Simulating the Spatial Mismatch between Ecosystem Services’ (ESs’) Supply and Demand Based on Their Spatial Transfer in Urban Agglomeration Area, China

Abstract

Ecosystem service spatial transfer is considered a feature that can deliver ecosystem services at a distance to meet the demands in areas with uneven spatial distribution of natural and social economic development. The natural ES spatial transfer distance and intensity were simulated by using the modified breaking point model in the Central Plains urban agglomeration (CPUA) with the cities of Luoyang, Zhengzhou, Shangqiu, and Huaibei stretching across. It is shown that there is a spatial mismatch between ES supply from ecospace and its demands from cities; relying only on natural spatial transfer, none of the ESs of the ecospace can be transported to corresponding population centers; and a spatial gap between ES supply and demand is illustrated in urban agglomeration areas. Intercity cooperation in ecosystem management and landscape planning based on ES spatial transfer would be good choices for cities, giving full play to comparative advantages to achieve sustainable development for the entire CPUA.

1. Introduction

Ecosystem services are the goods and services that human populations benefit directly or indirectly from in the natural ecosystems, including provision services, regulating services, and cultural services that have a direct impact on human life, as well as support services necessary to maintain other services [1,2]. Based on categories of ecosystem services grouped by Costanza [1], follow-up researchers [3,4,5,6] have developed different ecosystem service definition, classification, and categorization systems. Provision services were referred to as gross primary production, including food production, raw materials, etc., which could benefit people with the production of fish, crops, nuts, fruits, lumber, etc. Regulating services are defined as ecosystem functions of regulation, such as gas regulation, climate regulation, disturbance regulation, and hydrological regulation. In the field of meteorology, the regulation services of gas regulation and climate regulation bring about changes in local climate components such as wind, precipitation, temperature, CO₂/O₂ balance, greenhouse, and radiation due to ecosystem properties [1,4]; therefore, ecospace in urban areas has been recognized as a promising approach in mitigating urban heat islands (UHI) [7]. Cultural services are the functions of ecosystems providing opportunities for recreational activities and noncommercial uses, such as ecotourism and aesthetic, artistic, educational, and spiritual development, etc., which have been substantiated to affect the physiological and psychological activities of residents by affecting the health of the human living environment [1,7].

The concept of ecosystem services emphasizes the core of being used by human beings, which itself contains two aspects of the meaning of natural supply and human demand [1,3,4,8]. Many different definitions of ecosystem service supply and demand have been developed in recent decades. Their definitions have been developed quite significantly already: For ecosystem service supply, this was referred to as the provision of a selected ecosystem service by a selected ecosystem [4,9]. In consequence, the ES supply of a city or a region was often defined as the provision of services by all of the ecosystems in that city or region [10]. For ecosystem service demand, this was referred to as ecosystem goods and services currently consumed or used in a particular area over a given time period [4,11]. It indicated that ecosystem service demand was space–time-dependent, which can change over time and space, and was independent from actual ecosystem service supply [11]. Although some studies have compared the ecosystem service demand with the actual use of ecosystem services, it has been becoming increasingly clear that ecosystem service use will be driven in the future by demand [4,9,12]. In some studies, integrating large-scale ecosystem service supply–demand assessments into ecological risk assessment, land use planning, and decision making has been considered as an effective method to define spatial relationships for intercity cooperation [10,11]. Additionally, it has been found that only when the supply of ecosystem services matches people’s demand for ecosystem services could the benefits of ecosystem services be fully obtained [10,13,14]. However, in today’s cluster development model, there is a widespread spatial mismatch between the supply and demand of ecosystem services because of large urban–rural gradient differences [12,15]. According to reports, today’s cities account for 75% of fresh water and 76% of wood products used by people [16]. Although specific ESs could be imported to ensure cities are independent from their rural hinterlands through globalization, urban areas still depend on surrounding rural regions for decoupled or directional supply of almost all regulating and provisioning ecosystem services produced from the largest material flux of natural ecosystems into urban ecosystems [12,17,18]. Usually, outside of cities, the natural ecosystems in rural areas contribute the most important and largest ESs to the quality of life in cities [12,15]. Owing to the COVID-19 lockdowns and the closure of parks and green spaces, the lacks of natural benefits from the natural ecosystems are impeding human living standards [19]. The urban demand for ecosystem services to relieve citizens’ pressures and for the healing of the body and the mind is increasing in the post-COVID-19 world.

It has been reported that ESs have the characteristics of spatial transfer, which enables ESs to transfer out of the habitat and play a role in a larger scope [17,20,21]. The ES spatial transfer characteristics provide a good way of understanding the spatial relationships of ecosystem services supply–demand for intercity cooperation [10]; thus, ES spatial transfer has attracted the attention of many researchers. At present, scholars at home and abroad have carried out quantitative studies on the ES spatial transfer of watershed scale, region scale, and city scale. The establishment of watershed ecological compensation policy [10,21,22], regional sustainable development strategy [5,23], and ecosystem planning and management strategy [24,25] shows good theoretical support and important practical application value. Commonly, urban agglomeration is the focal area of transregional eco-environmental problems, and there is a strong, realistic demand for understanding transregional ES linkages of ecosystem services [26,27,28,29].

At present, studies on natural ES spatial transfer in urban agglomerations are relatively rare. In urban agglomeration areas with overall regional urbanization and dense populations, the regional natural ecosystems were seriously fragmented and scattered in the surrounding hinterland [30,31,32]. The mismatch between the supply and demand of ecosystem services in urban agglomerations has broken through the boundaries of individual cities and become prominent in transregional areas [20]. In this context, simulating the spatial mismatch status based on the ES spatial transfer characteristics would provide feasibility for assisting local administrations in measuring the ecological relationships between the ecospace and city and seeking transregional ecological cooperation to realize sustainable development in urban agglomerations.

Combining the value equivalent method [5,6], multisource remote sensing data based method [33], and spatial analysis functions of the ArcGIS10.5 platform, this study introduces the breaking point model to quantify the spatial characteristics of ES supply–demand dependent on the ES spatial transfer from the ecospace to the cities of Luoyang, Zhengzhou, Shangqiu, and Huaibei in Central Plains urban agglomeration (CPUA). The aim of this study is to assess whether the natural ecosystem could transfer its ES spatially to a target city. The objectives of this study are to (1) test a breaking model approach for simulating ES spatial transfer in an urban agglomeration area, (2) assess ES spatial transfer distance, predicting ES spatial transfer boundary and scope, and (3) identify the spatial relationships between ES supply and demand dependent on ES spatial transfer in Central Plains urban agglomeration. This study may provide a better understanding for intercity environmental cooperation to solve transboundary ES supply–demand problems.

2. Methods

2.1. Study Area

The CPUA comprises the largest city clusters in China, with Zhengzhou as the central city; Kaifeng, Xuchang, Xinxiang, and Jiaozuo as the metropolitan area; and Xinyang, Nanyang, and another 25 cities as the hinterland (Figure 1). An urban development zone comprising Luoyang, Zhengzhou, Shangqiu, and Huaibei is distributed from west to east and the population is highly concentrated in cities of different scales.

With a land area of 287,000 km², the CPUA is an important hub “connecting the East and the west” and “connecting the North and the South” [34,35], and it is rich in natural resources covering three mountain ranges of Liankangshan Mountains, Taihangshan Mountains, and Funiushan Mountains. The three big mountains, within which are numerous nature reserves and all kinds of large parks, constitute the three ecological barriers and are the main ecological spatial distribution of the urban agglomeration. The Yellow River and Huaihe River are the main ecological corridors running through the east and west of urban agglomeration, which split the ecospace into three ecological barrier zones, named Funiushan, Taihangshan, and Lianangshan [36].

2.2. Data and Pretreatment

The remote sensing data of the year 2015 selected for this study all came from the Resource and Environment Science and Data Center (https://www.resdc.cn/, accessed on 1 May 2020), including land use types, annual net primary production (NPP), and annual normalized difference vegetation index (NDVI) with a resolution of 1000 m; the data of main grain market price and grain yield per unit area are from the National Bureau of Statistics of China (http://www.stats.gov.cn/, accessed on 7 July 2020) and the National Food and Strategic Reserves Administration of China (http://www.lswz.gov.cn/html/zmhd/lysj/lsjg.shtml, accessed on 13 July 2020), respectively. The ecosystem types were obtained from the reclassification of the original land use/land cover data (

Table 1. Definition of terrestrial ecosystem types in ecospace of CPUA.

Ecosystems	Land Use/Land Cover
Forest	mainly includes dense forest land (forest land), shrub land, sparse forest land, and other forest land;
Grassland	mainly includes high-coverage grassland, medium-coverage grassland, and low-coverage grassland;
Wetland	mainly includes marsh and beach;
Rivers/lakes	mainly includes rivers, lakes, reservoirs, glaciers, and permanent snow;
Desert	Sandy land, Gobi desert, saline-alkali land; Alpine desert.

).

2.3. ES Calculation Method and Process

In the studies of ecosystem service evaluation for integrative large-scale ecosystem service supply and demand assessments in China [24,37,38], the number of ecosystem services were often valued in the economic value according to the “Equivalent Table Per Unit Area of Terrestrial Ecosystem in China” (

Table 2. Equivalent table per unit area of terrestrial ecosystem in China (data based on reference [5]).

Ecosystem Services		Forest	Grassland	Farmland	Wetland	Rivers/Lakes	Desert
Provision services	Food production	0.33	0.43	1.00	0.36	0.53	0.02
	Raw material production	2.98	0.36	0.39	0.24	0.35	0.04
Regulatory services	Gas regulation	4.32	1.50	0.72	2.41	0.51	0.06
	Climate regulation	4.07	1.56	0.97	13.55	2.06	0.13
	Hydrological regulation	4.09	1.52	0.77	13.44	18.77	0.07
	Waste disposal	1.72	1.32	1.39	14.4	14.85	0.26
Support services	Soil conservation	4.02	2.24	1.47	1.99	0.41	0.17
	Biodiversity	4.51	1.87	1.02	3.69	3.43	0.40
Culture services	Aesthetic landscape	2.08	0.87	0.17	4.69	4.44	0.24

), which determined that the economic value of 1 equivalent ecosystem service was equal to 1/7 of the national average market value of grain yield per unit area of farmland in that year [5,6].

In this study, ecosystems were regrouped according to the land cover types in CPUA (Table 1), and ecosystem services were classified into 9 single ecosystem services (Table 2). Firstly, the unit equivalent value (E) was calculated according to Formula (1) and the parameters in

Table 3. Parameters and reference parameter values of ES calculation formulas.

Parameter	Definition or Value
E	the unit equivalent value (E) of ecosystem services in CPUA, 1894.61 rmb·hm−2·a−1
Q	the average yield per unit area of main grain in CPUA, 5310.22 kg·hm−2 [20]
F	the average price of the main grain in China, RMB 2497.50·t−1 [20]
aci	the ESV equivalent of different ecosystems, see reference [20]
Vci	the unit area value of the type i ecosystem service of the category c ecosystem
ESV	the total ES amount in measure of monetary value
c	refers to the type of ecosystem, c = 1, 2,…, n
Vc	the ESV value of category c ecosystem
i	the ith ecosystem service function of the category c ecosystem, i = 1, 2,…, n
Vci	the unit area value of the ith ecosystem service type of the category c ecosystem, see reference [20]
j	refers to the number of patches of Vci in a certain area, j = 1, 2,…, m
Sij	the area of patch j in ecosystem i
Rij	adjustment coefficient of ecosystem quality, usually characterized by the fractional vegetation cover (FVC)
NPPmean	the average values of NPP
fmean	the average values of FVC
NPPj	the NPP of the jth patch.
fj	the FVC of the jth patch.
f	the FVC
NDVI	the vegetation index of the plot or pixel
NDVIv	the vegetation indexes corresponding to pure vegetation
NDVIs	the vegetation indexes corresponding to pure soil pixels

. Secondly, per unit area (V_ci) values of every single ecosystem service of different ecosystems in ecospaces (transfer-out area: Funiushan, Taihangshan, and Liankangshan) and cities (transfer-in area: Huaibei, Luoyang, Shangqiu, Zhengzhou) were calculated according to Formula (2), with the results listed in

Table 4. ESVs of different ecosystems per unit area (V_ci) in the Central Plains urban agglomeration (rmb·hm⁻²·a⁻¹).

Ecosystem Services		Forest	Grassland	Farmland	Wetland	Rivers/Lakes	Desert
Provision services	Food production	625.22	814.68	1894.61	682.06	1004.14	37.89
	Raw material production	5645.94	682.06	738.9	454.71	663.11	75.78
Regulatory services	Gas regulation	8184.72	2841.92	1364.12	4566.01	966.25	113.68
	Climate regulation	7711.06	2955.59	1837.77	25,671.97	3902.9	246.3
	Hydrological regulation	7748.95	2879.81	1458.85	25,463.56	35,561.83	132.62
	Waste disposal	3258.73	2500.89	2633.51	27,282.38	28,134.96	492.6
Support services	Soil conservation	7616.33	4243.93	2785.08	3770.27	776.79	322.08
	Biodiversity	8544.69	3542.92	1932.5	6991.11	6498.51	757.84
Culture services	Aesthetic landscape	3940.79	1648.31	322.08	8885.72	8412.07	454.71

. Then, the total ecosystem service value (V_c) of 1 ecosystem type can be obtained according to Formulas (2), (4), and (5) in correspondence with the ecosystem properties. Finally, the total ecosystem service value (ESV) of all ecosystem services of all ecosystems in the ecospaces and cities were calculated according to Formula (3). Therefore, the calculated total ecosystem service values of the ecospaces (transfer-out area: Funiushan, Taihangshan, and Liankangshan) were referred to as ecosystem service supply for transfer out (V_o), and the total ecosystem services values of cities (transfer-in area: Huaibei, Luoyang, Shangqiu, Zhengzhou) were referred to as total ecosystem services for transfer in (V_i).

The calculation formulas [33] and parameters are as follows:

E = QF/7

(1)

V_ci = Ea_ci

(2)

$$\mathrm{ESV}={\displaystyle \sum }_{\mathrm{c}=1}^{\mathrm{n}}{\mathrm{V}}_c$$

(3)

$${\mathrm{V}}_c={\displaystyle \sum }_{\mathrm{i}=1}^{\mathrm{n}}{\displaystyle \sum }_{\mathrm{j}=1}^{\mathrm{m}}{\mathrm{R}}_{\mathrm{ij}}\times {\mathrm{V}}_{\mathrm{ci}}\times {\mathrm{S}}_{\mathrm{ij}}$$

(4)

R_ij = (NPP_j/NPP_mean + f_j/f_mean)/2

(5)

F = (NDVI − NDVI_s)/(NDVI_v − NDVI_s)

(6)

2.4. ES Spatial Transfer (the Breaking Point Model)

The ES spatial transfer characteristics in the CPUA were quantified by introducing the breaking point model [20,21,39], and the ES radiation ranges through spatial transfer were calculated using the buffer analysis function and overlay analysis function of the ArcGIS10.5 platform. The calculation formulas and parameters (

Table 5. Parameters and reference parameter values of breaking point model formulas.

Parameter	Definition or Value
Do	the radius of ES spatial transfer
Doi	the distance from the core point of the transfer-out area to the core point of the transfer-in area
o	the transfer-out area core point, i.e., Taihangshan, Funiushan, Liankangshan
i	the transfer-in area core point, i.e., Luoyang, Zhengzhou, Shangqiu, Huaibei
Vo	the ES in the transfer-out area, i.e., Taihangshan, Funiushan, Liankangshan
Vi	the ES in the transfer-in area, i.e., Luoyang, Zhengzhou, Shangqiu, Huaibei
Ioi	the average transfer intensity of ESV from o region to i region, i.e., radiation intensity
K	the influencing factor of ESV in natural circulation from the transfer-out area o to the transfer-in area i, in CPUA, i = 0.6, see references [20,21]
A	the ES spatial transfer radiation area
Voi	the ES in transfer radiation scope

) are as follows:

$${\mathrm{D}}_{\mathrm{o}}=\raisebox{1ex}{${\mathrm{D}}_{\mathrm{oi}}$}\!\left/ \!\raisebox{-1ex}{$1+\sqrt{\raisebox{1ex}{${\mathrm{V}}_{\mathrm{i}}$}\!\left/ \!\raisebox{-1ex}{${\mathrm{V}}_{\mathrm{o}}$}\right.}$}\right.$$

(7)

$${\mathrm{I}}_{\mathrm{oi}}=\raisebox{1ex}{${\mathrm{V}}_{\mathrm{o}}$}\!\left/ \!\raisebox{-1ex}{${\mathrm{D}}_{\mathrm{oi}}^2$}\right.$$

(8)

$${\mathrm{V}}_{\mathrm{oi}}={\mathrm{kI}}_{\mathrm{oi}}\mathrm{A}$$

(9)

$$\mathrm{A}=3.14{\mathrm{D}}_{\mathrm{o}}{}^2$$

(10)

2.5. Spatial Boundary Condition Determination

In this study, we set a boundary condition of ES spatial transfer for the calculation formula of the breaking point model: V_oi ≤ V_o. That is, the amount of ES transferred out is less than or equal to the amount of ES in the transfer-out area. Taking V_oi = V_o as the boundary condition, we carry out the following derivation to identify and filter the model calculation results:

i. When V_oi < V_o, D_o is true, D_o is the actual transfer radius of ES, which is calculated by Formula (7);

ii. When V_oi = V_o D_o = D_max, the true ES transfer radius is predicted as the ES space transfer limit boundary, namely, the theoretical maximum boundary (D_max). D_max is calculated by Formula (11), deduced from Formulas (8) and (9):

$${\mathrm{D}}_{\mathrm{o}}=\raisebox{1ex}{${\mathrm{D}}_{\mathrm{oi}}$}\!\left/ \!\raisebox{-1ex}{$\sqrt{3.14\mathrm{K}}$}\right.$$

(11)

iii. When V_oi > V_o, D_o = D_max, which is the boundary of spatial transfer limit calculated by Formula (11).

The core point of ecospace (the ES transfer-out area): Due to natural endowment distribution, ecological space (ecospace) consisting of natural ecosystems in CPUA is mostly distributed in three green ecological barriers region in the west, i.e., Tongbai–Dabie Mountains Ecological Barrier Region (shortened to Lianangshan), Funiushan Ecological Barrier Region (shortened to Funiushan), and Taihangshan Ecological Barrier Region (shortened to Taihangshan), and their ESV amount accounts for more than 95% of the entire CPAU ecospace ESV amount. Twenty-one national nature reserves (NNR) are located in the region, of which Liankangshan, Funiushan, and Taihangshan (macaques as a protected species) are the top three in the area and are located in the core area of the entire ecospace. The core points of the Liankangshan NNR, Funiushan NNR, and Taihangshan NNR are the ES hotspot and chosen as a core point of the ES transfer-out area.

The core point of population centers (the ES transfer-in area): The population is mainly concentrated in the urban built-up area, which overlaps with the urban center in space. In order to make ES demand represent the whole region, an urban development belt, from west to east, and Luoyang, Zhengzhou, Shangqiu, and Huaibei are selected as four representative cities. These four cities have a concentrated population and run through the whole CPUA in space, which could cover the whole CPUA ES demand in space and scale. From the view of people’s demand for ES, we set the population centers, as well the city core point, as the center of the ES transfer-in area.

Boundary of ES demand: Buffer analysis was performed on the ArcGIS10.5 platform with the core points of ecospace, i.e., Liankangshan, Funiushan, and Taihangshan, as the center of the circle and the straight-line distance (D_oi) from the ecospace core point to population center points, i.e., Luoyang, Zhengzhou, Shangqiu, and Huaibei, as the radius.

Boundary of ES spatial transfer space: Buffer analysis was performed on the ArcGIS10.5 platform with the core point of ecospace, i.e., Liankangshan, Funiushan, and Taihangshan, as the center of the circle and the actual Do to cities as the radius.

3. Results

3.1. ES amount and Distribution

As shown in

Table 6. ES amount of ecospace and city (population center point).

Ecospace and Population Center		ESV (108 rmb)
Vo	Funiushan	1424.22
	Liankangshan	304.66
	Taihangshan	1110.97
Vi	Huaibei	8.26
	Luoyang	374.85
	Shangqiu	11.86
	Zhengzhou	59.48

, CPUA, V_o, and ecospace ES potentials measured in monetary value for transfer-out were 2839.85 × 10⁸ rmb, which concentrated in three ecological barrier regions represented by Funiushan, Liankangshan, Taihangshan, respectively. Among the ecospaces, Funiushan had the most ES potential at 1424.22 × 10⁸ rmb, while Liankangshan had the least ES potential at 304.66 × 10⁸ rmb. The four cities with high population had ES 454.45 × 10⁸ rmb for transfer-in (V_i), with Huaibei having the least ES at 8.26 × 10⁸ rmb, while Luoyang had the most ES at 374.85 × 10⁸ rmb.

In comparison of V_o with V_i, we can clearly see that the ES stock of ecospace was more than six times that of the population-concentrated area, which also confirms that there is a huge imbalance and spatial mismatch between ecological supply and demand in the CPUA region from this side.

3.2. ES in the Transfer-Out Area and Transfer Radiation Scope

We compared V_o and V_oi and judged the status of ES spatial transfer based on the comparison results. The ES spatial transfer with negative V_o and V_oi is considered to be invalid. In this case, we use the limit radius or maximum radius (D_max) to predict the ES potential transfer boundary between ecospace and population cluster center. The ES spatial transfer with positive Vo and V_oi is considered to be effective transfer. In this case, we take the actual transfer radius (D_o) as the actual transfer boundary of ES between ecospace and population cluster center. As shown in Figure 2, ES effective transfers generated by breaking point model calculation were Taihangshan_Luoyang, Liankangshan_Luoyang, Liankangshan_Zhengzhou, and Funiushan_Luoyang. Except for these, the other ES spatial transfer associations are invalid, and the possible transition range boundary can only be predicted by the maximum transition radius (D_max).

3.3. ES Spatial Transfer Radius

As shown in the

Table 7. Distance and radius of ecospace and population center.

Ecospace and Population Center	Distance (Doi) (km)	Radius (Do)
		Actual Do (km)	Dmax (km)
Funiushan_Huaibei	456.22	/	332.38
Funiushan_Luoyang	124.48	82.27	/
Funiushan_Shangqiu	363.67	/	264.95
Funiushan_Zhengzhou	210.03	/	153.02
Liankangshan_Huaibei	320.15	/	233.25
Liankangshan_Luoyang	408.63	193.73	297.70
Liankangshan_Shangqiu	325.85	/	237.40
Liankangshan_Zhengzhou	370.08	256.67	269.62
Taihangshan_Huaibei	380.93	/	277.52
Taihangshan_Luoyang	75.65	47.86	/
Taihangshan_Shangqiu	269.35	/	196.24
Taihangshan_Zhengzhou	90.31	/	65.79

, based on breaking point model and spatial maximum boundary determination conditions, we found that the ES spatial transfer association groups generated in the actual transfer were Funiushan_luoyang, Liankangshan_Luoyang, Liankangshan_Zhengzhou, and Taihangshan_Luoyang. Their actual D_o are 82.27 km, 193.73 km, 256.67 km, and 47.86 km, respectively. The maximum radius (D_max) was used to predict the spatial transfer radius of the other ES spatial transfer association groups, and the maximum transfer radius ranged from 65.79 km to 332.38 km for the ES spatial transfer boundary and ES demand boundary.

3.4. ES supply and Demand Patterns Based on Its Spatial Transfer

The distances (D_oi) from ecospace Funiushan, Liankangshan, and Taihangshan to the cities Luoyang, Zhengzhou, Shangqiu, and Huaibei was taken as the radius, and the buffer analysis tool of ArcGIS was used to obtain the ES demand boundary. The boundaries were the minimum distances of the population centers to obtain the ES supply of Funiushan through ES spatial transfer in the natural state. The transfer distance calculated above was taken as the radius, and the actual or maximum boundaries of ES spatial transfer were obtained by using the buffer analysis tool of ArcGIS—that is, the maximum distance that Funiushan ES can reach through spatial transfer in natural state.

As shown below, the ecospace ES supply boundary through ES spatial transfer could not reach the corresponding ES demand boundary. There is a spatial gap between ES supply and ES demand in all the four cities. That is to say, relying on natural spatial transfer, none of the ES in ecospace can be transported to population centers of Luoyang (Figure 3), Zhengzhou (Figure 4), Shangqiu (Figure 5), and Huaibei (Figure 6) by natural ecosystems themselves currently.

Although the ES of the three ecospaces did not spatially transfer to the corresponding urban centers, when we integrate the ecospace ES supplies to Luoyang (Figure 7), Zhengzhou (Figure 8), Shangqiu (Figure 9), and Huaibei (Figure 10), we find that the integrated actual transfer scope of ES still covers several population centers, such as Jiaozuo, Jincheng, Luohe, Zhoukou, Bozhou, Fuyang, Xinyang, and Zhumadian. If auxiliary effective ecological system construction and the ecological system management is “a porter”, the ES transfer boundaries of the ecospace can cover most of the population centers, which potentially means that the ecospace can transfer the ES from the remote ecospace to the population centers. By such remote connecting, home delivery of ESs from an ecospace faraway could be achieved.

4. Discussion

The ES value quantity method has the advantages of convenience, quickness, and ease of understanding. More importantly, it can carry out quantitative fusion and horizontal analogy among multiple ESs [1,5,6,38]. In this study, the ES value of ecospace was calculated by the combined ES economic value quantity method with multisource remote sensing data and with the support of the spatial statistics function of the ArcGIS10.5 platform. Therefore, the ES amount was given the property of spatial attributes, which provide the valued ES basis for evaluation of ES spatial transfer status. In terms of calculation results, the ES amount and its spatial distribution in this study are in good agreement with the studies in CPUA [20,37,40].

For the aspect of ES spatial transfer, the gravity model and its deformation formula are often used to study the degree of economic interaction between regions and cities in regional economic studies [41]. Basically, the ES also decreases with the increase in spatial distance [20,21,39]. Based on this, the breaking point model and field strength formula were introduced into ES assessment to quantify the ES spatial transfer intensity and radiation radius, and then calculate the ES spatial transfer amount and spatial radiation range. However, in previous studies, the maximum boundary problem of ES spatial transfer and the constraints on model application were rarely involved [20,21,39], which might ignore the validity of ES spatial transfer. In this study, we set a restriction condition of ES spatial transfer for the calculation model: when V_oi ≤ V_o, V_oi = V_o was used as the maximum boundary condition to identify and filter the calculation results of the model. Thus, we could determine the maximum spatial boundary of ES spatial transfer and the maximum ES supply boundaries.

In terms of the calculation of ES spatial transfer range, the starting point (transfer-out point) and ending point (transfer-in point) for ES spatial transfer in this study was defined as the geometric center point of ecospace and the population center of transfer-in cities. By using the buffer function of the ArcGIS10.5 platform, the spatial transfer range of ES was obtained. In this evaluation method, ecospace was replaced with a point, and the point location was one of the important factors affecting the spatial position and scope of ES transfer range, which may be an important factor limiting the spatial accuracy of research results.

For the ES spatial transfer intensity (I_oi), I_oi changes greatly with different transfer distances in different ES spatial transfer ranges. As shown in

Table 8. ES intensity (I_oi) of ecospace to the cities (population center point).

Ecospace and City	Ioi 108 (rmb/km2)
Funiushan_Huaibei	0.0068
Funiushan_Luoyang	0.0919
Funiushan_Shangqiu	0.0108
Funiushan_Zhengzhou	0.0323
Liankangshan_Huaibei	0.0030
Liankangshan_Luoyang	0.0018
Liankangshan_Shangqiu	0.0029
Liankangshan_Zhengzhou	0.0022
Taihangshan_Huaibei	0.0077
Taihangshan_Luoyang	0.1941
Taihangshan_Shangqiu	0.0153
Taihangshan_Zhengzhou	0.1362

, in the spatial transfer relations Funiushan_Luoyang, Funiushan_Zhengzhou, Funiushan_Shangqiu, and Funiushan_Huaibei, the range of transfer was different due to the difference in distance between the transfer-out point and the transfer-in point. Thus, the same Funiushan ES quantity (V_o) produced different levels of dilution within different transfer ranges, resulting in I_oi differences in Funiushan_Luoyang, Funiushan_Zhengzhou, Funiushan_Shangqiu, and Funiushan_Huaibei, i.e., the I_oi values were 0.092∗108 RMB/km², 0.032/km², 0.011/km², and 0.0068/km², respectively (Table 8). This study focuses on whether the ES of ecospace can reach the population gathering area with the help of the spatial transfer properties. The issue of whether the strength of spatially transferred ES I_oi can meet the ES demand of the corresponding population concentration area needs further research.

4.1. Implications

As shown in the Results section, there is a spatial gap between ES supply space and ES demand space to corresponding cities. Only relying on natural spatial transfer, none of the ESs in ecospace can be autonomously transferred into corresponding population centers, i.e., Luoyang, Zhengzhou, Shangqiu, and Huaibei, by natural ecosystems themselves. How and where to balance the spatial mismatch between ES supply and ES demand in urban agglomeration areas such as CPUA would be an important issue in local management and landscape planning. What implications can this study provide for local administrators in choosing ecological construction and management strategies in urban agglomeration area? According to this study, promoting ecosystem service connectivity between ecospace and demanding cities through landscape connectivity may be the solutions to some extent. Landscape connectivity is particularly important for maintaining ecosystem structural integrity and ecosystem function stabilization; therefore, it is able to exert ecosystem services and keep ecosystem health within human-dominated landscapes that have experienced extensive fragmentation [15,42,43,44]. Urban landscapes related to ecosystem processes are often mosaics of land cover types along an urban–rural gradient. The patches of typical land covers may functionally provide connectivity for promoting ecological processes during ecosystem service spatial transfer. In such areas, conservation management should focus on preserving and restoring regions of highly ecological linkages [42]. This focus may be particularly relevant for ecosystem services (genetic resources, soil formation, etc.) that are not easily transferable in space and, therefore, are able to maintain ecosystem services’ spatial transferring dynamics. Many different ecological linkage definition, classification, and categorization systems have been developed in recent decades. For simplicity, we will refer to ecological linkages as continual linkage, broken linkage, and stepping-stone linkage in this study based on previous studies [45,46,47,48,49]. Continual linkage refers to ribbons of natural landscapes, such as rivers, continuous green corridors, etc. Broken linkage refers to the landscape that is not spatially connected but is functionally connected, either autonomously or in conjunction with services from manufactured inputs, such as islet forests along the middle route of the South-to-North Water Diversion Project in CPUA. Stepping-stone linkage refers to a series of small patches forming migration channels between large ecosystem patches, which can increase the connectivity between large patches. Specifically, using the ecological linkages provided by landscape connectivity to increase ES stocks of ecospace, smooth the transfer path, and expand the ES receiving capacity and storage space of the inner city in the future would be the main points.

For improving ES stock in the ecospace [34,50], firstly, strengthen the construction and protection of nature reserves in ecospace; this may improve ES output per unit area by improving the quality of the natural ecosystem. Secondly, combined with promoting the construction of national parks, the implementation of red-line planning, and ecological protection policies in and around ecospace, i.e., returning farmland to forest, farmland and grassland, farmland to wetland, ecological immigration policy, etc., local administrators may expand the area of current ecospace size to increase the output of ES.

For unblocking the ES spatial transfer path, building an ecological network system to increase the connectivity of the ecological network would be a core job [44,50,51]. Firstly, managers should establish the guiding principle of coordinated management of urban and rural ecology to integrate urban and rural ecosystems to optimize the allocation of ES supply. Secondly, the key path of ES spatial transfer should be identified by organizing investigation and research, and the ecological network should be built to enhance the ecological interaction between hinterland ecospace and cities with dense populations, for example, urban and rural river corridor system or urban and rural road corridor system construction. In CPUA, the Yellow River and Huaihe River are the main ecological corridors running through the east and west of urban agglomeration. Strengthening the ecospace construction of cities along the Yellow River and Huaihe River would have an important impact on the delivery of ESs from ecospace in the western and southern area of the CPUA.

Thirdly, the stepping-stone within ES transfer scope is very important [18]. This study shows that the farther ES is transferred, the thinner/weaker ES intensity is within the transfer scope. The fragmentated patches, stepping-stone system, of natural ecosystems between Funiushan, Liankang, Taihangshan, and corresponding cities may be considered as “the porter” or ES transport carrier, which is a “refueling station/gas station” to enhance ES transfer intensity and breadth along the transfer route—in this case, for example, the Yellow River wetland natural patches between Taihang and Zhengzhou, Funiushan and Luoyang, and Funiushan and Zhengzhou. Finally, and most easily ignored, the fragmented ecological patches around the city should be paid more attention. They are the key “last kilometer” of linkage for connectivity between the external ecospace ES and the urban internal ecospace.

In terms of expanding the receiving and storage space in urban areas, it is mainly important to integrate the isolated ecospaces inside the city such as urban parks and urban water bodies/rivers. In this case, enough attention should be paid to the planning and construction of Longhu Lake, Longzihu Lake, Yanminghu Lake, and the greenway and parks in around Zhengzhou. Most importantly, a spatial perspective of the transregional interactions and the holistic view in intercity cooperation and ecosystem management are the top principles in urban agglomerations to solve intercity environmental issues in urban agglomeration areas and landscape planning, which are guarantees for realizing the effectiveness of remote linkage between ecospace ES supply and urban ES demand through spatial transfer. Local statistics and monitoring data in related cities [52,53] show that the climate regulation service of ecospace has contributed to the air quality improvement, urban climate evolution, and alleviation of urban heat island effect in Luoyang city, and the water provision service has contributed to water resource supply in Zhengzhou city to a certain extent. This proves the benefits and prospects of transregional ecological cooperation based on the fact of spatial transfer of ecosystem services.

4.2. Limitations

It is undeniable that there are also some limitations in this study: First of all, this study used an expert-based method in ES quantity calculation. Although this study has carried out the localization according to the actual situation of the CPUA, these expert-based parameters are still limited by the expert’s cognitive level, professional skills, and practices, which will lead to errors in the calculation results [38]. Secondly, based on the ES spatial transfer model, the buffer analysis is simplified to a circle with the transfer-out/transfer-in point chosen as starting point/ending point. Consequently, this will lead to deviation between the simplified ES supply boundary and the actual ES transfer space range as well as the simplified ES demand boundary. Finally, the data accuracy of this study is 1000 m. Previous studies showed that the optimal spatial resolution of remote sensing data for the ES value calculation of Central Plains urban agglomeration is 30–1000 m, and within this range of spatial resolution, the relative deviation of estimated results is less than 0.4% [37]. In this study, land use types, annual net primary productivity (NPP), annual vegetation index (NDVI), and other data with a resolution of 1000 m were selected to spatialize ecospace ES. However, coarse data accuracy may lead to the omission of some spatial details [33,37], for example, ecospace with an area less than 1 km² may be ignored.

As outlined above, the study was conducted with multiple limitations, and our work indicated that the calculation parameters in ES calculation and its spatial transfer related to ecosystem services would be the key factors most directly affecting the research topic. In this study, the calculation of the total ecosystem services includes nine single-ecosystem service categories, such as food production, raw material production, climate regulation, gas regulation, hydrology regulation, disturbance regulation, soil formation, maintenance of biodiversity, and aesthetic landscape. According to Costanza [1], single-ecosystem services are interdependent, and they can be added in many cases because they represent ‘joint products’ of the ecosystem. Based on this, the nine single services in this paper were added together and the value of total ecosystem services was calculated, but there may be a problem of double counting, which might make the calculation results biased: V_o value or V_i value, for example. For the ES spatial transfer constant, 0.6 was applied for every single service in the process of calculating ESV spatial transfer amount and radius. The uniform constant 0.6 simplifies the calculation process, but it might ignore one fact that there are differences in spatial transfer capacity between different services, for instance, the difference between regional climate regulation and soil conservation [21]. For the practicability of the results, the amount and radius of ES spatial transfer based on the ES amount added up from nine single ecosystem services may have deviations in the actual verification, which would reduce the practical guidance value of this study. In subsequent studies, services should be further separated from each other at different scales according to the attributes of different ecosystem service categories, as well as their spatial transfer characteristics, to provide a more accurate scientific reference for local management practices.

Generally speaking, a more refined classification of service categories, more precise parameters, and higher resolution data may be needed for future analysis to improve the accuracy of research results, identify more and richer details of ES spatial transfer, and provide clear spatial guidance for ecosystem protection and management for local administrators [54].

5. Conclusions

The spatial mismatch between ecospace and population space leads to spatial mismatch between ES supply and demand in CPUA. Limited by distance and ES spatial transfer conditions, most ESs of the ecospace could not be autonomously transferred into the four cities to meet their demands in space in the natural state.

To balance the spatial gap between the ES supply boundary and demand boundary, promoting ecosystem services’ connectivity between ecospace and demanding cities through landscape connectivity may be the solution to some extent in urban agglomeration areas such as CPUA. Additionally, transregional ecological integration and intercity cooperation are realization paths to guarantee ES spatial transfer from ecospace so as to improve the effectiveness of remote ecological linkage between ecospace ES supply and ES demand in the inner city.

In addition, in urban agglomeration areas such as CPUA, conservation management should focus on preserving and restoring regions of highly ecological linkages in space to maintain ecosystem services’ spatial transferring dynamics.