Construction and Application of Urban Green Space Ecosystem Service Assessment Indicator System and Assessment Method: A Case Study of Chifeng Central Urban Area, China

Abstract

The assessment of urban green space ecosystem services is an important basis for urban ecosystem management and decision-making. To further enhance the accuracy of this evaluation, this paper refers to the main research results of international urban ecosystem service assessment and puts forward the main principles for the construction of an urban green space ecosystem service assessment index system. Based on this principle, the framework of the assessment indicator system was carried out by adopting the expert consultation method and frequency analysis method. This system includes the functions of leisure and recreation and the landscape premium in cultural services, air purification, noise abatement, carbon sequestration, oxygen release, and precipitation storage in regulatory services, and biodiversity conservation in supply services. The assessment methods for each functional category are chosen for their appropriateness, or the inadequacies of the existing methods are refined and improved. Then, we take the urban center of Chifeng City, China, as an example to carry out an empirical study on the urban green space ecosystem service assessment index system and assessment methods proposed in this paper. This study shows that the total value of green space ecosystem services in the downtown area of Chifeng City is CNY 229.91 million (Chinese Yuan). Among them, cultural services, regulating services, and supporting services accounted for 89.62%, 9.06%, and 1.32% of the total value, respectively. This study has important reference significance for the comprehensive quantitative assessment of the ecological service function value of urban green spaces, revealing the socio-economic value of urban green space resources while providing insights and references for the environmental assessment, planning, construction, and management decision-making of urban green spaces.

1. Introduction

With the acceleration of urbanization, the rapid development of the regional economy, and the gradual increase in the proportion of the urban population, the scale of residential areas has become larger and larger, crowding out the natural reserves of cities, and at the same time bringing many difficulties to the management of the urban environment [1]. As a component of the urban environment, green spaces provide urban residents with places for leisure, recreation, and physical exercise within the city, carry multiple social and cultural functions, and provide a variety of ecological services which help to reduce or mitigate urban environmental problems and improve the quality of the human habitat, and are thus irreplaceable by other non-biological facilities [2,3]. Urban planning decision-makers usually ignore or underestimate the benefits of green spaces, resulting in the gradual encroachment of remnant urban green spaces by the urban sprawl. A sound assessment of these services from both environmental and economic perspectives can provide a more comprehensive and accurate assessment of the value of urban green spaces and contribute to possible future market transactions. Therefore, many scholars have conducted research on the value assessment of urban green spaces. Research on ecosystem services can be traced back to the 1970s, when the concept of ecosystem services was proposed by Holden and Ehrlich [4]. Since the 1990s, the valuation of ecosystem services has gradually become a research hotspot in related fields. The most representative figures are Daily and Robert Costanza. Daily (1997) defined ecosystem services as the conditions and utility of the natural environment as shaped by the ecosystems and ecological processes that sustain humans [5]. Costanza et al. (1997) quantified the value of ecosystem services using the equivalent factor method [6]. Since then, researchers have selected different valuation methods for different ecosystems to evaluate the service value and functions of each ecosystem. Research on urban green space ecosystems has been increasing.

Currently, the accounting methods for evaluating the value of urban green space ecosystem services mainly include the functional value method [7,8,9,10], the model method [11,12], the equivalent factor method [6,13], and the energy value analysis method [14]. These methods have their own applicability and limitations under different research circumstances. In recent years, the equivalent factor method and the functional value method have commonly been used to assess the value of forest ecosystem services in China [13]. The equivalent factor method was proposed by Costanza [6], which constructs the value equivalents of different types of ecosystem services based on certain quantitative standards, on the basis of classifying ecosystem service systems, and then combines them with their corresponding distribution areas for value assessment. In 2003, the Chinese scholar Xie Gaodi proposed the “Equivalent Factor Table of Terrestrial Ecosystem Service Value in China” based on the equivalent factor method proposed by Costanza [6]. The advantage of this method lies in its simplicity of operation, low data requirements, ease of access, and intuitive and usable results, making it suitable for large-scale assessment studies. This method is mainly based on expert experience for evaluation, so its disadvantage is that it struggles to reflect the actual changes in ecosystem services, resulting in lower accuracy of the assessment results. The functional value method estimates the functional value of ecosystem services in the study area by establishing a relationship equation between the level of ecosystem service functions and the ecological environment variables of the assessment area. It generally consists of two steps: first, quantifying and statistically analyzing the quantity of ecosystem service supply, and then evaluating the value of the quantified ecosystem services through relevant monetary values. In 2008, the State Forestry Administration of China issued the forestry industry standard “Guidelines for the Assessment of Forest Ecosystem Service Functions” (LY/T 1721-2008), and a large number of research studies on forest ecosystem service assessments have been conducted in academia under the guidance of this standard method. In 2016 and 2017, the “Long-term Positioning Observation Methods for Forest Ecosystems” (GB/T 33027-2016) and the “Long-term Positioning Observation Indicator System for Forest Ecosystems” (GB/T 35377-2017) were successively released, accelerating the research on forest system services in China and achieving fruitful results. In 2020, the national standard “Specifications for Assessment of Forest Ecosystem Services” (GB/T 38582-2020) [15] was officially released (hereinafter referred to as “Specifications”). The Specification stipulates the terms and definitions, basic requirements, data sources, assessment indicator system, distributed calculation methods, and assessment formulas for forest ecosystem service function assessment and is currently the main method used in China [7]. Many scholars have conducted theoretical studies on monitoring and evaluating forest ecological benefits at various scales, from national to ecological engineering, provincial, municipal, and below, achieving significant results [8,9,10]. The functional value method is a quantitative assessment of various material quantities in forest ecosystems based on the guidance of the “Specifications”. The input parameters for the functional value method are numerous, and the calculation process is relatively complex. Compared to the equivalent factor method, this approach is more complicated, but its accuracy is greatly improved. The model methods mainly include CITYgreen, i-Tree, and UFORE [14]. CITYgreen can evaluate remote sensing images at different scales, but this model only considers factors such as canopy and tree height, and cannot set tree species parameters; i-Tree has reliable basic data sources, but the original data are only applicable to regions such as the Americas and Europe, and for other international regions, it is necessary to match the species of trees. When the system lacks tree species data, data correction is required; UFORE is applicable at a broad scale, but requires a large amount of data, high precision, and high investment costs [14]. At the same time, the indicators involved in these three models are relatively limited, generally including carbon fixation and oxygen release, rainwater interception, air purification, and energy conservation, which can lead to the underestimation of the overall value of ecosystem service functions. The energy value method is a new scientific concept and measurement method established by American ecologist H.T. Odum based on previous energy value theories. According to the energy value method, any form of energy flowing through nature and human society ultimately comes from solar energy, which is the primary energy source for all other forms of energy in the biosphere [16]. The energy value analysis method can overcome the problem of inconsistent dimensions of different ecological products and services, but the underlying theoretical system still has flaws.

Since urban green spaces are somewhat similar to forest ecosystems, many scholars have drawn on this standard to assess the ecosystem service functions of urban green spaces [17,18]. However, the assessment indicators are limited to those characteristic of forest ecosystems and lack indicators that reflect the service characteristics of urban green spaces. The completeness of the indicators for assessing the value of forest ecosystem services determines the scientific and accurate nature of the assessment of ecosystem service value. The construction of the evaluation index system for China’s forest ecosystem services has gone through three important stages: the first stage originated from Costanza’s classification standards, establishing 17 categories of ecological service indicators [6]; the second stage, based on the methods of the Millennium Ecosystem Assessment (MA), established 11 functional categories [19]; the third stage is based on the “Specifications” that are more in line with China’s actual situation, which constructed 9 major functional categories and 18 categories of ecological service indicators. Currently, the construction of indicators for the assessment of the value of forest ecosystem services in China is mainly guided by the “Specifications”. For the construction of indicators for the assessment of the value of urban green space ecosystem services, most scholars also refer to the “Specifications”, but there are differences in the selection of indicators among various researchers. Analysis of the research on urban green space ecosystem services reveals that the five main categories of services—carbon fixation and oxygen release, air purification, water conservation, landscape recreation, and biodiversity protection—are the most important evaluation indicators in value assessments, all of which are included in the research cases. However, urban-specific ecological service functions such as noise reduction and the landscape premium have not been evaluated in many studies. This also reflects the fact that during the construction of the indicator system, many scholars actively abandon certain indicators due to the difficulty in obtaining and implementing them, leading to significant uncertainty regarding the authority and scientific validity of the final assessment results.

There have been a large number of studies on the framework of indicator systems and assessment methods for the assessment of ecosystem services in urban green areas, but the lack of a unified indicator system and technical methods has led to poor comparability of assessment results between different regions. In addition, in terms of the scope of the study, research at the scale of urban built-up areas is relatively scarce. Therefore, the framework of a set of scientific, systematic, and standardized indicator systems for the assessment of ecosystem services of green space applicable to urban built-up areas has become an urgent problem to be researched and solved at present. Referring to the main research results of urban ecosystem service assessment, this paper puts forward the main principles for the construction of the ecosystem service assessment index system of green areas in urban built-up areas. Based on these principles, the frequency analysis method and expert consultation method are adopted and combined with the “Specifications for Assessment of Forest Ecosystem Services” (GB/T 38582-2020) [15], and the indicator system for the assessment of ecosystem services in urban green areas is constructed. The applicability of the existing assessment methods of each functional category is assessed and their deficiencies are refined and improved.

For the past few years, Chifeng City in China has strengthened its construction of gardens, increasing the area and number of park green spaces in conjunction with urban planning and construction, thereby enhancing the quality of the landscape. This research takes the central urban area of Chifeng City as an example to conduct a study on the benefits of urban green spaces. It can provide certain suggestions for the design of park green spaces in Chifeng City, and can also serve as a reference for the evaluation of ecosystem services in parks and green spaces in other cities.

2. Construction of the Assessment Indicator System

2.1. Principles of Construction of the Assessment Indicator System

The urban green space ecosystem service function evaluation index system is the main tool for conducting assessments. The establishment of the principles of this system is a prerequisite for ensuring the objectivity, fairness, and operability of the indicator system. By drawing on the experience of constructing related indicator systems [20], and based on the characteristics of urban green space ecosystem services, the existing research results are organized and summarized to assess the indicators. Referring to the principles of describable, measurable, and measurable in the construction of the indicator system in the “Specifications for Assessment of Forest Ecosystem Services” (GB/T 38582-2020) [15], the main principles for the construction of an indicator system for the assessment of ecosystem services of green space are established, as follows: (1) Typicality of indicators. The indicators applied by international organizations or national assessments should be used as assessment indicators. (2) Evaluation objectivity. Quantifiable (including direct observation, calculation or simulation) indicators should be considered, so as to rely on data to ensure the objectivity of evaluation and avoid the influence of subjective factors. (3) Parameter availability. Direct observational data or parameters that are easy to collect should be chosen, along with authoritative and reliable data sources. (4) Service finality. The classification system of final ecosystem service indicators should be adopted. From a practical point of view, the CICES (Common International Classification of Ecosystem Services) framework’s indicator classification system for final ecosystem goods and services can be drawn on [21]. This avoids the inherent ambiguity of definitions and effectively reduce double accounting. (5) Appropriateness of scale. Indicators suitable for the scale of urban built-up areas should be selected.

2.2. Method of Construction of the Assessment Indicator System

In this study, the expert consultation method and frequency analysis method were comprehensively applied. Firstly, the frequency analysis method was adopted to statistically analyze the practical indicators in the main research results of urban green space ecosystem assessment in many countries and select those indicators with a higher frequency of use [22,23]. The weights of various indicators calculated through frequency analysis were sorted from largest to smallest as follows: air purification (0.177) > precipitation regulation (0.170) > carbon fixation and oxygen release (0.163) > leisure and recreation (0.095) > soil conservation (0.088) > biodiversity protection (0.082) > noise reduction (0.068) > food supply (0.035) > aesthetics (0.034) > education (0.027) > landscape premium (0.021) > energy conservation (0.020) > nutrient accumulation (0.014) > heritage (0.007). If the frequency value was greater than the average frequency value (0.071), it was considered important. This study found that the important indicators are the first six items, so the first step was to filter out these six functional category indicators. On this basis, relevant experts were consulted to screen and adjust the indicators. The cohort of experts included 10 experts from universities, colleges, research institutes, and urban planning institutes who are engaged in the research and construction management of urban green space and ecology-related disciplines. The six indicators were scored with 1 point for very unimportant, 2 points for not important, 3 points for average, 4 points for important, and 5 points for very important, and the indicators with more than 30 points were selected, meaning that soil conservation was excluded. At the same time, experts proposed additional indicators, using those proposed by more than one-third of the participants as the basis for the increase, which added noise reduction and the landscape premium. At the same time, combined with the characteristics of the ecosystem services of green space, based on the “Specifications for Assessment of Forest Ecosystem Services” (GB/T 38582-2020) [15], screened the indicators and methodology system suitable for urban green space. Ultimately, the functional categories for the assessment of urban green space ecosystem services were obtained.

The system includes three service categories, namely cultural services, regulating services, and supporting services: cultural services include two functional categories, namely leisure and recreation and the landscape premium; regulating services include four functional categories, namely air purification, noise abatement, carbon sequestration and oxygen release, and precipitation storage; and supporting services include one functional category, namely biodiversity protection (Figure 1).

3. Methodology for the Assessment of Indicators

3.1. Leisure and Recreation

The recreational and leisure value of urban green space is obtained by the product of the green space area and the recreational and leisure value per unit area of green space. The formula for calculating the recreational value of urban green space is shown in Equation (1).

$$U_l=V_l \times A$$

(1)

where U_l is the urban green space leisure and recreation value (CNY/a); V_l is the recreational and leisure value of urban green space per unit area [CNY/ (m²·a)]; and A is the urban green space area (m²).

3.2. Landscape Premium

The value of the landscape premium is obtained by multiplying the additional value per unit of housing promoted by the park by the overall area of housing that benefits from the landscape premium [24]. The specific accounting method was as follows:

• Based on the data of land use types, urban residential land use and parkland were selected to extract green spaces with landscape premium features. A previous study found that park green space with an area below 2 hm² is clustered with cold spots of house prices [25]. Therefore, only parks and green spaces larger than 2 hectares were selected as the subjects of this study.
• We set the radiation range of the park and green space landscape premium. At the same time, combined with the city class and size, we determined the influence of the distance from park green space on house prices. The area within with park and green space affects property value-added was assessed using buffer tool of ArcGIS 10.8 to calculate the influence range by using the distance of park and green Esri space’s influence on house prices as the buffer radius.
• We calculated the total building area of residential properties within the landscape premium impact area, specifically by multiplying the dwelling district land area within the influence area by the dwelling district land plot ratio. Referring to the “Planning and Design Standards for Urban Residential Areas” (GB 50180-2018), the dwelling district volume ratio was classified into four classes based on the number of building stories, and the method of obtaining the plot ratio for each residential plot involved firstly adopting the typical sampling method to obtain the investigation. The volume ratio of the sample plots was firstly obtained by means of a typical sampling method, and the volume ratio of the urban residential building plots within the premium influence range was manually interpreted by remote sensing images.
• To calculate the added value of landscape premium, the specific method involved multiplying the total construction area in the district by the average price of local property, and then multiplying the appreciation coefficient of park green space to real estate by the average annual income value based on years of property ownership.

The formula for calculating the total floor area of the benefiting scope is shown in Equation (2), and the formula for calculating the landscape premium value is shown in Equation (3).

$$G_l=A_l\times P_r$$

(2)

where G_l is the gross area of urban residential properties that gained appreciation from urban parkland landscaping (m²); A_l is the area of urban residential land that receives an appreciation of value from the urban green space landscape (m²); and P_r is the floor area ratio for residential sites that derive added value from the urban greenscape.

$${ U}_l=G_l \times P_a \times V_a÷ n$$

(3)

where U_l is the premium value of the urban park landscape (CNY/a); P_a is the price of residences (CNY/m²); V_a is the value-added factor of parkland to neighboring properties; and n is the age of properties.

3.3. Purification of Atmospheric Environment

The value of the air purification function is accounted for by selecting the main substances involved in air pollution (sulfur dioxide, fluorides, and nitrogen oxides), the indicators of air dust (TSP, PM_2.5, and PM₁₀) and a negative air ion count to reflect the ability of green space to purify the atmospheric environment [26,27], referring to the “Specifications for Assessment of Forest Ecosystem Services” (GB/T 38582-2020) [15]. The value of air purification was then accounted for through the equivalent tax amount for air pollutants in the Law of the People’s Republic of China on Environmental Protection Tax.

3.3.1. Negative Ion Supply

The formula for calculating the annual number of negative air ions released from urban green spaces is shown in Equation (4), and the formula for calculating the value of the negative ion supply by urban green spaces is shown in Equation (5).

$$G_n=5.256 \times {10}^{15} Q_n \times A \times H/L$$

(4)

where G_n is the annual number of negative ions provided by urban green spaces (pieces/a); Q_n is the green space negative ion concentration (pieces/cm³); A is the urban green space area (hm²); H is the height of green space forest stands (m); and L is the negative ion lifespan (min).

$$U_n=5.256 \times { 10}^{15}(Q_n-600) \times A \times H \times K/L$$

(5)

where U_n is annual value of negative ions provided by urban green spaces (CNY/a) and K is the cost of negative ion production (CNY/piece).

3.3.2. Absorption of Gaseous Pollutants

The formula for calculating the annual absorption of gaseous pollutants is shown in Equation (6), and the formula for calculating the value of the absorption of gaseous pollutants is shown in Equation (7).

$$G_{S/N/F}={ Q}_{S/N/F} \times A$$

(6)

where G_S/N/F is the annual absorption of air pollution (sulfur dioxide, fluorides, and nitrogen oxides) by urban green spaces (kg/a); Q_S/N/F is the air pollution (sulfur dioxide, fluorides, and nitrogen oxides) absorption per unit area of urban green space [kg/(hm²·a)]; and A is the area of urban green space (hm²).

$$U_{S/N/F}={ G}_{S/N/F}/N_{S/N/F} \times K$$

(7)

where U_S/N/F is the annual value of air pollutants (sulfur dioxide, fluorides, and nitrogen oxides) absorbed by urban green spaces (CNY/a); N_S/N/F is the equivalent value of air pollution (sulfur dioxide, fluoride and nitrogen oxides) (kg); and K represents air pollutant taxes.

3.3.3. Retention of Airborne Particulate Matter

The formula for calculating the annual retention of airborne particulate matter by urban green space is shown in Equation (8), and the formula for calculating the value of airborne particulate matter trapped by urban green spaces is shown in Equations (9) and (10).

$$G_{TSP/{PM}_{10}/{PM}_{2.5}}=10 \times { Q}_{TSP/{PM}_{10}/{PM}_{2.5}} \times A \times n \times \mathit{LAI}$$

(8)

where G_{TSP/PM10/PM2.5} is the annual retention of airborne particulate matter (TSP, PM_2.5, and PM₁₀) by urban green spaces (kg/a); Q_{TSP/PM10/PM2.5} is the amount of particulate matter (TSP, PM_2.5 and PM₁₀) absorbed per unit area of leaves (g/m²); A is the area of urban green space (hm²); n is the number of elutions; and LAI is the leaf area index.

$$U_{TSP}=(G_{TSP}-G_{{PM}_{10}})/N_{TSP} \times K +U_{{PM}_{10}}$$

(9)

$$U_{{PM}_{10}/{PM}_{2.5}}=G_{{PM}_{10}/{PM}_{2.5}}/N_{{PM}_{10}/{PM}_{2.5}} \times K$$

(10)

where U_{TSP/PM10/PM2.5} is the annual value of airborne particulate matter (TSP, PM_2.5, and PM₁₀) absorbed by urban green spaces (CNY/a); N_{TSP/PM10/PM2.5} is the equivalent value of airborne particulate matter (TSP, PM_2.5 and PM₁₀) (kg); and K represents airborne particulate matter taxes.

3.4. Noise Reduction

The noise reduction effect of green space is mostly related to the width of green space [28]. This paper evaluates the noise reduction effect of green space on the basis of considering the width of green space [29], combined with the length of green space. Based on the above principles and assumptions, roadside green space is subdivided into five levels of 0–10 m, 10–20 m, 20–30 m, 30–40 m and more than 40 m according to the width. Although green space has the function of noise reduction, only green space between a road where noise is generated and the working and living areas of citizens will generate noise reduction services, meaning that green spaces on the roadside that provide actual noise reduction services for the citizens should be selected for the accounting of the value of noise reduction services.

Noise abatement function value accounting adopts the shadow engineering method [30,31,32], i.e., the cost of constructing soundproof walls next to high-speed roads is used as a substitute for calculation. According to the relevant public data, soundproof walls can reduce the noise volume by 75% on average. The existing volume of this green space is converted into a soundproof wall with a certain length, assuming that the soundproof wall is 4 m high, and the construction cost of the unit length of the soundproof wall is CNY 400/m which can be obtained from the cost of noise reduction. Then, we calculate the value of green space noise reduction alternatively based on the construction cost of the soundproof wall. The calculation formula is shown in Equation (11).

$$U_{N }={\sum }_i^n{\displaystyle \frac{\alpha }{\beta }} \times P \times L$$

(11)

where U_N is the value of noise reduction in green spaces on urban roads (CNY/a); i is the green space width level; α is the noise reduction rate of different green space structures (%); β is the noise reduction rate of a 4 m high soundproof wall (%); P is the cost per unit length of a soundproof wall (CNY/m); and L is the length of road green space (m).

3.5. Carbon Sequestration and Oxygen Release

The value of the carbon sequestration and oxygen release function of urban green space is calculated with reference to the “Specifications for Assessment of Forest Ecosystem Services” (GB/T 38582-2020) [15]. The first priority is to calculate the net primary productivity (NPP) of plants, i.e., the amount of organic carbon fixed by plants minus the amount consumed by their own respiration. According to the chemical reaction equation of photosynthesis, vegetation can absorb and fix 1.63 g of carbon dioxide and release 1.19 g of oxygen for every 1.00 g of dry matter accumulated. We used the photosynthesis equation to account for the functional value of urban green space ecosystem’s carbon sequestration and oxygen release services, and then used the price of carbon sequestration and oxygen release to account for this value. The cost of the carbon tax in China’s carbon trading market was used for this assessment. The annual oxygen release value forests was calculated using the oxygen commodity price announced by the national authority.

The formulas for calculating the annual carbon sequestration and oxygen release in urban green spaces are shown in Equations (12) and (13), while the formula for calculating the functional value of carbon sequestration and oxygen release in urban green spaces are shown in Equations (14) and (15), respectively.

$$G_c=1.63 B \times R \times A$$

(12)

$$G_o=1.19 B \times R \times A$$

(13)

where G_c is the annual carbon sequestration in urban green space (t/a); G_o is the annual oxygen release in green space (t/a); B is the net productivity of urban green space [t/(hm²·a)]; R is the content of carbon in carbon dioxide (27.27%); and A is the area of urban green space (hm²).

$$U_c={ G}_c \times { C}_c$$

(14)

$$U_o={ G}_o \times C_o$$

(15)

where U_c is annual value of carbon sequestration in urban green spaces (CNY/a); G_c is the annual carbon sequestration of urban green space (t/a); C_c is the price of carbon sequestration (CNY/t); U_o is the annual value of oxygen release in urban green spaces (CNY/a); G_o is the annual oxygen release of urban green space (t/a); and C_o is the price of oxygen production (CNY/t).

3.6. Rainfall Storage

The rainwater retention and purification function of urban green spaces is mainly manifested in the detention and storage of rainwater. The accounting of water conservation functions in forest ecosystems generally uses the water balance method, while in urban green spaces, water sources include not only natural rainfall but also artificial irrigation. Therefore, the water balance method is not applicable to the study of urban green space rainwater detention. Due to the relatively flat nature of urban green spaces, the sponge body principle can be used to calculate the precipitation regulation and storage function. The rainwater retention capacity of urban green spaces is affected by soil properties. Soil permeability is an important physical characteristic parameter that describes the rate of soil infiltration [33]. Under the same conditions, the better the soil permeability, the less surface runoff there will be. Therefore, the adjustment of water volume is calculated using three methods based on rainfall rate and rainfall amount.

Method 1: When the rainfall rate does not exceed the soil infiltration rate and the infiltrated water does not reach the saturated water-holding capacity, then the annual regulated water in urban green space ecosystems is the amount of rainfall.

Method 2: When the rainfall rate exceeds the soil infiltration rate and the infiltrated water volume does not reach saturation capacity, then the annual water regulation volume of urban green space ecosystem is the rainwater infiltrated into the soil within a certain rainfall period.

Method 3: When the amount of rainfall that infiltrates the soil within a certain period reaches the saturation holding capacity, the annual water regulation amount of the urban green space ecosystem is equal to the saturation holding capacity.

The formulas for calculating the annual rainfall storage in urban green spaces are shown in Equations (16)–(19), while the formulas for calculating the functional value of rainfall storage in urban green spaces are shown in Equations (20) and (21).

$$G_s={ A\times R}_r/1000$$

(16)

$$G_s={ S}_p\times t\times A/1000$$

(17)

$$G_s={ S}_s\times A$$

(18)

$$G_s=G_p$$

(19)

where G_s is the annual water regulation volume of urban green spaces (m³/a); R_r is the annual precipitation (mm/a); A is the urban green space area (m²); S_p is the rainwater infiltration rate (mm/min); t is the rainfall duration (min); S_s is the saturated water-holding capacity (m/a); and G_p is the annual water purification volume of urban green space (m³/a).

We then use the market price method to calculate the value of annual water regulation and storage of urban green space.

$$U_s={ G}_s \times { C}_s$$

(20)

$$U_p={ G}_p \times { C}_p$$

(21)

where U_s is the annual value of regulated water in urban green space (CNY/a); C_s is the water resource market transaction price (CNY/m³); U_p is the annual value of water purification by urban green spaces (CNY/a); and C_p is the water purification cost (CNY/m³).

3.7. Biodiversity Conservation

The accounting of the value of biodiversity conservation function was carried out with reference to the “Specifications for Assessment of Forest Ecosystem Services” (GB/T 38582-2020 [15]. In this study, the value of biodiversity was calculated according to the Shannon–Wiener index, which was divided into seven levels: when the index < 1, the value of biodiversity was CNY 3000/(ha·year); when 1 ≤ index < 2, it was 5000/(ha·year); when 2 ≤ index < 3, it was CNY 10,000/(ha·year); when 3 ≤ index < 4, it was CNY 20,000/(ha·year); when 4 ≤ index < 5, it was CNY 30,000/(ha·year); when 5 ≤ index < 6, it was CNY 40,000/(ha·year); and when the index ≥ 6, it was CNY 50,000/(ha·year).

The formula for calculating the functional value of Biodiversity conservation in urban green spaces is shown in Equation (22).

$$U_{B }=(1+\sum _{m=1}^x{E}_m \times 0.1+\sum _{n=1}^y{B}_n \times 0.1+\sum _{r=1}^z{O}_r \times 0.1) \times S_B \times A$$

(22)

where U_B is the conservation value of urban green space species resources (CNY/a); E_m is the rarity and endangered index of species m in urban green spaces; B_n is the endemic species index of species n in urban green spaces; O_r is the ancient tree age index of species r in urban green spaces; x is the number of rare and endangered species; y is the number of endemic species; z is the number of ancient tree species; S_B is the conservation value of species resources per unit area [CNY/(hm²·a)]; and A is the urban green space area (hm²).

4. Empirical Research on the Evaluation of the Ecosystem Service Function Value of Urban Green Spaces in Chifeng City

4.1. Overview of the Research Area

The study area is the central urban area of Chifeng City (118°46′39.332″~119°9′20.314″ E, 42°8′48.043″~42°24′27.228″ N), including the concentrated built-up area of Hongshan District and Songshan District and the surrounding control area, covering a total area of 387.49 km². According to the Statistical Yearbook of Chifeng (2021) [34], the scale of construction land in Chifeng City in 2020 was 114.18 km². The permanent urban population of the central urban area is 875,800. The overall area of green spaces in the central urban area of Chifeng is 665.07 hm². According to the Chifeng City Land Spatial Master Plan (2021–2035), the central urban area of Chifeng City is composed of Hongshan District, Songshan District, Bajia District, Xiaoxindi District, Qiaobei District, and Beiwazi District (Figure 2). Referring to the “Urban Green Space System Planning in Chifeng” (2015–2030), the green spaces planned and constructed in Chifeng City in recent years are not only large in number and area, but also have a relatively well structured spatial distribution (Figure 3).

4.2. Data Sources and Parameter Selection

In this empirical study, land use data were based on the Third National Land Survey Data and high-resolution remote sensing imagery maps of Chifeng city’s main urban area from WorldView-2, with a resolution of 0.5 m. The area of park green spaces is as follows: Hongshan District (101.68 hm²), Songshan District (130.53 hm²), Bajia District (181.40 hm²), Xiaoxindi District (51.53 hm²), Qiaobei District (124.82 hm²), and Beiwazi District (18.39 hm²). The data sources and parameter selection for calculating the service value of each evaluation indicator were as follows.

4.2.1. Leisure and Recreation Services

Using the equivalent substitution method, the recreational value of green space per unit area is based on the average leisure and recreation value of park green spaces in Beijing, which was found to be CNY 19.10/m² by Li Xiang et al. [35]. According to the “Statistical Bulletin of National Economic and Social Development of Chifeng City in 2020” [36], the per capita consumption expenditure of urban residents in Chifeng in 2020 was CNY 19,048. According to the China Network Real Estate [37], the per capita consumption expenditure of urban residents in Beijing in 2020 was CNY 41,726. It can be concluded that the per capita consumption expenditure of urban residents in Beijing is 2.19 times that of Chifeng. The leisure and recreation value of parks and green spaces in Chifeng is calculated to be CNY 8.72/m².

4.2.2. Landscape Premium Services

For housing prices, this study uses the second-hand housing prices obtained from the residential transaction intermediary platform https://chifeng.anjuke.com/ (accessed on 8 May 2024). Because the housing feature data in the residential transaction intermediary platform include various types of houses and have a broad coverage, they are sensitive to the surrounding environment. Housing prices are based on the average houses price in the central urban area of Chifeng City in the last three years. Previous studies have found that the impact radius of the landscape premium from green spaces is mostly around 1000 m, with a stronger premium effect within a range of 500 m [38,39,40]. According to the Urban Park Law of Japan, the minimum impact distance for regional parks is 1000 m, for community parks it is 500 m, and for children’s parks it is 250 m [41]. Referring to the “Urban Green Space System Planning of Chifeng (2015–2030)”, the area of man-made parks is generally greater than 10 hectares. Therefore, the scope of the landscape premium impact of comprehensive parks in the central urban area of Chifeng City with an area greater than 10 hectares is 1000 m, while the radiation scope of green spaces with an area less than 10 hectares is 500 m. In this study, 177 communities in Chifeng City were sampled. According to the types of residential buildings within the communities, 3–7 samples were randomly selected from each community, totaling 495 house samples. This paper selected the factors that have a significant impact on house prices based on existing research, including four aspects (house features, neighborhood features, location features, and distance to park features). House characteristics included the type of house, number of floors, total floor area, orientation, and completion year. Neighborhood features included business supporting facilities (whether there is a comprehensive supermarket within 500 m of the neighborhood) and school supporting facilities (whether there is a nursery or elementary school within 500 m of the neighborhood). Location features included transportation convenience (quantity of bus stops within 1000 m of the neighborhood). The park location factor refers to whether the distance from the residence to the park is within 500 m or 1000 m. The data on housing characteristics features were from https://chifeng.anjuke.com/ (accessed on 8 May 2024), the data on supermarkets, kindergartens, and elementary schools were from https://map.baidu.com/ (accessed on 16 Jun 2024), and the distance data were measured using the distance tool (Near) in ArcGIS (10.8) software.

4.2.3. Purification of Atmospheric Environment Services

The annual average concentration of air negative ions in urban green spaces, according to Huang Xianghua’s research, is 900 pieces/cm³ [42]. The negative air ion lifespan is 10 min in reference to the forest ecosystem service assessment norms. The absorption capacity of urban green space for air pollutants refers to the “China Biodiversity National Research Report” [43], in which the absorption capacity of broad-leaved forests for SO₂ averaged 88.7 kg/(hm²·a), the absorption capacity for fluorides averaged 4.7 kg/(hm²·a), the absorption capacity for NO_x averaged 6.0 kg/(hm²·a), and the dust-reducing capacity averaged 10,110 kg/(hm²·a). Adopting the equivalent values of air pollutants in the “Standards and Calculation Methods for the Collection of Emission Charges” [44], the value for SO₂ is 0.95, that for fluorides is 0.87, that for NO_x is 0.95, that for TSP is 4, that for PM_2.5, and that for PM₁₀ is 0.59. According to the “Environmental Protection Tax Law of the People’s Republic of China” [45], in Inner Mongolia, the applicable tax amount for taxable air pollutants is CNY 3.9 per pollutant equivalent.

4.2.4. Noise Reduction Service

A combination of GIS and remote sensing image interpretation was used to measure the width and length of five levels of green space. According to the “Environmental Noise Quality Standards” (GB3096-2008), we selected green spaces that provide noise reduction services between residential, educational, cultural, health, and administrative office areas and streets. According to the measured results of the noise reduction effects of five levels in the urban area of Beijing by Chen Long [30], combining the results with the width and length of the green space in the central urban area of Chifeng, and combining these with the average road noise value (65.9 dB (A)) in the urban area of Chifeng in 2020 [46], the noise reduction function of the green space in the central urban area of Chifeng was obtained.

4.2.5. Carbon Sequestration and Oxygen Release Services

The net productivity of urban green space adopts the research results of Liu Li [11] on the carbon fixation of park green space in North China, and the annual net primary productivity of urban green spaces in northern cities is 5.24 t/hm²·a. The carbon sequestration price adopts the carbon price of China’s carbon market in 2020 announced by the “Carbon Trading Network” to be CNY 74/t [47]. This price is the carbon dioxide price, which is converted into a carbon equivalent price of CNY 271.37/t. The price of released oxygen is based on the average price of oxygen published by the Ministry of Health of the People’s Republic of China, which is CNY 1000/t.

4.2.6. Rainfall Storage Services

Based on field observations, the maximum single rainfall event in the central urban area of Chifeng City in 2020 was 50.77 mm, which is less than the soil saturation water holding capacity of 77.10 mm. The soil permeability is based on the research of Zhang Biao et al. [33]. The transaction price of the water resources market adopts the water price of CNY 7.05/m³ published by “China Water Network” for Chifeng City in 2020 [48]. The cost of water purification is based on the sewage treatment price of CNY 0.95/m³ published by “China Water Network” for Chifeng City in 2020 [48].

4.2.7. Biodiversity Conservation Services

According to the research results of Wu Wenting [49] and Zhang Wei [50] on the biodiversity of urban green spaces, the Shannon–Wiener index level of urban green spaces is rated as level 2.

4.3. Research Results

4.3.1. Results of the Physical Assessment

The ecosystem service functions of urban green spaces are affected by the ecological background of the urban space, the intensity of green space planning and construction, and the area of green spaces. The functions of precipitation storage services, carbon sequestration and oxygen release services, and atmospheric purification services are expressed in terms of physical quantity, the function of landscape premium services is expressed in terms of benefited area, and the function of noise abatement services is expressed in terms of the length of green space along the road.

The research results on the material quality of the functions of precipitation regulation and storage services, carbon sequestration and oxygen release services, and air purification services are shown in

Table 1. Quality assessment results of ecosystem services of green space in the central urban area of Chifeng City.

District	Rainfall Storage (Ten Thousand t/a)	Carbon Sequestration and Oxygen Release		Air Purification
		Carbon Sequestration (t/a)	Oxygen Release (t/a)	Absorption of Gaseous Pollutants			Adsorption of Dust	Negative Ion Supply
				Sulfur Dioxide (kg/a)	Fluoride (kg/a)	Nitrogen Oxide (kg/a)	Adsorption of PMTSP (t/a)	Amount of Negative Ions Provided × 1019 (pieces/a)
Hongshan	19.13	236.84	172.91	9019.38	498.25	610.10	1028.02	48.10
Qiaobei	23.49	290.73	212.25	11,071.72	611.63	748.93	1261.95	59.05
Xiaoxindi	9.70	120.01	87.63	4570.92	252.51	309.19	520.99	24.38
Songshan	24.56	304.02	221.96	11,577.80	639.59	783.17	1319.63	61.74
Beiwazi	3.46	42.84	31.28	1631.62	90.13	110.37	185.97	8.70
Bajia	34.13	422.51	308.46	16,090.06	888.85	1088.39	1833.94	85.81
Total	114.47	1416.98	1034.48	53,961.50	2980.96	3650.16	6150.51	287.78

, which are ordered as follows: Bajia District > Songshan District > Qiaobei District > Hongshan District > Xiaoxindi District > Beiwazi District.

Regarding the landscape premium features, the green space in the downtown area of Chifeng City can promote the appreciation of 1369.21 hectares of residential land, accounting for 58.13% of the residential area in the areas that have been built (Figure 4). The residential land area within the radius of beneficial impact in different districts and its share in the total area of residential land are ordered as follows: Qiaobei District (95.52%) > Bajia District (84.81%) > Songshan District (56.96%) > Chengnan District (53.65%) > Hongshan District (37.03%) > Beiwazi District (29.98%).

For noise reduction functions, the annual noise reduction value in the central urban area of Chifeng City is 1.63 × 10⁵ dB (A)/a. The length of roadside green spaces providing noise reduction services is 8.59 × 10⁴ m, accounting for 20.27% of the total road length. The lengths of road green spaces providing noise reduction services in different districts are ordered as follows: Bajia District (3.49×10⁴ m) > Songshan District (2.31×10⁴ m) > Hongshan District (1.06×10⁴ m) > Qiaobei District (0.93×10⁴ m) > Chengnan District (0.44×10⁴ m) > Beiwazi District (0.25×10⁴ m). The proportions of the total road length are ordered as follows: Bajia District (51.22%) > Songshan District (25.19%) > Qiaobei District (14.67%) > Chengnan District (9.38%) > Beiwazi District (9.28%) > Hongshan District (8.37%) (Figure 5).

4.3.2. Results of the Value Assessment

The ecosystem service value of the green space in the center of Chifeng is displayed in

Table 2. Results of ecosystem service assessment of green space in Chifeng city center (hundred thousand CNY).

Service Category	Functional Category	HS	QB	XXD	SS	BWZ	BJ	Total		Percent (%)
Cultural service	Leisure and recreation	88.67	108.85	44.94	113.82	16.04	158.18	530.49	2060.36	89.62
	Landscape premium	257.61	166.64	106.05	498.53	67.04	434.00	1529.87
Regulatory services	Air purification	12.17	14.78	6.10	15.46	2.18	21.49	72.18	208.29	9.06
	Noise abatement	4.02	5.08	1.82	7.51	0.85	11.08	30.36
	Carbon sequestration and oxygen release	2.37	2.91	1.20	3.04	0.42	4.23	14.17
	Rainfall storage	15.31	18.79	7.76	19.65	2.77	27.31	91.58
Support services	Biodiversity conservation	5.08	6.24	2.58	6.53	0.92	9.07	30.42		1.32
Total		385.23	323.29	170.45	664.54	90.22	665.36	2299.08		100
Percentage (%)		16.76	14.06	7.41	28.90	3.92	28.94	100

“HS” is Hongshan District; “QB” is Qiaobei District; “XXD” is Xiaoxindi District; “SS” is Songshan District; “BWZ” is Beiwazi District; “BJ” is Bajia District.

and Figure 6, with a total value of CNY 239.69 million, and the ecosystem service value of the green space per unit area is CNY 37.79. The ecosystem service value per unit area of green space in each district is ordered as follows: Songshan District (CNY 50.91) > Beiwazi District (CNY 49.05) > Hongshan District (CNY 37.88) > Bajia District (CNY 36.68) > Xiaoxindi District (CNY 33.08) > Qiaobei District (CNY 25.90). According to the classification of ecosystem service categories, the cultural services, regulating services, and supporting services of green spaces in the central urban area of Chifeng City account for 89.62%, 9.06%, and 1.32% of the total value, respectively. The value of each function is ranked as follows: landscape premium (CNY 152.99 million) > recreational leisure (CNY 53.05 million) > precipitation regulation and storage (CNY 9.16 million) > air purification (CNY 7.22 million) > biodiversity (CNY 3.04 million) > noise reduction (CNY 3.04 million) > carbon fixation and oxygen release (CNY 1.42 million). The green space ecosystem service value ranking for each district is as follows: Bajia District (28.94%) > Songshan District (28.90%) > Hongshan District (16.76%) > Qiaobei District (14.06%) > Xiaoxindi District (7.41%) > Beiwazi District (3.92%).

4.4. Analysis of Results

The ecosystem service value of green space per unit area in the central urban district of Chifeng City is CNY 37.79/(m²·a), which is about twice the standard of premium green space maintenance and management (CNY 15 /m²·a) [40]. From the evaluation results of various service functions, it was found that cultural services account for the largest proportion in urban green space ecosystem services, with a total proportion of 91.36%, making cultural services the dominant service. Their main manifestations are leisure and recreation and landscape premium functions. The share of cultural services in Chifeng is similar to that of cultural services in China’s Shenzhen mangrove ecosystem (88.10%) [51] and cultural services in the urban green space ecosystem of China’s Mengzi City (93.63%) [10]. This study and the research on the Shenzhen mangroves and Mengzi City both consider the landscape recreation function and landscape premium function, while many other studies only calculate the landscape recreation function without considering the value of the landscape premium function, resulting in an undervaluation of cultural functional value. This can cause a loss of benefits due to the underestimation of ecological value when land transfer is carried out. Previous studies on the landscape premium function were mostly based on the landscape premium appreciation coefficient and the distance of influence, and with the advancement of research techniques and methods, research on the overall value of the landscape premium has been carried out in recent years. Therefore, when using frequency analysis for indicator selection, some indicators may be overlooked due to the relatively small number of studies on landscape premium functions. Therefore, a method that combines expert consultation should be used to supplement the overlooked indicators. Based on their years of professional experience, experts believe that landscape premium function indicators are of high importance, and this study further confirms these experts’ judgments. Research has found that the proportion of supply services is very small, as this indicator system only considers biodiversity conservation indicators, while studies on the functions of forest ecosystem services also include the supply of timber products. Compared to forests, urban green spaces primarily function to provide citizens with a high-quality ecological environment and recreational areas, and they basically do not have the function of supplying timber products.

The functions of precipitation storage services, carbon sequestration and oxygen release services, and air purification services are directly related to the area of green space and the type of forest classification. Bajia District has the highest value of these three functions, mainly because the green spaces under the guidance of green space system planning are not only large in number and area, but also have a relatively rational spatial distribution structure. Songshan District, Qiaobei District, and Hongshan District are next in the rankings, while Xiaoxindi District and Beiwazi District are ranked the lowest, because they are located in the periphery of urban core area, and so have relatively less greenery. Regarding the landscape premium function, as the green space system in the main urban area of Chifeng City relies on the structure of the Xipo River, the half-branch Arrow River, and the Yin River for its layout and construction, the radiation scope of the landscape premium service of park green spaces is concentrated in the vicinity of the three branches of the river. Among the districts, the radiation range of Songshan District and Bajia District accounts for a relatively high proportion, while the radiation range of Hongshan District and Beiwazi District accounts for a relatively low proportion. Because Hongshan District comprises the old town, while Beiwazi District is located on the periphery of the urban core area, the planning and construction of green space parks in both places are insufficient, with a low quantity and a small area. The radiation range of the landscape premium reflects the scope of influence of leisure and recreational services to a certain extent. For the noise abatement function, the supply demand for roadway green space is judged based on the ratio of the length of roadway green space to the length of roadways that provide noise abatement services. Among the districts, Bajia District accounts for half of these services, Songshan district accounts for a quarter, and other districts account for less than 15%. Therefore, there is a need to improve the planning, design, and construction of roadside green spaces, improve the overall noise reduction level of urban green space, and realize the balanced distribution of noise reduction services.

This study found that the data regarding the park green space varieties, area difference, distribution type, and quality in Chifeng city center’s urban area can basically cover a variety of urban green spaces, so this research on Chifeng city center urban area can be used as an example to conduct research on the benefits of urban green space, with good applicability. The evaluation of the service functions of urban green spaces in other regions can use the method proposed in this study and select the parameters relevant to the research area. Therefore, this study can not only provide certain construction guidance for park green space in Chifeng, but also provide reference for the evaluation of the ecosystem service functions of park green spaces in other cities.

5. Conclusions

5.1. Progressiveness and Prospects for Research Methods

Since the air purification function, carbon sequestration and oxygen release function, and biodiversity protection function of the urban green space system are similar to those of forest ecosystems, the assessment of these three services is based on the “Specifications for Assessment of Forest Ecosystem Services” (GB/T 38582-2020). This assessment specification, as a national standard, is widely used in the evaluation of the value of ecosystem service functions in China’s forest ecosystems.

For the assessment of the recreational and leisure functions of urban green spaces, future studies can use the urban green space leisure service indicators (green space rate, green coverage, accessibility, per capita occupancy, and functional integrity) [52] for evaluation.

To evaluate the value-added function of urban green space landscapes, some studies have taken all the areas within the service radiation scope of green spaces as value-added areas, which may lead to overestimation [40]. This study quantified the floor area benefiting based on the physical location of dwellings and the height of buildings. Using GIS technology and national land spatial survey data, we extract urban residential land parcels within the service radiation range of landscape and determine the residential land plot ratio through remote sensing imagery interpretation and on-site investigation, thereby determining the total building area of dwelling district parcels. For the landscape premium function, combined with the hedonic pricing method [53] widely used in current research, the value-added coefficient is calculated based on the physical distribution of residential buildings of the research area, making the calculation of the landscape premium of real estate more accurate. The determination of the radiation range of the impact of park green spaces on residential housing prices uses the method of defining the radiation range with park green spaces at the core and with the influence distance as the radius, ignoring the influence of the park entrance location and walking traffic routes. Therefore, future research can adopt network analysis methods to comprehensively determine the radiation range based on elements such as park gates and pedestrian traffic routes.

For the assessment of noise reduction function, some studies have summed up all green space areas and then converted them. This method accounts for green spaces that do not generate noise reduction services or those that are smaller in width and have a weaker noise reduction function, resulting in an overestimation of the results. The green space selected for this study is the green space around residences that provides actual noise reduction services to the city residents, and the existing volume of this green space is converted into a soundproof wall with a certain length.

For the assessment of precipitation storage function in park green spaces, most of the existing studies refer to the water balance method, which is a method for assessing the precipitation storage function of forest ecosystems that takes into account the amount of rainfall, runoff, and evapotranspiration. However, in urban green space, in addition to rainfall, there is also artificial irrigation, so the water balance method cannot be used. This study adopts the principle of a sponge body, eliminating the influence of artificial irrigation on the evaluation.

5.2. Guidance on Urban Planning Policies in This Study

The ecological service functions of urban green space systems depend not only on the quantity and area of urban green spaces but also on their quality, such as the composition, structure, landscape pattern, and management level of the green space system. The focus of urban green space planning is not on the coverage rate and area of green spaces, but on how green spaces are distributed spatially. Exploring urban green space planning issues from an ecological perspective is a sustainable approach that respects the natural evolution process. Therefore, the reasonable layout of urban green spaces is a vital means to enhance the comprehensive benefits of urban land use and fully realize the value of urban green spaces. This article concludes that the scope of benefits derived from the landscape premium function can basically reflect the service radiation range of urban green spaces. Therefore, this range is used as an important reference, and combining it with the value of green space service functions in each district, suggestions for further improving park and green space planning and construction are proposed. It is recommended to appropriately increase the quantity and area of park green spaces outside the benefit area, while reasonably arranging the space, expanding the benefit range of park green spaces, and increasing the per capita area of park green spaces to ensure that citizens have fairer access to parks and green spaces. For areas where green space is relatively well established (Bajia and Qiaobei districts), green space can also be pocketed, decentralized, and three-dimensionalized to further increase the size of the green space. For the old urban areas (Hongshan District and Songshan District), there is a natural deficit of green space. It is essential to first ensure that the existing green spaces are not encroached upon, while also integrating a consideration of green space with the renovation of old districts to fully tap into the potential of the land, actively developing new green spaces of considerable scale and green spaces interspersed within residential areas and communities and next to homes. For the areas located on the periphery of the city (Beiwazi District and Xiaoxindi District), large-scale green space should be reserved in strict accordance with the national construction land standards and the land use planning of the new district, in accordance with the scattering of district green space, set block park space, and dedicated park space. In addition, due to the provision of noise reduction services on the road, the green space length to road length ratio has not reached 50%. Therefore, the number of strips of road green space and separated green belts should be increased throughout the entire center of the city.