Evaluation of Ecosystem Protection and Restoration Effects Based on the Mountain-River-Forest-Field-Lake-Grass Community Concept: A Case Study of the Hunjiang River Basin in Jilin Province, China

Abstract

The protection and restoration projects of the mountain-river-forest-field-lake-grass (MRFFLG) system are the mainstream focus of China’s current ecological environment protection. A reasonable method for calculating ecosystem service values (ESVs) is a prerequisite for determining the ecological service functions of a watershed. However, how to effectively implement and evaluate the systematic nature of the ecological protection and restoration of the MRFFLG system remains one of the pressing issues. This paper takes the protection and restoration project of the MRFFLG system in the Hunjiang River Basin (HRB) of Jilin Province, China, as an empirical case. Firstly, it constructs an ESVs system to quantify the comprehensive ecological protection and restoration effects of the MRFFLG system. The results show that the forest ecosystem in the HRB has the highest ecological value. Furthermore, by introducing the interval planning method, an uncertain optimization model is constructed with the objective function of maximizing the ecosystem service value of the HRB, and constraints such as restoration costs, unit restoration price, and restoration area. The results show that the total ESVs has increased, with a maximum increase of 348,413.79 × 10⁴ CNY. Finally, the introduction of the fuzzy method reduced the total interval of ESVs by 49.89%, effectively shortening the assessment interval. This study applies the interval-fuzzy method to the protection and restoration projects of the MRFFLG system, effectively measuring the comprehensive management effects of the MRFFLG system in the HRB. This paper provides a theoretical basis for the development of subsequent MRFFLG projects and offers theoretical references for promoting the ecological environment assessment of the comprehensive MRFFLG system.

1. Introduction

The ecological environment serves as the cornerstone for human existence and the progression of societal civilization. Ecosystems not only directly supply various raw materials vital for human sustenance but also play pivotal roles in climate regulation, water conservation, and biodiversity conservation [1]. The processes inherent within ecosystems that underpin human well-being and the resources they generate are collectively designated as ecosystem services (ES) [2]. Nevertheless, the escalating demand for natural resources in contemporary society has imposed significant stress on ecosystems, resulting in the gradual degradation of ES [3,4]. People are becoming increasingly cognizant of the fact that ineffective environmental stewardship can lead to the depletion of ecosystem service values (ESVs). This realization has fueled a burgeoning interest in the study of ESVs, transforming it into a prevalent research trajectory in ecology and economics [5,6,7]. The Millennium Ecosystem Assessment categorizes ES into four primary groups: supporting, regulating, cultural, and provisioning services [8]. Appraising ES and integrating their values into assessment frameworks facilitates the harmonization between upstream conservation efforts and downstream economic endeavors, thereby aiding the enactment of sustainable development policies [9,10].

Amidst China’s rapid economic development and industrialization, there has been a notable toll on its ecosystems. Consequently, restoring these ecosystems and bolstering their ESVs have emerged as paramount measures for ecological conservation in China [11]. Within the framework of forging a new era of ecological civilization, the notion of a “mountain-river-forest-field-lake-grass (MRFFLG) community of life” has gained prominence in China [12]. This community of life underscores the interconnectedness and mutual influence among MRFFLG, forming an inseparable unity [13]. In recent times, the significance of the MRFFLG life community in ecological civilization construction has grown substantially, intimately tied to China’s overarching strategy for modernizing its territorial management systems and governance capabilities [14]. By 2019, China had embarked on three rounds of pilot initiatives aimed at the ecological protection and rehabilitation of the MRFFLG system, yielding favorable outcomes in land rejuvenation, soil pollution remediation, mine environment restoration, watershed water quality management, and biodiversity preservation [15]. These endeavors not only contribute to the restoration and enhancement of ESVs but also underscore the immense potential of the MRFFLG community concept in ecosystem restoration endeavors.

The assessment of ESVs serves as a pivotal instrument for gauging the efficacy of ecological restoration endeavors, encompassing considerations of environmental protection, social development, and economic imperatives. It quantifies the production, allocation, and utilization of ES, thereby quantifying the outcomes of restoration measures [16]. Presently, models for appraising ESVs are ubiquitous in ecological restoration studies globally [17,18,19]. Nevertheless, the parameters employed in these models frequently confront uncertainties stemming from the intricacy and variability of the natural environment, leading to notable deviations between model simulations and reality [20,21,22]. Thus, accurately valuing and addressing uncertainties in ESVs calculations can bolster the models’ adaptability to real-world scenarios and elevate the credibility of their findings.

This paper delves into a case study of the Hunjiang River Basin (HRB)’s MRFFLG protection and restoration project in Jilin Province, China (hereinafter, “Project Recommendation”). By integrating the concept of the MRFFLG community into ecosystem restoration, we establish an ESVs index system. This system utilizes ESVs as metrics to evaluate the efficacy of ecosystem protection and restoration projects. Furthermore, we refine the ESVs model by incorporating interval planning and fuzzy planning methodologies, aiming to devise an efficient and low-risk assessment framework for ecosystem restoration. Our study not only elucidates the ecological protection and restoration effects of the MRFFLG system but also optimizes the comprehensive management effectiveness of this system within the HRB. By striking a balance between ESVs and economic benefits, we offer insightful recommendations for harmonizing ecological environmental protection with economic gains.

2. Case Introductions

2.1. Overview of the Physical Geography of HRB

The Administrative Region of the HRB encompasses the urban areas of Baishan City and Tonghua City, as well as 49 townships, housing a total population of approximately 3.37 million individuals [23]. The geographical location of the study area is shown in Figure 1. Within this basin, three medium-sized reservoirs and seven smaller ones have been constructed, playing pivotal roles in managing floods, facilitating irrigation, supplying water, generating electricity, and supporting fish farming activities [23]. Located in the southeastern region of Northeast China, the HRB spans 435 km and covers an extensive area of 15,144 km². During the flood season, from June to September, the basin experiences approximately 70% of its annual rainfall, with the heaviest precipitation occurring primarily in July and August. The average annual rainfall amounts to 860 mm, while the average annual evaporation ranges between 1000 and 1100 mm. Characterized by a monsoon climate, the region maintains an average annual temperature of 4.7 °C [24,25]. Due to the frequent and concentrated heavy rainfall, the basin is prone to flooding and is identified as a critical zone for flash flood prevention and control within China. It is equipped with 41 key flood control villages, 69 rainfall stations, and three hydrological stations to address these challenges [26]. The current state of forest communities in the HRB is concerning, marked by low forest quality and compromised ecological service functions. Biodiversity faces significant threats, particularly the scarcity of red pine species essential for community development. The native zonal vegetation, which was once dominated by broad-leaved red pine forests, has degraded, transitioning into Mongolian oak forests, mixed wood forests, and scrub vegetation. Consequently, biodiversity has declined, and the genetic diversity of protected species such as red pine and northeastern purple shirt has been lost [27]. Furthermore, water resources within the basin have dwindled, and the water quality of the main streams has deteriorated. Certain tributaries fail to meet water quality standards, and the connectivity of the water is poor. The encroachment on buffer zones and wetlands has intensified, exacerbating soil erosion and the formation of erosion gullies [28,29,30]. Additionally, 48 abandoned mines along the HRB continue to emit harmful gases, posing substantial geological hazards and contributing to the degradation of vegetation in low-lying and mid-mountain gullies [31]. These factors have severely damaged the ecosystem of the HRB, leading to a decrease in the ESVs. Consequently, urgent restoration efforts are imperative to rehabilitate the diverse ecosystems within this basin.

2.2. Effect of the Protection and Restoration of the MRFFLG Project in the HRB

The successful implementation of the project, aimed at protecting and restoring the MRFFLG of the HRB in Jilin Province, China, would yield several noteworthy outcomes. Firstly, it would lead to the restoration of 520.45 km² of forest ecosystems, complemented by the introduction of an additional 4.75 km² of ecological and economic forests. This restoration endeavor would significantly enhance the water conservation capacity and carbon sequestration capabilities of the HRB, thereby providing a material foundation and technical support for the establishment of an ecological security barrier. Concurrently, the project would contribute to the conservation of biodiversity in the HRB by achieving the dual objectives of protecting and restoring 17.73 km² of valuable wildlife habitat and rejuvenating populations of key protected wild plant species in 13.33 km². Furthermore, the project would encompass the comprehensive improvement and restoration of 321.04 km of rivers within the HRB, along with the rehabilitation of riverbanks. To ensure stable water quality, erosion ditches would be treated to meet Class III standards in the mainstream of the basin. Moreover, the treatment of 3.65 km² of sloping land and the restoration of 4.16 km² of mines would lead to improved land use efficiency and enhanced soil and water conservation capabilities. This, in turn, would facilitate the creation of 2.67 km² of new forest land and 1.44 km² of arable land. In this study, the MRFFLG ecosystems have been further categorized, with mountains, forests, and partially grasses representing forest ecosystems. The restoration effects of these ecosystems will be evaluated through the measurement of ESVs. Similarly, lakes and partially grasses represent wetland ecosystems, while fields represent cropland ecosystems. The restoration effects of these ecosystems would also be assessed using ESVs. It is important to note that, at present, water variables in the landscape, forest, field, lake, and grass ecosystems are not taken into account due to a lack of available data. (All data in this section are sourced from the project report).

3. Materials and Methods

3.1. Construction of ESVs Indicator System

Developing valuation systems for ES is a valuable approach to assessing their worth [32]. Indicators serve as effective tools for comprehending intricate systems, as they quantify ecosystem phenomena and visualize abstract information [33,34]. Each indicator captures specific characteristics or functions of an ecosystem, and selecting appropriate indicators enhances our understanding of complex ecosystems while maintaining ecological structure and biological relationships in balance [35]. As a result, the establishment of an effective ESVs system that addresses the needs of policymakers becomes a pressing challenge [36]. Such a valuation system should adhere to several fundamental principles, including representativeness, relevance, independence, and feasibility, among others [37]. In a study conducted by Cai et al., they constructed a system of 4 primary indicators, 12 secondary indicators, and 15 tertiary indicators based on these principles [37]. Building upon Cai’s ESVs indicator system [37], this research presents a framework consisting of three primary indicators, four secondary indicators, and 15 tertiary indicators under each secondary indicator, as outlined in

Table 1. ESVs system.

First Index	Second Index	Third Indexes
Forest land	Regulating services	Gas conditioning
		Climate control
		Clean-up operation
		Hydrological regulation
		Fixed carbon
		Oxygen release
		Waste disposal
		Dust catching
	Provision services	Food production
		Production of material
	Support services	Water conservation
		Soil conservation
		Nutrients cycle maintenance
		Biodiversity
	Cultural services	Aesthetic landscape
Wetland	Regulating services	Gas conditioning
		Climate control
		Clean-up operation
		Hydrological regulation
		Fixed carbon
		Oxygen release
		Storage flood volume
	Provision services	Food production
		Production of material
		Water conservation
	Support services	Soil conservation
		Nutrients cycle maintenance
		Biodiversity
		Pollution degradation
	Cultural services	Aesthetic landscape
Arable land	Regulating services	Gas conditioning
		Climate control
		Clean-up operation
		Hydrological regulation
		Fixed carbon
		Oxygen release
		Waste disposal
	Provision services	Food production
		Production of material
		Water supply
		Water conservation
	Support services	Soil conservation
		Nutrients cycle maintenance
		Biodiversity
	Cultural services	Aesthetic landscape

. Figure 2 illustrates the research roadmap of this study.

3.2. Accounting Method for the ESVs in the HRB

Utilizing the scale of terrestrial ESVs per unit area [38,39,40], this study integrates the three land types under consideration and summarizes the value equivalent factors for each ES function within the HRB. These equivalent factors signify the contribution capacity of ES provided by the ecosystems, as measured through the service value equivalent factor [41]. The specific equivalent factors for each ESVs per unit area within the HRB are detailed in

Table 2. ESVs per hectare of the terrestrial ecosystem in the HRB.

Type	Forest Land	Wetland	Cultivated Land
Gas conditioning	2.35	1.90	0.67
Climate control	7.03	3.60	0.36
Clean-up operation	1.99	3.60	0.10
Hydrological regulation	3.51	24.25	0.27
Fixed carbon	2.43	0.34	3.24
Oxygen release	2.60	3.32	8.59
Waste disposal	1.70	0.00	0.12
Storage flood volume	0.00	3.34	0.00
Dust catching	1.98	0.00	0.00
Food production	0.31	36.62	0.85
Production of material	0.71	0.50	0.40
Water supply	0.00	0.00	0.02
Water conservation	0.37	2.59	0.60
Soil conservation	2.86	2.31	1.03
Nutrients cycle maintenance	0.22	0.18	0.12
Biodiversity	2.60	7.88	0.13
Pollution degradation	0.00	26.31	0.00
Aesthetic landscape	1.14	4.73	0.06

.

To estimate the unit ESVs, this study is based on the grain prices and yields from the “Jilin Statistical Yearbook 2023”. Subsequently, the total ESVs is calculated by multiplying the per-unit ESVs by the service value equivalent factor, as represented by the equations provided in [42,43]:

$$V_e={\displaystyle \frac{1}{7}}·T_a·T_b$$

(1)

$$T{V}_e=V_e·E_f·S$$

(2)

where $Ve$ denotes the value of ES per unit area in the HRB; $T_a$ denotes the average baseline grain yield in the HRB at the time of the study; $T_b$ is the price of food at the time of the HRB study; ${\displaystyle \frac{1}{7}}$ indicates that the value of natural ES within the HRB in the absence of human intervention is 1/7 of the economic value of services provided by a unit area of farmland; $T{V}_e$ denotes the total value of each service in the ecosystem of the HRB; $E_f$ denotes the ES value equivalent factor per unit area of the HRB; and $S$ denotes the area of each ecosystem within the HRB.

3.3. Construction of Interval-Fuzzy ESVs Optimization Model in HRB

This study focuses on the HRB as the primary research subject, to maximize the ESVs within the basin through the optimization of ecosystem restoration areas. To achieve this, the paper introduces interval planning methods as an initial step. These methods involve setting parameter values within reasonable intervals, thereby providing decision makers with a range of viable options. Furthermore, a fuzzy planning approach is incorporated to address the inherent uncertainty surrounding renovation costs and the potential extreme values of restoration costs for each ecosystem. This approach aims to mitigate decision-making risks and enhance the robustness of the planning process. In the context of the interval planning model, “+” is used to denote the maximum value of a parameter, while “−” represents its minimum value. The deterministic model for ESVs, which serves as the foundation for the interval planning approach, is presented as follows:

Objective function:

$$\mathit{max}={\sum }_{i=1}^3{\sum }_{j=1}^{18}{U}_{ij}\times A_i\times Y_{ij}$$

(3)

Constraints:

(1)

The sum of ecosystem renovation costs is less than or equal to the cost specified in the plan:

$${\sum }_{i=1}^I{U}_{W_I}\times A_i\le Q_n$$

(4)

(2)

There is an extreme value for the repair cost in the ecosystem and the renovation cost in the optimization plan should be less than or equal to the extreme value:

$$U_{w_i}\times A_i\le W_i,\forall i$$

(5)

(3)

The ecosystem restoration area should be greater than or equal to the originally planned area:

$$A_i>M_i,\forall i$$

(6)

(4)

Each functional category is not used for every ecological service functional area, so a parameter of 0 and 1 is required for adjustment, where 0 represents not applicable and 1 represents applicable:

$$Y=0 or 1, \forall i, j$$

(7)

(5)

Non-negative constraint: All variables in the entire optimization model are greater than 0.

The interval model for optimizing ESVs is presented as follows:

Objective function:

$$\mathit{max}={\sum }_{i=1}^3{\sum }_{j=1}^{18}{U}_{ij}^{\pm }\times A_i^{\pm }\times Y_{ij}$$

(8)

Constraints:

(1)

The sum of ecosystem renovation costs is less than or equal to the cost specified in the plan:

$${\sum }_{i=1}^I{U}_{w_i}^{\pm }\times A_i^{\pm }\le Q_n^{\pm }$$

(9)

(2)

There is an extreme value for the repair cost in the ecosystem, and the renovation cost in the optimization plan should be less than or equal to the extreme value:

$$U_{w_i}^{\pm }\times A_i^{\pm }\le W_i^{\pm },\forall i$$

(10)

(3)

The ecosystem restoration area should be greater than or equal to the originally planned area:

$$A_i^{\pm }>M_i,\forall i$$

(11)

(4)

Each functional category is not used for every ecological service functional area, so a parameter of 0 and 1 is required for adjustment, where 0 represents not applicable and 1 represents applicable:

$$Y=0or1,\forall i,j$$

(12)

(5)

Non-negative constraint: All variables in the entire optimization model are greater than 0.

The interval-fuzzy planning approach model for optimizing ESVs is presented as follows:

Objective function:

$$\mathit{max}={\lambda }^{\pm }(0\le {\lambda }^{\pm }\le 1)$$

(13)

where ${\lambda }^{\pm }$ denotes the fuzzy affiliation function.

Binding conditions:

(1)

Benefit constraints:

$${\sum }_{i=1}^3{\sum }_{j=1}^{18}{U}_{ij}^{\pm }\times A_i^{\pm }\times Y_{ij} \ge f^{″}-\left(1-{\lambda }^{\pm }\right)\ast (f^{″}-f^{\prime })$$

(14)

where $i$ denotes ecosystem type; $j$ represents functional categories; $U$ denotes the value of ecological services per unit; $A$ is the area of each ecosystem restored after optimization; $Y$ denotes 0–1 integer; $f^{″}$ represents interval model upper-bound results; and $f^{\prime }$ indicates the lower-bound result of the interval model.

(2)

The total costs associated with ecosystem modifications should be equal to or lower than the specified costs outlined in the program.

$$\sum _{i=1}^I(U_{w_i}^{\pm }\times A_i^{\pm })\le Q_n^{-}+\left(1-{\lambda }^{\pm }\right)\ast (Q_n^{+}-Q_n^{-})$$

(15)

where $U_w$ denotes restoration price per unit area; $Q_n$ denotes the sum of investments in the rehabilitation of each ecosystem.

(3)

Each ecosystem has varying extreme values for restoration costs, and in an optimized solution, the cost of renovation should be equal to or less than these extreme values.

$$U_{w_i}^{\pm }\times A_i^{\pm }\le W_i^{-}+\left(1-{\lambda }^{\pm }\right)\ast (W_i^{+}-W_i^{-}),\forall i$$

(16)

where $W$ denotes the extreme value of the restoration costs.

(4)

The restoration area of the ecosystem should be equal to or greater than the original area.

$$A_i^{\pm }\ge M_i,\forall i$$

(17)

where $M$ denotes the original area of ecosystem restoration.

(5)

To regulate the usage of each functional category for ecological service functional areas, a binary parameter (0 or 1) is required. A value of 0 indicates that the functional category is not applicable, while a value of 1 indicates its applicability.

$$Y=0 or 1,\forall i,j$$

(18)

(6)

The optimization model imposes a non-negative constraint, requiring that all variables in the model have values greater than zero.

3.4. Solution of Service Value of Interval-Fuzzy Ecosystem

The objective function of the model is formulated to maximize the total ESVs in the HRB. Given the application of interval planning methods, the model is initially solved for the upper-bound scenario. Specifically, Lingo 11 software is utilized to solve the upper-bound sub-model, which provides an estimate of the maximum achievable ESVs under the given constraints and parameter intervals. This solution serves as a benchmark for further analysis and decision making.

Objective function:

$$\mathit{max}= {\lambda }^{+} (0\le {\lambda }^{+}\le 1)$$

(19)

Binding conditions:

$${\sum }_{i=1}^3{\sum }_{j=1}^{18}{U}_{ij}^{+}\times A_i^{+}\times Y_{ij} \ge f^{″}-\left(1-{\lambda }^{+}\right)\ast (f^{″}-f^{\prime })$$

(20)

$${\sum }_{i=1}^I(U_{w_i}^{-}\times A_i^{+}) \le Q_n^{-}+\left(1-{\lambda }^{+}\right)\ast (Q_n^{+}-Q_n^{-})$$

(21)

$$U_{w_i}^{-}\times A_i^{+} \le W_i^{-}+\left(1-{\lambda }^{+}\right)\ast (W_i^{+}-W_i^{-}), \forall i$$

(22)

$$A_i^{+} \ge M_i, \forall i$$

(23)

$$Y=0 or 1, \forall i, j$$

(24)

The optimized ecosystem restoration area $A^{+}$ and the upper bound of the affiliation function $t^{+}$ are solved by means of an upper-bound sub-model. The upper-bound model is employed to determine the maximum achievable value of optimized ES. Subsequently, the lower-bound sub-model is solved.

Objective function:

$$\mathit{max}= {\lambda }^{-} (0\le {\lambda }^{-}\le 1)$$

(25)

Binding conditions:

$${\sum }_{i=1}^3{\sum }_{j=1}^{18}{U}_{ij}^{-}\times A_i^{-}\times Y_{ij} \ge f^{″}-\left(1-{\lambda }^{-}\right)\ast (f^{″}-f^{\prime })$$

(26)

$${\sum }_{i=1}^I(U_{w_i}^{+}\times A_i^{-}) \le Q_n^{-}+\left(1-{\lambda }^{-}\right)\ast (Q_n^{+}-Q_n^{-})$$

(27)

$$U_{w_i}^{+}\times A_i^{-} \le W_i^{-}+\left(1-{\lambda }^{+}\right)\ast (W_i^{+}-W_i^{-}), \forall i$$

(28)

$$A_i^{-} \ge M_i, \forall i$$

(29)

$$Y=0 or 1, \forall i, j$$

(30)

4. Results and Discussion

4.1. Analysis of the Results of ESVs in the HRB

The unchecked development of heavy industries, particularly mining and iron and steel production, has inflicted severe damage on the ecosystems of the HRB. Specifically, forest ecosystems, wetland ecosystems, and arable land have borne the brunt of this development, resulting in forest degradation, biodiversity loss, wetland ecosystem damage, and acute soil erosion [44,45,46,47]. To assess the potential ecological benefits of restoring the HRB under the original project recommendation scheme, this study calculates the total ESVs. The deterministic optimization model of ESVs in the basin reveals intriguing insights, as presented in

Table 3. The total ESVs in the Hunjiang River (×10⁴ CNY).

Type	Forest Land	Wetland	Cultivated Land
Gas conditioning	34,869.33	46.45	55.53
Climate control	104,311.24	88.00	29.84
Clean-up operation	29,527.65	88.00	8.29
Hydrological regulation	52,081.43	592.30	22.38
Fixed carbon	36,063.64	8.25	268.53
Oxygen release	38,605.80	81.06	711.89
Waste disposal	25,224.62	0.00	10.11
Storage flood volume	0.00	81.46	0.00
Dust catching	29,430.41	0.00	0.00
Food production	4599.78	894.54	70.45
Production of material	10,534.99	12.22	33.15
Water supply	0.00	0.00	1.66
Water conservation	5490.07	63.31	49.73
Soil conservation	42,436.72	56.47	85.37
Nutrients cycle maintenance	3264.36	4.40	9.95
Biodiversity	38,578.84	192.38	10.77
Pollution degradation	0.00	642.64	0.00
Aesthetic landscape	16,915.34	115.62	4.97
Total	471,934.22	2967.10	1372.62

. Notably, the forest ecosystem emerges as the most valuable contributor to ecological services, surpassing both wetland and cropland ecosystems. This dominance can be attributed to the increased forest cover following restoration efforts and the larger land area occupied by forests compared to other ecosystems. The wetland ecosystem, despite its vulnerability [48], benefits significantly from restoration initiatives, as evidenced by its mitigation of wetland degradation and its higher ESVs equivalent factor compared to cropland. Figure 3 vividly illustrates the proportion of ESVs attributed to these three ecosystems. Forests primarily contribute to climate and hydrological regulation, with a relatively lower impact on food production and nutrient cycling, underlining their crucial role in addressing climate change [49]. Additionally, forests offer valuable services in landscape aesthetics and raw material production, consistent with previous research [50,51], thereby validating the reliability of the constructed ecological value accounting model. Wetland ecosystems, on the other hand, are primarily involved in hydrological regulation, food production, and pollution degradation. Notably, artificial wetlands in the HRB play a pivotal role in alleviating the pressure on natural wetlands for pollutant treatment and preventing their degradation [52]. Cultivated ecosystems, while demonstrating significant service value in climate regulation and food production, exhibit relatively low service value in terms of biodiversity and aesthetic landscape, indicating limitations in their biological regulation capabilities [53]. Collectively, these findings underscore the enhanced ESVs of the MRFFLG ecosystem and the success of restoration and protection efforts in the MRFFLG system. However, it is important to acknowledge that the deterministic optimization scheme for the HRB, while taking into account restoration area and ecosystem value, may not always align with reality due to the inherent complexity and variability of the real environment. This poses significant challenges for decision makers in making informed and effective choices.

This study evaluates the potential ecological benefits of restoring the HRB by calculating the ESVs, revealing the negative impact of heavy industries such as mining and steel development on the basin’s ecosystem. The results showed that forest ecosystems exhibited the highest ESVs among the three considered ecosystems, surpassing wetland and cultivated land ecosystems. This is mainly due to the increase in forest coverage and its larger land area. Forest ecosystems contribute the most to climate and hydrological regulation, followed by oxygen release and carbon fixation, indicating the crucial role of forests in addressing climate change. Wetland ecosystems have outstanding service value in hydrological regulation, food production, and pollution degradation. The ecosystem of cultivated land exhibits significant service value in climate regulation and food production, but its value in biodiversity and landscape aesthetics is relatively low, indicating the limitations of cultivated land in biological regulation functions. Special attention should be paid to the vulnerability and diversity of service functions of wetland and farmland ecosystems. In wetland management, it is necessary to strengthen the control of pollution sources and monitoring of water quality to prevent further degradation. At the same time, more ecological agricultural practices should be introduced in farmland management to enhance its ecological service value, especially in terms of biodiversity and landscape aesthetics.

4.2. Analysis of Ecological System-Optimized Area Based on Interval-Fuzzy Method

To address the constraints of deterministic modeling and enhance its practicality in real-world applications, this study integrates interval programming to accommodate parameter uncertainties within the initial project recommendation framework, thereby augmenting the credibility of model outcomes. Figure 4 depicts the refined outcomes of ecological restoration zones in the HRB. Under the interval optimization paradigm, the forest ecosystem restoration area spans [95,894.30, 143,249.50] hectares, signifying an augmentation of [4777.45, 52,132.65] hectares compared with the initial restoration area. Similarly, the wetland ecosystem restoration area is projected to be [186.43, 272.06] hectares, displaying an expansion of [36.44, 122.07] hectares compared to the baseline. Likewise, the arable ecosystem restoration area varies between [594.63, 883.23] hectares, marking an increase of [85.69, 374.29] hectares from the original plan. Although the interval optimization model offers decision-makers a breadth of alternatives, the resultant span of the ecological service value optimization model for the HRB, predicated on interval planning, is excessively vast. The substantial disparity between the lower and upper bounds of the outcomes impedes decision-making processes. To mitigate this, the fuzzy approach is incorporated to refine the decision spectrum.

The HRB, situated in the northeastern region of China and serving as a tributary of the Songhua River, is a treasure trove of ecological resources. Encompassing diverse ecosystems like forests, wetlands, and cultivated land, this watershed serves a dual purpose: offering vital ecological services such as water and soil conservation, as well as biodiversity protection, while also playing a pivotal role in promoting regional economic and social development. However, the escalating economic activities in the area have placed immense pressure on the ecological environment of the HRB, leading to discernible degradation of its ecosystems. Consequently, the undertaking of ecological restoration initiatives holds paramount importance. Acknowledging the unique characteristics of the HRB and adhering to the integrated management framework of MRFFLG, this article endeavors to optimize the restoration areas for each ecosystem through innovative methodologies, namely interval optimization and fuzzy methods. By implementing these strategies, we aim to not only refine the decision-making process for policymakers by providing a more precise range of options but also amplify the overall benefits derived from the ecosystem. Furthermore, this approach serves to mitigate decision-making risks, ensuring that ecological restoration efforts are both effective and sustainable in the long run.

Within the ESVs optimization model for the HRB, constructed adopting the interval-fuzzy methodology, the affiliation function λ^± values range from [0.21, 0.71]. The forest ecosystem restoration area, under the interval-fuzzy optimization model, is narrowed to [112,685.56, 124,672.12] hectares, signifying an augmentation of [21,568.71, 33,555.27] hectares over the initial area. The wetland ecosystem restoration area is refined to [219.07, 236.78] hectares, an increase of [69.08, 86.79] hectares from the original. Similarly, the arable ecosystem restoration area is specified as [698.74, 768.70] hectares, representing an expansion of [189.80, 259.76] hectares compared to the baseline. These findings underscore that the integration of interval and fuzzy methodologies augments the restoration areas and optimizes resource allocation within a prescribed restoration cost. In comparison to the interval optimization model, the interval-fuzzy approach reduces the restoration area intervals for the three ecosystems by 74.68%, 79.32%, and 75.76%, respectively. This signifies that the fuzzy approach elevates the lower-bound values while diminishing the upper bounds, thereby narrowing the restoration area optimization intervals. Consequently, more precise decision intervals are furnished for the preservation and rehabilitation of the MRFFLG system, comprising the forest, wetland, and cropland ecosystems, while mitigating decision-making risks for stakeholders.

In recent years, China has actively promoted the integrated management plan of MRFFLG, emphasizing comprehensive management and systematic restoration. This plan aims to achieve the overall restoration and sustainable development of ecosystems by comprehensively considering the interactions between natural elements such as MRFFLG and promoting the synergistic effects of ecosystems. In this context, the combination of interval and fuzzy optimization methods is of great significance. Firstly, these methods can provide scientific decision support for the MRFFLG plan. By considering uncertainty and providing precise restoration area ranges, decision makers can more effectively develop and adjust ecological restoration strategies to meet the specific needs of different ecosystems. For example, in the comprehensive management of forests, wetlands, and farmland, interval and fuzzy optimization methods can help determine the optimal restoration area and resource allocation plan, ensuring the restoration effect and sustainability of each ecosystem. Secondly, these methods can improve the efficiency and effectiveness of ecological restoration projects. In the implementation process of the MRFFLG plan, actual ecological restoration work often faces complex natural conditions and changing environmental factors. The combination of interval and fuzzy optimization methods can provide flexible response plans for ecological restoration in different scenarios, ensuring the smooth progress of restoration work and the achievement of expected results. For example, in the face of sudden climate change or other environmental disturbances, these methods can quickly adjust recovery strategies and minimize the impact of uncertainty on restoration effectiveness. Overall, the combination of interval and fuzzy optimization methods provides an effective optimization strategy for ecological restoration in the HRB. These methods not only optimize the restoration area but also reduce decision-making risks, providing a scientific basis for the restoration and protection of ecosystems. Combined with the integrated management plan of MRFFLG in China, these methods have broad application prospects and can help achieve more efficient and sustainable ecological restoration environmental management goals.

4.3. Analysis of ESVs Based on Interval-Fuzzy Method

The outcomes of optimizing the ESVs within the HRB utilizing the interval planning approach are detailed in

Table 4. Optimization results of ESVs in HRB cased on the interval model method (×10⁴ CNY).

Type	Forest Land	Wetland	Cultivated Land
Gas conditioning	[33,027.85, 60,301.87]	[51.96, 92.67]	[58.39, 106.00]
Climate control	[98,802.45, 180,392.40]	[98.44, 175.58]	[31.37, 56.96]
Clean-up operation	[27,968.26, 51,064.14]	[98.44, 175.58]	[8.71, 15.82]
Hydrological regulation	[49,330.95, 90,067.90]	[662.58, 1181.77]	[23.53, 42.72]
Fixed carbon	[34,159.08, 62,367.26]	[9.23, 16.47]	[282.37, 512.62]
Oxygen release	[36,566.98, 66,763.58]	[90.68, 161.73]	[748.57, 1358.99]
Waste disposal	[23,892.48, 43,622.63]	[0.00, 0.00]	[10.63, 19.30]
Storage flood volume	[0.00, 0.00]	[91.13, 162.54]	[0.00, 0.00]
Dust catching	[27,876.16, 50,895.97]	[0.00, 0.00]	[0.00, 0.00]
Food production	[4356.86, 7954.71]	[1000.68, 1784.81]	[74.08, 134.48]
Production of material	[9978.63, 18,218.86]	[13.67, 24.39]	[34.86, 63.29]
Water supply	[0.00, 0.00]	[0.00, 0.00]	[1.74, 3.16]
Water conservation	[5200.13, 9494.34]	[70.82, 126.32]	[52.29, 94.93]
Soil conservation	[40,195.59, 73,388.66]	[63.17, 112.67]	[89.76, 162.96]
Nutrients cycle maintenance	[3091.97, 5645.28]	[4.92, 8.78]	[10.46, 18.99]
Biodiversity	[36,541.45, 66,716.96]	[215.21, 383.85]	[11.33, 20.57]
Pollution degradation	[0.00, 0.00]	[718.89, 1282.22]	[0.00, 0.00]
Aesthetic landscape	[16,022.02, 29,252.82]	[129.34, 230.70]	[5.23, 9.49]
Total	[447,010.85, 816,147.38]	[3319.17, 5920.08]	[1443.33, 2620.28]

. Specifically, focusing on the forest ecosystem, the application of interval planning led to the following enhancements: gas conditioning services increased by a range of [−1841.48, 25,432.54] × 10⁴ CNY, climate control services by [−5508.79, 76,081.16] × 10⁴ CNY, clean-up operation services by [−1559.39, 21,536.49] × 10⁴ CNY, hydrological regulation services by [−2750.48, 37,986.47] × 10⁴ CNY, fixed carbon services by [−1904.56, 26,303.62] × 10⁴ CNY, oxygen release services by [−2038.82, 28,157.78] × 10⁴ CNY, waste disposal services by [−1332.14, 18,398.01] × 10⁴ CNY, dust catching services by [−1554.25, 21,465.56] × 10⁴ CNY, food production services by [−242.92, 3354.93] × 10⁴ CNY, material production services by [−556.36, 7683.87] × 104 CNY, water conservation services by [−289.94, 4004.27] × 10⁴ CNY, soil conservation services by [−2241.13, 30,951.94] × 10⁴ CNY, nutrients cycle maintenance services by [−172.39, 2380.92] × 10⁴ CNY, biodiversity services by [−2037.39, 28,138.12] × 10⁴ CNY, and aesthetic landscape services by [−893.32, 12,337.48] × 10⁴ CNY. Notably, the interval model’s results indicate a general decrease in lower-limit values and an increase in upper-limit values, offering a broad spectrum of options to decision makers. However, the lower-limit values signify a potential decline in ecological value services, which, while reducing decision risk, may compromise economic benefits, thereby conflicting with maximizing ES value. To mitigate this, the fuzzy programming methodology is incorporated to assess the risks associated with various economic benefit decisions through the utilization of fuzzy affiliation functions. This approach endeavors to achieve a balance between maximizing economic benefits and minimizing decision risk.

The outcomes of optimizing the ESVs within the HRB, utilizing the interval-fuzzy planning methodology, are systematically presented in

Table 5. Optimization results of ESVs in HRB based on the interval-fuzzy model method (×10⁴ CNY).

Type	Forest Land	Wetland	Cultivated Land
Gas conditioning	[38,810.57, 52,482.10]	[61.05, 80.65]	[68.61, 92.26]
Climate control	[116,101.41, 156,999.65]	[115.68, 152.81]	[36.87, 49.57]
Clean-up operation	[32,865.12, 44,442.29]	[115.68, 152.81]	[10.24, 13.77]
Hydrological regulation	[57,968.13, 78,388.16]	[778.59, 1028.53]	[27.65, 37.18]
Fixed carbon	[40,139.86, 54,279.66]	[10.85, 14.33]	[331.81, 446.15]
Oxygen release	[42,969.36, 58,105.88]	[106.55, 140.76]	[879.64, 1182.76]
Waste disposal	[28,075.73, 37,965.78]	[0.00, 0.00]	[12.50, 16.80]
Storage flood volume	[0.00, 0.00]	[107.09, 141.46]	[0.00, 0.00]
Dust catching	[32,756.89, 44,295.93]	[0.00, 0.00]	[0.00, 0.00]
Food production	[5119.69, 6923.17]	[1175.88, 1553.36]	[87.05, 117.04]
Production of material	[11,725.75, 15,856.29]	[16.07, 21.22]	[40.96, 55.08]
Water supply	[0.00, 0.00]	[0.00, 0.00]	[2.05, 2.75]
Water conservation	[47,233.29, 63,871.84]	[83.22, 109.94]	[61.44, 82.62]
Soil conservation	[47,233.29, 63,871.84]	[74.23, 98.06]	[105.48, 141.83]
Nutrients cycle maintenance	[3633.33, 4913.22]	[5.78, 7.64]	[12.29, 16.52]
Biodiversity	[42,939.36, 58,065.30]	[252.89, 334.07]	[13.31, 17.90]
Pollution degradation	[0.00, 0.00]	[844.76, 1115.94]	[0.00, 0.00]
Aesthetic landscape	[188,274.26, 25,459.40]	[151.99, 200.78]	[6.14, 8.26]
Total	[525,276.36, 710,311.82]	[3900.31, 5152.38]	[1696.04, 2280.49]

. By seamlessly integrating the fuzzy programming approach, an interval-fuzzy ESVs optimization model was devised. Specifically, for the forest ecosystem, the enhancements in various services can be quantified as follows: gas conditioning services by [38,810.57, 52,482.10] × 10⁴ CNY, climate control services by [116,101.41, 156,999.65] × 10⁴ CNY, clean-up operation services by [32,865.12, 44,442.29] × 10⁴ CNY, hydrological regulation services by [57,968.13, 78,388.16] × 10⁴ CNY, fixed carbon services by [40,139.86, 54,279.66] × 10⁴ CNY, oxygen release services by [42,969.36, 54,279.66] × 10⁴ CNY, waste disposal services by [28,075.73, 37,965.78] × 10⁴ CNY, dust catching services improvement by [32,756.89, 44,295.93] × 10⁴ CNY, food production services improvement by [5119.69, 6923.17] × 10⁴ CNY, material production services improvement by [11,725.75, 15,856.29] × 10⁴ CNY, water conservation services improvement by [6110.60, 8263.14] × 10⁴ CNY, soil conservation services by [47,233.29, 63,871.84] × 10⁴ CNY, nutrients cycle maintenance services by [3633.33, 4913.22] × 10⁴ CNY, biodiversity services by [42,939.36, 58,065.30] × 10⁴ CNY, and aesthetic landscape services by [18,827.26, 25,459.40] × 10⁴ CNY. The introduction of the fuzzy planning method has led to an improvement in both the lower- and upper-limit values, as compared to the original service valuations. Notably, the ESVs interval has undergone a reduction of 49.87% in comparison to the preceding interval model, underscoring the enhanced precision of this approach. Consequently, decision makers are now equipped with more refined decision solutions and a narrower, yet more accurate, decision space. Furthermore, the simulation conducted using the interval-fuzzy method underscores the substantial ecological value that ecosystem restoration endeavors in the HRB are poised to bring, while also highlighting the potential for further optimization in these restoration efforts.

The ESVs of forest, wetland, and cropland ecosystems, categorized under the MRFFLG ecosystem framework, have undergone notable transformations. Under the interval optimization model, the forest ESVs stands at [447,010.85, 816,147.38] × 10⁴ CNY, showcasing an increase of [−24,923.37, 344,213.16] × 10⁴ CNY compared to the deterministic model. Similarly, the wetland ESVs is [3319.17, 5920.08] × 10⁴ CNY, an augmentation of [352.07, 2952.98] × 10⁴ CNY over the deterministic model. The cropland ESVs also exhibits an enhancement, amounting to [1443.33, 2620.28] × 10⁴ CNY, with an improvement of [70.71, 1247.66] × 10⁴ CNY over the deterministic model. The adoption of the interval planning method, though reducing the lower-limit value of the forest ecosystem’s ES, poses challenges in optimizing forests due to the relatively smaller increment in the restoration area, which fails to fully compensate for the negative impacts on the per-unit-area ESVs. Upon constructing the interval-fuzzy programming model, the ESVs for forest, wetland, and cropland ecosystems are refined to [525,276.36, 710,311.82] × 10⁴ CNY, [3900.31, 5152.38] × 10⁴ CNY, and [1696.04, 2280.49] × 10⁴ CNY, respectively. Notably, the interval range of ESVs is significantly reduced by 49.87%, 51.86%, and 50.34%, respectively, compared to the interval optimization model. These improvements, in addition to surpassing the deterministic model’s ESVs, equip decision makers with a more precise decision spectrum and foster both ESVs enhancement and ecosystem restoration area expansion. This underscores the pivotal role and imperative of MRFFLG protection and restoration projects, as evidenced by the modeling outcomes, which offer a potent strategy for ecological benefit augmentation.

Figure 5 presents the total ESVs for the HRB, analyzed through both interval and interval-fuzzy planning models. The interval planning model yields a total ESVs of [451,773.35, 824,687.73] × 10⁴ CNY, an alteration of [−24,500.59, 348,413.79] × 10⁴ CNY from the original scenario. While the lower limit detracts from the total ESVs, the upper limit substantially elevates it, presenting a challenge for informed decision making due to the wide disparity between the two. Conversely, the interval-fuzzy planning model offers a total ESVs of [530,872.71, 717,744.69] × 10⁴ CNY, an augmentation of [54,598.77, 241,470.75] × 10⁴ CNY over the original scenario. Notably, the interval range is 49.89% narrower than that of the interval model, affording decision makers a more concise decision space. Furthermore, the affiliation function underscores a low decision risk (0.21) but moderate benefits for the lower limit, while the upper limit, at 0.71, signifies a higher ESVs albeit with increased decision risk. This equilibrium between service value and risk facilitates informed and balanced decision making.

The plan of MWFFLG emphasizes the integrity and systematic nature of the ecosystem, considering the interactions between MWFFLG. In this context, the changes in the value of forest, wetland, and agricultural ES reflect the synergistic effects between different ecosystems. By optimizing the restoration of forests and wetlands, not only have their respective ESVs been enhanced, but the ESVs of farmland has also been indirectly improved, achieving overall optimization of the ecosystem. Interval and fuzzy optimization methods help decision makers maximize the value of ecological services in situations where resources are limited. This is consistent with the concept of comprehensive management and efficient utilization of resources in MWFFLG. In the case of limited resources, optimizing the restoration area and ecological service value can achieve the optimal allocation of resources and ensure the sustainability of ecological restoration projects. In the process of ecological restoration, environmental uncertainties such as climate change and natural disasters are often encountered. Interval and fuzzy optimization methods can provide flexible recovery strategies in different environments, ensuring the continuity and effectiveness of recovery work. This method can effectively address the uncertainty in ecosystem management and ensure the stability of ecological restoration effects. Combining the comprehensive management planning of MWFFLG in China, interval and fuzzy optimization methods provide effective decision-making tools for ecological restoration. By optimizing the restoration area and ESVs, these methods improve the overall efficiency of the ecosystem, reduce decision-making risks, and provide a scientific basis for the restoration and protection of the ecosystem. Future research can further explore the application of these methods in other ecosystems and environmental management to enhance their broad applicability and practical value. In the context of the HRB, the climate and terrain characteristics of the region have a significant impact on ecological restoration. The HRB is located in the temperate monsoon climate zone, with distinct four seasons and abundant precipitation, which provides favorable conditions for the restoration of forests and wetlands. At the same time, the terrain of the basin is complex, with many mountains and hills, undulating terrain, and fertile soil. These natural conditions present both challenges and opportunities for ecological restoration. Interval and fuzzy optimization methods can be used to develop more effective ecological restoration strategies based on the specific climate and terrain characteristics of the HRB, maximizing the ESVs of forests, wetlands, and farmland, and ensuring the long-term stability and effectiveness of restoration work.

5. Conclusions

The protection and restoration of the MWFFLG system are crucial for ecosystem recovery. This paper combines the protection and restoration projects of the HRB’s MWFFLG system, calculating the ESVs under the original restoration plan using ESVs equivalent factors. Additionally, interval planning and fuzzy planning methods are introduced to evaluate the restoration effects of the HRB’s ecosystem. Firstly, the calculation results of ESVs indicate that the HRB is dominated by forest ecosystems. The primary services of forest ecosystems are climate regulation and hydrological regulation. Wetland ecosystem services are mainly hydrological regulation and pollution degradation, while cultivated land ecosystem services are primarily climate regulation and food production. Secondly, through interval planning, the restoration areas of forest, wetland, and cultivated land ecosystems increased by [4777.45, 52,132.65] ha, [36.44, 122.07] ha, and [85.69, 374.29] ha, respectively. The total ESVs increased by [−24,500.59, 348,413.79] ×10⁴ CNY, although the obtained ecosystem value assessment interval is relatively large. Finally, the introduction of the fuzzy method further shortened the result interval range, reducing the restoration area intervals of forest, wetland, and cultivated land ecosystems by 74.69%, 79.32%, and 75.76%, respectively. The total ESVs interval of the MWFFLG system decreased by 49.89%, mitigating the issue of large ecosystem value assessment intervals caused by environmental uncertainties in the HRB. However, this paper only conducts theoretical calculations of the ESVs of the HRB and provides theoretical assessment intervals. In the actual implementation of the project, it is necessary to conduct evaluations based on the local actual conditions.