Identifying Cross-Regional Ecological Compensation Based on Ecosystem Service Supply, Demand, and Flow for Landscape Management

Abstract

Clarifying the issues related to the supply, demand, and flow of ecosystem services is crucial for regional landscape management. This study employs the equivalence factor method and demand index quantification to analyze the supply and demand of ecosystem services in the Zheng-Bian-Luo region in 2000 and 2020. We used hotspot analysis tools and the minimum cumulative resistance model to establish the ecological corridors, identifying the spatial flow paths of ecosystem services in our site. By calculating the flow volume of the key corridor value through the breakpoint formula and field strength theory and combining this with the ratio of the regulating service value, we computed the ecological compensation amount, thereby realizing the value of the ecosystem service. The results indicate that the area of balance between ecosystem service supply and demand gradually decreased and the deficit area in the Zheng-Bian-Luo region increased 43.62% from 2000 to 2020 along with rapid urbanization. The total value flow of ecosystem services by the important ecological corridors in 2000 and 2020 was USD 242.40 million and USD 365.92 million, respectively. In 2020, it was predicted that Luanchuan County would receive ecological compensation totals of USD 237.76 million from each ecological demand area, and mainly from Jinshui District. Our findings support enhancing the quality of the ecological environment and optimizing the landscape management of the Yellow River’s Henan section.

1. Introduction

The accelerating process of urbanization has established cities as primary spaces for human survival and production. Incorporating the development of urban ecological security patterns into the establishment of ecological safety structures is a crucial component [1,2,3]. The acceleration of urbanization processes has led to human-induced disruptions in the ecosystems, causing imbalances in the supply and demand of regional ecosystem services. Ecosystems supply humans with products and services, creating demand and consumption for these products and services [4]. This dynamic process, in which the interaction between demand and supply shapes the flow of ecosystem services from natural ecosystems to human societies, is spatially impacted by biophysical factors and changes in land use resulting from human activities [5], and quantitatively by the natural capital supply capacity and human welfare objectives [6]. Ecosystem service demand, influenced by individuals and social groups, represents the total of all ecosystem services consumed or used in a specific region during a given period [5,7]. The demand for ecosystem services by economic entities can cause changes in the service status of the ecosystem [8]. As socioeconomic development progresses, human intervention in ecosystems results in the degradation or loss of diverse ecosystem services. This, in turn, contributes to regional disparities in the supply and demand of these services, posing risks to ecological safety and the prospects of sustainable development. The equilibrium between the supply and demand of ecosystem services is crucial for maintaining ecological safety and promoting the sustainable growth of regional ecosystems [9,10]. The identification, measurement, spatial analysis, and examination of supply and demand are fundamental aspects of the study of ecosystem services. These actions not only support the effective management of ecosystems, but also aid in the rational distribution of natural resources [11]. Increasingly, attention has been given to the quantification of ecosystem service supply and demand [12] and their matching relationships [13]. Theoretical research has introduced various conceptual models to explore the correlation between the supply and demand of ecosystem services, such as the integrated assessment framework for ecosystem service supply and demand [7] and the framework linking ecosystem services and human welfare using elements as carriers [14,15]. Research methods commonly used include those based on the land use/land cover change matrix for ecosystem service supply and demand [8], the ecological footprint method, the public participation method [16], model calculation based on ecological processes [17], and the market value method [18]. Each of these methods has its applicable scenarios. Among them, the matrix method based on land use and land cover [5] has been widely applied by numerous scholars to assess regional ecosystem service supply and demand conditions in places such as eastern Germany [19], central Europe [20], the Jingjinji region [21], the Dongting Lake Eco-region [22], and the Hangzhou metropolitan area [23], offering a certain pathway for quantitative, standardized, and visualized research on regional ecosystem service supply and demand status. Model simulation also serves as a critical method in connecting ecosystem service supply and demand. Several researchers have employed model simulation as the primary quantitative method to identify the status and matching relationships of ecosystem services on the urban [24,25] and provincial scales [26,27]. This application of model simulation provides valuable insights into how different scales of ecosystems interact and balance their resource supply and demand.

Balancing ecosystem service supply and demand is the key to achieving ecological security and human welfare [6,28,29]. The flow of ecosystem services is pivotal in connecting the provision of these services with human needs. This aspect represents a central area of focus and challenge within the field of ecosystem services research, which is currently in its early stages, trending towards quantification and spatial analysis [30,31,32]. As our understanding of the interplay between ecosystem services and the economy deepens, issues concerning the cross-regional and cross-boundary flow of ecosystem services have received attention. Certain research efforts have focused on the process of ecosystem services moving from the source regions to the beneficiary regions [33]. They have also put forth a conceptual framework outlining the spatial pathways of these service flows. Several scholars have compiled an overview of the existing research on ecosystem service flows [34,35,36]. Their findings suggest that, depending on the specific research objectives, this can encompass the tangible provision of ecosystem services, their ultimate realization, or the intermediary channels through which they travel. Additionally, they have outlined the steps involved in quantitatively assessing the flow of ecosystem services within regions. The issue of the cross-boundary flow of ecosystem services in special regions, such as the Inner Mongolia area, has gradually received attention [37]. For specific ecosystem services, some quantitative methods have been applied in the evaluation of ecosystem service flows. For instance, the Service Path Attribute Network model is utilized to simulate the flow process of ecosystem services from the source regions to the beneficiary regions [38]; the Hybrid Single-Particle Lagrangian Integrated Trajectory model is employed to quantify the physical flow, flow path, and beneficiary areas of PM_2.5 reduction services in Beijing’s wetlands [39]; and the Bayesian Network model is used to simulate the flow of carbon fixation services and water production services [40,41].

Research on the flow of ecosystem services is essentially about establishing spatiotemporal correlations between supply and demand, clarifying where and when the benefits produced by ecosystem services are enjoyed, and providing information for the formulation of policy measures such as payment for ecosystem services [28]. Analyzing the spatial dynamics of ecosystem services through the lens of supply and demand relationships allows for a clearer understanding of the origins, dissipation pathways, and the regions that derive benefits from these services [42]. This approach aids in optimizing ecosystem service management, improving regional ecological environmental conditions, providing theoretical support for payment for ecosystem services, and facilitating policymakers in formulating long-term development plans. Research on the quantification of specific ecosystem service flows has made some progress in policy practices such as ecological compensation. For example, regarding the issue of transboundary water resource flows, the quantification of water production service flows can more accurately determine the beneficiary areas and standards for cross-regional ecological compensation, and has been applied in the Weihe River Basin [43], Miyun Reservoir [44], the middle and lower reaches of the Yellow River [45], and Yan River Basin [46]. For the transregional issue of windbreaks and sand fixation, the precise quantification of the flow of windbreaks and sand fixation services helps to determine that areas such as Inner Mongolia and Yanchi County should benefit from ecological compensation [47,48]. At the regional scale, ecosystem service flows help to define the scope of the ecosystem service supply and demand areas and to calculate the value of ecosystem service compensation, and can become a new entry point for ecological compensation research [49]. Overall, current research on the spatial flow of ecosystem services can quantify how much of the ecosystem services received by beneficiary areas come from the spatial flow of services [50,51,52,53,54,55], and some models can simulate the flow process for individual services such as carbon fixation and water production. However, few studies displayed the flow path and volume of integrated ecosystem services through intuitive spatial methods, especially for the study of ecosystem service flows across urban areas. For integrated ecosystem services, current research on the flow of ecosystem services has not yet applied ecological compensation.

In recent years, Henan Province has encouraged all regions to explore the value accounting of ecological products, research the establishment of compensation standards, and investigate the development of a lateral ecological compensation mechanism in the Yellow River Basin [56,57]. They plan to raise funds and coordinate within the city to provide ecological compensation to the Yellow River Basin. At present, the question of how to calculate the compensation fund according to the ecological service function lacks scientific argumentation; in terms of ecological compensation standards, the country is still in the exploration stage and lacks precedents to follow. The Zheng-Bian-Luo region, as the most economically active part of the Henan section of the Yellow River Basin, faces tremendous pressure from the imbalance of regional ecosystem supply and demand, including issues such as low forest resource quality, uneven spatial distribution of water resources, diminishing arable land, and increasingly prominent man–land conflicts [58,59,60]. Analyzing the supply and demand patterns, elucidating the spatial flow routes and quantities of ecosystem services, and identifying ecological compensation within the Zheng-Bian-Luo region can establish a scientific foundation for refining and executing the ecological compensation mechanism in the Henan section of the Yellow River. This process contributes to enhancing the Yellow River’s environmental governance capabilities. In the above context, this study focuses on the following research questions and objectives: (1) determining the ecological source and demand areas in the Zheng-Kai-Luo region based on the supply and demand of ecosystem services; (2) identifying ecological corridors and calculating the flow of ecosystem service value from the source to the demand area; and (3) determining the ecological compensation area and ecological compensation amount in the Zheng-Kai-Luo region.

2. Materials and Methods

2.1. The Study Area

The Zheng-Bian-Luo area is situated in the north-central region of Henan Province, on the southern bank of the middle and lower sections of the Yellow River. Comprising 35 counties and cities within its administrative boundaries, the Zheng-Bian-Luo region is arranged in an east-to-west pattern (Figure 1). It covers a combined land area of 29,098.2 km² and hosts a population of approximately 24.59 million. This study area experiences well-defined seasons, encounters both rainy and hot periods concurrently, and exhibits a wide range of weather conditions. The Zheng-Bian-Luo region is the main artery, with the strongest economic ties in the Central Plains city group, and with enormous development strength and potential [59]. According to the 2021 Henan Provincial Statistical Yearbook, the urbanization rate in the Zheng-Bian-Luo area stands at 70%. Meanwhile, the 2020 land use classification data reveal that our specific region contains approximately 14.6% built-up land. This region represents the most vibrant and economically crucial area within the province, and the shifts in land use composition due to rapid urbanization have a direct impact on human livelihoods. Furthermore, the disparity between the supply and demand of ecosystem services intensifies ecological security risks. It is imperative to ensure sustainable regional economic development and guarantee that ecological safety is not threatened.

2.2. Research Design

This paper revolves around the research design shown in Figure 2 to study and analyze the ecosystem services flow and ecological compensation in our site. The research design is divided into the following four steps. First, according to the equivalent factor method, the supply value of each district/county in our site is calculated, and at the same time, the demand index of each district/county in our site is calculated based on the selected per capita population density, per capita GDP (Gross Domestic Product), and proportion of construction land. After normalizing the value supply and demand index, the supply–demand ratio is calculated. The spatial pattern of ecosystem service supply and demand in our site is obtained through the hotspot analysis tool in spatial autocorrelation analysis. Second, the source and demand locations of the ecosystem are extracted through the spatial pattern of ecosystem service supply and demand, and the ecological corridor is constructed through the minimum cumulative resistance (MCR) model. The important ecological corridors, i.e., the optimal paths for the spatial flow of ecosystem services, are extracted based on the gravity model, which intuitively displays the flow path of ecosystem services. Third, based on the important ecological corridors, the radiation area and flow intensity from the source to the demand locations are calculated according to the breakpoint formula and field strength model, the flow value of ecosystem services is finally accounted for, and the temporal and spatial changes in the value flow of ecosystem services are analyzed. Fourth, the ratio of ecosystem regulating service value is calculated to determine the ecological compensation coefficient, and the ecological compensation amount is calculated based on the value flow of ecosystem-regulating services in the important ecological corridor.

2.3. Data Sources

Through field data collection and online downloading, we obtained the data required for studying the spatial flow in our site, as shown in

Table 1. Data sources.

Data Types	Data Details	Data Source
Land Use Data	The land use data of the Zheng-Bian-Luo region in 2000 and 2020, with a spatial resolution of 30 m × 30 m. The land use data for 2000 were generated from Landsat-TM/ETM remote sensing imagery data through manual visual interpretation. The 2020 land use data update mainly used Landsat 8 remote sensing imagery data.	Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences (RESDC) (http://www.resdc.cn, accessed on 3 January 2023)
Food Production and Prices	The planting area and yield of the main grain crops in the Zheng-Bian-Luo region in 2000 and 2020.	Statistical Yearbook of Henan Province
	Average national prices of major grain crops in 2000 and 2020.	National compilation of agricultural revenue information
Population, GDP	The urban resident population, population density, and gross production value of each district/county.	Statistical Yearbook of Henan Province, Zhengzhou City, Luoyang City and Kaifeng City
Roads, Elevation	National and provincial highways, roads, railways, and DEM data in Henan Province.	RESDC (http://www.resdc.cn, accessed on 5 January 2023)

. The data in question predominantly comprise information related to land use, socioeconomic statistics, and natural geography. The GDP data for each district/county mainly came from the statistical yearbooks of Henan Province, Zhengzhou City, Kaifeng City, and Luoyang City. Some data, including the GDP data for Luoyang Old Town, Xigong District, Chanhe Huizu District, etc., in 2000, and the GDP data for Longting District, Shunhe Huizu District, Drum Tower District, etc., in Kaifeng City in 2000, were mainly calculated by multiplying the city’s per capita GDP by the total population of each district.

2.4. Accounting for the Ecosystem Services Supply and Demand

2.4.1. Ecosystem Services Value Supply

Based on the “equivalent value of unit area ecosystem services in China”, revised in the referenced literature [61], we further fixed the value of ecosystem services per unit area in our site. The net profit for food production within the agricultural ecosystem per unit area (1 hm²) was established as the value for a single standard equivalent factor of ecosystem service. By referring to the “Henan Province Statistical Yearbook 2001–2022” and the “National Compilation of Agricultural Product Cost and Income Data” and other basic materials, the average price were calculated as 3507.44 USD/hm². The value of the equivalent factor in our site was determined by Formula (1), as follows:

$$E_{\mathit{no}}={\displaystyle \frac{1}{7}}{\displaystyle \sum _{i=1}^n\frac{s_i{p}_i{q}_i}{S}}$$

(1)

In the formula, $E_{no}$ is the value of the standard equivalent factor of ecosystem services (USD·hm⁻²); $i$ represents the types of food crops in our site; $n$ is the total number of food crop types in our site, in this case $n=4$; $S_i$ is the planting area of the $i$ food crop (hm²); $S$ is the total planting area of $n$ food crops in the $t$ year (hm²); $p_i$ is the national average price of the ith food crop (USD·kg⁻²) (

Table 2. Seeding area, yield per unit, and national average price of main grain crops in our site in 2000 and 2020.

Calculated Indicator	Grain Crop	2000	2020	Calculated Average
Agricultural Product Price (USD/kg)	Rice	0.15	0.40	0.27
	Wheat	0.15	0.33	0.24
	Corn	0.12	0.33	0.23
	Soybean	0.30	0.70	0.50
Yield per Unit (kg/hm2)	Rice	21,126.00	21,470.21	21,298.10
	Wheat	11,929.00	16,662.87	14,295.94
	Corn	14,912.00	15,809.75	15,360.87
	Soybean	6613.00	6440.81	6526.90
Seeding Area (hm2)	Rice	22,640.00	8300.00	15,470.00
	Wheat	729,560.00	678,140.00	703,850.00
	Corn	339,370.00	502,770.00	421,070.00
	Soybean	68,100.00	39,370.00	53,735.00
	Total Seeding Area	1,159,670.00	1,228,580.00	1,194,125.00

); and $q_i$ is the yield per unit area of the $i$ food crop (kg·hm⁻²). The ratio of 1/7 signifies that the economic worth derived from natural ecosystems, without human intervention, accounts for approximately one-seventh of the economic value generated by food production services on a per unit of farmland basis. The calculated economic value of food production services provided by the existing unit area of farmland is 3507.44 USD/hm², so the value of the standard equivalent factor in our site is 501.06 USD/hm².

Based on the ecosystem services equivalent factor from Xie et al. [61] and the actual land use characteristics of the Zheng-Bian-Luo region, we improved the ecosystem services equivalent values of our site (

Table 3. The ecosystem service equivalent values in our site.

Primary Category	Secondary Category	Arable Land	Forest Land	Grassland	Water Body	Unused Land
Provisioning Services	Food Production	0.93	0.25	0.23	0.66	0.00
	Raw Material Production	0.35	0.58	0.34	0.37	0.00
	Water Supply	−0.42	0.30	0.19	5.44	0.00
Regulating Services	Gas Regulating	0.74	1.61	1.21	1.34	0.02
	Climate Regulating	0.39	5.71	3.19	2.95	0.00
	Environmental Purification	0.11	1.67	1.05	4.58	0.10
	Hydrological Regulating	0.68	3.74	2.34	63.24	0.30
Supporting Services	Soil Conservation	0.86	2.32	1.47	1.62	0.02
	Maintaining Nutrient Cycle	0.13	0.18	0.11	0.13	0.00
	Biodiversity	0.14	2.12	3.77	5.21	0.02
Cultural Services	Aesthetic Landscape	0.06	0.93	2.08	3.31	0.01

).

Based on the improved ecosystem service equivalent (Table 3) and the value of the standard equivalent factor, the average values of ecosystem services per unit area of our site were calculated (

Table 4. The value of ecosystem services per unit area in our site (USD/hm²).

Primary Category	Secondary Category	Arable Land	Forest Land	Grassland	Water Body	Unused Land
Provisioning Services	Food Production	465.99	125.27	115.24	330.70	0.00
	Raw Material Production	175.37	290.61	170.36	185.39	0.00
	Water Supply	−210.45	150.32	95.20	2725.77	0.00
Regulating Services	Gas Regulating	370.78	806.71	606.28	671.42	10.02
	Climate Regulating	195.41	2861.05	1598.38	1478.13	0.00
	Environmental Purification	55.12	836.77	526.11	2294.85	50.11
	Hydrological Regulating	340.72	1873.96	1172.48	31687.03	150.32
Supporting Services	Soil Conservation	430.91	1162.46	736.56	811.72	10.02
	Maintaining Nutrient Cycle	65.14	90.19	55.12	65.14	0.00
	Biodiversity	70.15	1062.25	1889.00	2610.52	10.02
Cultural Services	Aesthetic Landscape	30.06	465.99	1042.20	1658.51	5.01
Total		1989.21	9725.57	8006.94	44519.18	235.50

). The ecosystem service values of Zheng-Bian-Luo in 2000 and 2020 were obtained by using Formulas (2) and (3).

$$ES{V}_i={\displaystyle \sum A_i\times V{C}_i}$$

(2)

$$ESV={\displaystyle \sum ES{V}_i}$$

(3)

where ${ESV}_i$ and $ESV$ represent the values of the ith type of ecosystem service and the total value of ecosystem services, respectively; $A_i$ is the land area of the $i$ type of ecosystem (km²); and ${VC}_i$ is the value coefficient, representing the ecosystem service value of the unit area of the $i$ type of ecosystem.

2.4.2. Ecosystem Services Demand Index

This study, based on the view of the existing literature [5,62], believes that the demand for ecosystem services refers to the quantity of ecosystem services that are consumed (or can be obtained) or hoped to be obtained by human society. Our site’s assessment of ecosystem service demand draws from previous research [63], utilizing three socioeconomic indicators: land use development level, population density, and per capita GDP. These factors are employed to compute the ecosystem service demand index (X) and provide a characterization of ecosystem service demand. The formula is as follows:

$$X=\partial \times \lg \rho \times \lg E$$

(4)

where $X$ represents the demand index for ecosystem services; $\partial$ represents the proportion of construction land (%); $\rho$ represents population density (people/km²); and $E$ represents per capita GDP (10,000 USD/km²), reflecting the wealth of the region.

2.5. Spatial Pattern of Ecosystem Service Supply and Demand

The purpose of analyzing the spatial pattern of ecosystem service supply and demand is mainly to identify the ecological source and sink region, which includes two steps. Firstly, we normalize the value supply and demand index to calculate the ecosystem service supply–demand ratio (ESDR). Secondly, we conduct the spatial autocorrelation analysis of ESDR through the hotspot analysis tool in ArcGIS (Arc Geographic Information System) and identify the ecological source and sink region of Zheng-Bian-Luo.

2.5.1. Ratio of Ecosystem Service Supply and Demand

This paper uses the ecosystem service supply and demand ratio to quantitatively analyze the supply and demand relationship of the ecosystem in each district/county of our site, revealing the nature of a surplus or deficit in quantity [64,65,66]. The calculation formula is as follows:

$$ESDR={\displaystyle \frac{S-D}{(S_{\mathit{max}}+D_{\mathit{max}})/2}}$$

(5)

where $ESDR$ is the ecosystem service supply and demand ratio; $S$ and $D$ are the supply and demand quantities of ecosystem services in each district/county of our site (calculated after data standardization); $S_{max}$ is the maximum supply value of ecosystem services; and $D_{max}$ is the maximum demand value of ecosystem services. When $ESDR>0$, the supply of ecosystem services exceeds the demand. When $ESDR<0$, the supply cannot meet the demand. When $ESDR=0$, it indicates a state of supply–demand balance.

2.5.2. Spatial Autocorrelation Method

By using the hotspot analysis tool of the ${G_i}^{*}$ statistical index in the ArcGIS platform, the aggregation locations and degrees of the high-value areas (hotspot areas) and low-value areas (coldspot areas) of the ecosystem service supply and demand ratio are directly reflected in the space. This is used to analyze the profit and loss relationship of the ecosystem supply and demand spatial pattern [67]. When ${G_i}^{*}$ equals zero, it indicates that there is no aggregation of the ESDR, and that the area is in a demand–supply balance. When ${G_i}^{*}$ is positive, it indicates that the supply in this region greatly exceeds the demand, constituting a high-value agglomeration surplus area. Conversely, when ${G_i}^{*}$ is negative, it indicates that the demand in this region is remarkably less than the supply, making it a low-value agglomeration deficit area. As the absolute value of ${G_i}^{*}$ increases, it signifies a greater degree of low- or high-value aggregation of the ecosystem service supply–demand ratio, pointing to an imbalance in supply and demand.

Based on the ArcGIS hotspot analysis tool, the ${G_i}^{*}$ of ESDR are 0, ±1, ±2, and 3. According to the values of ${G_i}^{*}$, areas of imbalance can be divided into four types: areas with ${G_i}^{*}$ = 3 are areas with sufficient surplus; areas with ${G_i}^{*}$ = 1, 2 are areas with a general surplus; areas with ${G_i}^{*}$ = −1 are areas with a general deficit; areas with ${G_i}^{*}$ = −2 are areas with a severe deficit; and areas with ${G_i}^{*}$ = 0 are areas of balanced supply and demand. Areas with sufficient and general surplus are also known as areas of surplus supply and demand due to their supply exceeding their demand. Areas with severe and general deficits are also known as areas of deficit supply and demand because their supply does not meet the demand.

2.6. Determining the Flow Value Quantity of Ecosystem Services

Determining the ecosystem service flow value quantity includes two steps. The first is identifying the flow paths of ecosystem service values. The ecological source and sink region of Zheng-Bian-Luo are extracted through the spatial patterns of ecosystem service supply and demand. We construct the ecological corridor through the minimum cumulative resistance (MCR) model and extract the important ecological corridors, i.e., optimal paths of ecosystem service values, using the gravity model. The second step is calculating the ecosystem service flow value quantity. According to the breakpoint formula, the radiation area from ecological source to sink is calculated. According to the field strength model, the ecosystem service flow intensity from the source to the sink is calculated. Based on the important ecological corridors, the radiation area and the flow intensity, the flow value quantity of ecosystem services is finally accounted for.

2.6.1. Identifying the Paths of Spatial Flow of Ecosystem Services

In this paper, surplus patches identified through hotspot analysis are treated as ecological source areas. These can provide a high ecological service value, making them suitable as the source of ecosystem service flows. Deficit patches are treated as ecological demand areas due to their high demand indices, making them the main beneficiary areas of ecosystem services. The centroids of the source and demand areas were used as the source points and target points, respectively. Ecological corridors are key channels for the flow of matter and energy within ecosystems and an essential part of the ecological safety pattern. Multiple ecological corridors between the source and demand areas were generated using an MCR model. The MCR model, which reflects accessibility by calculating the cost of overcoming resistance through different landscape types, can be used to calculate the MCR distance between ecological source areas and demand areas, thus identifying the potential route for the spatial flow of ecosystem services, i.e., the ecological corridor [46,68,69,70,71]. The formula is as follows:

$$MCR=f_{\min }{\displaystyle {\sum }_{j=n}^{i=m}{D}_{\mathit{ij}}\times R_i}$$

(6)

where $MCR$ represents the minimal cumulative resistance value; $D_{ij}$ represents the distance from source $j$ to landscape unit $i$; $R_i$ represents the resistance factor of landscape unit $i$ to species movement; and $f$ indicates the positive correlation between MCR and ecological processes.

The detailed processes of extracting ecological corridors are as follows when using the MCR model. A resistance evaluation system is constructed (

Table 5. Resistance factor assignment.

Resistance Factors	Resistance Values
Forest Land	5
Grassland	30
Arable Land	50
Water Body	80
Unused Land	100
Construction Land	200
30 m Buffer Zone from Railway	300
15 m Buffer Zone from Road	150

), including six types of land use categories (i.e., forests, grasslands, cultivated land, waters, unused land, and built-up land), railway buffer zones, and road buffer zones [72]. Using the reclassification and buffer tools, the resistance factors are assigned, and then the raster layers of various resistance factors are superimposed to generate a resistance cost surface using the embed-to-new-raster tool. Using the cost distance tool, the cost distance from the ecological source to the ecological demand site is calculated. Using the cost path tool, with different ecological source centroids as the source and all ecological demand centroids as targets, multiple least-resistant cost paths are generated. Finally, the generated cost paths are converted to lines using the raster-to-polyline tool, and all the cost paths are merged to obtain all potential ecological corridors between the ecological source and demand areas.

The interaction between the ecological source and demand areas is calculated using the gravity model [73,74], resulting in a gravity matrix. This approach facilitates a quantitative assessment of the interaction strength between various source and demand regions. It also helps to ascertain the relative significance of corridors within the area, enabling the identification of the most suitable path for the spatial flow of ecosystem services. The stronger the interaction is, the simpler the exchange and transmission of material energy between the source and demand areas, which can be extracted as important corridors. Conversely, a weaker interaction indicates a smaller possibility of overcoming obstacles for biological species to migrate between the two places. The exchange and transmission of material energy between the source and demand areas are also more difficult and thus are not suitable as the optimal path for the spatial flow of the ecosystem. The formula is as follows:

$$G_{ab}={\displaystyle \frac{N_a{N}_b}{{D_{ab}}^2}}={\displaystyle \frac{\left[\frac{1}{P_a}\times \ln S_a\right]\left[\frac{1}{P_b}\times \ln S_b\right]}{{\left(L_{ab}/L_{ab}\right)}^2}}={\displaystyle \frac{{L_{max}}^2\ln S_a\ln S_b}{{L_{ah}}^2{P}_a{P}_b}}$$

(7)

where $G_{ab}$ is the interaction between source $a$ and demand $b$; $N_a$ and $N_b$ are the weight values of the two ecological patches; $D_{ab}$ is the standardized potential corridor resistance value between source $a$ and demand $b$; $P_a$ is the resistance value of source $a$ and $P_b$ is the resistance value of demand $b$; $S_a$ is the area of source $a$ and $S_b$ is the area of demand $b$; $L_{ab}$ is the cumulative resistance value of the corridor between source $a$ and demand $b$; and $L_{max}$ is the maximum cumulative resistance value of all corridors in the study area.

2.6.2. Calculating the Flow of the Ecosystem Services Value

The value of ecosystem service transfers generally follows the law of distance decay, i.e., it decreases as the distance of ecological space increases [75]. The breakpoint model can simulate the boundary of ecosystem services flowing from the ecological source to the ecological demand [76,77,78,79]. The breakpoint formula of the ecosystem is as follows:

$$D_i={\displaystyle \frac{D_{y,x}}{1+\sqrt{E_y/E_x}}}$$

(8)

$$A=D_i^2\pi$$

(9)

where $D_i$ is the distance from the centroid of the ecological source to the breakpoint (km); $E_y$ and $E_x$ are the ecosystem service values of the ecological source and demand (USD), respectively; $D_{y,x}$ is the distance along the important corridor from the centroid of the ecological source to the demand (km); and $A$ is the radiating area of ecosystem services flowing from the ecological source to the demand (km²).

Based on the boundaries and range obtained from Formulas (10) and (11), the transfer area A of ecosystem services is estimated. Based on the field strength model, the transfer intensity of ecosystem services per unit area is calculated to obtain the value of ecosystem service transfer. The formula is as follows:

$$I_{y,x}={\displaystyle \frac{E_y}{D_{y,x}^2}}$$

(10)

$$E_{y,x}=k_{y,x}{I}_{y,x}A$$

(11)

where $E_{y,x}$ is the value of ecosystem service transfer from the ecological source to the demand (million USD); $k_{y,x}$ is the factor affecting the spatial transfer of ecosystem services from the source to the demand, and the value is generally [0~1], since rivers are an important factor in the arid inland river basin [75]. In this paper, $k_{y,x}$ is taken as 0.8; $I_{y,x}$ is the intensity of ecosystem service transfer from the ecological source to the demand; and $A$ is the radiating area of ecosystem services flowing from the ecological source to the demand (km²).

2.7. Ecological Compensation Calculation Method

To calculate ecological compensation, we need to know the ecological compensation coefficient and the flow of ecological value from the source area to the sink area. The flow value quantity of ecosystem services has been accounted for in Section 2.6, and determining the ecological compensation coefficient is the priority and key. Referring to the previous literature [80], the regulating services directly benefit human well-being and the value could not be realized in the market. Therefore, the output parts of regulating service value need to be compensated. To calculate the ecological compensation for different ecological sources, the ecological system in our site is regarded as an independent system, and ecological services will flow from the ecological sources in the region to the ecological demands in the region. Since ecological functions have value, and ecological functions are proportional to their ecological values, as the regulating service functions flow, the ecological sources transfer a huge value of regulating services to the ecological demands. Since there is a difference in the value of the regulating service functions per unit area between the ecological sources and demands, the output value per unit area of the ecological sources can be determined. Based on the method of calculating the value of ecosystem service transfer in the previous section, the transfer amount of multiple ecological corridors in 2020 is calculated, and then the value of ecosystem regulating services is calculated using the equivalent factor method to obtain the ecological compensation coefficient. Finally, the total amount of ecological compensation ($E_s$) obtained is determined through Formula (12). The formula is as follows:

$$E_s=\beta E_{y,x}$$

(12)

where $E_s$ is the total ecological compensation obtained for each ecological source (million USD); $\beta$ is the ecological compensation coefficient, i.e., the proportion of output value in the total value; and $E_{y,x}$ is the value of ecosystem service transfer from the ecological source to the ecological demand (million USD).

3. Results

3.1. Spatial–Temporal Changes in Ecosystem Service Supply and Demand

3.1.1. Spatial–Temporal Changes in Ecosystem Service Supply

The values of ecosystem service supply for 2000 and 2020 were USD 17.72 billion and USD 18.04 billion, respectively, showing an increase (Figure 3). Fourteen counties/districts, including Xin’an County, Zhongmu County, Xingyang City, and Mengjin County, showed an increase in supply value, while regions such as Jinshui District, Xinmi City, and Dengfeng City showed a decrease of over USD 54.48 million in supply value from 2000 to 2020. The change in supply value was evident in seven counties/districts, including Xin’an County, Zhongmu County, Xingyang City, and Mengjin County, with increases greater than USD 54.48 million.

The total value of ecosystem service supply in our site presented a pattern of being higher in the west and lower in the east, with high-value areas mainly concentrated in the southwest, distributed in Songxian County, Luanchuan County, and Luoning County of Luoyang City. Luoyang City, with its rich water system and numerous mountains and forest resources, particularly the Yi River basin originating in the Funiu Mountain region of Luanchuan County, which flows into the Luo River at Yanshi, has a strong ability to provide ecosystem services. In 2000 and 2020, the supply values of Songxian County and Luanchuan County both exceeded USD 2.60 billion, with the value in Songxian County reaching as high as USD 3.12 billion in 2000, making it the county with the highest supply value. The areas with low supply value were mainly concentrated in economically developed counties/districts such as Luoyang City’s Old Town, Xigong District, Chanhe Huizu District, Kaifeng’s Yuwangtai District, Gulou District, Shunhe Huizu District, Zhengzhou’s Erqi District, Zhongyuan District, and Guancheng Huizu District, which are densely populated.

3.1.2. Spatial–Temporal Changes in Ecosystem Service Demand

The ecosystem service demand indices for the Zheng-Bian-Luo region in 2000 and 2020 (Figure 4) indicate that the demand indices of most counties/districts increased remarkably. From 2000 to 2020, the ecosystem service demand indices of Jinshui District, Zhongyuan District, and Guancheng Huizu District in Zhengzhou City showed the most significant growth. The ecosystem service demand is closely related to the regional economic development level, with obvious differences between counties/districts. The overall distribution was high in the east and low in the west; high-demand index areas were mainly concentrated in the central urban areas of economically developed cities in our site. The area with the highest demand index was Jinshui District in Zhengzhou City, with a GDP per capita of up to 16481.89 million USD/km² in 2020, and the proportion of construction land was 71%. Rapid urbanization in Jinshui District has led to a surge in the level of economic development and a series of environmental problems. Natural ecological land such as forest land has been converted to industrial land use in Jinshui District, resulting in the fragmentation of the ecological land landscape, a decline in urban habitat quality, the reduction in key ecosystem services, and a sharp increase in the urban greenhouse effect.

3.2. Balance of Ecosystem Service Supply and Demand

In 2000 and 2020, the areas with ${G_i}^{*}$ = 0, representing balanced supply and demand, accounted for 57.22% and 56.32% of the total area, respectively, showing a decrease in the areas of balanced supply and demand. The spatial patterns of supply and demand in our site in 2000 and 2020 (Figure 5) show that there were six surplus patches, located in Songxian County, Luanchuan County, Luoning County, Yiyang County, Yichuan County, and Ruyang County of Luoyang City, with a total area of 11,793.04 km². In 2000, there were three deficit patches, located in the main urban area of Luoyang City, with a total area of 599.32 km². In 2020, there were five deficit patches, with a total area of 860.77 km², an increase of 261.45 km². In 2020, the deficit areas were Zhongyuan District, Jinshui District, Erqi District, and Guancheng Huizu District in Zhengzhou City, and Laocheng District in Luoyang City.

3.3. Flow Paths and Value Quantity of Ecosystem Services

3.3.1. Spatial Flow Paths of Ecosystem Services

The spatial distribution pattern of the ecological corridors in our site in 2000 and 2020, constructed based on supply and demand relations, is shown in Figure 6. The interaction matrix between the source and demand areas calculated using the gravity model shows that three and five important ecological corridors can be extracted in 2000 and 2020, respectively (

Table 6. Interaction matrix of patches in our site in 2000 and 2020.

Year	Patches	Songxian County	Luanchuan County	Luoning County	Yiyang County	Yichuan County	Ruyang County
2000	Luolong District	0.60	1.05	0.33	0.45	1.36	0.33
	Laocheng District	0.27	0.48	0.16	0.20	0.15	0.15
	Chanhe Huizu District	0.20	0.35	0.11	0.14	0.18	0.11
2020	Jinshui District	0.14	0.22	0.08	0.07	0.08	0.09
	Zhongyuan District	0.24	0.37	0.13	0.12	0.17	0.16
	Guancheng Huizu District	0.18	0.29	0.10	0.10	0.12	0.12
	Laocheng District	0.54	0.93	0.31	0.37	0.28	0.29
	Erqi District	0.34	0.51	0.18	0.18	0.26	0.23

). In 2000, the interaction between the two ecological source areas, the Laocheng District and the Chanhe Huizu District, and Luanchuan County was relatively strong. The interaction between the ecological source area, Yichuan County, and the demand area, Luolong District, was the strongest, indicating the strongest association between the two places and highlighting its suitability for the construction of important ecological corridors. In 2020, all five ecological demand areas had the strongest interaction with Luanchuan County, from which five important ecological corridors were extracted as the flow path for ecosystem services.

3.3.2. Ecosystem Services’ Flow Value Quantity

The size of the radiation area from the ecological source areas to the ecological demand areas of the three and five important corridors extracted based on the gravity model in 2000 and 2020, respectively, the intensity of flow, and the quantity of ecosystem service flow are shown in

Table 7. Transfer volume of ecosystem service value and related parameters based on important corridors in 2000 and 2020.

Year	Source of Ecology–Demand for Ecology	$\mathit{A}$/km2	${\mathit{I}}_{\mathit{y},\mathit{x}}$	${\mathit{E}}_{\mathit{y},\mathit{x}}$/USD Million
2000	Yichuan County–Luolong District	503.89	0.00857	200.08
	Luanchuan County–Laocheng District	286.64	0.00229	30.72
	Luanchuan County–Chanhe Huizu District	112.83	0.00217	11.60
2020	Luanchuan County–Jinshui District	6902.41	0.00065	208.76
	Luanchuan County–Zhongyuan District	1056.79	0.00077	37.68
	Luanchuan County–Guancheng Huizu District	1201.62	0.00068	37.68
	Luanchuan County–Laocheng District	135.51	0.00228	14.48
	Luanchuan County–Erqi District	1865.65	0.00078	67.28

Note: $A$ refers to the corridor radiation area; $I_{y,x}$ refers to the transfer intensity; $E_{y,x}$ refers to the value of ecological service transfer.

. The total value of ecosystem service flow in 2000 was USD 242.40 million; due to the much larger radiation area and transfer intensity from Yichuan County to Luolong District compared to the rest of the corridors, the flow capacity of the corridor was relatively strong, hence the highest value of ecosystem service flow between the two areas. The radiation area from Luanchuan County to Chanhe Huizu District was much smaller than other demand areas, resulting in a value of ecosystem service flow of only USD 11.60 million. In 2020, the total value of ecosystem service flow was USD 365.92 million; the value of ecosystem service flow from Luanchuan County to Jinshui District was the highest. The flows from Luanchuan County to Erqi District, Zhongyuan District, Guancheng Huizu District, and the Laocheng District were USD 67.28, 37.68, 37.68, and 14.48 million, respectively. From 2000 to 2020, the flow of ecosystem services in our site increased. The increase in the area of supply–demand imbalance led to an increase in the value flow of ecosystem services.

3.4. Ecological Compensation Amount

In 2020, Luanchuan County received ecological compensation totals of USD 237.76 million from each ecological demand area. The flow of ecosystem services from Luanchuan County to the patches in Jinshui District of Zhengzhou City was the largest, so the ecological compensation amount of Luanchuan County received from Jinshui District was largest (

Table 8. Ecological compensation obtained from the demand site by the source site.

Year	Source Site	Demand Site	$\mathit{\beta }$	${\mathit{E}}_{\mathit{s}}$/USD million
2000	Yichuan County	Luolong District	57.45%	114.84
	Luanchuan County	Laocheng District	65.20%	19.72
		Chanhe Huizu District		7.52
2020	Luanchuan County	Jinshui District	65.17%	136.28
		Zhongyuan District		24.36
		Guancheng Huizu District		24.36
		Laocheng District		9.28
		Erqi District		43.48

Note: $\beta$ represents the coefficient of the output value of ecosystem services, and $E_s$ represents the theoretical ecological compensation amount.

). Jinshui District needed to pay Luanchuan County ecological compensation amounts of USD 136.28 million. The flow of ecosystem services from the ecological source areas to the patches in the Laocheng District was the smallest, so the ecological compensation amount of Luanchuan County received from the Laocheng District of Luoyang City was also the smallest. The Laocheng District needed to pay Luanchuan County ecological compensation amounts of USD 9.28 million. In 2000, Luanchuan County received ecological compensation totals of USD 27.24 million from each ecological demand area. The LuoLong District needed to pay Yichuan County ecological compensation amounts of USD 114.84 million.

4. Discussion

4.1. Methodology Advantages

Ecosystem service flow links the supply and demand of ecosystem services [81]. It is in the initial stage of development and is the focus and difficulty of current research [31]. Individual cities [82,83,84], small watersheds, or large watersheds [32,46,50,78,85,86,87,88] are often the research hotspots for ecosystem service flow, because the research results can provide scientific support for ecological conservation. Previous studies [82,83,84] on the flow of urban ecosystem services in multiscale and cross-scale studies are few, and due to the small scale of urban ecosystems, the amount of ecosystem services they can provide is small; this has been less studied in the past. As the process of urbanization continues to develop, cities are more populated, and there is an urgent need to solve the efficient schemes of landscape patterns and natural resource management. This paper innovatively carried out research on urban spatial cross-regional spatial flow, providing a reference for future urban spatial and multiscale research on ecosystem service flows. In the initial stage of ecosystem service flow research, since service supply is the source of service flow, the actual services of natural ecosystems are considered to be ecosystem service flow and receive attention [33,89,90,91]. Unlike some studies [33,90,91] that focused mainly on the flow of services provided by a single region, they only considered the natural supply of regional ecology and ignored the demand of humans for it. This paper takes into consideration the relationship between supply and demand and selects the source and demand areas of ecosystem flow through the spatial pattern of supply–demand balance, thus clarifying the source, dissipation process, and benefit area of ecosystem services.

Ecosystem service flows describe the supply and demand process of ecosystem services and simulate the flow path of ecosystem services. Some models have been employed to simulate the flow process for individual services such as carbon fixation and water production [88,91,92,93,94]. However, few studies displayed the flow path and volume of integrated ecosystem services through intuitive spatial methods, especially for the study of ecosystem service flows across urban areas. By giving the exact path of the spatial flow of ecosystem services and simulating its spatial flow process, this paper visually displays the spatial flow path of ecosystem services, constructs the “source–demand” ecological corridor, quantifies the spatial flow volume of important ecological corridors, and compensates for the lack of visual display of flow paths in previous research. At the same time, the calculation of flow volume provides a basis for ecological compensation. Ecosystem service flows have great application potential in specific practice and application fields. Ecological compensation polices based on service flows have been applied in water yield [43,44,45,46] and sand fixation [47,48,95]. For integrated ecosystem services, current research on the flow of ecosystem services has not yet applied ecological compensation. Based on the theoretical compensation amount calculated by the flow volume and ecological compensation coefficient, this paper first takes the regulatory service value as the output part of ecosystem services to calculate the value, proposing a relatively new method for calculating the ecological compensation amount, providing a scientific basis for relevant departments to explore diversified compensation methods and gradually forming a comprehensive ecological compensation policy system.

4.2. Factors Affecting Ecosystem Service Supply, Demand, and Flow

In this paper, we conduct a comprehensive analysis of the changes in spatiotemporal patterns of the supply–demand relationship of ecosystem services in our study area for both 2000 and 2020. The study found that the overall supply value of ecosystem services in our site in 2000 and 2020 showed a slow increase, but the supply values in high-value areas such as Songxian County, Luanchuan County, and Yiyang County all decreased, necessitating the restoration and enhancement of the ecosystem services by strengthening the conservation of key ecological function areas. The ecosystem service demand index of each district/county remarkably increased with economic development and population growth. Since 2000, the development of our site has mainly revolved around the secondary and tertiary industries. Rapid economic development has caused a series of environmental problems and prominent contradictions between resource protection and utilization. The differences in regional industrial structure and the impact of national policies have led to the excessive occupation of ecological land during the accelerated development of urban construction, causing some districts/counties to experience imbalances in the supply and demand of regional ecosystem services. From 2000 to 2020, the area of supply–demand imbalance in our site gradually increased. The imbalance area in our site gradually shifted from the main urban area of Luoyang City to the core urban area of Zhengzhou City. Luoyang City has relatively abundant water resources, high forest coverage, and a high value of ecosystem services, and the city’s efforts to promote ecological protection and strengthen watershed management since 2000 have been effective. Zhengzhou City, while vigorously promoting the construction of “urbanism on rails”, had a significant increase in the proportion of construction land, and the developed industry led to a severe supply–demand imbalance in regional ecosystem services. Lv et al. [96] identified the ecosystem services supply–demand balance status in Nanjing Metropolitan Area and found that land use changes were the driving forces of the supply and demand. However, other studies revealed that temperature [97] and precipitation [98] were also the main factors in ecosystem service supply–demand balance. Therefore, the natural geographical characteristics and socioeconomic characteristics of the study area may affect the supply and demand balance of ecosystem services, which is closely related to the attributes of the study area.

Zhang et al. [99] considered ecosystem service values to flow from lakes to cities, and the intensity of the flow increased with the urbanization process of Suzhou city. In Shanghai, ecosystem services flow from the suburbs to the city center, and the flow intensity of different types of ecosystem services varies [82]. Wei et al. [100] found that as downstream ecosystem service supply increased, upstream ecosystem service flows decreased in the Hexi Corridor. Our research results showed that the flow of ecosystem service value in the Zheng-Bian-Luo region showed an increase during the study period, which was in agreement with Zhang et al. [99]. On the one hand, due to the accelerating process of urbanization, the people’s demand index is constantly increasing, leading to an insufficient supply of ecosystem services. Conversely, rapid economic development has led to significant human interventions in the natural ecosystem. These interventions have given rise to a range of ecological and environmental issues, including water resource scarcity, reduced biodiversity, and a deterioration of agricultural ecosystems, ultimately causing a decrease in the supply of ecosystem services. While certain regions have reacted to ecological conservation policies by enhancing their environments and augmenting the value of their ecosystem services, the general trend suggests that the extent of supply–demand disparities is on the rise, leading to an increase in the flow of ecosystem service values.

4.3. Policy Recommendations for Landscape Management

Based on the supply–demand pattern analysis of Zheng-Bian-Luo, the question of how to carry out reasonable and effective ecological protection has become an urgent problem to solve. It is crucial to bolster ecological protection and restoration in key ecological areas. Large-scale and high-intensity industrialization and urbanization development should be restricted, with a focus on enhancing the supply capacity of ecosystem services. The following policy recommendations outline a comprehensive approach to this:

(1)

Based on the research results of ecosystem service supply–demand balance, the protection and restoration of crucial ecosystems should be continuously promoted, with an emphasis on the construction of key projects such as mountains, waters, forests, fields, lakes, and grasslands in essential ecological areas (i.e., six ecological sources including Songxian County, Luanchuan County, Luoning County, Yiyang County, Yichuan County, and Ruyang County). Urban areas with extensive urbanization (i.e., deficit areas including Zhongyuan District, Jinshui District, Erqi District, and Guancheng Huizu District in Zhengzhou City, and Laocheng District in Luoyang City) should focus on ensuring that construction land expansion does not proceed haphazardly. For the situation of decline in the ecosystem service value of the core urban area of Zhengzhou City, ecological protection work should be performed well, further strengthening the restoration and protection work of the source of ecology and optimizing the planning of the ecological network of ecological corridors. The construction of ecological corridors can serve as a means for Zhengzhou City to alleviate environmental pressure. Based on the identification of important ecological corridors, the districts/counties of Luoyang City are the source of ecology. The important corridors in Luoyang need to be carefully managed in future planning and construction, and strict measures and policies should be adopted for protection. From the perspective of maintaining the stable development of the ecosystem, the expansion of ecological source areas and the planning of ecological corridors in the southwestern part of Luoyang should be strengthened to optimize the ecological network.

(2)

The value realization of ecosystem services could be explored using the ecological compensation amount calculation method proposed in this paper. The government can use the ecological compensation system proposed in this paper to carry out pilot work in some areas. Financial incentives should be provided to cities and counties that demonstrate excellent ecosystem service supply improvement, substantial contributions to good ecological products, and comprehensive progress in establishing compensation mechanisms. This should encourage local areas to prioritize the construction of ecological compensation mechanisms. Government departments should leverage big data and information technology to supervise progress, strengthen communication, and monitor the use of compensation funds. Research should be conducted to establish a compensation standard system, promoting the transition of horizontal ecological compensation from a single ecological element to multiple ecological elements. This will allow for the in-depth implementation of upstream and downstream ecological compensation mechanisms within the basin.

5. Conclusions

Using the districts and counties of the Zheng-Bian-Luo region as research units, this study applied the equivalence factor method and demand index quantification to examine the supply and demand of ecosystem services in 2000 and 2020. Utilizing the MCR model, breakpoint formula, and field strength theory, we established ecological corridors, calculated the value flow of important corridors, and determined the ecological compensation amount. The key findings are as follows.

(1)

From 2000 to 2020, the supply value of ecosystem services in the Zheng-Bian-Luo region exhibited a gradual, albeit modest, increase. Simultaneously, there was a significant surge in the demand for ecosystem services across various districts and counties within the area. Notably, the supply values followed a distribution pattern, with higher values in the western part and lower values in the eastern part, while the demand index displayed a contrasting trend, being higher in the east and lower in the west.

(2)

The supply–demand deficit area in the Zheng-Bian-Luo region increased 43.62% from 2000 to 2020. In 2000, the supply–demand deficit area was mainly found in the Luoyang city area, and this deficit shifted towards the core city area of Zhengzhou. The supply–demand surplus areas were primarily located in Songxian, Luanchuan, Luoning, Yichuan, Yiyang, and Ruyang in Luoyang City, covering a total area of 11,793.04 km².

(3)

We identified three and five crucial ecological corridors in 2000 and 2020, respectively. The total value of ecosystem services flow in 2000 and 2020 was USD 242.40 million and USD 365.92 million, respectively.

(4)

In 2020, Luanchuan County should have received ecological compensation totals of USD 237.76 million from each ecological demand area, including Jinshui District, Zhongyuan District, Guancheng Huizu District, Laocheng District, and Erqi District. Jinshui District needed to pay Luanchuan County ecological compensation amounts of USD 136.28 million. The calculation of the ecological compensation amount provides a scientific basis for relevant departments to construction the ecological compensation mechanism, explore diversified compensation methods, and gradually form a sound ecological compensation policy system, contributing to the continuous improvement of the ecological environment quality.

The ecosystem service flow model constructed in our study has certain shortcomings. Different ecosystem services have different flow directions, while this study makes the flow directions of different ecosystem service types consistent. In addition, the impact of natural factors and human activities is not comprehensively considered in the process of ecosystem service flow. Future research needs to use a variety of technical means and data information to calculate and identify the path direction, flow size, and attenuation characteristics of ecosystem service flow in a specific and accurate manner. The issue of regional ecological compensation is complex. When calculating the specific amount of ecological compensation, our study did not take into account the socioeconomic status of the ecological compensation area and the payment area. In the future, it will be necessary to further optimize the ecological compensation model and establish a reasonable ecological compensation system.