A Supply-Demand Framework for Eco-Compensation Calculation and Allocation in China’s National Key Ecological Function Areas—A Case Study in the Yangtze River Economic Belt

Abstract

China’s National Key Ecological Function Areas (NKEFAs) provide important ecosystem services but lose significant development opportunities. An NKEFA consists of a few National Key Ecological Function Counties (NKEFCs). China’s central government annually makes fiscal transfers to NKEFCs to compensate for their fiscal imbalance and ecosystem protection costs. The eco-compensation coefficient (ECC), together with the fiscal revenue and expenditure gap (FREG), determines the transfer payment, but the central government fails to provide practical methods for its estimation. This article proposes a framework for ECC estimation by integrating ecosystem service supply (ESS), ecosystem protection cost (EPC), and public service provision capability (PSC) of NKEFCs, and clarifies the criteria and indicators for ESS, EPC, and PSC evaluation. The framework was implemented in the Yangtze River Economic Belt (YZEB), and the results were compared with the payments in the current central-to-local fiscal transfer (CTLFT) system. The key findings and conclusions include: (1) The payment in the current CTLFT system mainly depends on the FREG rather than ESS and EPC of NKEFCs. (2) Some counties are notably under-compensated because their ESS or EPC are underestimated, or the province that administers them has a stronger fiscal capability. (3) The framework contributes to fair allocation and efficient use of eco-compensation payments by improving the ECC estimation method and identifying the main stressors and public service weaknesses in NKEFAs. This study gives the following policy implications: (1) Inner-provincial and cross-provincial watershed eco-compensation programs need to be developed to supplement the central-to-local eco-compensation program in the YZEB. (2) Environmental management strategies should be based on the characteristics of stressors and people’s livelihood in NKEFAs.

1. Introduction

Ecological compensation (EC) and payment for ecosystem services (PES) have become increasingly essential policies internationally to protect natural ecosystems and reconcile development with conservation [1,2,3]. The two terms are often used interchangeably but have some differences. PES, also called payment for environmental services, occurs when the beneficiaries of an ecosystem service (ES) make payments to its providers. It primarily addresses positive environmental externalities to ensure the sustainable supply of ES. EC, in contrast, has a broader scope by dealing with both positive and negative environmental externalities. It includes rewards for ecosystem protection efforts and compensations for disturbed or impaired ecosystems. Some EC programs focus on the protection of natural ecosystems, such as forests [4,5], grassland [6], and protected areas [7,8,9], by making payments to people who make efforts to preserve these natural ecosystems and lose development opportunities. Other EC programs address the adverse impacts of development projects, such as mining, hydropower, oil and gas development, and infrastructure construction [1,10,11,12]. In addition, PES is usually made in monetary form, while EC could be made through monetary payments or conservation actions.

More than 40 countries have implemented EC or PES programs around the world, such as the federal-level wetland preservation program in the US, the BushBroker Program in the state of Victoria in Australia, the Biodiversity Offsetting Pilots program in England, the Compensation Pools program in Germany, the Sloping Land Conservation Program in China, and the environmental compensation program in Brazil [1,13]. Protecting biodiversity is often the priority of conventional EC programs. A No Net Loss (NNL) policy has been widely used for designing and evaluating the performance of an EC program that emphasizes no net loss or even a net gain of biodiversity [1,14]. However, the NNL policy neglects the welfare consequences that the policy exerts on the impacted population. Many recent EC programs have both environmental and social objectives. In response, the No Worse-Off principle was introduced into EC programs, which insists that “the social gains associated with the changes in biodiversity caused by development and accompanying offsets must at least equal any social losses” [1,3,14].

EC and PES are often called “eco-compensation” in Chinese researchers’ publications [12,15]. The eco-compensation to National Key Ecological Function Areas (NKEFAs) is a national-level government-financed EC program to support China’s major function-oriented zoning (MFOZ) policy [16,17,18]. A county is the basic unit for MFOZ; all counties in the country are categorized into one of the three types based on their primary function, including urbanized areas, agricultural areas, and important ecological areas. The national-level MFOZ delimited 25 NKEFAs in 2011, which provide important ESs and play a vital role in securing the ecological safety of a large region or even the whole country. Each NKEFA is composed of some counties that are set as national key ecological function counties (NKEFCs). Later, provincial governments developed their MFOZ and set a few province-level ecological function areas, which were gradually upgraded to NKEFAs from 2011 to 2016. By the end of 2016, the number of NKEFCs increased to 676, and their area accounts for 53% of China’s land area.

These NKEFAs (NKEFCs) are planned to mainly play ecological roles where large-scale urbanization and industrialization are not encouraged, and energy-intensive and highly polluting industries are forbidden. Thus, NKEFCs provide essential ESs to urbanized and agricultural counties but lose significant development opportunities. The central government annually makes fiscal transfers to NKEFCs to compensate for their direct and opportunity cost of ecosystem protection [16,18]. Such eco-compensation has been found to give NKEFCs incentives to protect ecosystems, help improve their environmental quality, and alleviate poverty [19,20,21,22]. Nevertheless, since there are problems both in the eco-compensation payment calculation and allocation, the environmental and socioeconomic effects of the program have been found to vary among counties and provinces [16,22,23,24].

The eco-compensation payment to NKEFCs is made through the central-to-local government fiscal transfer (CTLFT). The following problems exist in the payment calculation and allocation. Firstly, the payment is calculated based on these counties’ fiscal revenue and expenditure gap (FREG) and eco-compensation coefficient (ECC). According to the official guideline, the ECC should be estimated based on the size of the ecological redline area (ERA), as well as the restriction on industrial development and its impact on local government revenue [25]. However, the former can only partially indicate a county’s ES contribution and protection cost, while the latter is practically hard to approximate. Secondly, the central government does not directly make the payment to NKEFCs but transfers it to provincial governments. Then, provincial governments determine how to distribute the payment among counties under their jurisdiction. In practice, some provincial governments allocate the payment to counties with poor fiscal capability but are not planned as NKEFCs. As a result, there exist controversies regarding payment allocation both among provinces and NKEFCs [16,24]. Moreover, based on the current policy, the eco-compensation payment could be used for either ecosystem protection or public service improvement. The central government annually evaluates the performance of NKEFCs in the two fields, and the evaluation result impacts the payment made to the county the next year. Since investments in ecological and environmental protection projects usually take a longer time to be productive, county governments are more likely to allocate the payment to public service areas to achieve quick outcomes. Consequently, the performance of some NKEFCs on ecosystem preservation and restoration is unsatisfying. Further, the quantity and value of ESs have been widely recognized as the most critical criterion for determining eco-compensation payment. However, they are not incorporated into the payment calculation in the CTLFT system.

The eco-compensation system reform is on the agenda of the Chinese government, in which the eco-compensation to NKEFAs is an essential component. To protect ecosystems, secure ecological security, and narrow regional development disparity, the Chinese government proposes establishing an eco-compensation system that compensates for the protection cost and helps improve the basic public services of ecologically important areas. As for the eco-compensation to NKEFAs, the central government suggests taking care of the heterogeneity of ecological contributions, functions, sensitivity, and vulnerability among NKEFAs and NKEFCs in payment calculation and allocation [25]. Furthermore, besides the central-to-local eco-compensation program, developing horizontal eco-compensation programs among provinces along the Yangtze River and the Yellow River has also been put forward as a reform goal.

The Chinese government has put forward the goal and direction of eco-compensation system reform, while theoretical, methodological, and empirical research needs to be undertaken in academia to support the reform practice. With this background, this paper focuses on improving the method for eco-compensation payment calculation to NKEFAs, especially the ECC estimation, by establishing a framework that integrates ES supply and eco-compensation demand evaluation. The framework will support decision-making on eco-compensation payment allocation among different NKEFCs, NKEFAs, and provinces, and between ecosystem protection and public service improvement objectives. As a case study, the framework was implemented in NKEFAs in the Yangtze River Economic Belt (YREB).

2. Study Area

The Yangtze River Economic Belt (YREB) is a national-level strategic development area and economic zone in China. It includes nine provinces (Sichuan, Yunnan, Guizhou, Hubei, Hunan, Jiangxi, Anhui, Jiangsu, and Zhejiang) and two municipalities (Chongqing, Sichuan), accounting for over 40 percent of China’s population and total GDP [26,27] (Figure 1). Based on the reach division of the Yangtze River, the YREB is divided into the upper reach, middle reach, and lower reach regions. The upper reach region includes the provinces of Sichuan, Yunnan, Chongqing, and Guizhou, the middle-reach region includes Hubei, Hunan, and Jiangxi, and the lower reach region includes Anhui, Jiangsu, Zhejiang, and Shanghai. The eleven provincial-level administrative areas, linked by the Yangtze River, are ecologically interactive and socioeconomically interrelated. Meanwhile, there exists apparent inequality in economic development, infrastructure construction, and household income among them. The Chinese government has set ecosystem protection and restoration as a priority for the YREB and has chosen the YREB as an important eco-compensation area in the CTLFT program since 2018 [26,28,29]. Two hundred and fifty-five counties have been planned as NKEFCs in the YREB, distributed in nine provinces and 21 NKEFAs (Figure 2). This study estimates ECC for these counties, analyzes their spatial heterogeneity, and, accordingly, provides suggestions on eco-compensation allocation and ecosystem management.

The YREB and the Yangtze River Basin (YRB) are geographically overlapped but do not completely cover the same area. This article takes YREB instead of the YRB as the study area because of the following considerations. The YRB is the area where the main stream and tributaries of the Yangtze River flow through [30]. It is a geographical area independent of administrative boundaries. In contrast, the YREB is an economic area with 11 provincial-level administrative areas, and the connection and collaboration among them are highly emphasized. Since implementing the eco-compensation program to NKEFAs requires the participation and cooperation of different levels of government, it is more appropriate to take the YREB as the study area. Nevertheless, since the two areas are largely overlapped, the study in the YREB will give support or implications for the development of horizontal eco-compensation programs in the YRB.

3. Methodology

This study has both methodological and empirical focuses. Methodologically, a framework was proposed for ECC estimation in NKEFCs. Empirically, the framework was implemented in the YREB with county, province, and NKEFA level study designations. The methods for the framework establishment and implementation are illustrated as follows.

3.1. The Supply–Demand Framework for Eco-Compensation Coefficient Estimation

$$ECP=F(FREG,ECC)$$

(1)

The eco-compensation payment (ECP) to NKEFCs is made through central-to-local government fiscal transfer. The payment is calculated based on the fiscal revenue and expenditure gap (FREG) and the eco-compensation coefficient (ECC) of NKEFCs [23]. Since the FREG can be straightforwardly calculated based on fiscal data, determining the value of ECC becomes the key problem for ECP estimation.

A supply–demand framework was proposed for ECC estimation (Figure 3). The framework consists of four levels: target, structure, criterion, and indicator level. The target level is the eco-compensation coefficient (ECC) to be estimated. The structure level is composed of ES supply (ESS) and eco-compensation demand; the eco-compensation demand is evaluated with ecosystem protection cost (EPC) and public service provision capability (PSC). Finally, the criterion and indicator level comprise criteria and measurements for EPC and PSC evaluation, which are selected according to the specific situation of the YREB for this study.

3.1.1. Ecosystem Service Supply

Four key ESs are selected for evaluating ESS, including soil retention, water retention, biodiversity conservation, and carbon storage. The reasons are as follows. Firstly, the four ESs have been regarded as highly important in China’s national ecosystem assessment, especially for the YREB [31,32]. Secondly, NKEFAs in the YREB are categorized into one of three types: soil retention, water retention, and biodiversity conservation. Besides, China’s terrestrial ecosystem carbon sink is currently offsetting 7–15% of anthropogenic emissions. The carbon sequestration service of the terrestrial ecosystem has been catching much attention from the Chinese government and academia to support the carbon neutrality strategy [32]. Thus, it is necessary to incorporate the carbon sequestration service into the ECC estimation.

3.1.2. Eco-Compensation Demand

Eco-compensation compensates NKEFCs for their cost in protecting ecosystems and supports public service improvement to benefit residents. In response, the eco-compensation demand analysis is designed to integrate EPC and PSC evaluations. A higher EPC or a lower PSC value indicates a higher demand for eco-compensation.

EPC is evaluated with the impact of both natural and anthropogenic stressors and policy restrictions.

National stressors include water erosion and geological hazards. Firstly, storms and flooding are the most frequent and severe natural hazards that strike the YREB. Water erosion happens as rain falls or moves as part of a storm or the result of flooding. Therefore, water erosion’s quantity and spatial distribution indicate where and how serious the impact of storms and flooding is [33,34]. Secondly, geological hazard vulnerable areas are widely distributed in NKEFAs of the YREB [35]. The impact of geological hazards is measured by the number of geological hazard sites.

The impact of anthropogenic stressors is evaluated with three indicators: population density, population growth rate, and cropland area on slopes steeper than 15 degrees. Population density and land use have been widely used for evaluating and mapping human impacts on ecosystems [36,37]. The population growth rate is supplemented to measure the impact of human stressors from a dynamic perspective. NKEFAs in the YREB are featured with a large percentage of the mountainous landscape, especially in the upper and middle reach regions. Slope cropland is a widespread and significant impact that humans impose on nature [38,39,40]. The Chinese government has requested the afforestation of cropland on slopes larger than 25 degrees or 15–25 degrees but located at important water source areas through implementing the Grain to Green program. In addition, soil and water conservation measures are requested to be implemented on other slope cropland, especially on slopes steeper than 15 degrees. Therefore, the area of slope cropland (>15 degrees) is an appropriate land-use indicator for evaluating the anthropogenic impact on ecosystems and EPC.

The policy restriction criterion is evaluated with the ecological redline area (ERA) percentage in a county. The ecological redline policy is one of the most important national-level environmental policies to support China’s sustainable development. ERA is defined as “the minimum ecological area needed to guarantee and maintain ecological safety and functionality, and biological diversity for national security” [17,41,42]. ERA needs to be preserved and prohibited from development. The percentage of ERA has a direct impact on the protection cost of a county.

PSC is evaluated by two criteria: the fiscal capability of local government and the public service provision status. The former criterion is evaluated with fiscal revenue and public service expenditure indicators. As for evaluating public service provision status, four public services were evaluated: education, healthcare, social security, and transportation. On the one hand, education, healthcare, and social security, together with employment and housing services have been regarded as basic public services in China [43,44]. In NKEFAs, most people who are self-employed as farmers live in rural areas and build their houses on rural homesteads. Public service data on employment and housing is hard to collect. Therefore, the basic public services of education, healthcare, and social security were evaluated. On the other hand, even though transportation is not classified as a basic public service, the state of transport infrastructure significantly impacts rural people’s livelihood. Road networks affect people’s access to daily necessities and other public services, such as education and healthcare [43]. Therefore, transportation, especially road networks, is also selected as an indicator for evaluating public service provision status.

3.2. County-Level Study

A county is the basic unit of NKEFAs and the basic administrative unit that accepts fiscal transfers for eco-compensation. The county-level study estimates the ECC for each NKEFC and analyzes its spatial heterogeneity.

ECC of an NKEFC was estimated based on the integration of ESS, EPC, and PSC. The calculation of ESS, EPC, and PSC can be regarded as a multi-criteria evaluation (MCE) problem. Hierarchical MCE models were established for ESS, EPC, and PSC estimation based on the proposed framework (Figure 3). The weighted linear combination method was used for multiple criteria integration in each level; criteria (indicators) in the same level were assigned equal weights for their importance. The measurement and calculation method of indicators are illustrated in Table 1 (ESS indicators), Table 2 (EPC indicators), and Table 3 (PSC indicators). Since these indicators are measured with different units and scales, the min–max normalization was used to convert the indicators to the same scale [0, 1] with Formula (2).

$$X_i^{\text{'}}=\frac{X_i-X_{\min }}{X_{\max }-X_{\min }}$$

(2)

For an indicator, X_i is the original indicator value for county i, X_i’ is the normalization indicator value for county i, and X_max and X_min are, respectively, the maximum and minimum indicator values for all counties.

The eco-compensation coefficient (ECC) was calculated by multiplying ESS, EPC, and PSC results. ESS and EPC are positive factors—a higher payment needs to be made to a county with a larger ESS or EPC value. In contrast, PSC is a negative factor—a county with poorer public service provision capability needs to be compensated more to support public well-being improvement. To handle the different directions of ESS, EPC, and PSC, a mean-value standardization method was applied to process the ESS, EPC, and PSC values before the multiplication.

$$EC{C}_i=ES{S}_{i-std}\times EP{C}_{i-std}\times \frac{1}{PS{C}_{i-std}}$$

(3)

$$ES{S}_{i-std}=\frac{ES{S}_i}{\overline{ESS}}$$

(4)

$$EP{C}_{i-std}=\frac{EP{C}_i}{\overline{EPC}}$$

(5)

$$PS{C}_{i-std}=\frac{PS{C}_i}{\overline{PSC}}$$

(6)

where ESS_i, EPC_i, PSC_i are, respectively, the original ESS, EPC, PSC values for county i. ESS_i-std, EPC_i-std, PSC_i-std are, respectively, the standardized values of ESS_i, EPC_i, and PSC_i. are, respectively, the average ESS, EPC, and PSC values of the 255 NKEFCs in the study.

Such a standardization method has the following advantages: (1) The variance among the variables is kept in the standardization results. (2) Outliers do not impact the standardization results too much. ESS values are taken as an example for illustrating the standardization results. If ESS_i-std > 1, the ESS of county i is higher than the average of the 255 counties. In contrast, if ESS_i-std < 1, the ESS of county i is lower than the average.

Table 1. Criteria and indicators for evaluating ecosystem service supply (ESS).

Criteria	Indicators	Indicator Calculation	Data Source and References
Key ecosystem service (C1)	Soil retention (I1)	$\begin{array}{l}SR=(A_{potential}-A_{actural})\times A_{county}\\ A_{potential}=R\times K\times LS\\ A_{actual}=R\times K\times LS\times C\end{array}$ SR represents soil retention service. Apotential is potential annual soil loss in tons per hectare, Aactual is actual soil loss, R is the rainfall and runoff factor, K is the soil erodibility factor, LS is the slope length-gradient factor, C is the land cover factor. Acounty is area of the county in the unit hectare.	http://cstr.cn/31253.11.sciencedb.458.CSTR:31253.11.sciencedb.458 (accessed on 6 June 2022). [31,45]
	Water retention (I2)	$WR={\displaystyle \sum _{i=1}^m(P_i-R_i-E{T}_i)}\times A_i$ WR represents water retention service, Pi is precipitation, Ri is storm runoff, ETi is evapotranspiration, and Ai is the area of the ecosystem defined with land cover. m is the number of ecosystems.
	Biodiversity conservation (I3)	The number of indicator species in the county.
	Carbon sequestration (I4)	$\begin{array}{l}ACS=\left(BC{S}_{2010}-BC{S}_{2000}\right)/10\\ BC{S}_t={\displaystyle \sum _i^m{\displaystyle \sum _j^n\left(B_{ijt}\times C{C}_i\right)}}\end{array}$ACS is the carbon sequestration service, measured by the average annual carbon storage from 2000 to 2010. BCSt is the carbon storage in year t. Bijt is the biomass carbon density of ecosystem i in unit j in year t. CCi is the carbon content in the biomass of ecosystem i, which is 0.5 for forest and wetland, and 0.45 for grassland.

Table 1. Criteria and indicators for evaluating ecosystem service supply (ESS).

Criteria	Indicators	Indicator Calculation	Data Source and References
Key ecosystem service (C1)	Soil retention (I1)	$\begin{array}{l}SR=(A_{potential}-A_{actural})\times A_{county}\\ A_{potential}=R\times K\times LS\\ A_{actual}=R\times K\times LS\times C\end{array}$ SR represents soil retention service. Apotential is potential annual soil loss in tons per hectare, Aactual is actual soil loss, R is the rainfall and runoff factor, K is the soil erodibility factor, LS is the slope length-gradient factor, C is the land cover factor. Acounty is area of the county in the unit hectare.	http://cstr.cn/31253.11.sciencedb.458.CSTR:31253.11.sciencedb.458 (accessed on 6 June 2022). [31,45]
	Water retention (I2)	$WR={\displaystyle \sum _{i=1}^m(P_i-R_i-E{T}_i)}\times A_i$ WR represents water retention service, Pi is precipitation, Ri is storm runoff, ETi is evapotranspiration, and Ai is the area of the ecosystem defined with land cover. m is the number of ecosystems.
	Biodiversity conservation (I3)	The number of indicator species in the county.
	Carbon sequestration (I4)	$\begin{array}{l}ACS=\left(BC{S}_{2010}-BC{S}_{2000}\right)/10\\ BC{S}_t={\displaystyle \sum _i^m{\displaystyle \sum _j^n\left(B_{ijt}\times C{C}_i\right)}}\end{array}$ACS is the carbon sequestration service, measured by the average annual carbon storage from 2000 to 2010. BCSt is the carbon storage in year t. Bijt is the biomass carbon density of ecosystem i in unit j in year t. CCi is the carbon content in the biomass of ecosystem i, which is 0.5 for forest and wetland, and 0.45 for grassland.

Table 2. Criteria and indicators for evaluating ecosystem protection cost (EPC).

Criteria	Indicators	Indicator Calculation	Data Source and References
Natural stressors (C2)	Soil erosion (I5)	$SE=(R\times K\times LS\times C\times P)\times A_{county}$ P is the supporting practice factor. Other factors are the same as that in soil retention service calculation	https://doi.org/10.3974/geodb.2021.05.03.V1 (accessed on 14 June 2022). [46,47]
	Geological hazards (I6)	The number of geological hazard sites in the county.	https://www.resdc.cn/data.aspx?DATAID=290 (accessed on 16 June 2022)
Anthropogenic stressors (C3)	Population density (I7)	The population density in 2019.	* Statistical Yearbook of provinces or cities, Statistical Bulletin on Economic and Social Development of counties
	Population growth rate (I8)	$PGR=\frac{PO{P}_{2019}-PO{P}_{2010}}{PO{P}_{2010}}\times 100\%$ POP2010 and POP2019 are, respectively, the permanent resident population in 2010 and 2019.
	Area of slope cropland (I9)	The area of cropland on slopes steeper than 15 degrees.	GLC_FCS30-2020 https://data.casearth.cn/sdo/detail/6123651428a58f70c2a51e49 (accessed on 20 June 2022)
Policy restriction (C4)	Ecological redline area percentage (I10)	$ERLP=\frac{A_{redline}}{A_{county}}$ Aredline is the area of ecological redline. Acounty is the county area.	* Ecological Redline Delimitation Report of Sichuan, Yunnan, Chongqing, Guizhou, Hubei, Hunan, Jiangxi, Anhui, and Zhejiang

* The data were collected from the statistical yearbook of 9 provinces and 65 cities, the Statistical Bulletin of 255 counties, and Ecological Redline Delimitation Report of 9 provinces in the Yangtze River Economic Belt. The name of provinces, cities, and counties are shown in Appendix A.

Table 2. Criteria and indicators for evaluating ecosystem protection cost (EPC).

Criteria	Indicators	Indicator Calculation	Data Source and References
Natural stressors (C2)	Soil erosion (I5)	$SE=(R\times K\times LS\times C\times P)\times A_{county}$ P is the supporting practice factor. Other factors are the same as that in soil retention service calculation	https://doi.org/10.3974/geodb.2021.05.03.V1 (accessed on 14 June 2022). [46,47]
	Geological hazards (I6)	The number of geological hazard sites in the county.	https://www.resdc.cn/data.aspx?DATAID=290 (accessed on 16 June 2022)
Anthropogenic stressors (C3)	Population density (I7)	The population density in 2019.	* Statistical Yearbook of provinces or cities, Statistical Bulletin on Economic and Social Development of counties
	Population growth rate (I8)	$PGR=\frac{PO{P}_{2019}-PO{P}_{2010}}{PO{P}_{2010}}\times 100\%$ POP2010 and POP2019 are, respectively, the permanent resident population in 2010 and 2019.
	Area of slope cropland (I9)	The area of cropland on slopes steeper than 15 degrees.	GLC_FCS30-2020 https://data.casearth.cn/sdo/detail/6123651428a58f70c2a51e49 (accessed on 20 June 2022)
Policy restriction (C4)	Ecological redline area percentage (I10)	$ERLP=\frac{A_{redline}}{A_{county}}$ Aredline is the area of ecological redline. Acounty is the county area.	* Ecological Redline Delimitation Report of Sichuan, Yunnan, Chongqing, Guizhou, Hubei, Hunan, Jiangxi, Anhui, and Zhejiang

* The data were collected from the statistical yearbook of 9 provinces and 65 cities, the Statistical Bulletin of 255 counties, and Ecological Redline Delimitation Report of 9 provinces in the Yangtze River Economic Belt. The name of provinces, cities, and counties are shown in Appendix A.

Table 3. Criteria and indicators for evaluating public service provision capacity (PSC).

Criteria		Indicators	Data Source and Reference
Fiscal capability of the local government (C5)	Public revenue of county government (C5-1)	General public budget revenue (I11)	Statistical Yearbook of provinces or cities, Statistical Bulletin on National Economic and Social Development of counties, county government work report, Gaode electronic map. [48,49,50,51]
		General public Budget Revenue per capita (I12)
	Public expenditure of county government (C5-2)	General public budget expenditure (I13)
		General public budget expenditure per capita (I14)
Public service provision status (C6)	Education (C6-1)	Teacher–student ratio of primary and secondary school (I15)
		School–student ratio of primary and secondary school (I16)
	Health care (C6-2)	Number of hospital beds per 1000 Population (I17)
		Number of healthcare professionals per 1000 population (I18)
	Social security (C6-3)	Subsistence allowances standard for urban residents (I19)
		Subsistence allowances standard for urban residents(I20)
	Infrastructure (C6-4)	Road length (I21)
		Road density (I22)

Table 3. Criteria and indicators for evaluating public service provision capacity (PSC).

Criteria		Indicators	Data Source and Reference
Fiscal capability of the local government (C5)	Public revenue of county government (C5-1)	General public budget revenue (I11)	Statistical Yearbook of provinces or cities, Statistical Bulletin on National Economic and Social Development of counties, county government work report, Gaode electronic map. [48,49,50,51]
		General public Budget Revenue per capita (I12)
	Public expenditure of county government (C5-2)	General public budget expenditure (I13)
		General public budget expenditure per capita (I14)
Public service provision status (C6)	Education (C6-1)	Teacher–student ratio of primary and secondary school (I15)
		School–student ratio of primary and secondary school (I16)
	Health care (C6-2)	Number of hospital beds per 1000 Population (I17)
		Number of healthcare professionals per 1000 population (I18)
	Social security (C6-3)	Subsistence allowances standard for urban residents (I19)
		Subsistence allowances standard for urban residents(I20)
	Infrastructure (C6-4)	Road length (I21)
		Road density (I22)

3.3. Province-Level Study

The central government makes fiscal transfers to provincial governments, and then provincial governments are responsible for distributing the money among NKEFCs. Therefore, the focus of the province-level study is to examine whether the eco-compensation payment allocation among the nine provinces is reasonable and suggest possible adjustments if it is not.

The fiscal transfer payments to the nine provinces were calculated based on the fiscal revenue and expenditure gap (FREG) and the estimated ECC. They were compared with the payments made by China’s Ministry of Finance (MOF) in 2018 and 2019. Four scenarios are designed. In practice, the payment made to each province depends not only on the FREG and ECC of NKEFCs but also on the total fiscal transfer payments made by the central government. For the convenience of scenario comparison, the total payment made to the nine provinces was set the same as that made by the MOF of China and we compared the payment allocation among them in different scenarios.

Scenario 1: This scenario only accounts for FREG of NKEFCs in eco-compensation payment allocation.

Scenario 2: Both FREG and ECC of NKEFCs are incorporated into the payment calculation. The estimated ECC values of NKEFCs are directly used for the payment calculation with the range [0.09, 4.87].

Scenario 3: Both FREG and ECC of NKEFCs are incorporated into the payment calculation. The estimated ECC values are rescaled to the range [1,2]. Compared to scenario 2, ECC differences among NKEFCs are given a lower weight in the eco-compensation payment calculation.

Scenario 4: Fiscal transfer payments made by the MOF of China in 2018 and 2019.

The eco-compensation payment in Scenarios 1, 2, and 3 are calculated based on Equations (7) and (8). Where PECP_j is the eco-compensation payment made to province j. RECP_j is the relative eco-compensation payment made to province j. k is the total number of provinces and k = 9 for this study. TECP is the total payment made to the nine provinces. County i is in province j. FREG_i and ECC_i are the fiscal revenue and expenditure gap, and the eco-compensation coefficient of county i; n is the total number of NKEFCs in province j.

$$PEC{P}_j=TECP\times \frac{REC{P}_j}{{\displaystyle \sum _{j=1}^{k}REC{P}_j}}$$

(7)

$$REC{P}_j={\displaystyle \sum _{i=1}^n\left(FRE{G}_i\times EC{C}_i\right)}$$

(8)

In Scenario 1, ECC_i = 1, all NKEFCs are given the same ECC value. In scenario 2, ECC_i is the same as that in the county-level study, within the range [0.09–4.87]. In scenario 3, ECC_i is rescaled to the range [1,2] based on Equation (9).

$$EC{C}_{i-new}=a+\frac{(b-a)(EC{C}_i-EC{C}_{\min })}{EC{C}_{\max }-EC{C}_{\min }}$$

(9)

where, ECC_i-new is the rescaled value of ECC_i, a = 1, b = 2, ECC_max = 4.87 and ECC_min = 0.09.

3.4. NKEFA-Level Study

An NKEFA is a geographical area with critical ecological functions and is composed of counties with similar physical characteristics and socioeconomic contexts. The central government requested Environmental Protection and Development Planning for each NKEFA to instruct ecosystem management and public service system construction. Eco-compensation payment is an important funding source for NKEFAs’ protection and development, which needs to be primarily allocated to programs or projects that address the main environmental stressors and public service weaknesses. Additionally, large-scale ecosystem protection and restoration programs often involve a few counties in an NKEFA. Thus, it is practically significant to perform NKEFA-level studies to identify the leading stressors and public service weaknesses of each NKEFA to support the efficient use of eco-compensation payment. In response, for the NKEFA-level analysis, EPC and PSC of each NKEFA was analyzed, and the individual criteria or indicators for EPC and PSC calculation were examined.

EPC or PSC of an NKEKA was evaluated with the average status of all NKEFCs within it, according to Equations (10) and (11).

$$EAEP{C}_j=\frac{{\displaystyle \sum _{i=1}^{m}EP{C}_i}}{m}$$

(10)

$$EAPS{C}_j=\frac{{\displaystyle \sum _{i=1}^{m}PS{C}_i}}{m}$$

(11)

where county i is in NKEFA j, EAEPC_j and EAPSC_j are, respectively, EPC and PSC values of NKEFA j; EPC_i and PSC_i are EPC and PSC values of county i, respectively; m is the total number of counties in NKEFA j.

To identify the main natural or anthropogenic stressor(s) and outstanding public service weakness(es), I₅, I₆, I₇, I₈, I₉ (in Table 2) and C_6-1, C_6-2, C_6-3, C_6-4 (in Table 3) were calculated for each NKEFA, measured with the average value of all NKEFCs for the corresponding indicator or criterion in an NKEFA. Then, the twenty-one NKEFAs were ranked in ascending order for each indicator or criterion to support the analysis.

3.5. Quadrant Chart Analysis for NKEFCs and NKEFAs Categorization

Quadrant chart analysis was applied for county-level and NKEFA-level analysis. There are 255 counties distributed in 21 NKEFAs for the study. It is necessary and beneficial for policymaking to divide them into categories and suggest eco-compensation payment allocation, ecosystem management, and public service improvement strategies by category.

The county-level analysis is used to illustrate how the quadrant chart works. The 255 counties were plotted onto the quadrant chart based on their standardized ESS, EPC, and PSC values. The X-axis indicates the EPC_std value, the Y-axis indicates the PSC_std value, and the plot size indicates the ESS_std value (Figure 4). The characteristics of the counties in each quadrant are illustrated in

Table 4. Quadrant analysis for National Key Ecological Function County categorization.

Quadrant	Ecosystem Protection Cost (EPC) and Public Service Provision Capacity(PSC): Values and Features
Quadrant I	EPCstd > 1 and PSCstd > 1	The ecosystem protection cost of these counties is higher than the average, but their public service provision capacity is stronger than the average.
Quadrant II	EPCstd < 1 and PSCstd > 1	The ecosystem protection cost of these counties is lower than the average, and their public service provision capacity is stronger than the average.
Quadrant III	EPCstd < 1 and PSCstd < 1	The ecosystem protection cost of these counties is lower than the average, but their public service provision capacity is poorer than the average.
Quadrant IV	EPCstd > 1 and PSCstd < 1	The ecosystem protection cost of these counties is higher than the average, and their public service provision capacity is poorer than the average.

. Additionally, ESS_std values and plot size indicate the ES supply of counties in each category. NKEFA-level analysis can be similarly interpreted.

4. Results

4.1. County-Level and Province-Level Study

4.1.1. Spatial Heterogeneity of the Eco-Compensation Coefficient

The range of ESS, EPC, and PSC values is [0.02–0.82], [0.13–0.49], and [0.15–0.49], respectively, which indicates that the ESS disparity among counties is larger than that of EPC and PSC. ESS, EPC, PSC, and ECC values of the 255 counties are mapped with the quantile classification method for spatial pattern and heterogeneity analysis (Figure 5). For an individual attribute, the 255 counties are categorized into four classes: low (bottom 25%), low-medium (final 25–50%), medium-high (top 25–50%), high (top 25%).

The mapping of each attribute displays a clustered distribution pattern. For ESS, the high and medium-high classes are mainly distributed in the upper reach region, in the provinces of Yunnan and Sichuan. These counties supply a high level of ES and are ecologically highly important. For EPC, the high and medium-high classes mainly cluster in the provinces of Chongqing, Hunan, and Jiangxi, indicating that the ecosystem protection cost of counties is high. For PSC, the high and medium-high classes are mainly distributed in Chongqing, Hubei, Anhui, and Zhejiang provinces, showing that these counties’ public service provision capability is strong. In contrast, the low and low-medium classes are mainly distributed in Sichuan, Yunnan, Guizhou, and Hunan provinces. For ECC, the spatial pattern can be described as upper reach region > middle reach region > lower reach region.

When the mappings of ESS (Figure 5a), EPC (Figure 5b), and PSC (Figure 5c) are compared to the spatial distribution map of NKEFAs (Figure 3), counties in the same NKEFA tend to be classified in the same or neighboring classes for the attributes of ESS, EPC, and PSC. This implies that it is necessary and reasonable to analyze the values of ESS, EPC, and PSC by NKEFAs.

4.1.2. Categorization of National Key Ecological Function Counties

The 255 NKEFCs are categorized according to the combination of EPC and PSC values through the quadrant chart analysis. The categorization results are shown in

Table 5. Categorization of National Key Ecological Function Counties in the Yangtze River Economic Belt.

Category	Number of Counties	Number and Percentage of Counties with ES std > 1
Quadrant I	55	31(56.4%)
Quadrant II	56	20 (35.7%)
Quadrant III	88	26 (29.5%)
Quadrant IV	56	20 (35.7%)

and Figure 6.

Fifty-five counties are categorized in Quadrant I, characterized by high ecosystem protection cost but strong public service provision capability. They are scattered in nine provinces. Most NKEFCs in Chongqing province belong to this category. Additionally, 56.4 percent of these counties have a higher ES supply than the average. For counties in this category, local governments are suggested to allocate a large percentage of the eco-compensation payment to fund ecosystem restoration or protection projects and subsidize residents who make sacrifices for ecosystem conservation.

Fifty-six counties in Quadrant II are evaluated with low ecosystem protection costs and strong public service capability. The situation of these counties is the most favorable among the four categories. They are mainly distributed in the provinces of Hubei, Anhui, and Zhejiang, in the middle or lower reach region. For counties in this category, it is critical to determine the key ecosystem stressors and specific public service weaknesses and give them prior consideration for eco-compensation payment allocation.

The number of counties in Quadrant III is the largest among the four categories, whose features can be described as low ecosystem protection cost but poor public service provision capability. These counties are mainly distributed in Yunnan, Sichuan, and Guizhou provinces. Projects that ensure people’s livelihood and improve basic public services should be given priority for eco-compensation payment allocation.

Fifty-six counties are categorized in Quadrant IV. These counties’ situation is the worst among the four categories, with high protection costs and poor public service provision capability. Both ecosystem conservation and public service improvement programs require support and investments from the central and local governments. These counties are mainly distributed in Sichuan, Yunnan, Hunan, and Jiangxi provinces.

4.1.3. Eco-Compensation Payment Allocation among Provinces

The eco-compensation payments to NKEFAs are provided to provincial governments through central-to-local fiscal transfer. The (estimated) fiscal transfer payments to each province under the four scenarios are calculated and shown in Figure 7.

Firstly, the payment allocation in Scenario 1 is mostly close to that in Scenario 4, followed by Scenario 3. This implies that the current central-to-local fiscal transfer system mainly takes care of the FREG of NKEFCs when determining the eco-compensation payment. Their ecosystem protection costs are considered but not given a high weight.

Secondly, when payment distribution in Scenario 4 is compared to that in Scenario 1, Zhejiang province is notably under-compensated, while Guizhou province is over-compensated. This implies that the central government considers the fiscal capability of both NKEFCs and provinces when determining the payment made to each province. Consequently, provinces with stronger fiscal capability tend to be under-compensated.

Thirdly, when Scenario 4 is compared to Scenarios 2 and 3, Zhejiang, Jiangxi, and Yunnan are under-compensated, which indicates that the current central-to-local fiscal transfer system underestimates ES supply and eco-compensation demand of NKEFCs in these three provinces.

Finally, ECC disparity among NKEFAs is given a higher weight in Scenario 2 than in Scenario 3. As a result, the eco-compensation payments made to Zhejiang, Anhui, Jiangxi, Hubei, Chongqing, and Guizhou increase, while that made to Sichuan and Yunnan decrease. This indicates that the estimated eco-compensation payment is sensitive to the scale of ECC. Therefore, setting an appropriate scale for ECC is important for reaching an agreement among provinces on payment allocation.

4.2. NKEFA-Level Study

The twenty-one NKEFAs were classified into four categories based on their EPC and PSC values (Figure 8,

Table 6. Main stressors and public service weaknesses for the 21 National Key Ecological Function Areas in the Yangtze River Economic Belt.

Category	NKEFA	Natural Stressors		Anthropogenic Stressors			Public Service
		Water Erosion (I5)	Geological Hazards (I6)	Population Growth (I7)	Population Density (I8)	Slope Cropland (I9)	Education (C6-1)	Healthcare (C6-2)	Social Security (C6-3)	Transportation (C6-4)
Quadrant I	WYM (5)	13	5	20	11	21	15	6	4	7
	TGR (8)	7	1	15	8	1	10	11	3	6
	DXZYL (11)	3	6	21	3	12	17	8	21	2
Quadrant II	ZGWM (1)	16	9	17	9	20	7	15	2	5
	SZM (2)	15	14	5	13	14	12	1	1	14
	QBM (7)	14	4	14	12	4	4	10	11	11
	DBM (9)	12	19	9	1	17	9	19	6	4
Quadrant III	REG (12)	21	20	3	21	15	8	7	18	18
	DXLM (13)	10	15	2	14	6	20	14	15	19
	WNC (14)	19	17	1	20	10	5	2	17	20
	GQDKSD (16)	2	16	10	10	8	14	17	12	10
	MM (17)	8	21	12	15	7	16	4	13	16
	CY (19)	6	8	11	17	5	2	18	8	3
	NYUTGGR (21)	4	10	6	6	3	6	20	9	9
Quadrant IV	MFM (3)	11	3	18	7	16	19	16	5	13
	LXM (4)	18	7	16	4	18	21	12	14	8
	WLM (6)	9	13	13	5	11	13	5	20	12
	NLM (10)	17	12	7	2	19	18	9	19	15
	MQLM (15)	20	2	4	19	2	1	3	16	21
	NWY (18)	5	11	8	18	9	3	21	10	17
	YSB (20)	1	18	19	16	13	11	13	7	1

The grey fields are identified as main stressors or public service weaknesses in each NKEFA.

). Then, the main stressors and public service weaknesses were analyzed for each NKEFA by category.

Three NKEFAs are categorized in Quadrant I, including WYM, TRG, and DXZYL. They are characterized by strong public service provision capability but high protection cost. Geological hazard is the primary environmental stressor for WYM and TGR, while water erosion threatens ecological safety in DXZYL. Additionally, TGR faces land use pressure from humans, and DXZYL is under strong population stress. The overall public service provision capacity of the three areas is relatively strong among the 21 NKEFAs. However, each area has weak fields, such as education in WYM, education and healthcare in TGR, and education and social security in DXZYL.

There are four NKEFAs in quadrant II, including ZGWM, SZM, QBM, and DBM. They are mainly distributed in middle and lower reach regions. The protection cost of these NKEFAs is relatively lower, while the public service provision capacity is stronger than other NKEFAs. However, geological hazards need to be carefully handled in ZGWM, and population pressure needs to be addressed in QBM, ZGWM, DBM, and SZM. Controlling and mitigating human impacts on natural ecosystems should be the focus of environmental management for these areas. Additionally, the provision of basic public services is also unbalanced. Healthcare in ZGWM, education in SZM, and healthcare in DBM require more financial outlays and support from the government.

Seven NKEFAs are in Quadrant III, distributed in Sichuan, Yunnan, and Guizhou provinces, the upper reach region of the YREB. They are concentration areas of China’s ethnic minority groups and also poverty clustering areas. For REG, DXLM, and WNC, the stress mainly comes from population growth, while the other areas face pressures from both natural and human sources, mainly water erosion and slope farming. These areas’ public service provision capacity is at a lower level. Some especially weak areas need to be given priority for fiscal outlays, such as social security and transportation in REG and WNC, education and transportation in DXLM, education and healthcare in GQDKSD, education and transportation in MM, and healthcare in NWY and YSB.

Quadrant VI includes seven NKEFAs, distributed in upper and middle reach regions, mainly in Sichuan, Yunnan, Guizhou, Hunan, and Jiangxi. These areas face tremendous challenges both in ecosystem management and public livelihood improvement and therefore require more support from the central government. NWY, YSB, WLM, and LXM overlap with China’s poverty concentration areas. MFM, LXM, WLM, NWY, and MQLM are under natural and human pressures. NLM mainly faces population pressure. For YSB, the pressure mainly comes from water erosion. The public service provision status of these areas is overall poor. Some fields require special attention, including education and healthcare in MFM, education in LXM, social security in WLM, education and social security in NLM, and transportation and healthcare in NWY.

5. Discussion

5.1. Implications for Policy and Practice

Eco-compensation to NKEFAs can also be regarded as payments for ecosystem services provided by NKEFAs. However, the payment is not directly made between ES supplies and beneficiaries. NKEFCs are suppliers of ES, but the beneficiaries are hard to identify accurately. The eco-compensation payments are not directly made to NKEFCs, but transferred to provincial governments. In addition, the payment amount is determined not based on the ES values or ecosystem protection cost but on the fiscal capability of NKEFCs and that of the province administering them. As a result, some NKEFCs in a province with stronger fiscal capabilities are under-compensated, such as those in Zhejiang province. In such a circumstance, the provincial government needs to develop inner-province eco-compensation programs to pay for the ES provided by these counties and compensate for their protection cost.

Besides, some provinces, such as Zhejiang, Jiangxi, and Yunnan, are under-compensated because their ES supply and eco-compensation demand are under-estimated in the central-to-local fiscal transfer system. On the one hand, the central government needs to modify the payment calculation method by giving careful consideration to ES value, ecosystem protection cost, and the public service provision capability of NKEFCs. On the other hand, horizontal eco-compensation programs among provinces need to be developed to supplement the central-to-local eco-compensation program. There are many watersheds in the YZRB and many of them cover more than one province. It is necessary for the relevant provincial governments to identify ES suppliers and beneficiaries, evaluate the ES values and protection costs, and develop horizontal eco-compensation programs among them.

After eco-compensation payment is allocated, how to effectively use the payment to protect and manage ecosystems becomes a critical problem. Specifically, how to manage the natural and anthropogenic stressors in different NKEFAs needs to be discussed. The following paragraphs discuss the management of geological hazards, water erosion, population pressure, and slope farming, which have been found to be significant stressors for NKEFAs in the YREB.

Geological hazards are a threatening stressor for a few NKEFAs in the middle or lower reach region, such as WYM, ZGWM, MFM, and LXM, or in the junction area of the Yangtze River’s upper and lower reach, such as TGR and QBM. The effective management of geological hazards relies on establishing a hazard monitoring and warning system, which requires the collaboration of academia, industry, and different levels of government. On the one hand, the central government or provincial government needs to assemble academic or industrial professionals to develop a hazard monitoring and warning system and provide technical support to system operation and data collection and processing. On the other hand, the local government should be responsible for setting up monitoring facilities and employing staff to perform daily maintenance work. Therefore, for these NKEFAs, the hazard monitoring system’s establishment, operation, and maintenance should be given preferential consideration for eco-compensation payment use.

NKEFAs with intense population pressure are often overlapped with the poverty concentration area. Mitigating population pressure in these areas is critical for both ecosystem conservation and poverty alleviation. These NKEFAs can be classified into two categories based on their location and social characteristics. The first category includes MFM, LXM, NLM, DBM, and DXZYL, distributed in the middle or lower reach regions and characterized by high population density. The second category includes REG, DXLM, WNC, NWY, and WLM, distributed in the upper reach region, in Sichuan, Guizhou, and Yunnan provinces. These NKEFAs featured a higher population growth rate and are inhabited by ethnic minority groups. There are two approaches to population pressure mitigation: either reducing the population in relation to available resources or increasing the capability of the population to extract resources through improving technology and skills [52,53]. The first approach is more suitable for NKEFAs in the first category, while the second approach is suggested for NKEFAs in the second category.

The NKEFAs in the first category are geographically close to the Yangtze River Delta Urban Agglomeration or the Yangtze River Middle-Reach Urban Agglomeration. The laboring population should be encouraged to work in big cities to mitigate population pressure on ecosystems. The local government needs to take responsibility for offering employment training services to the public to help migrant workers find suitable jobs in cities. In contrast, NKEFAs in the second category are inhabited by ethnic minority groups. The living and production of minority groups are deeply impacted by their custom and culture, which might contribute to rapid population growth, widespread slope farming, and a low emigration rate. Local people traditionally make a living through agriculture, which strongly impacts ecosystems. The local government is advised to diversify the industries. For instance, they could take advantage of the natural landscape and cultural resources to develop modern tourism or the handicraft industry. The development of these industries also requires the government to improve the transport infrastructure and provide employment training services to residents to make them qualified for the new industries.

Water erosion and slope farming often coexist, such as in GQDKSD, MM, CY, and NYUTGGR. Therefore, the management of the two stressors needs to be integrated. Although water erosion was classified as a natural stressor closely related to topography, soil, and precipitation, human land use, especially slope farming, will aggravate water erosion. To afforest slope cropland is an effective measure for water erosion control. However, in these areas, many rural households’ livelihood relies on slope farmland because of the limited flat land. To control water erosion caused by slope farming, on one hand, the local government needs to implement the grain to green program (GTGP) on cropland on slopes steeper than 25 degrees [39]. On the other hand, it is more important to take water control measures on cropland with slopes lower than 25 degrees, such as maintaining vegetation cover, improving irrigation practices, and constructing contour banks or drains. Accordingly, implementing the GTGP and water control projects needs to be prioritized for allocating eco-compensation payments.

5.2. Limitations

The proposed framework integrates ecological importance, protection cost, and public service provision capacity evaluation, and thus matches the goal of China’s eco-compensation system reform. However, both the proposed framework and the study designation have some limitations.

Firstly, the ECC for NKEFCs was measured with relative compensation standards rather than monetary values in this study. This approach is suitable for large-scale eco-compensation programs involving multiple ES suppliers and beneficiaries and a combination of multiple ESs. For these programs, it is difficult to recognize the beneficiaries of the ES services or reach an agreement between the ES providers and beneficiaries on the compensation amount. Under such a circumstance, it requests an upper-level government to get involved in the payment determination and benefit redistribution based on the relative ES supply, eco-compensation demand, and fiscal capability of ES providers and beneficiaries. However, both the scale of the ECC and the weights given to the factors of ESS, EPC, and PSC impact the estimated payment value. In practice, it is necessary to involve all stakeholders in the decision-making process and make multiple scenario simulations to reach an agreement among them.

Secondly, the ESS and EPC evaluation indicators are selected based on the specific situation of the YZRB, which might not work equally with other areas. For instance, in North or Northwest China, sander storm is a significant natural stressor, and the wind prevention and sand fixation service is one of the key ESs. In coastal areas, the coastal protection service is of high importance. When it goes to the watershed-level study, the impact of the pollution stressor and the pollution control cost need to be incorporated into the ECC estimation.

Finally, eco-compensation to NKEFAs is an environmental management policy, as well as a regional development policy. In our study, a limited number of public services are selected for evaluating the PSC of NKEFCs because of data limitations. Statistical data on county-level socioeconomic development, including public services, are incomplete in China, especially in underdeveloped counties. Additionally, the construction of new infrastructure, such as the 5G network, is not only public service itself but also impacts the accessibility of other public services, such as education and healthcare, by making distance education or telemedicine accessible. However, statistical data on the coverage of these new infrastructures are also very limited. Therefore, creating a more comprehensive database for nationwide county-level socioeconomic statistics and making it accessible to both governments and the public will significantly benefit China’s regional study and policymaking.

6. Conclusions

Eco-compensation to NKEFAs in China is an environmental management and regional development policy to protect ecosystems and promote social equity. The eco-compensation payment is made from the central government to NKEKCs through central-to-local fiscal transfer. However, the current payment allocation mainly considers the fiscal revenue and expenditure gap (FREG) of NKEFCs, but underestimates the heterogeneity of ES supply and ecosystem protection costs among them. As a result, some NKEFCs are under-compensated in the YREB, such as those in Zhejiang, Yunnan, and Jiangxi provinces.

This study proposes a framework for estimating the eco-compensation coefficient of NKEFCs by integrating ES supply and eco-compensation demand evaluation; the eco-compensation demand estimation incorporates the ecosystem protection cost and public service provision capability. The ECC, together with the FREG, will support calculating eco-compensation payments to each NKEKC and province. The case study in the YREB indicates that the framework improves the method for calculating and allocating eco-compensation payments and helps identify critical natural or anthropogenic stressors and main public service weaknesses in NKEFAs. As a result, it will contribute to more reasonable allocation and efficient use of eco-compensation payments, thus benefiting both ecosystem management and public well-being improvements in China’s NKEFAs.