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Article

Assessment of the Payments for Watershed Services Policy from a Perspective of Ecosystem Services: A Case Study of the Liaohe River Basin, China

by
Manman Guo
,
Xu Lu
* and
Qing Ma
College of Architecture and Urban Planning, Shenyang Jianzhu University, Shenyang 110168, China
*
Author to whom correspondence should be addressed.
Water 2025, 17(15), 2328; https://doi.org/10.3390/w17152328
Submission received: 24 June 2025 / Revised: 31 July 2025 / Accepted: 1 August 2025 / Published: 5 August 2025
(This article belongs to the Special Issue NBS for Watershed Management: From Ecological Health Assessment to Ecosystem Restoration)
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Abstract

Payments for Watershed services (PWSs) have been emerging as a critical tool for environmental governance in watershed, yet their comparative effectiveness across implementation models has remained poorly understood. Based on a comparative analysis of Eco-Compensation (EC) and Payments for Ecosystem Services (PESs) frameworks, examining both theoretical foundations and implementation practices, this study aims to quantitatively assess and compare the effectiveness of two dominant PWSs models—the EC-like model (Phase I: October 2008–April 2017) and the PESs-like model (Phase II: 2017–December 2021). Using the Liaohe River in China as a case study, utilizing ecosystem service value (ESV) as an indicator and employing the corrected unit-value transfer method, we compare the effectiveness of different PWSs models from October 2008 to December 2021. The results reveal the following: (1) Policy Efficiency: The PESs-like model demonstrated significantly greater effectiveness than the EC-like model, with annual average increases in ESV of 3.23 billion CNY (491 million USD) and 1.79 billion CNY (272 million USD). (2) Functional Drivers: Water regulation (45.1% of total ESV growth) and climate regulation (24.3%) were dominant services, with PESs-like interventions enhancing multifunctionality. (3) Stakeholder Impact: In the PESs-like model, the cities implementing inter-county direct payment showed higher growth efficiency than those without it. The operational efficiency of PWSs increases with the number of participating stakeholders, which explains why the PESs-like model demonstrates higher effectiveness than the EC-like model. Our findings offer empirical evidence and actionable policy implications for designing effective PWSs models across global watershed ecosystems.

1. Introduction

As a subset of Payments for Ecosystem Services (PESs), Payments for Watershed Services (PWSs) adopts the environmental economic definition [1,2]: a voluntary, conditional transaction with at least one provider, one user, and a well-defined environmental service [3,4]. It is a market model that depends on the Coase Theorem and the private property rights system, stressing private property rights, free markets, and conditional transactions. The implementation of PWSs as an economic incentive policy tool to enhance watershed management has transitioned from theoretical discourse to policy debate and practical application over several years. Nevertheless, practical experiences indicate that few PWSs schemes in both developed nations, such as the United States [5], European [6], Brazil [7], and Mexico [8], and developing regions, including Costa Rica [9], Madagascar [10], and Vietnam [11], conformed to this definition. From the perspective of property rights, the publicity of water resources usually makes the land managers of watersheds the public sector. In addition, from the perspective of river ecosystems, which are different from ecosystems that transmit ecosystem externalities through atmospheric motion like forests and grasslands, the environmental externalities of rivers are mainly transmitted by water movement. The unchangeable nature of the river flow direction determines a fixed relationship between the provider (upstream) and the user (downstream). As such, unlike other kinds of PESs, in the case of PWSs, it was not possible to freely decide who is the buyer and who is the seller. From both theoretical analysis and practical experience, the environmental economics definition is too narrow for PWSs. The ecological economics approach of PWSs, described by Muradian et al. (2010) [12], focuses on the just distribution and economic efficiency and favors a variety of payment mechanisms to achieve these goals, both market and non-market, is an available perspective.
In China, Payments for Ecosystem Services and ecological compensation are typically regarded as synonymous [2,13], although the latter term is predominantly employed in most contexts [14]. According to Pigou’s tax theory, EC, characterized as a policy instrument designed to sustain or enhance ecological conditions via diverse incentives in China, posits that the externality of ecological benefits arises from market mechanism failures. It advocates for governmental intervention to motivate individuals to preserve ecosystem services through mandatory policy instruments such as fiscal transfer payments and/or tax subsidies [15].
The conceptual definitions of PESs (Payment for Ecosystem Services) and EC (Environmental Compensation) remain under development, and the distinction between these two concepts continues to be debated. At present, no consensus has been reached. Although PESs and EC are both economic strategies aimed at promoting ecosystem restoration and development, their theoretical underpinnings, implementation mechanisms, and benefits differ significantly. Consequently, they are fundamentally distinct frameworks. EC is a public policy based on the principles of public property rights and Pigovian tax theory. PESs, based on an efficient business-oriented framework, represents a novel conservation paradigm that markedly diverges from conventional public subsidy models [1,16].
Comparative studies on PESs schemes remain scarce, particularly quantitative analyses, due to spatial heterogeneity and variations in institutional policy across regions. A subset of the scarce literature focused on theoretical comparisons, utilizing methods like conceptual analysis and logical derivation, such as Wunder [4,17] and Yu [13]. Payment for ecosystem services, payments for environmental services, ecological compensation, eco-compensation, PESs, and EC are commonly used terms. The comparative research of PESs and EC as policy instruments for environmental governance continue unabated [18]. Another approach involves systematic literature reviewed to compare payment mechanisms qualitatively among nations and regions, like China [19] and Mexico [20]. The research, constrained to case selection and data collection, mostly emphasize comparisons among various situations across distinct locations (i.e., different watersheds or cities). Statistical data show/reveal that the global portfolio of PESs programs is estimated to represent an annual investment of more than USD 36 billion. Despite substantial financial investments, rigorous effectiveness evaluations of Payments for Ecosystem Services (PESs) programs remain inadequate, particularly quantitative assessments. This evidence gap perpetuates ongoing debates regarding the efficacy of this policy instrument.
The effectiveness evaluation methods for both PESs and EC can be systematically categorized into three distinct approaches. Primarily, studies employed environmental monitoring data—including water quality measurements, air quality indices, and forest cover effects [21]—to assess ecological effectiveness. The key objective of Watershed ecological restoration is to enhance both water quality and quantity. While water quality [22], water quantity, and soil erosion provide the most direct assessment measures, their application in research has been hindered by uneven regional monitoring capabilities across study areas. Secondarily, as a kind of policy tool, the policy performance assessment was also an applicable method. Mainly according to the local statistical data, adopting models like TOPSIS [23], the Entropy Method, AHP-Fuzzy Comprehensive Method [24], Principal Component Analysis (PCA) [25], and Structural Equation Modeling (SEM) [26], researchers assessed the benefits with comprehensive indicators including ecological effectiveness, economic efficiency, and social equity [27,28]. Estimates based on official statistics data made this method capable of only assessing the benefits of administrative districts but not the watersheds. In addition, the benefits covered by this method were too broad to reject the benefits generated from other policies. Therefore, it is difficult to use a single economic methodology or policy performance assessment method to assess PWSs from the perspective of theoretical analysis. Additionally, “Ecosystem services” (ESs) constitute a defined component of ecological economics [29], referring to the benefits that people drive from functioning ecosystems. The evaluation and valuation of ESs have increasingly become critical metrics in assessing the effectiveness. In particular, the ecosystem service value (ESV)—which consolidates various divergent variables into a singular monetary unit that incorporates both ecological and socioeconomic objectives of PWSs—is a suitably adequate assessment tool for evaluating PWSs. However, most studies have predominantly employed the valuation of one or several ESs that are more amenable to marketization or quantification as their primary assessment metrics, such as carbon stocks and carbon sequestration [30], normalized difference vegetation index (NDVI) [8,28], pollination [31], wood [7], water purification [32], water supply [33], soil conservation [7], and cultural impact [7]. However, ESs possessing non-use values [34] or low perceptibility are frequently neglected, potentially leading to substantial underestimation of the effectiveness of PESs or EC models. The PWSs policy generates advantages by incorporating the externalities of land use into the production processes of land managers [35]. While numerous methodologies exist for quantifying ecosystem services value (ESV), the unit-value transfer method [36,37], based on land use change data, is appropriate for PWSs [38].
In order to take this research direction further, this study adopts the Liaohe River mainstream basin as a case study, and conceptualizes the basin’s ecological compensation mechanism as a two-phase process—namely the EC-like model (2008~2017) and the PESs-like model (2017~2021). The watershed management of the Liaohe River in China did not strictly adhere to either the PESs or EC model; yet, its growth exhibited distinct stages characteristic of both models. In addition, employing the corrected unit-value transfer method, this study utilizes ESV as an evaluation metric to evaluate and compare the economic effectiveness or efficiency of the two models.

2. Materials and Methods

2.1. Study Area

The Liaohe River (Figure 1a,b), one of China’s seven principal rivers, is referred to as the “mother river” of Liaoning Province. The river starts in Hebei and traverses Inner Mongolia, Jilin, and Liaoning, with its watersheds encompassing the entirety of Liaoning Province. The primary sources of pollution in the Liaohe River are industrial wastewater, home sewage, and agricultural waste, all in substantial volumes, which rendered it the most polluted river in China from the mid-1980s to the early 21st century [39].
The Liaohe River mainstream Basin (Figure 1b,c) represents one of the earliest watersheds in the Liaohe River Basin to implement both ecological restoration and compensation. In 2008, building on the cross-city water quality monitoring network, the Liaoning Provincial Government established a PWSs system between upstream and downstream cities, using cross-city water quality as the key performance indicator. The number of cross-city water quality monitoring stations in Liaoning Province increased substantially from 27 (including 12 along the mainstream) in 2008 to 186 in 2021. Since 2008, guided by the successful practices in the Liaohe River mainstream Basin, Liaoning Province has rapidly expanded its PWSs program.
In order to adhere to the principle of ecological integrity, based on Strahler–Horton [40], we performed a hydrological simulation and watershed division of the Liaohe River mainstream and defined its specific watershed scope using the hydrological analysis method (Figure 1c). To meet the need for river flood control, in 1958, the intersection of the Liaohe River mainstream and the Daliao River was artificially blocked in the Liujianfang area, such that the two rivers became and have since remained entirely unconnected from each other. From the perspective of watershed division, both rivers belong to the Liaohe River Basin. Yet, due to independent flow movement, the ecological compensation measures of Daliao River have very little effect, if any, on the water quality of the mainstream of the Liaohe River. For this reason, we did not include the Daliao River basin in our account of ecological benefits arising from the two models. Additionally, the mainstem Liao River Basin was delineated into 8 sub-basins, including Zhaosutai River basin, Qing River basin, Chai and Fan River basin, Lama River basin, Xiushui River basin, Liu River basin, Liaohe downstream basin, and Raoyang River basin. The watershed covers Tieling City and Shenyang City, including four counties, including Kangping and others; Anshan City including Tai’an County; Fuxin City including Zhangwu County and Fuxin County; Jinzhou City including Heishan County and Panshan County; and Panjin City including the Dawa district. The watershed area is about 63,709.4 km2 with 6 cities and 19 counties, accounting for 43% of the whole area of Liaoning Province.

2.2. Division of Phases in the Liaohe River Mainstream Basin

While PESs build on voluntary Coasean bargaining, EC embodies Pigouvian welfare economics, resulting in fundamentally different transaction structures: market-based contracts vs. fiscal transfer systems. Therefore, PESs and EC represent two distinct PWSs models, characterized by divergent theoretical foundations, implementation mechanisms, and advantages. Building on the empirical evidence from the Liao River mainstem basin, we find that distinct phases of the watershed’s PWSs program exhibited operational characteristics analogous to both EC and PESs models. In addition, using ESV as the core indicator, we systematically evaluated and compared the performance of both models through an integrated research framework (Figure 2).
Under the influence of national ecological governance policies, the PWSs program was officially launched in the Liaohe River mainstem Basin in 2008, marked by the promulgation of Document No. 71 (2008): Interim Measures of Liaoning Province for Assessment of Water Quality Objectives of Rivers Across Administrative Regions. In accordance with national water quality standards for the Liaohe River mainstream, the Liaoning Provincial Government established binding performance targets for cross-city water quality monitoring stations and formalized accountability through signed responsibility agreements with prefecture-level municipal governments. The agreement mandated that upstream cities exceeding the contracted water quality standards must pay compensation to downstream cities. For mainstream cities, the payment is 500,000 CNY/month if the water quality exceeded the standard by 0.5-fold or less, with an additional 500,000 CNY/month for every subsequent 0.5-fold increase. Tributary cities were subject to half of these payment rates. On a monthly basis, each city underwent performance evaluation, and the provincial government centrally withheld and annually redistributed these funds. Based on water quality monitoring data from the mainstream, by 2015 the river showed improvement compared to 2008 levels, yet still fell short of national water quality standards for trunk streams. In 2017, under mounting pressure to achieve national water quality standards, Liaoning Province enacted comprehensive reforms to its PWSs program through Water Pollution Compensation Measures for River Sections (No. 45, 2017), which introduced the following: ① Doubled compensation rates, increasing the original payment standards by 100%; ② decentralized payment procedures, allowing direct inter-municipal negotiations and payments rather than provincial-level redistribution and ③ expanded program coverage, incorporating more tributaries into the assessment system following the deployment of additional monitoring stations. Water quality monitoring data revealed significant improvements in 2020, with all measured parameters meeting national assessment standards. In addition, the decentralized intergovernmental negotiation mechanism created a policy window for Shenyang and Tieling to reach their first-ever cross-jurisdictional agreement on water quality governance for the Zhu’er Mountain monitoring station through bilateral negotiations.
From October 2008 to December 2021, there were two phases in the management of the river (Table 1); Phase I spanned from October 2008 to April 2017 (8.5 years), while Phase II covered May 2017 to December 2021 (4.6 years). In terms of definition, the two phases of the PWSs in the Liaohe River basin cannot be considered either EC or PESs in the strict sense. However, from a practical point of view, Phase I is closer to the EC model, and Phase II to the PESs model. In Phase I, the provincial government, as the higher-level administrative unit, issues mandatory instructions that the municipal governments are obliged to follow. This model is similar to the EC based on government intervention so it is referred to as the “EC-like model”. In Phase II, the upstream cities pay compensation directly to the downstream cities in the form of a monthly settlement. Although the provincial government still decides the amount of compensation, the municipal governments, through a process of consultation, determine the form of payment (e.g., horizontal transfer payment, direct payment, and project investment). Further, the municipal governments that receive compensation funds have autonomy in regard to fund allocation, unlike the first phase in which municipal governments had very little autonomy. Although the payment model in this phase does not fully comply with the principles of voluntary and conditional transactions, we refer to this phase as a “PESs-like model” given that the following conditions are in place: clearly defined property based on administrative boundaries, a buying and selling relationship based on upstream and downstream locations, and negotiable forms of payment.

2.3. Methods for Effectiveness Evaluation

The boundaries of the Liaohe River mainstream basin are not consistent with those of the administrative region; therefore, the statistical data based on administrative region cannot support the relevant calculation of the watershed. In addition there is only limited monitoring of the local ecological environment quality monitoring level, so it cannot meet the data requirements of the eco-service function method. Recognizing the ESV across diverse policy contexts, including the Grain for Green Program [41], the Key Ecological Functional Zones [42], and urbanization [43], this study adopts ESV as the core indicator to evaluate and compare the effectiveness and efficiency of the two payment models. The present study, therefore, relies primarily on the unit-value transfer method proposed by Constanza [14,44] and Xie Gaodi [5] to calculate the ESV in each period. In order to bring the equivalent ESV factor closer to the reality of the Liaohe River basin, based on the data including the output value and planting area of grain crops in Liaoning, the ESV equivalent of the Liaohe River basin is calculated according to the following formula:
D = 1 7 × A O V S ,
where D represents the standard ESV equivalent per unit area; AOV is the total output value of grain crops in Liaoning (unit: CNY/USD); S is the total sown area of all grain crops in Liaoning (unit: hm2); and 1/7 means that the standard value equivalent of Liaoning Province is 1/7 of the output value per unit area of local food crops [5]. To ensure comparability of ESV across different phases, a unified baseline year (2008) was adopted for equivalent factor calculations of ESV. Through calculation, the average equivalent factor of ESV in Liaoning is 2686.5 CNY/hm2 (408.19 USD/hm2).
According to the literature, the ESV is related to multiple factors, including Net Primary Productivity (NPP), rainfall, soil erosion, and habitat quality. In order to make the assessment results more consistent with the local reality, NPP was selected as the adjustment factor [6] to modify the equivalent factors in space:
D ij = ( bj / B ) × D ( i = 1 , 2 , 3 … ; j = 1 , 2 … 8 ) ,
where Dij is the ESV equivalent per unit area that has been modified; i represents distinct ecosystem service types in Table 2, while j denotes ecosystem categories, primarily encompassing 8 types: farmland, forestland, grassland, bush, wetland, water, unused land, and built-up land.; bj is the NPP of ecosystem j; and B is the average NPP per unit area of the Liaohe River mainstream ecosystem.
Based on the land use data and field survey data, the table of ecosystem service equivalent values in China was modified to obtain a table suitable for the Liaohe River (Table 2). The ESV of each ecosystem was calculated based on obtaining the equivalent factor and the equivalent scale. Then, the ESV in Tieling, Shenyang, Anshan, Fuxin, Jinzhou, and Panjin were calculated as follows:
E S V i j = D i j × S j   ( i = 1,2 , 3 … … ; j = 1,2 , … 8 ) ,
E S V λ = ∑ j n E S V i j ( λ = 1,2 , 3 … 6 )
where ESVij is the ESV of ecosystem j; Sj (hm2) is the area of ecosystem j; ESVλ (yuan) is the ESV of λ; and λ represents Tieling, Shenyang, Anshan, Fuxin, Jinzhou, and Panjin. In order to compare the two phases in relation to the service efficiency of PWSs, the increase or decrease in ESV is calculated based on the ESV in 2008, 2017, and 2021.

2.4. Materials

Aligned with critical phases of ecological compensation policy evolution in the mainstream of Liaohe River, this analysis focuses on three benchmark years (2008, 2017, 2021), with dataset details provided in Table 3. The watershed boundaries were delineated using DEM data, while Landsat imagery and land use data were employed to calculate ESV. Net Primary Productivity (NPP) was derived from precipitation and temperature data, with NPP subsequently used to adjust ESV estimates. Water quality monitoring data served for ESV result validation and comparative analysis.
Given that publicly available land use datasets lack the required accuracy and temporal continuity for this study, we generated customized land use maps for the target years by interpreting Landsat 4–5 TM and Landsat 8 OLI imagery through a hybrid approach combining automated classification and manual visual interpretation. Based on the land use/cover classification method developed by the Chinese Academy of Sciences combined with the ecosystem types in the table of ecosystem service equivalent value, land use in the Liaohe River mainstream basin is divided into 8 types: farmland, forestland, grassland, bush, wetland, water, unused land, and built-up land. To account for cloud interference and regional climatic/vegetation characteristics, only cloud-free images (cloud cover <10%) acquired were selected for analysis. Image preprocessing and enhancement in ArcGIS 10.8 (Esri, Redlands, CA, USA) and ENVI 5.6 (Harris Geospatial Solutions, Boulder, CO, USA) was performed and the remote sensing images were explained and classified based on eCognition 9.0 (Trimble Geospatial, Munich, Germany). For the baseline year of 2021, field surveys and supplementary studies were conducted in key, challenging, and disputed areas to enhance data accuracy through ground-truthing and validation. To ensure scientific rigor, 1000 sample points were randomly selected for accuracy validation, yielding a final land use data with an overall accuracy exceeding 85% and a Kappa coefficient above 0.8.
Based on monthly precipitation and temperature data, we generated continuous gridded datasets through spatial interpolation using ANUSPLIN v4.4. To minimize bias from anomalous years, annual precipitation was derived from monthly gridded data. The results demonstrated that although interannual fluctuations existed, the precipitation levels in 2008, 2017, and 2021 did not deviate significantly from the long-term average (592.68 mm) (Figure 3), with none of these years classified as statistically extreme (i.e., neither drought- nor flood-dominated). This approach ensured the robustness of subsequent analyses. Moreover, these monthly climate surfaces were then employed as inputs for the Thornthwaite Memorial model to derive annual Net Primary Productivity (NPP) data [45].
The selection of water quality monitoring stations adhered to two fundamental principles: (1) prioritizing cross-city stations to ensure direct relevance to ecological compensation policies, and (2) rigorously maintaining temporal data continuity across all three observation periods (2008, 2017, 2021). Following the established spatiotemporal parameters, 11 monitoring stations were identified, including Tongjiangkou, Qingliao, No. 1 Bridge of Fan River, Zhu’er Mountain, Lama Bridge, Juliu River Bridge, Liu River Bridge, Hongmiaozi, Panjin Xing’an, Shengli Tang, and Zhaoquan River (Figure 1c). The water quality rank for each monitoring station is from the official website of the Liaoning Province’s Department of Ecological Environment, and mainly from reports of surface water quality.

3. Results

3.1. Spatial Differences in ESV Changes

The calculation process described generated the following figures: The total value of ESV growth in Phase I is 15.23 billion CNY (2.31 billion USD), with an average value of 1.79 billion CNY (272 million USD) per year. In comparison, the total value of ESV growth in Phase II is slightly lower at 14.86 billion CNY (2.26 billion USD). However, the average value of 3.23 billion CNY (491 million USD) per year in this phase is higher than the corresponding value in Phase I. Above all, whether it is the EC-like model in Phase I or the PESs-like model in Phase II, the ESV showed an obvious growth trend. Secondly, however, in relation to the total value of ESV changes, although Phase II (4.6 years) spanned approximately half the time of Phase I (8.5 years), the two phases were very close in the total value of ESV changes. Further, considering the annual average ESV changes, the value of Phase II is more than twice that of Phase I. Compared with Phase I, the PWSs efficiency of the Liaohe River mainstream basin in Phase II improved significantly.
In regard to the spatial distribution of ESV changes (Figure 4), in Phase I, the following observations are made: Firstly, the regions that showed significant ESV changes are mainly located along the Liaohe River mainstream and at the estuary and are in the form of bands and blocks. Secondly, as important tributaries, the Zhaosutai River and Liu River also realized obvious ESV changes in the form of growth space. Thirdly, Zhangwu County and Xinmin County, which are located in the west of the central Liaoning Plain, gained ESV changes in the form of plaque. In Phase II, the following observations were made: The ESV changes space along the Liaohe River mainstream, the Liu River, and the estuary were still obvious, but the growth rate was lower than that of Phase I. At the same time, the growth rate along the tributaries such as the Raoyang River, Qing River, and Fan River is self-evident, especially in the upstream regions.

3.2. Regional Differences in ESV Changes

The PWSs and eco-environment management of rivers in Liaoning Province has always been the responsibility of the prefecture-level cities, although these cities differ in terms of their natural environment and their administrative strategies. Taking prefecture-level cities as the unit to determine the extent to which ESVs have changed in two phases is helpful in comparing the focal ecological compensation models in terms of efficiency. As shown in Table 4, firstly, although Phase II covers only more than 4 years, which is half the length of Phase I, from the overall growth trend, with the exceptions of Anshan City and Panjin City, the total ESV growth of Phase II is higher than that of Phase I. Different cities take over different areas of the Liaohe River mainstream basin and have different space scales, which is also likely to have an effect on the change efficiency of ESV. Secondly, in order to further compare the changes in the ESV of the respective areas, the average growth is represented by the annual growth in the ESV per unit area. The calculation shows that the average change in Phase II is far higher than—in fact, more than twice—that of the first. At first glance, one might conclude that the higher payment standard led to the double growth of ESV. However, this is not the case. Taking the mainstream regions as an example, the cardinal payment in Phase II was 1 million CNY/month (0.15 million USD/month), which is double that of Phase I. On this basis, each 1-fold increase would bring an increase of 1 million CNY/month (0.15 million USD/month); yet, the number in Phase I is 0.5 million CNY/month (0.08 million USD/month) per 0.5-fold; the incremental payment standards increased to the same extent in the two phases. So there was no doubling relationship between them. In addition, we found that an improved standard does not mean that the amount of compensation paid increases proportionally. On the basis of the relevant statistics from the Liaoning Provincial Government, with the exception of the first two years following the policy’s launch in 2008, the total cost incurred by the entire province was low given that this constituted the exploratory stage. However, the average annual PWSs payment in Liaoning Province was about 150 million CNY (22.79 million USD) in most of the following years. The increased payment standard had a stronger binding force on the cities than before. In order to avoid paying high compensation fees, cities mobilized their own efforts to control water quality, so that the actual payment did not double with the doubling of the standard. Finally, the average growth of Panjin in the two phases is much higher than that of the other cities. The main reason for this greater growth is that Panjin is located at the estuary of the Liaohe River mainstream. This special location means Panjin’s ecological control methods focus primarily on wetland engineering and marsh construction, which have a higher ESV than other methods such as growing trees and recovering grassland and can, therefore, bring greater ESV growth to the same area.
Another interesting finding is that the average growth in Phase II is nearly three times higher than that in Phase I in Tieling, Shenyang, and Fuxin, for which growth is higher than in the other cities. The reasons for growth vary from place to place: Growth in Shenyang was mainly achieved through the construction of national projects such as Seven Star Wetland Park and Kangping Wetland Park—a consequence of its advantages as a provincial capital city combined with loans from the provincial and central governments. Fuxin, located at the southern edge of Horqin Sandy Land, is the main source of sand and dust in the central Liaoning urban agglomeration. On behalf of the overall interests of Liaoning Province, the Liaoning Water Resource Management Group invested in the management of the upstream Liu River and other tributaries in Fuxin. For this reason, Fuxin’s growth in terms of ESV was realized based on financial support from the provincial state-owned enterprise, i.e., the Liaoning Water Resource Management Group, on the one hand and the participation of county governments on the other. Tieling’s changes in this regard was mainly due to the introduction of a local policy to establish a PWS among counties by adding municipal water quality monitoring stations in the region. This policy subdivided the water quality requirements of the county governments as set out by the provincial government with the goal of improving water quality by mobilizing county governments to join in ecological management action focused on rivers. For example, in 2018, Changtu County and Kaiyuan County invested 50 million CNY (7.60 million USD) and 20 million CNY (3.04 million USD), respectively, in projects such as dredging and building overflow dams for tributaries in the region. In addition to the provincial and municipal governments, which were responsible for initiating and directing these projects in the early days, county governments also began to join the PWSs with a high level of engagement in terms of securing funds and taking action.

3.3. Functional Differences in ESV Changes

According to the main ecosystem services classification system, the ecosystem services of the Liaohe River are divided into provisioning services, regulating services, supporting services, and cultural services. The ESV generated by regulating services are the greatest, with 82.72%, followed by supporting services and cultural services, with 14.04% and 5.00% (Table 5). The spatial distribution of ESV generated by these services is also basically similar, being mainly concentrated along the Liaohe River, the Liaohe estuary, and mountains in the east and west areas (Figure 5). On the contrary, the ESV produced by provisioning services has decreased (Table 5). Based on the land use data of the Liaohe River mainstream Basin, the ESV changes in this basin is mainly due to the conversion of farmland into forest, grassland, wetland, and water. The provisioning value of the farmland and these land uses have little difference, so the increase in green space did not bring obvious change in provisioning benefits. At the same time, combined with the reduction in provisioning value caused by the increase in urban and rural construction land in these phases, the overall decline of provisioning value is generated. The regulating value of forest, bush, wetland, and water is higher, especially the water regulating value of water and wetland, leading to an obvious increase in regulating value.
In order to further explore the specific ESV types generated by PWSs in the Liaohe River mainstream Basin, regulating services are further subdivided into four: In terms of the change scale of ESV, the growth of water regulating service value is the highest, accounting for 45.12%, followed by the climate regulating service value with 24.32%, and then the waste treatment service value and gas regulating service value, accounting for 9.06% and 4.22% (Table 5). In terms of spatial distribution, the change space of gas regulating service value, climate regulating service value, and waste treatment service value are similar, with a larger space scale and similar spatial distribution. In Phase I, the main growth space was concentrated along the mainstream and a few tributaries such as Zhaotai River, while in Phase II, the space spread to more tributaries and the forested areas in their upper reaches. The ecological restoration means in Liaohe River mainstream Basin are mainly natural close hillsides along the rivers, water storage projects with rubber dam in cut-off rivers, and artificial forestation in desertified areas. Phase I of PWSs is the early stage of natural close hillsides along the rivers, so the ecosystem was mainly restored to simple ecosystems such as grassland in this phase, and then to more complex ecosystems such as bush land and forests in Phase II. This is the reason why, from the perspective of spatial scale, the growth space along the river in Phase I was larger, but the growth of ESV was not as high as that in Phase II. In addition, in Phase II, provincial and municipal governments invested more heavily in upstream areas for artificial forestation, thus resulting in more forest land and more ESV. The growth space of water regulating value is smaller, distributed in the mainstream, estuary, and other important tributaries of the Liaohe River in the form of water growth, which benefits from the construction of rubber dam (Figure 6).

4. Discussion

4.1. Why Are PESs More Efficient than EC?

Using ESV as the evaluation metric, this study’s comparative analysis of PWSs models in the Liaohe River mainstream Basin revealed that the PESs-like model demonstrates significantly higher efficiency than the EC-like model. This finding aligns with the conclusions drawn by Ezzine-de-Blas et al. [46] and Kaiser J et al. [47]. Their meta-analysis similarly found that market-oriented approaches with direct stakeholder engagement consistently outperformed top-down compensation schemes in terms of both ecological outcomes and cost-effectiveness. In the PESs-like model of the Liaohe River mainstream Basin, bilateral negotiations such as contracts and direct payments between upstream and downstream governments can be seen as constituting a form of broad marketization, and the horizontal transfer payment institution between cities created a market between them. It is this market effect that incentivizes cities to take steps to participate in PWSs in a fully engaged way. While direct compensation costs are quantifiable, the substantial human and material resources mobilized by local governments to achieve water quality targets remain largely unquantified and likely exceed formal payment amounts by orders of magnitude. Additionally, in comparison with others such as Anshan and Jinzhou, Tieling, Fuxin, and cities with an inter-counties, the PWSs structure shows more growth relative to ecological benefits. It is the cooperation mechanism between them that has produced more active agents, and as more agents participate, ecosystem services become more efficient.
The study reveals that PESs have demonstrated significant effectiveness not only in the PWSs policy implemented in the Liaohe River mainstream Basin, but also across various other policy contexts including agri-environmental policy [48] and regional applications throughout Latin America [49] and other regions. Building upon the seminal review by Salzman et al. (2018) [50], this study corroborates that the presence of motivated buyers and motivated sellers constitutes a fundamental prerequisite for successfully scaling PESs models. Their global analysis, synthesizing evidence from 550 PESs initiatives across 51 countries, identified this dual motivation as the most critical enabling factor—a finding strongly supported by our empirical results from the Liaohe River mainstream Basin. Even Wunder acknowledges that the initial development of PESs emphasized diversified market-based mechanisms, including bilateral negotiated agreements and small-scale collective action approaches, rather than being confined solely to direct cash payments [4,16]. Enhancing PWSs efficiency critically depends on incentivizing broader stakeholder participation in ecological restoration and governance, for which PESs or PESs-like models have proven effective.

4.2. Policy Implications: Designing Effective PWSs Mechanisms

Unlike PESs targeting nondirectional ecosystem services such as forest cover [51] or carbon sequestration [52], PWSs focus on directional ecosystem services—including water purification, hydrological regulation, and water conservation—where the unidirectional flow of water inherently constrains the flexibility in designating providers and users [53]. Thus, although PESs demonstrate higher efficiency, implementing a pure PESs model in PWSs policy remains challenging. However, our research provides new insights: establishing a PESs-like model in watershed ecological governance can still enhance efficiency, even within public ownership systems like China’s. In China, the land and water resources are public property, which means that local governments are agents of public resources and thus the providers and users of PWSs. In addition, the layer-by-layer structure of responsibility with Chinese characteristics has the effect of connecting the layers vertically through responsibility contracts, although horizontally there are no contractual linkages. Given this structure, market competition occurs horizontally but not vertically [54]. Therefore, the PESs-like model can only occur within the same layer between provinces, cities, or counties. Phase II in the Liaohe River mainstream Basin exemplifies this very model. To further enhance the efficiency of PWSs, scaling up PESs-like models by incorporating broader participation represents a critical pathway forward. In China, breaking administrative boundaries to enable direct inter-provincial city-to-city and county-to-county transactions could significantly expand the PWSs market. In Europe, the United States, and other regions, more diverse transactional relationships can be established, such as between governments and social organizations, governments and enterprises, or communities and private entities.

4.3. Limitations

From theoretical analysis, ecological benefits refer to the goods and benefits that come from the final services. For the PWSs in the Liaohe River mainstream Basin, the most direct indicator is water quality monitoring data. According to the process data from 2008 to 2021 (Figure 7), the overall trends in water quality changes between the two phases (2008–2017 and 2017–2021) were consistent with the ESV. However, the fluctuating variations in water quality rank throughout these phases made it challenging to accurately assess and compare the efficiency between the two models. In contrast, the ESV assessment method based on land use change proved more straightforward and stable, making it particularly suitable for evaluating and comparing the effectiveness of PWSs programs.
Nevertheless, this study still has limitations due to inherent methodological constraints of the unit-value transfer method. The ESV assessment relies on land use data while encompassing both intermediate and final ecosystem services, which carries a risk of overestimating the total ESV. Future research should incorporate multi-source monitoring data for cross-validation to enhance the reliability of ESV assessments. The unit-value transfer method operates on the assumption of spatial homogeneity, making it more suitable for large- to medium-scale ESV assessments [55]. Although this study has implemented methodological refinements, the approach still cannot adequately capture fine-scale spatial variations in ESV. Future advancements, particularly in machine learning techniques [56], are expected to significantly enhance the precision of ESV calculations. Moreover, this study’s observation period remains limited in duration. Given the time-lag effects characteristic of environmental policies, the long-term impacts of PWSs policies—particularly the PESs-like model—require continued monitoring and evaluation. We will continue long-term monitoring of the PWSs and comprehensively assessing the social and economic impact on local communities.

5. Conclusions

In this paper, the Liaohe River mainstream Basin in China was investigated as an example of a comparative study of the efficiency of the EC-like model and the PESs-like model by evaluating the increase in ESV. The comparative analysis of PWSs models in the Liaohe River mainstream Basin reveals significant insights into the efficiency and design of ecological compensation mechanisms. The study demonstrates that the PESs-like model, characterized by decentralized negotiations and direct payments between upstream and downstream governments, achieves higher efficiency in enhancing ESV compared to the top-down EC-like model. This finding underscores the importance of market-oriented approaches and stakeholder engagement in improving the effectiveness of watershed management policies.
The methodological approach of using ESV based on land use change data provides a stable and practical tool for evaluating the ecological benefits of PWSs programs. With future advancements in multi-source monitoring data, the accuracy of ESV assessments will be significantly enhanced, consequently improving its utility in PWSs and related application domains.
In summary, the study advocates for the adoption of PESs-like models in watershed management, emphasizing the role of market mechanisms and broader stakeholder participation. These findings offer valuable policy implications for designing effective PWSs programs not only in China but also in other regions facing similar challenges in balancing ecological and economic objectives.

Author Contributions

Conceptualization, X.L. and M.G.; methodology, X.L.; software, M.G.; validation, X.L., M.G. and Q.M.; formal analysis, M.G.; investigation, Q.M.; resources, Q.M.; data curation, Q.M.; writing—original draft preparation, M.G. and X.L.; writing—review and editing, M.G.; visualization, M.G.; supervision, X.L.; project administration, Q.M.; funding acquisition, X.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China (No. 52178045).

Data Availability Statement

The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

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Water 17 02328 g001
Figure 1. Location of Liaohe mainstream Basin.
Figure 1. Location of Liaohe mainstream Basin.
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Figure 2. Methodological flow diagram about the assessment of PWSs.
Figure 2. Methodological flow diagram about the assessment of PWSs.
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Figure 3. Average precipitation in the Liaohe River mainstream Basin (2008–2021).
Figure 3. Average precipitation in the Liaohe River mainstream Basin (2008–2021).
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Figure 4. Spatial distribution of the ESV changes in models (the average exchange rate in the figure is 6.5815 CNY/USD).
Figure 4. Spatial distribution of the ESV changes in models (the average exchange rate in the figure is 6.5815 CNY/USD).
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Water 17 02328 g005aWater 17 02328 g005b
Figure 5. The spatial distribution of ESV changes of 4 major services from October 2008 to December 2021 (the average exchange rate in the figure is 6.5815 CNY/USD).
Figure 5. The spatial distribution of ESV changes of 4 major services from October 2008 to December 2021 (the average exchange rate in the figure is 6.5815 CNY/USD).
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Figure 6. The spatial distribution of ESV changes of 4 regulating services in models (the average exchange rate in the figure is 6.5815 CNY/USD).
Figure 6. The spatial distribution of ESV changes of 4 regulating services in models (the average exchange rate in the figure is 6.5815 CNY/USD).
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Figure 7. Changes in water quality at water quality monitoring stations in Liaohe mainstream River Basin.
Figure 7. Changes in water quality at water quality monitoring stations in Liaohe mainstream River Basin.
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Table 1. Two models of PWSs in Liaohe River mainstream Basin.
Table 1. Two models of PWSs in Liaohe River mainstream Basin.
ModelEC-like modelPESs-like model
PhasePhase I: October 2008–April 2017.Phase II: May 2017–December 2021
PolicyInterim Measures of Liaoning Province for Assessment of Water Quality Objectives of Rivers Across Administrative Regions (No. 71, 2008)Compensation Measures of Water Pollution at River Sections in Liaoning Province (No. 45, 2017)
Measures of Liaoning Province for the Administration of Special Compensation Funds for Water Quality Objectives of Rivers Across Administrative Regions (No. 678, 2011)Measures for the Administration of Compensation Funds for Water Pollution at River Sections in Liaoning Province (No. 293, 2018)
ProviderThe municipal government
UserThe provincial government: the allocation and specific use of funds is determined by the provincial financeThe municipal government: the specific use of funds is determined by the cities
Way of paymentYearly payment by the provincial governmentMonthly direct payment between municipal governments; the method
of payment is negotiable
Basis of paymentThe water quality objectives of rivers across administrative regions
Payment standard
(Unit: CNY/month (USD/month))		≤0.5-foldEach extra 0.5-fold≤1-foldEach extra 1-fold
Mainstream500,000 (75,970)500,000 (75,970)1,000,000 (151,941)500,000 (75,970)
Tributary250,000 (37,985)250,000 (37,985)1,000,000 (151,941)500,000 (75,970)
Table 2. The ecosystem service equivalent value per unit area.
Table 2. The ecosystem service equivalent value per unit area.
Provisioning ServiceRegulating ServiceSupporting ServiceCulture
Food Pro-
duction		Raw MaterialWater SupplyGas RegulationClimate RegulationWaste TreatmentWater RegulationSoil FormationNutrient CyclingBiodiversityCulture
Farmland0.850.400.020.670.360.100.271.030.120.130.06
Forestland0.290.660.342.176.501.934.742.650.202.411.06
Grassland0.220.330.181.143.021.002.211.390.111.270.56
Bush land0.380.560.311.975.211.723.822.400.182.180.96
Wetland0.510.502.591.903.603.6024.232.310.187.874.73
Water0.800.238.290.772.295.55102.240.930.072.551.89
Unused0.000.000.000.020.000.100.030.020.000.020.01
Built-up0.000.000.000.000.000.000.000.000.000.000.00
Total3.052.6811.738.6420.9814137.5410.730.8616.439.27
Table 3. Summary of data sources.
Table 3. Summary of data sources.
Data TypeData NameResolutionData Source
TemporalSpatial
The remote sensing dataThe DEM data--30 mGeospatial Data Cloud
(http://www.gscloud.cn, accessed on 5 August 2024)
The Landsat 4-5 TM data in 31 October 2008Daily30 m
The Landsat 8 OLI data in 30 April 2017, 31 December 2021Daily30 m
The land use data in 31 October 2008, 30 April 2017, 31 December 2021Daily30 mClassified based on eCognition based on the Landsat data
The Net Primary Productivity (NPP) data in 2008, 2017, 2021Annual30 mCalculated based on the Thornthwaite Memorial Model
The precipitation dataMonthly--China Meteorological Administration (CMA) (http://data.cma.cn, accessed on 5 August 2024)
Environmental monitoring dataThe temperature dataMonthly--
The water quality monitoring dataAnnual--Department of Ecology and Environment of Liaoning Province (http://www.ln.gov.cn, accessed on 5 August 2024)
Table 4. Changes in ESV of PWSs. (Units: million CNY (USD), CNY (USD)/hm2·year).
Table 4. Changes in ESV of PWSs. (Units: million CNY (USD), CNY (USD)/hm2·year).
CityPhase IPhase IIEntire Period
TotalMeanTotalMeanTotalMean
Tieling4989.72 (758.05)232.19 (35.28)5769.45 (876.56)604.06 (91.79)9154.48 (1391.04)294.91 (44.81)
Shenyang3207.95 (487.41)259.44 (39.42)4083.38 (620.47)743.02 (112.90)7529.35 (1144.03)408.23 (62.04)
Anshan394.34 (59.92)228.47 (34.72)308.92 (46.94)402.71 (61.19)703.27 (106.86)282.08 (42.86)
Fuxin1210.60 (183.96)117.34 (17.83)2738.82 (416.17)597.28 (90.76)3949.42 (600.09)265.01 (40.26)
Jinzhou924.73 (140.51)120.45 (18.30)1586.97 (241.13)465.10 (70.67)2511.70 (381.64)226.50 (34.41)
Panjin2385.25 (362.42)633.91 (96.31)2116.04 (321.52)1265.32 (192.27)4501.29 (683.93)828.19 (125.83)
Total15,227.15 (2313.37)265.57 (40.35)14,859.32 (2257.79)583.09 (88.61)30,086.47 (4571.58)363.27 (55.20)
Note: The average exchange rate for 2008–2021 was 6.5815 CNY/USD.
Table 5. The ESV changes in different services in phases (Units: million CNY (million USD), %).
Table 5. The ESV changes in different services in phases (Units: million CNY (million USD), %).
Ecosystem ServicePhase IPhase IIEntire Period
Value%Value%Value%
Provisioning−666.85 (−101.32)−4.38137.41 (20.88)0.92−529.45 (−80.44)−1.76
Regulating12,705.20 (1930.43)83.4412,182.60 (1851.05)81.9924,887.81 (3781.43)82.72
Gas regulating609.64 (92.63)4.00661.26 (100.48)4.451270.90 (193.11)4.22
Climate regulating4175.82 (634.48)27.423138.79 (476.93)21.127314.61 (1111.49)24.32
Waste treatment1511.38 (229.66)9.931215.72 (184.73)8.182727.10 (414.38)9.06
Water regulating6408.37 (973.80)42.097166.83 (1089.03)48.2413,575.20 (2062.89)45.12
Supporting2283.63 (347.01)15.001940.80 (294.90)13.064224.43 (641.90)14.04
Culture905.17 (137.53)5.94598.51 (90.94)4.021503.68 (228.48)5.00
Total15,227.15 (2313.37)10014,859.32 (2257.79)10030,086.47 (4571.58)100
Note: The average exchange rate for 2008–2021 was 6.5815 CNY/USD.
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MDPI and ACS Style

Guo, M.; Lu, X.; Ma, Q. Assessment of the Payments for Watershed Services Policy from a Perspective of Ecosystem Services: A Case Study of the Liaohe River Basin, China. Water 2025, 17, 2328. https://doi.org/10.3390/w17152328

AMA Style

Guo M, Lu X, Ma Q. Assessment of the Payments for Watershed Services Policy from a Perspective of Ecosystem Services: A Case Study of the Liaohe River Basin, China. Water. 2025; 17(15):2328. https://doi.org/10.3390/w17152328

Chicago/Turabian Style

Guo, Manman, Xu Lu, and Qing Ma. 2025. "Assessment of the Payments for Watershed Services Policy from a Perspective of Ecosystem Services: A Case Study of the Liaohe River Basin, China" Water 17, no. 15: 2328. https://doi.org/10.3390/w17152328

APA Style

Guo, M., Lu, X., & Ma, Q. (2025). Assessment of the Payments for Watershed Services Policy from a Perspective of Ecosystem Services: A Case Study of the Liaohe River Basin, China. Water, 17(15), 2328. https://doi.org/10.3390/w17152328

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