal regulation, making the optimization strategies for community supply–demand matching more practical.

5.2. Optimization Strategies for Supply-Demand Alignment of Urban Parks CES in Central Urban Areas

As a plateau city located in an ethnic region, Lhasa has consistently prioritized the protection of historical and cultural heritage alongside the development of its green space system. The “Lhasa Territorial Spatial Master Plan (2021–2035)” and the “Lhasa Green Space System Plan (2021–2035)” both emphasize integrating ethnic cultural features and ecological functionality into urban planning. This approach aims to create an open spatial landscape that organically blends urban areas with natural mountain and water features, gradually increasing the area and diversity of urban parks. However, due to factors such as the late start of planning, complex terrain, and the cultural significance of mountain and water worship, Lhasa residents have become highly dependent on natural spaces within the city. This has led to a significant spatial mismatch in the supply and demand of CES in urban parks, presenting a major challenge to sustainable urban development.

The optimization method for urban green space systems, based on the balance of supply and demand and efficiency improvements, serves as an effective approach to enhancing the construction and management of urban living environments, promoting high-quality development [60]. A study on the primary uses of parks in five European cities found that respondents’ socio-cultural and geographical backgrounds play a significant role in the CES provided by parks [37]. The plateau mountain–water landscape and political–cultural influences have shaped the urban spatial structure of Lhasa [61], profoundly affecting the demand patterns of its social groups and the utilization of urban parks. Therefore, in optimizing CES, it is essential to fully consider the coupling relationship between regional topography, cultural traditions, and resident needs. Based on the spatial assessment of supply–demand balance and coordination levels in CES in the central urban area of Lhasa, this study identifies four typical community types with specific issues: undersupply, oversupply, imbalanced decline, and low-level balance. Targeted optimization strategies are proposed, considering recreational potential, opportunities, and local residents’ needs.

In the study area, the supply–demand imbalance is primarily reflected in the insufficient CES supply in the old town’s central cluster and the oversupply in key development clusters. To address the issue of limited land availability in the residential areas of the old town, a strategy of placing small green spaces into the urban environment is proposed. It is recommended to explore local cultural activity routes in detail and increase small green spaces, such as pocket park and community parks, based on targeted demand, in order to improve the efficiency of CES supply and demand in the old town’s parks. The LNHZ and the Cijuelin cultural group, which currently exhibit an oversupply of green space, do not face a lack of green resources at present. However, to prevent resource wastage, it is recommended that future urban development integrate population resettlement from the old town with the city’s economic and demographic growth directions, ensuring that park resources are used efficiently and promoting equitable development.

The uncoordinated supply–demand development is primarily concentrated in the near-mountain areas and the peripheral clusters of the city center, characterized by supply lag and low-level balance. To enhance the supply capacity in near-mountain areas, a strategy is proposed to expand countryside parks along the northern and southern mountain ranges, aiming to optimize the use of ecological land and cultural resources. It is recommended to make full use of the valley resources while providing space for local folk activities, thereby enhancing the availability of green spaces and optimizing the integration of suburban green spaces with urban development. Additionally, leveraging Lhasa’s southern and northern mountains, a greenway network should be established to improve access to suburban recreational resources and strengthen the connection between suburban areas and the urban core. Low-level balance communities are mostly located in the peripheral clusters of the city center. The communities show a general supply–demand match but fail to meet the specific needs of certain groups. Therefore, optimization is required to align with changes in population and economic development. It is recommended to prioritize the renewal of existing park infrastructure, taking into account the specific needs of target groups, and to expand leisure spaces and services tailored to the elderly and children.

5.3. Limitations and Future Prospects

The intangible, subjective, complex, and dynamic nature of CES presents a challenge in accurately selecting indicators to quantify supply and demand levels. Due to limited data availability, the selected indicators may still fail to fully capture all aspects of the supply–demand system. Some non-material cultural values within CES, such as “inspirational value” and deep spiritual connections, remain difficult to fully quantify using existing indicator systems. Future studies should consider addressing this limitation through more in-depth qualitative methods, such as narrative analysis and cultural mapping. In this study, Lhasa, being a tourism city, primarily focuses on the park CES needs of urban residents while neglecting the needs of tourists. Additionally, values shape how people interpret their environment and make decisions [62]. Local residents in Lhasa, influenced by their beliefs, follow fixed patterns of behavior. However, there is a lack of comprehensive long-term data to quantitatively evaluate these cultural and spiritual needs. Future research should combine multi-source data analysis (such as social media photos and park visitor numbers) with long-term field surveys to quantify cultural ecosystem services in urban parks from the perspectives of both local residents and tourists. This approach will enhance the applicability of the research and provide a more accurate and comprehensive framework for CES evaluation, further expanding the precision and depth of the analysis [63]. To further reflect the dynamics of urban development and its impact on the CES supply–demand relationship, temporal variations in demand at different stages of urban development will be considered, when data is available, to provide a more forward-looking planning pathway.

6. Conclusions

This study developed a CES supply–demand assessment system for urban parks in the central urban area of Lhasa, suitable for the characteristics of typical plateau river valley cities. Based on spatially explicit methods, the study analyzed the supply–demand matching characteristics at the community scale and proposed optimization strategies with high feasibility. The main conclusions are as follows:

(1) Influenced by the natural geographic environment and urban development level of plateau river valley cities, the spatial distribution of CES supply and demand in urban parks in Lhasa showed significant variation. The supply was higher in the central area and lower in the eastern and western wings, while the demand decreases from the old city center toward the periphery.

(2) The phenomenon of CES supply–demand imbalance was widespread. In total, 25.67% of the communities experienced an undersupply, 16.22% exhibited oversupply, and 58.11% maintained a balanced supply–demand relationship, with just 3.73% of the communities achieving a high-level balance. The overall supply–demand coupling coordination degree was poor. In total, 55.11% and 38.14% of communities were in declining and transitional stages, respectively.

(3) Differences in CES supply–demand relationships were significant within urban clusters. The imbalance in supply and demand was mainly reflected in the seven communities in the center of the old city cluster, where CES supply was insufficient, and the five communities in the key development clusters, where CES supply was excessive. The issue of supply–demand uncoordinated development was concentrated in 23 communities in the near-mountain areas and the periphery of the city center.

The findings of this study show that the supply-demand relationship of urban park CES is driven by park landscape attributes and resident needs, and is significantly influenced by sociocultural and geographical background, the timing of urban development, and the completeness of the urban park system. This study integrates non-monetary qualitative assessment with quantitative methods to develop an indicator system centered on “recreational potential–recreational opportunities” and “social needs–material needs.” It enhances the understanding of the supply–demand mechanisms of urban park CES and provides an operational and replicable evaluation framework and optimization path for cities with geographical particularities and cultural diversity. The four strategies proposed to address the CES supply–demand issues in the research area, namely “culturally driven urban park development,” “demand-oriented park planning,” “expanding countryside parks along mountain ridges,” and “revitalizing existing parks,” are both practical and targeted. These strategies are designed to address specific local challenges identified in the study, offering actionable guidance for the future optimization and development of urban parks.

Appendix A.1. Score of 113 City Park Indicators

The content of this appendix lists the types of the 113 parks, the data of the eight indicators. The subsequent study used this as the source data for the production of the bubble map, which visually reflects the CES capacity of each park in the central urban area of Lhasa.

Table A1. Park type classification.

ID	Type	Name
1	Comprehensive parks	Zongjiao Lukang Park
2	Comprehensive parks	Liuwu Park
3	Comprehensive parks	Rende Park
4	Comprehensive parks	Norbulingka Square Park
5	Comprehensive parks	Cisongtang Water Park
6	Comprehensive parks	Qihui Lake Park
7	Comprehensive parks	Gaji Park
8	Comprehensive parks	Cijuelin Park
9	Specialized parks	The East Side of Luding South Road Park
10	Specialized parks	The Economic Development Zone Health Park
11	Specialized parks	Lhasa Riverside Park
12	Specialized parks	Princess Wencheng Cultural Tourism Theme Park
13	Specialized parks	Wellness Park
14	Specialized parks	Duilong Riverside Sports Park
15	Specialized parks	Tibet Astronomical Park
16	Specialized parks	Gymnasium Recreational Park
17	Specialized parks	Tax Linka
18	Specialized parks	Sports Park
19	Specialized parks	Sichuan-Tibet Monument Park
20	Specialized parks	Lhasa River Sports Park
21	Specialized parks	Mopan Mountain Park
22	Specialized parks	Yaowang Mountain Greenland Park
23	Specialized parks	The Potala Palace Park
24	Specialized parks	Norbulingka
25	Specialized parks	Yingqin Bridge Bridgehead Park
26	Specialized parks	Children’s Park
27	Specialized parks	Riverfront Park
28	Specialized parks	Chinese Cultural Park
29	Specialized parks	Along Tanga Avenue South Park
30	Specialized parks	Nanshan Park
31	Community parks	Naigang Park
32	Community parks	Century Avenue Park
33	Community parks	Duilong Avenue North Park
34	Community parks	The East Side of Langkang 2nd Road Park
35	Community parks	South Mudui Community Park
36	Community parks	Senzu Island Ribbon Park
37	Community parks	Linqiong Park
38	Community parks	Qumi Road Park
39	Community parks	Tongniu Park
40	Community parks	Gesang Flower Park
41	Community parks	Harmony Park
42	Community parks	Cisongtang Middle Road South Park
43	Community parks	Noba Garden
44	Community parks	Heba Forest Park
45	Community parks	Dazi Industrial Park
46	Community parks	Zhenjiang Park
47	Community parks	Dazi District Deqing Park
48	Community parks	Rurulinka
49	Community parks	Sango Park
50	Community parks	Guanshan Road Park
51	Community parks	Chagu Avenue East Park
52	Community parks	Zijin East Road Park
53	Community parks	The West Side of Lingyue Mansion Park
54	Community parks	Kangsang Garden
55	Community parks	Xicheng Anju Garden
56	Community parks	Kelsang Linka Community Park
57	Community parks	In the Economic Development Zone Ecological Park
58	Community parks	Laqing Park
59	Community parks	Duilong Avenue North Park
60	Community parks	Yangda Street Park
61	Community parks	Naiqiong Temple Park
62	Community parks	North Loop Park
63	Community parks	National Unity Park
64	Recreational parks	Tiger Peak Road Park
65	Recreational parks	Resiga Park
66	Recreational parks	Duilong Avenue North Recreational Park
67	Recreational parks	Yangda Street Park
68	Recreational parks	Both Sides of the Student Avenue Recreational Park
69	Recreational parks	Tour the Island and Visit Recreational Park
70	Recreational parks	Children’s Palace Recreational Park
71	Recreational parks	Zhongsa Road Recreational Park
72	Recreational parks	Opposite the Sixteenth Regiment Recreational Park
73	Recreational parks	Liuwu Bridge Recreational Park
74	Recreational parks	Quicksand River Ribbon Recreational Park
75	Recreational parks	Jinzhu Middle Road North Recreational Park
76	Recreational parks	Potala Palace Park
77	Recreational parks	Beijing West Road Ribbon Recreational Park
78	Recreational parks	Wetland Drainage Canals Are Strip-Shaped Recreational Park
79	Recreational parks	Kang Yuen Park West Recreational Park
80	Recreational parks	Niangregou Recreational Park
81	Recreational parks	Aba Linka Community Recreational Park
82	Recreational parks	The West Side of Niangre North Road Recreational Park
83	Recreational parks	Sera North Road Recreational Park
84	Recreational parks	Cisongtang Middle Road North Community Recreational Park
85	Recreational parks	Snowy Pearl Park Recreational Park
86	Recreational parks	Zaki Garden
87	Recreational parks	Cisongtang East Road North Recreational Park
88	Recreational parks	Herdsmen Settle Down Recreational Park
89	Recreational parks	Balku North Recreational Park
90	Recreational parks	The Eastern Convention and Exhibition Center Recreational Park
91	Recreational parks	Yangcheng Recreational Park
92	Recreational parks	The North Side of Jiangsu Avenue Recreational Park
93	Recreational parks	The South Side of Jiangsu Avenue Recreational Park
94	Recreational parks	The Middle Trunk Canal Recreational Recreational Park
95	Recreational parks	Duoren Garden
96	Recreational parks	Duilong New Town Group Recreational Park
97	Recreational parks	Nine Children’s Paradise Park
98	Recreational parks	The City Hall Circles Recreational Park
99	Recreational parks	The North of the Chengnan Traffic Police Brigade Recreational Park
100	Recreational parks	Pumpbare Recreational Park
101	Recreational parks	Polinka Park
102	Recreational parks	South of the Bureau of Culture and Tourism Recreational Park
103	Recreational parks	Jiangsu East Road Monument Recreational Park
104	Recreational parks	Ruyi Garden
105	Recreational parks	Pearl Garden
106	Recreational parks	Gyatso Garden
107	Recreational parks	Duilong Recreational Park
108	Recreational parks	Nagin Bridge Recreational Park
109	Recreational parks	North Ring Riverside Road Recreational Park
110	Recreational parks	South Ring Road Riverside Park
111	Recreational parks	Lhasa Riverside Recreational Park
112	Recreational parks	Linkuo East Road Recreational Park
113	Recreational parks	Welcome Park

Table A1. Park type classification.

ID	Type	Name
1	Comprehensive parks	Zongjiao Lukang Park
2	Comprehensive parks	Liuwu Park
3	Comprehensive parks	Rende Park
4	Comprehensive parks	Norbulingka Square Park
5	Comprehensive parks	Cisongtang Water Park
6	Comprehensive parks	Qihui Lake Park
7	Comprehensive parks	Gaji Park
8	Comprehensive parks	Cijuelin Park
9	Specialized parks	The East Side of Luding South Road Park
10	Specialized parks	The Economic Development Zone Health Park
11	Specialized parks	Lhasa Riverside Park
12	Specialized parks	Princess Wencheng Cultural Tourism Theme Park
13	Specialized parks	Wellness Park
14	Specialized parks	Duilong Riverside Sports Park
15	Specialized parks	Tibet Astronomical Park
16	Specialized parks	Gymnasium Recreational Park
17	Specialized parks	Tax Linka
18	Specialized parks	Sports Park
19	Specialized parks	Sichuan-Tibet Monument Park
20	Specialized parks	Lhasa River Sports Park
21	Specialized parks	Mopan Mountain Park
22	Specialized parks	Yaowang Mountain Greenland Park
23	Specialized parks	The Potala Palace Park
24	Specialized parks	Norbulingka
25	Specialized parks	Yingqin Bridge Bridgehead Park
26	Specialized parks	Children’s Park
27	Specialized parks	Riverfront Park
28	Specialized parks	Chinese Cultural Park
29	Specialized parks	Along Tanga Avenue South Park
30	Specialized parks	Nanshan Park
31	Community parks	Naigang Park
32	Community parks	Century Avenue Park
33	Community parks	Duilong Avenue North Park
34	Community parks	The East Side of Langkang 2nd Road Park
35	Community parks	South Mudui Community Park
36	Community parks	Senzu Island Ribbon Park
37	Community parks	Linqiong Park
38	Community parks	Qumi Road Park
39	Community parks	Tongniu Park
40	Community parks	Gesang Flower Park
41	Community parks	Harmony Park
42	Community parks	Cisongtang Middle Road South Park
43	Community parks	Noba Garden
44	Community parks	Heba Forest Park
45	Community parks	Dazi Industrial Park
46	Community parks	Zhenjiang Park
47	Community parks	Dazi District Deqing Park
48	Community parks	Rurulinka
49	Community parks	Sango Park
50	Community parks	Guanshan Road Park
51	Community parks	Chagu Avenue East Park
52	Community parks	Zijin East Road Park
53	Community parks	The West Side of Lingyue Mansion Park
54	Community parks	Kangsang Garden
55	Community parks	Xicheng Anju Garden
56	Community parks	Kelsang Linka Community Park
57	Community parks	In the Economic Development Zone Ecological Park
58	Community parks	Laqing Park
59	Community parks	Duilong Avenue North Park
60	Community parks	Yangda Street Park
61	Community parks	Naiqiong Temple Park
62	Community parks	North Loop Park
63	Community parks	National Unity Park
64	Recreational parks	Tiger Peak Road Park
65	Recreational parks	Resiga Park
66	Recreational parks	Duilong Avenue North Recreational Park
67	Recreational parks	Yangda Street Park
68	Recreational parks	Both Sides of the Student Avenue Recreational Park
69	Recreational parks	Tour the Island and Visit Recreational Park
70	Recreational parks	Children’s Palace Recreational Park
71	Recreational parks	Zhongsa Road Recreational Park
72	Recreational parks	Opposite the Sixteenth Regiment Recreational Park
73	Recreational parks	Liuwu Bridge Recreational Park
74	Recreational parks	Quicksand River Ribbon Recreational Park
75	Recreational parks	Jinzhu Middle Road North Recreational Park
76	Recreational parks	Potala Palace Park
77	Recreational parks	Beijing West Road Ribbon Recreational Park
78	Recreational parks	Wetland Drainage Canals Are Strip-Shaped Recreational Park
79	Recreational parks	Kang Yuen Park West Recreational Park
80	Recreational parks	Niangregou Recreational Park
81	Recreational parks	Aba Linka Community Recreational Park
82	Recreational parks	The West Side of Niangre North Road Recreational Park
83	Recreational parks	Sera North Road Recreational Park
84	Recreational parks	Cisongtang Middle Road North Community Recreational Park
85	Recreational parks	Snowy Pearl Park Recreational Park
86	Recreational parks	Zaki Garden
87	Recreational parks	Cisongtang East Road North Recreational Park
88	Recreational parks	Herdsmen Settle Down Recreational Park
89	Recreational parks	Balku North Recreational Park
90	Recreational parks	The Eastern Convention and Exhibition Center Recreational Park
91	Recreational parks	Yangcheng Recreational Park
92	Recreational parks	The North Side of Jiangsu Avenue Recreational Park
93	Recreational parks	The South Side of Jiangsu Avenue Recreational Park
94	Recreational parks	The Middle Trunk Canal Recreational Recreational Park
95	Recreational parks	Duoren Garden
96	Recreational parks	Duilong New Town Group Recreational Park
97	Recreational parks	Nine Children’s Paradise Park
98	Recreational parks	The City Hall Circles Recreational Park
99	Recreational parks	The North of the Chengnan Traffic Police Brigade Recreational Park
100	Recreational parks	Pumpbare Recreational Park
101	Recreational parks	Polinka Park
102	Recreational parks	South of the Bureau of Culture and Tourism Recreational Park
103	Recreational parks	Jiangsu East Road Monument Recreational Park
104	Recreational parks	Ruyi Garden
105	Recreational parks	Pearl Garden
106	Recreational parks	Gyatso Garden
107	Recreational parks	Duilong Recreational Park
108	Recreational parks	Nagin Bridge Recreational Park
109	Recreational parks	North Ring Riverside Road Recreational Park
110	Recreational parks	South Ring Road Riverside Park
111	Recreational parks	Lhasa Riverside Recreational Park
112	Recreational parks	Linkuo East Road Recreational Park
113	Recreational parks	Welcome Park

Table A2. Eight CES recreational potential indicators for parks.

ID	PA	RV	PN	RW	RR	RI	RA	RH
1	0.135003	0.133583993	0.195451006	0.548187971	0.131951004	0.02733	0.197567999	0.52827
2	0.198951	0.151994005	0.219775006	0.248627007	0.101001002	0.00773	0	0
3	0.0804127	0.395496011	0.093835004	0.532693982	0.207782	0.01144	0	0
4	0.0406494	0.182929993	0.049715299	0.103395	0.162174001	0.03751	0	0.10238
5	0.0491823	0.163646996	0.062090199	1	0.186979994	0.03106	0	0
6	0.134878	0.430635005	0.067862898	0.414880991	0.200724006	0.00456	0	0
7	0.0487873	0.0277417	0.059812099	0	0.0300091	0.00626	0	0
8	0.0709151	0.460597008	0.038539	0.562379003	0.086089402	0.02593	0	0
9	0.0555512	0.450785011	0.055762101	0	0.23646	0.01101	0	0
10	0.337692	1	0.221193001	0.262221992	0.149314001	0.00274	0	0
11	0.0548799	0.312013	0.047955099	0.107591003	0.389741004	0.02787	0	0
12	0.0285779	0.322706997	0.0217086	0	0.167715997	0.01062	0	0
13	0.0474119	0.336510003	0.039826699	0	0.246586993	0.01933	0	0
14	0.076441	0.0133216	0.062105201	0.114598997	0.208470002	0.01604	0	0
15	0.120242	0.42760101	0.064102001	0.105731003	0.134164006	0.01022	0	0
16	0.043912	0.032035001	0.053670101	0.328875989	0.144363999	0.0139	0	0
17	0.215551	0.678801	0.229276001	0	0.00155003	0.00143	0	0.05846
18	0.0158826	0.487450004	0.0223065	0	0.214417994	0	0	0
19	0.0051675	0.063617401	0.00469406	0	0.158236995	0.05471	0	0.74664
20	0.198984	0.547429025	0.159937993	0.196110994	0.158310995	0.01083	0	0
21	0.0079487	0.035868499	0.00800138	0	0.377256989	0.10992	0	1
22	0.188736	0.585029006	0.150176004	0	0.0162024	0.01304	0	0.06675
23	0.0193279	0.037549999	0.017199	0	0.164516002	0.01558	0	0.42526
24	0.303507	0.337013006	0.358644009	0.015965899	0.011134	0.01218	1	0.12464
25	0.0374774	0.44075501	0.0212381	0	0.153549001	0.03252	0	0
26	0.0199568	0.562096	0.00900556	0	0.309735	0.04531	0	0
27	0.236844	0.65060401	0.227400005	0.069343701	0.109839998	0	0	0
28	1	0.718930006	1	0	0.194424003	0.0037	0.00382182	0
29	0.195679	0.508032024	0.159536004	0	0.313264012	0.01573	0	0
30	0.399285	0.499781996	0.323148996	0	−1.11 × 10−8	0.00154	0.00478245	0
31	0.0061189	0.163190007	0.0075926	0	0.574944973	0.04682	0	0
32	0.0107923	0.079244897	0.0150535	0	0.316511005	0.08217	0	0
33	0.0350613	0.409660012	0.033061199	0	0.309713006	0.01736	0	0
34	0.0188507	0.185568005	0.0194501	0	0.218844995	0.03193	0	0
35	0.0078398	0.238812998	0.00546359	0	0.294813007	0.03712	0	0
36	0.0674025	0.317220986	0.052857	0	0.26541701	0.01818	0	0
37	0.0224514	0.030506499	0.023814199	0.308964014	0.392645001	0.02692	0	0
38	0.0087546	0	0.0118818	0	0.281203985	0.06688	0	0
39	0.0187604	0	0.018151199	0.125490993	0.313466996	0.03208	0	0
40	0.0099713	0.000204332	0.0118244	0.135615006	0.079182401	0.05909	0	0
41	0.0046807	0	0.00634766	0	0.292867988	0.05988	0	0
42	0.0114519	0.015978601	0.0188317	0	0.123327002	0	0	0
43	0.0091299	0.000162289	0.0126046	0	0.462119997	0.03213	0	0
44	0.0114347	0	0.0100294	0	0.682112992	0.10365	0.160582006	0
45	0.0761557	0.230460003	0.084736302	0.466805995	0.281623989	0	0	0
46	0.0131856	0.065247297	0.0126964	0	0.537889004	0.06777	0	0
47	0.020208	0.152170002	0.0197697	0	0.229145005	0	0	0
48	0.0100333	0.246277004	0.00779081	0	0.447421014	0.08811	0	0
49	0.0202346	0.238817006	0.00948191	0.525066018	0.269187987	0.0298	0	0
50	0.0326186	0.227906004	0.030640099	0	0.167656004	0.01865	0	0
51	0.122055	0.0359	0.128893003	0.278681993	0.202621996	0.01007	0	0
52	0.0087944	0	0.0102058	0	0.33969301	0.0333	0	0
53	0.0137338	0.065404102	0.0131045	0	0.64021498	0.04344	0	0
54	0.0084295	0.011067	0.00759207	0	0.234871	0.03466	0	0.473
55	0.0023284	0	0.00163989	0	0.274145007	0.22042	0	0
56	0.0264869	0.00432089	0.018310901	0.055243202	0.314879	0.05722	0	0
57	0.0564607	0.085903198	0.0671831	0	0.202114999	0.01084	0	0
58	0.0052331	0.275247991	0.00527151	0	0.738063991	0.10817	0	0
59	0.0128866	0.796419024	0.0087177	0.307099015	0.286161005	0.04619	0	0
60	0.0189451	0.282889009	0.0193319	0	0.179249004	0.03178	0	0
61	0.0137876	0.203246996	0.015599	0	0.133805007	0	0	0.29525
62	0.0092811	0.341333985	0.00264487	0	0.087755501	0.03163	0	0
63	0.0698901	0.070120603	0.040872298	0	0.036079001	0.00877	0	0
64	0.0001463	0.0255032	0.0011364	0	0.893375993	1	0	0
65	0.0027125	0	0.00240264	0	0.651422977	0.19382	0	0
66	0.008012	0.122720003	0.00720139	0	0.429214001	0.1091	0	0
67	0.0041943	0.111235999	0.00366754	0	0.866623998	0	0	0
68	0.0117157	0.0187793	0.0152907	0	0.338881999	0.02531	0	0
69	0.028352	0.015859099	0.030502001	0.106105	0.463230014	0.04281	0	0
70	0.0004514	0	0	0	0.581104994	0.33456	0	0
71	0.0101689	0.011464	0.0157595	0	0.406601012	0.05799	0	0
72	0.0061873	0.00521654	0.00855768	0	0.520124018	0	0	0
73	0.0442671	0.195650995	0.056407701	0	0.070495799	0.01379	0	0
74	0.0790657	0.00694874	0.088705599	0	0.543156981	0.01551	0	0.05293
75	0.0026514	0.00994693	0.00385097	0	0.440131009	0	0	0
76	0.0752747	0.131720006	0.097050302	0.376058996	0.191854	0.02444	0.0757199	0.11116
77	0.0143952	0.079245098	0.00636402	0	1	0	0	0
78	0.0177983	0.315347999	0.0218213	0	0.350263	0	0	0
79	0.0012711	0.0078361	0.00288178	0	0.410946995	0	0	0
80	0.0015209	0.467886001	0.000758755	0	0.403753996	0	0	0
81	0.0005327	0.0014594	6.41164 × 10−5	0	0.193357006	0.30745	0	0
82	0.0020278	0.0462959	0.00151897	0	0.309305012	0	0	0
83	0.002465	0.00206807	0.00175894	0	0.32052201	0.31524	0	0
84	0.002833	0	0.00636388	0	0.483651996	0	0	0
85	0.0069829	0.836197972	0.0064135	0	0.41991201	0	0	0
86	0.0035588	0.0105759	0.0034579	0	0.41782099	0	0	0
87	0.0027861	7.20749 × 10−7	0.00201102	0	0.065836899	0.09472	0	0
88	0.0002391	0.565832973	0	0	0.400766999	0	0	0
89	0.0104399	0.250997007	0.0108119	0	0.281931013	0.02827	0	0
90	0.0121746	0.000710778	0.0128507	0	0.053385802	0.0244	0	0
91	0.007673	0.842885017	0.00803224	0	0.387448013	0	0	0
92	0.0154598	0.104497001	0.015072	0.411487997	0.229120001	0	0	0
93	0.0084081	0.328173012	0.00830879	0	0.28015399	0.03474	0	0
94	0.0658277	0.283410013	0.048449401	0	0.444795012	0	0	0.0635
95	0.0052119	0.000573716	0.00932069	0	0.204766005	0	0	0
96	0.0069512	0.0307859	0.00400129	0	0.233105004	0	0	0
97	0.0021213	0	0.00105739	0	0.455823988	0	0	0
98	0.01558	0.026327999	0.0185975	0	0.070188403	0	0	0
99	0.0023881	0	0.00516749	0	0.619035006	0	0	0
100	0.0057911	0.631897986	0.00411502	0	0.227663994	0	0	0
101	0.0029676	0.00648398	0.00245757	0	0.362269998	0.08972	0	0
102	0.0032272	0	0.0019892	0	0.546922982	0	0	0
103	0.0029942	0.0113786	0.000652471	0	0.352380991	0.08903	0	0
104	0.0017915	0	0.00336957	0	0.574238002	0	0	0
105	0.0054154	0.00188644	0.00859638	0	0.156046003	0	0	0
106	0.0003839	0.018330401	0.000573589	0	0.742215991	0	0	0
107	0.0829371	0.35611099	0.057770502	0	0.223286003	0.0074	0	0
108	0.0259015	0.478026003	0.0265444	0.379528999	0.392093986	0	0	0
109	0.0218169	0.000155441	0.0121203	0.139528006	0.323309988	0	0	0
110	0.0784036	0.540368021	0.064153597	0	0.134579003	0.00391	0	0
111	0.139652	0.383300006	0.090893798	0	0.197458997	0.01101	0	0
112	0	0.000427644	0.000166809	0	0.452659994	0	0	0
113	0.0100518	0.0255033	0.00756587	0	0.644833982	0.02932	0	0

Table A2. Eight CES recreational potential indicators for parks.

ID	PA	RV	PN	RW	RR	RI	RA	RH
1	0.135003	0.133583993	0.195451006	0.548187971	0.131951004	0.02733	0.197567999	0.52827
2	0.198951	0.151994005	0.219775006	0.248627007	0.101001002	0.00773	0	0
3	0.0804127	0.395496011	0.093835004	0.532693982	0.207782	0.01144	0	0
4	0.0406494	0.182929993	0.049715299	0.103395	0.162174001	0.03751	0	0.10238
5	0.0491823	0.163646996	0.062090199	1	0.186979994	0.03106	0	0
6	0.134878	0.430635005	0.067862898	0.414880991	0.200724006	0.00456	0	0
7	0.0487873	0.0277417	0.059812099	0	0.0300091	0.00626	0	0
8	0.0709151	0.460597008	0.038539	0.562379003	0.086089402	0.02593	0	0
9	0.0555512	0.450785011	0.055762101	0	0.23646	0.01101	0	0
10	0.337692	1	0.221193001	0.262221992	0.149314001	0.00274	0	0
11	0.0548799	0.312013	0.047955099	0.107591003	0.389741004	0.02787	0	0
12	0.0285779	0.322706997	0.0217086	0	0.167715997	0.01062	0	0
13	0.0474119	0.336510003	0.039826699	0	0.246586993	0.01933	0	0
14	0.076441	0.0133216	0.062105201	0.114598997	0.208470002	0.01604	0	0
15	0.120242	0.42760101	0.064102001	0.105731003	0.134164006	0.01022	0	0
16	0.043912	0.032035001	0.053670101	0.328875989	0.144363999	0.0139	0	0
17	0.215551	0.678801	0.229276001	0	0.00155003	0.00143	0	0.05846
18	0.0158826	0.487450004	0.0223065	0	0.214417994	0	0	0
19	0.0051675	0.063617401	0.00469406	0	0.158236995	0.05471	0	0.74664
20	0.198984	0.547429025	0.159937993	0.196110994	0.158310995	0.01083	0	0
21	0.0079487	0.035868499	0.00800138	0	0.377256989	0.10992	0	1
22	0.188736	0.585029006	0.150176004	0	0.0162024	0.01304	0	0.06675
23	0.0193279	0.037549999	0.017199	0	0.164516002	0.01558	0	0.42526
24	0.303507	0.337013006	0.358644009	0.015965899	0.011134	0.01218	1	0.12464
25	0.0374774	0.44075501	0.0212381	0	0.153549001	0.03252	0	0
26	0.0199568	0.562096	0.00900556	0	0.309735	0.04531	0	0
27	0.236844	0.65060401	0.227400005	0.069343701	0.109839998	0	0	0
28	1	0.718930006	1	0	0.194424003	0.0037	0.00382182	0
29	0.195679	0.508032024	0.159536004	0	0.313264012	0.01573	0	0
30	0.399285	0.499781996	0.323148996	0	−1.11 × 10−8	0.00154	0.00478245	0
31	0.0061189	0.163190007	0.0075926	0	0.574944973	0.04682	0	0
32	0.0107923	0.079244897	0.0150535	0	0.316511005	0.08217	0	0
33	0.0350613	0.409660012	0.033061199	0	0.309713006	0.01736	0	0
34	0.0188507	0.185568005	0.0194501	0	0.218844995	0.03193	0	0
35	0.0078398	0.238812998	0.00546359	0	0.294813007	0.03712	0	0
36	0.0674025	0.317220986	0.052857	0	0.26541701	0.01818	0	0
37	0.0224514	0.030506499	0.023814199	0.308964014	0.392645001	0.02692	0	0
38	0.0087546	0	0.0118818	0	0.281203985	0.06688	0	0
39	0.0187604	0	0.018151199	0.125490993	0.313466996	0.03208	0	0
40	0.0099713	0.000204332	0.0118244	0.135615006	0.079182401	0.05909	0	0
41	0.0046807	0	0.00634766	0	0.292867988	0.05988	0	0
42	0.0114519	0.015978601	0.0188317	0	0.123327002	0	0	0
43	0.0091299	0.000162289	0.0126046	0	0.462119997	0.03213	0	0
44	0.0114347	0	0.0100294	0	0.682112992	0.10365	0.160582006	0
45	0.0761557	0.230460003	0.084736302	0.466805995	0.281623989	0	0	0
46	0.0131856	0.065247297	0.0126964	0	0.537889004	0.06777	0	0
47	0.020208	0.152170002	0.0197697	0	0.229145005	0	0	0
48	0.0100333	0.246277004	0.00779081	0	0.447421014	0.08811	0	0
49	0.0202346	0.238817006	0.00948191	0.525066018	0.269187987	0.0298	0	0
50	0.0326186	0.227906004	0.030640099	0	0.167656004	0.01865	0	0
51	0.122055	0.0359	0.128893003	0.278681993	0.202621996	0.01007	0	0
52	0.0087944	0	0.0102058	0	0.33969301	0.0333	0	0
53	0.0137338	0.065404102	0.0131045	0	0.64021498	0.04344	0	0
54	0.0084295	0.011067	0.00759207	0	0.234871	0.03466	0	0.473
55	0.0023284	0	0.00163989	0	0.274145007	0.22042	0	0
56	0.0264869	0.00432089	0.018310901	0.055243202	0.314879	0.05722	0	0
57	0.0564607	0.085903198	0.0671831	0	0.202114999	0.01084	0	0
58	0.0052331	0.275247991	0.00527151	0	0.738063991	0.10817	0	0
59	0.0128866	0.796419024	0.0087177	0.307099015	0.286161005	0.04619	0	0
60	0.0189451	0.282889009	0.0193319	0	0.179249004	0.03178	0	0
61	0.0137876	0.203246996	0.015599	0	0.133805007	0	0	0.29525
62	0.0092811	0.341333985	0.00264487	0	0.087755501	0.03163	0	0
63	0.0698901	0.070120603	0.040872298	0	0.036079001	0.00877	0	0
64	0.0001463	0.0255032	0.0011364	0	0.893375993	1	0	0
65	0.0027125	0	0.00240264	0	0.651422977	0.19382	0	0
66	0.008012	0.122720003	0.00720139	0	0.429214001	0.1091	0	0
67	0.0041943	0.111235999	0.00366754	0	0.866623998	0	0	0
68	0.0117157	0.0187793	0.0152907	0	0.338881999	0.02531	0	0
69	0.028352	0.015859099	0.030502001	0.106105	0.463230014	0.04281	0	0
70	0.0004514	0	0	0	0.581104994	0.33456	0	0
71	0.0101689	0.011464	0.0157595	0	0.406601012	0.05799	0	0
72	0.0061873	0.00521654	0.00855768	0	0.520124018	0	0	0
73	0.0442671	0.195650995	0.056407701	0	0.070495799	0.01379	0	0
74	0.0790657	0.00694874	0.088705599	0	0.543156981	0.01551	0	0.05293
75	0.0026514	0.00994693	0.00385097	0	0.440131009	0	0	0
76	0.0752747	0.131720006	0.097050302	0.376058996	0.191854	0.02444	0.0757199	0.11116
77	0.0143952	0.079245098	0.00636402	0	1	0	0	0
78	0.0177983	0.315347999	0.0218213	0	0.350263	0	0	0
79	0.0012711	0.0078361	0.00288178	0	0.410946995	0	0	0
80	0.0015209	0.467886001	0.000758755	0	0.403753996	0	0	0
81	0.0005327	0.0014594	6.41164 × 10−5	0	0.193357006	0.30745	0	0
82	0.0020278	0.0462959	0.00151897	0	0.309305012	0	0	0
83	0.002465	0.00206807	0.00175894	0	0.32052201	0.31524	0	0
84	0.002833	0	0.00636388	0	0.483651996	0	0	0
85	0.0069829	0.836197972	0.0064135	0	0.41991201	0	0	0
86	0.0035588	0.0105759	0.0034579	0	0.41782099	0	0	0
87	0.0027861	7.20749 × 10−7	0.00201102	0	0.065836899	0.09472	0	0
88	0.0002391	0.565832973	0	0	0.400766999	0	0	0
89	0.0104399	0.250997007	0.0108119	0	0.281931013	0.02827	0	0
90	0.0121746	0.000710778	0.0128507	0	0.053385802	0.0244	0	0
91	0.007673	0.842885017	0.00803224	0	0.387448013	0	0	0
92	0.0154598	0.104497001	0.015072	0.411487997	0.229120001	0	0	0
93	0.0084081	0.328173012	0.00830879	0	0.28015399	0.03474	0	0
94	0.0658277	0.283410013	0.048449401	0	0.444795012	0	0	0.0635
95	0.0052119	0.000573716	0.00932069	0	0.204766005	0	0	0
96	0.0069512	0.0307859	0.00400129	0	0.233105004	0	0	0
97	0.0021213	0	0.00105739	0	0.455823988	0	0	0
98	0.01558	0.026327999	0.0185975	0	0.070188403	0	0	0
99	0.0023881	0	0.00516749	0	0.619035006	0	0	0
100	0.0057911	0.631897986	0.00411502	0	0.227663994	0	0	0
101	0.0029676	0.00648398	0.00245757	0	0.362269998	0.08972	0	0
102	0.0032272	0	0.0019892	0	0.546922982	0	0	0
103	0.0029942	0.0113786	0.000652471	0	0.352380991	0.08903	0	0
104	0.0017915	0	0.00336957	0	0.574238002	0	0	0
105	0.0054154	0.00188644	0.00859638	0	0.156046003	0	0	0
106	0.0003839	0.018330401	0.000573589	0	0.742215991	0	0	0
107	0.0829371	0.35611099	0.057770502	0	0.223286003	0.0074	0	0
108	0.0259015	0.478026003	0.0265444	0.379528999	0.392093986	0	0	0
109	0.0218169	0.000155441	0.0121203	0.139528006	0.323309988	0	0	0
110	0.0784036	0.540368021	0.064153597	0	0.134579003	0.00391	0	0
111	0.139652	0.383300006	0.090893798	0	0.197458997	0.01101	0	0
112	0	0.000427644	0.000166809	0	0.452659994	0	0	0
113	0.0100518	0.0255033	0.00756587	0	0.644833982	0.02932	0	0

Appendix A.2. The Supply-Demand Matching Index of Community CES

The data in this appendix lists 75 communities in the central urban area of Lhasa, along with the level of CES provided by parks to each community, the level of demand for parks from each community, the supply-demand ratio, and the coupling coordination degree (CCD).

Table A3. Community CES Supply-Demand Ratio and Coupling Coordination Degree.

	Community Name	Supply	Demand	S-D Ratio	CCD
1	Barkhor Street, Barkhor Community	0.38727391	0.882266056	0.438953658	0.72677
2	Barkhor Street, Bailin Community	0.45820266	0.689133938	0.664896376	0.71077
3	Barkhor Street, Chongsaikang Community	0.3733624	0.93754432	0.398234405	0.73157
4	Barkhor Street, Dangjeling Community	0.6043305	0.788366503	0.766560347	0.78891
5	Barkhor Street, Lugu Community	0.46255616	0.505818833	0.914470028	0.65664
6	Barkhor Street, Dangsai Community	0.54841283	0.560698337	0.978088925	0.70418
7	Barkhor Street, Xia Sasu Community	0.38370832	0.984713584	0.389664901	0.74598
8	Caigongtang Street, Bai Ding Village	0.00737402	0.077606602	0.095017895	0.13079
9	Caigongtang Street, Cai Village	0.18034854	0.111976318	1.61059538	0.3337
10	Caigongtang Street, Ciguoling Village	0.36124119	0.095751793	3.772683262	0.37549
11	Raidi Street, Luo’o Village	0.08535757	0.077811244	1.096982403	0.2415
12	Raidi Street, Sangyi Community	0.22781616	0.2616197	0.870791288	0.4593
13	Jiedi Street, Weiba Village	0.04318472	0.064375518	0.670825218	0.18728
14	Gamagongsang Street, Ojetang Community	0.3863209	0.428545715	0.901469515	0.60049
15	Gamagongsang Street, Gamagongsang Community	0.38018962	0.592273043	0.641916128	0.65191
16	Gamagongsang Street, Garma Gonsang Street, Najin Road North Community	0.3808865	0.485240706	0.784943419	0.61863
17	Garma Gonsang Street, Tujian Community	0.39160944	0.580688195	0.674388491	0.65334
18	Gongdeling Street, Gatso Community	0.83303631	0.287420757	2.898316467	0.65218
19	Gongdeling Street, Lalu Community	0.41295871	0.145978441	2.828902041	0.44797
20	Gongdeling Street, Happiness Community	0.67290402	0.418186218	1.609101375	0.68531
21	Gongdeling Street, Xue Community	0.79638833	0.513385791	1.551247318	0.75515
22	Gongdeling Street, Tsemenlin Community	0.81354782	0.52983114	1.535485103	0.76557
23	Jibungang Street, Jibungangang Community	0.3822715	0.781029845	0.489445445	0.70185
24	Jibungang Street, Mulu Community	0.3905168	0.779874224	0.500743307	0.70533
25	Jibungang Street, Jiemuqi Community	0.39337029	0.79652416	0.493858584	0.71051
26	Jibungang Street, Palangxue Community	0.49633361	0.665451536	0.745859886	0.71851
27	Jiri Street, Hebalin Community	0.56516132	0.454689878	1.242959978	0.67099
28	Jiri Street, Jiri Community	0.32614068	0.830380893	0.392760334	0.68536
29	Jiri Street, Tiebanggang Community	0.53485552	0.711625611	0.751596787	0.74503
30	Jinzhu West Road, Bayi Community	0.61299167	0.343951605	1.782203252	0.63485
31	Jinzhu West Road, Dangba Community	0.4128025	0.120781679	3.417757568	0.42114
32	Jinzhu West Road, Jinzhu West Road Community	0.42225321	0.22557623	1.87188699	0.51365
33	Jinzhu West Road, Lodu Community	0.46809681	0.164879317	2.839026846	0.48011
34	Liangdao Street, Karmalingka Community	0.60373227	0.393530854	1.534142137	0.65611
35	Liangdao Street, Xianzudao Community	0.45821968	0.218847962	2.093780879	0.51967
36	Najin Street, Zangge Community	0.30286837	0.288989476	1.048025605	0.5072
37	Najin Street, Garba Village	0.02096521	0.046917291	0.446854649	0.13022
38	Najin Street, Garong Community	0.28467853	0.425907103	0.668405215	0.55543
39	Najin Street, Najin Village	0.36106331	0.159752923	2.260135875	0.44558
40	Najin Street, Naryu Community	0.36245428	0.271893238	1.333075739	0.52148
41	Najin Street, Tama Village	0.40323209	0.255925174	1.575585864	0.52648
42	Nyange Street, Abalinka Community	0.56176655	0.321244525	1.748719466	0.60958
43	Nyange Street, Cisongtang Community	0.5976659	0.276916057	2.158292696	0.59399
44	Nyange Street, Jisu Village	0.17413063	0.132551772	1.31368016	0.34998
45	Nyange Street, Galsi Village	0.00755227	0.044277176	0.170568058	0.0965
46	Nyange Street, Rinchen Cai Village	0.30635572	0.185091146	1.65516138	0.44725
47	Zhaxi Street, Nizhulin Community	0.24914305	0.376898693	0.661034531	0.51973
48	Zhaxi Street, Unity New Village Community	0.5783097	0.528019117	1.095243864	0.70232
49	Zhaxi Street, Xiongha Community	0.49373854	0.568758954	0.868098061	0.68852
50	Zhaxi Street, Zhaxi Community	0.41580419	0.361287174	1.150896621	0.58396
51	Zhaxi Street, Zhaxi Xincun Community	0.50919273	0.4203835	1.211257652	0.64008
52	Bangdui Township, Bangdui Village	0.00369646	0.054141962	0.068273399	0.09252
53	Bangdui Township, Lin’a Village	0.0052399	0.030705832	0.170648468	0.00139
54	Bangdui Township, Yeba Village	0.00040244	0.032265261	0.012472932	0.02699
55	Deqing Township, Baina Village	0.031744	0.045888434	0.691764679	0.1421
56	Deqing Township, Deqing Village	0.14512217	0.087241122	1.663460663	0.28864
57	Deqing Township, Sangzhulin Village	0.0671828	0.064365406	1.043771853	0.20914
58	Deqing Township, Xincang Village	0.03823755	0.035408873	1.079886183	0.11106
59	Dongga Street, Dongga Village	0.36028291	0.193676894	1.860226612	0.47209
60	Dongga Street, Nanga Village	0.15690047	0.154658562	1.014495838	0.35814
61	Dongga Street, Sangmu Village	0.14250616	0.219458577	0.649353344	0.38839
62	Gurong Town, Baje Village	0.05555556	0.065071477	0.853762027	0.20047
63	Gurong Town, Join Village	0.08286145	0.042560595	1.946905239	0.16979
64	Liwu Street, Deyang Village	0.00016675	0.058065751	0.002871679	0.04432
65	Liwu Street, Liwu Village	0.43210284	0.138749984	3.114255052	0.4458
66	Liuwu Street, Sangda Village	0.09600911	0.055290993	1.736433164	0.21139
67	Naiqiong Street, Boma Village	0.15060741	0.060221676	2.500883769	0.24764
68	Naiqiong Street, Gangdelin Village	0.09942097	0.086278703	1.1523234	0.26147
69	Naiqiong Street, Jamu Village	0.02645005	0.061949959	0.426958315	0.16261
70	Naiqiong Street, Jiage Village	0.01658251	0.069131507	0.239869001	0.15237
71	Naiqiong Street, Naiqiong Village	0.12676762	0.095896866	1.321916181	0.28916
72	Naiqiong Street, Sema Village	0.02984781	0.092724268	0.321898567	0.19893
73	Yangda Street, Gangpu Village	0	0.050458244	0	0
74	Yangda Street, Yangda Village	0.10915017	0.096063005	1.136235202	0.27872
75	Yangda Street, Tongga Village	0.25504909	0.109792454	2.323011119	0.36143

Table A3. Community CES Supply-Demand Ratio and Coupling Coordination Degree.

	Community Name	Supply	Demand	S-D Ratio	CCD
1	Barkhor Street, Barkhor Community	0.38727391	0.882266056	0.438953658	0.72677
2	Barkhor Street, Bailin Community	0.45820266	0.689133938	0.664896376	0.71077
3	Barkhor Street, Chongsaikang Community	0.3733624	0.93754432	0.398234405	0.73157
4	Barkhor Street, Dangjeling Community	0.6043305	0.788366503	0.766560347	0.78891
5	Barkhor Street, Lugu Community	0.46255616	0.505818833	0.914470028	0.65664
6	Barkhor Street, Dangsai Community	0.54841283	0.560698337	0.978088925	0.70418
7	Barkhor Street, Xia Sasu Community	0.38370832	0.984713584	0.389664901	0.74598
8	Caigongtang Street, Bai Ding Village	0.00737402	0.077606602	0.095017895	0.13079
9	Caigongtang Street, Cai Village	0.18034854	0.111976318	1.61059538	0.3337
10	Caigongtang Street, Ciguoling Village	0.36124119	0.095751793	3.772683262	0.37549
11	Raidi Street, Luo’o Village	0.08535757	0.077811244	1.096982403	0.2415
12	Raidi Street, Sangyi Community	0.22781616	0.2616197	0.870791288	0.4593
13	Jiedi Street, Weiba Village	0.04318472	0.064375518	0.670825218	0.18728
14	Gamagongsang Street, Ojetang Community	0.3863209	0.428545715	0.901469515	0.60049
15	Gamagongsang Street, Gamagongsang Community	0.38018962	0.592273043	0.641916128	0.65191
16	Gamagongsang Street, Garma Gonsang Street, Najin Road North Community	0.3808865	0.485240706	0.784943419	0.61863
17	Garma Gonsang Street, Tujian Community	0.39160944	0.580688195	0.674388491	0.65334
18	Gongdeling Street, Gatso Community	0.83303631	0.287420757	2.898316467	0.65218
19	Gongdeling Street, Lalu Community	0.41295871	0.145978441	2.828902041	0.44797
20	Gongdeling Street, Happiness Community	0.67290402	0.418186218	1.609101375	0.68531
21	Gongdeling Street, Xue Community	0.79638833	0.513385791	1.551247318	0.75515
22	Gongdeling Street, Tsemenlin Community	0.81354782	0.52983114	1.535485103	0.76557
23	Jibungang Street, Jibungangang Community	0.3822715	0.781029845	0.489445445	0.70185
24	Jibungang Street, Mulu Community	0.3905168	0.779874224	0.500743307	0.70533
25	Jibungang Street, Jiemuqi Community	0.39337029	0.79652416	0.493858584	0.71051
26	Jibungang Street, Palangxue Community	0.49633361	0.665451536	0.745859886	0.71851
27	Jiri Street, Hebalin Community	0.56516132	0.454689878	1.242959978	0.67099
28	Jiri Street, Jiri Community	0.32614068	0.830380893	0.392760334	0.68536
29	Jiri Street, Tiebanggang Community	0.53485552	0.711625611	0.751596787	0.74503
30	Jinzhu West Road, Bayi Community	0.61299167	0.343951605	1.782203252	0.63485
31	Jinzhu West Road, Dangba Community	0.4128025	0.120781679	3.417757568	0.42114
32	Jinzhu West Road, Jinzhu West Road Community	0.42225321	0.22557623	1.87188699	0.51365
33	Jinzhu West Road, Lodu Community	0.46809681	0.164879317	2.839026846	0.48011
34	Liangdao Street, Karmalingka Community	0.60373227	0.393530854	1.534142137	0.65611
35	Liangdao Street, Xianzudao Community	0.45821968	0.218847962	2.093780879	0.51967
36	Najin Street, Zangge Community	0.30286837	0.288989476	1.048025605	0.5072
37	Najin Street, Garba Village	0.02096521	0.046917291	0.446854649	0.13022
38	Najin Street, Garong Community	0.28467853	0.425907103	0.668405215	0.55543
39	Najin Street, Najin Village	0.36106331	0.159752923	2.260135875	0.44558
40	Najin Street, Naryu Community	0.36245428	0.271893238	1.333075739	0.52148
41	Najin Street, Tama Village	0.40323209	0.255925174	1.575585864	0.52648
42	Nyange Street, Abalinka Community	0.56176655	0.321244525	1.748719466	0.60958
43	Nyange Street, Cisongtang Community	0.5976659	0.276916057	2.158292696	0.59399
44	Nyange Street, Jisu Village	0.17413063	0.132551772	1.31368016	0.34998
45	Nyange Street, Galsi Village	0.00755227	0.044277176	0.170568058	0.0965
46	Nyange Street, Rinchen Cai Village	0.30635572	0.185091146	1.65516138	0.44725
47	Zhaxi Street, Nizhulin Community	0.24914305	0.376898693	0.661034531	0.51973
48	Zhaxi Street, Unity New Village Community	0.5783097	0.528019117	1.095243864	0.70232
49	Zhaxi Street, Xiongha Community	0.49373854	0.568758954	0.868098061	0.68852
50	Zhaxi Street, Zhaxi Community	0.41580419	0.361287174	1.150896621	0.58396
51	Zhaxi Street, Zhaxi Xincun Community	0.50919273	0.4203835	1.211257652	0.64008
52	Bangdui Township, Bangdui Village	0.00369646	0.054141962	0.068273399	0.09252
53	Bangdui Township, Lin’a Village	0.0052399	0.030705832	0.170648468	0.00139
54	Bangdui Township, Yeba Village	0.00040244	0.032265261	0.012472932	0.02699
55	Deqing Township, Baina Village	0.031744	0.045888434	0.691764679	0.1421
56	Deqing Township, Deqing Village	0.14512217	0.087241122	1.663460663	0.28864
57	Deqing Township, Sangzhulin Village	0.0671828	0.064365406	1.043771853	0.20914
58	Deqing Township, Xincang Village	0.03823755	0.035408873	1.079886183	0.11106
59	Dongga Street, Dongga Village	0.36028291	0.193676894	1.860226612	0.47209
60	Dongga Street, Nanga Village	0.15690047	0.154658562	1.014495838	0.35814
61	Dongga Street, Sangmu Village	0.14250616	0.219458577	0.649353344	0.38839
62	Gurong Town, Baje Village	0.05555556	0.065071477	0.853762027	0.20047
63	Gurong Town, Join Village	0.08286145	0.042560595	1.946905239	0.16979
64	Liwu Street, Deyang Village	0.00016675	0.058065751	0.002871679	0.04432
65	Liwu Street, Liwu Village	0.43210284	0.138749984	3.114255052	0.4458
66	Liuwu Street, Sangda Village	0.09600911	0.055290993	1.736433164	0.21139
67	Naiqiong Street, Boma Village	0.15060741	0.060221676	2.500883769	0.24764
68	Naiqiong Street, Gangdelin Village	0.09942097	0.086278703	1.1523234	0.26147
69	Naiqiong Street, Jamu Village	0.02645005	0.061949959	0.426958315	0.16261
70	Naiqiong Street, Jiage Village	0.01658251	0.069131507	0.239869001	0.15237
71	Naiqiong Street, Naiqiong Village	0.12676762	0.095896866	1.321916181	0.28916
72	Naiqiong Street, Sema Village	0.02984781	0.092724268	0.321898567	0.19893
73	Yangda Street, Gangpu Village	0	0.050458244	0	0
74	Yangda Street, Yangda Village	0.10915017	0.096063005	1.136235202	0.27872
75	Yangda Street, Tongga Village	0.25504909	0.109792454	2.323011119	0.36143

Appendix A.3. Classification of Supply-Demand Balance and Coupling Coordination States

To ensure objectivity and transparency, this study compared four classification methods—K-means, Z-score, Jenks natural breaks, and IQR. A sensitivity analysis was conducted to assess robustness by introducing data perturbations. The Kappa coefficient was used to evaluate stability, and the Jenks method was found to be the most stable and consistent approach for classifying the supply-demand ratio and coupling coordination degree. The validation process is shown in Figure A1, Figure A2, and Figure A3.

Figure A1. Sensitivity Analysis of CES Supply–Demand Ratio Classification Method. X-axis: test data; Y-axis: perturbation coefficient. Shapes indicate classification types; red markers denote data points with classification changes.

Figure A2. Sensitivity Analysis of CES Coupling Coordination Degree Classification Method. X-axis: test data; Y-axis: perturbation coefficient. Shapes indicate classification types; red markers denote data points with classification changes.

Figure A3. Changes in Kappa Statistic with Perturbation. Y-axis: Kappa coefficient; X-axis: perturbation coefficient.