Forestry, Faculty of
Non UBC
Forest and Conservation Sciences, Department of
DSpace
Yan, Ke
Zhao, Bingting
Li, Yuanhui
Wang, Xiangfu
Jin, Jiaxin
Jiang, Jiang
Dong, Wenting
Wang, Rongnv
Yang, Hongqiang
Wang, Tongli (Forest geneticist)
Wang, Weifeng
Ecological engineering (EE) has a profound impact on land-use dynamics, leading to alterations in ecosystem services (ESs). However, an appropriate EE implementation intensity that can balance the tradeoffs associated with altered ESs well has always been a concern for researchers and policymakers. In this study, we set the transition probability of farmland, bare land, and desertification land to forest and natural shrub, with 2010–2020 as the natural implementation scenario, as 10% for the low-intensity implementation scenario (LIS), 30% for the medium-intensity scenario, and 50% for the high-intensity scenario. The patch-generating land-use simulation (PLUS) model was used to project land-use patterns and the Integrated Valuation of Ecosystem Service and Tradeoffs (InVEST) model was used to simulate changes in the quality of ESs under four EE implementation intensities in 2030. We then performed a quantitative tradeoff analysis on the dominant ESs under four scenarios and used the production possibility frontier (PPF) curve to identify the optimal EE implementation intensity scenario. Our results indicated that an increase in EE implementation intensity would lead to an increase in soil retention, water purification, habitat quality, and carbon storage, but also to a decrease in water yield, aggravating the tradeoffs between water yield and other ESs. In all EE implementation intensity scenarios, the LIS had the lowest tradeoff intensity index and balanced ESs well, and thus was the optimal EE implementation scenario in Qinghai province. Our results provide knowledge to help decision makers select the appropriate EE intensity to maintain sustainable development. The integrated methodology can also be applied in other conservation regions to carry out practical land management.
Citation: Yan, K.; Zhao, B.; Li, Y.;Wang, X.; Jin, J.; Jiang, J.; Dong, W.;Wang, R.; Yang, H.; Wang, T.; et al.Effects of the ImplementationIntensity of Ecological Engineeringon Ecosystem Service Tradeoffs inQinghai Province, China. Land 2024,13, 848. https://doi.org/10.3390/land13060848Academic Editor: GuillermoJ. Martinez-PasturReceived: 31 March 2024Revised: 7 June 2024Accepted: 8 June 2024Published: 14 June 2024Copyright: © 2024 by the authors.Licensee MDPI, Basel, Switzerland.This article is an open access articledistributed under the terms andconditions of the Creative CommonsAttribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).landArticleEffects of the Implementation Intensity of EcologicalEngineering on Ecosystem Service Tradeoffs in QinghaiProvince, ChinaKe Yan 1,2, Bingting Zhao 1, Yuanhui Li 3, Xiangfu Wang 3,*, Jiaxin Jin 4 , Jiang Jiang 5, Wenting Dong 3,Rongnv Wang 3, Hongqiang Yang 6, Tongli Wang 2,* and Weifeng Wang 1,*1 Co-Innovation Center for Sustainable Forestry in Southern China, College of Ecology and Environment,Nanjing Forestry University, Nanjing 210037, China2 Department of Forest and Conservation Sciences, University of British Columbia,Vancouver, BC V6T 1Z4, Canada3 Northwest Surveying and Planning Institute of National Forestry and Grassland Administration,Key Laboratory National Forestry Administration on Ecological Hydrology and Disaster Prevention in AridRegions, Xi’an 710048, China4 College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China5 College of Forestry, Nanjing Forestry University, Nanjing 210037, China6 College of Economics and Management, Nanjing Forestry University, Nanjing 210037, China* Correspondence: wxf5378@163.com (X.W.); tongli.wang@ubc.ca (T.W.); wang.weifeng@njfu.edu.cn (W.W.)Abstract: Ecological engineering (EE) has a profound impact on land-use dynamics, leading toalterations in ecosystem services (ESs). However, an appropriate EE implementation intensity thatcan balance the tradeoffs associated with altered ESs well has always been a concern for researchersand policymakers. In this study, we set the transition probability of farmland, bare land, anddesertification land to forest and natural shrub, with 2010–2020 as the natural implementationscenario, as 10% for the low-intensity implementation scenario (LIS), 30% for the medium-intensityscenario, and 50% for the high-intensity scenario. The patch-generating land-use simulation (PLUS)model was used to project land-use patterns and the Integrated Valuation of Ecosystem Serviceand Tradeoffs (InVEST) model was used to simulate changes in the quality of ESs under four EEimplementation intensities in 2030. We then performed a quantitative tradeoff analysis on thedominant ESs under four scenarios and used the production possibility frontier (PPF) curve toidentify the optimal EE implementation intensity scenario. Our results indicated that an increase inEE implementation intensity would lead to an increase in soil retention, water purification, habitatquality, and carbon storage, but also to a decrease in water yield, aggravating the tradeoffs betweenwater yield and other ESs. In all EE implementation intensity scenarios, the LIS had the lowesttradeoff intensity index and balanced ESs well, and thus was the optimal EE implementation scenarioin Qinghai province. Our results provide knowledge to help decision makers select the appropriateEE intensity to maintain sustainable development. The integrated methodology can also be appliedin other conservation regions to carry out practical land management.Keywords: ecological engineering; land management; multi-scenario simulation; production possibilityfrontier; tradeoff intensity1. IntroductionEcosystem services (ESs), including various products and services derived from thestructure and function of ecosystems, directly affect ecological security and human well-being [1,2]. Due to the heterogeneity of the spatial distribution of ecosystems and thecomplexity of ecological processes, the relationships among ESs mainly present as tradeoffsor synergies [3,4]. As the contradiction between the utilization of natural resources andecological protection becomes more prominent, exploring the tradeoff relationships amongLand 2024, 13, 848. https://doi.org/10.3390/land13060848 https://www.mdpi.com/journal/landLand 2024, 13, 848 2 of 18different ESs is crucial for determining the maximum benefit of regional ecology andimproving human well-being [5,6]. Land-use and land-cover (LULC) changes alter the mainecological functions and processes, leading to ES changes, which further significantly affectsthe ES tradeoffs [7]. Policy is one of the main driving forces of land-use change [8,9]. If theES tradeoffs caused by land-use changes are not considered in policy-making, an effectivebalance between economic development and ecological and environmental protectioncannot be achieved [10,11]. Therefore, clarifying the impact of policies on ESs and theirtradeoffs is essential for regional ecosystem management.To control the deterioration of ecosystems and promote the sustainable developmentof society, many policies related to ecological engineering (EE) have been carried out. Somecountries and regions have implemented various large-scale projects for ecological restora-tion, such as the Thur and Töss rivers restoration projects in Switzerland [12], the US federalforest restoration programs on national forests [13], the ecological restoration of peatlandsin Indonesia [14], the Mountain-River-Forest-Cropland-Lake-Grassland System Project [15],and the Natural Forest Protection Project (NFPP) [16] and Grain-for-Green Programme(GFGP) in China [17,18]. These ecological restoration projects are often accompanied bychanges in land-use patterns, with an initial aim of improving one or more ESs [19]. Asexpected, EE has played an important role in increasing vegetation cover [20], controllingsoil erosion [21], and improving habitats [22], achieving a remarkable effect in improvingecosystems from 2000 to 2020. However, the benefits of EE were often at the expense ofreducing water yield (WY), especially in arid and semiarid regions [23]. This indicates thatEE changes ES interactions and enhances the ES tradeoff effect, inducing a controversialbalance among ESs [24]. Thus, it is necessary to control the EE implementation intensityin the future. Previous studies have revealed the impact of EE on ESs [25] and selectedpriority conservation areas for EE implementation [21]. Due to the complexity of the EStradeoffs, the government is still unclear about how much forest should be planted andprotected through EE. There is also no consensus on the EE implementation intensity fromscientific and policy-based perspectives. None have attempted to explore the level of EEimplementation intensity that would best increase the provision of ESs while minimizingES tradeoffs. Thus, evaluating the impacts of different EE implementation intensities on ESsand determining an optimal intensity based on ES tradeoffs for policy-makers to developfuture ecosystem management is imperative.Land-use scenario simulation is an important method used to quantify the responseof ESs to different policies [26]. Identifying the potential ES changes and tradeoffs underdifferent land-use patterns by establishing future land-use scenarios consistent with eco-logical policies has a strong practical significance for local policy formulation [27]. Manyresearchers have adopted dynamic simulation models of land use, such as the CLUE-Smodel [28], CA-Markov model [29], and patch-generating land-use simulation (PLUS)model [30], to model land-use scenarios in the future based on specific development di-rections or different objectives, such as ecological protection [29], urban expansion [31],and farmland protection [32], and have evaluated ES changes. In addition, the productionpossibility frontier (PPF) is an economic concept that refers to the sum of one or moreproducts that can be produced under the scenario of a fixed resource quantity [33]. Thetradeoff between different ES combinations can be quantitatively described based on thePPF curve [34]. Because the types and quantities of ESs provided by ecosystem resourcesare limited, the selection of an optimal EE implementation scenario could be analyzed asan economic problem by the PPF method to realize the optimal allocation of resources.Therefore, land-use scenario simulation combined with the PPF method can quantitativelyevaluate the tradeoff intensity between different ES combinations under different scenariosto provide a more effective methodology for selecting optimal scenarios.Qinghai province has a fragile ecosystem in Western China. In the past 20 years,Qinghai province has mainly implemented ecological projects, such as the NFPP andthe project of returning farmland to forest. Land use has changed in the province, andthe ecosystem has achieved remarkable benefits [21]. These projects aim to establish aLand 2024, 13, 848 3 of 18sustainable landscape and functional forest ecosystem [35]. Several studies have reportedthe progress and capital operation of EE [20] and analyzed the effectiveness of EE in termsof ecological and social benefits in the province [22]. However, most of these studieshave focused on characterizing the impact of EE on resource protection and one or twoESs [21,36]. From a scientific perspective, there is still uncertainty regarding the optimallevel of intensity at which EE should be implemented to uphold the essential functionsof ESs.The primary objectives of this study were to: (1) model future land-use patternsunder various intensities of EE implementation, (2) evaluate the impacts of different EEimplementation intensities on ESs and assess the tradeoffs among them, and (3) proposean optimal EE implementation intensity for Qinghai province in the near future. Weformulated the following hypotheses: (1) an increase in EE implementation intensity wouldlead to a significant improvement in most ESs due to an associated increase in vegetationcoverage and (2) the optimal EE implementation intensity for Qinghai province would beexhibited in a scenario with a low implementation intensity that effectively balances all themajor ESs.2. Materials and MethodsWe first designed four land-use scenarios under different EE implementation intensi-ties using the PLUS model. Then, we used the integrated valuation of ecosystem servicesand tradeoffs (InVEST) model to quantify the impact of EE implementation intensities onESs. Next, we conducted a correlation analysis to determine whether there was a tradeoffbetween each pair of ESs. For those ESs that had a tradeoff relationship, the PPF curvebetween the ESs was drawn. Finally, we determined the optimal implementation intensityby calculating the tradeoff intensity index based on the PPF curve. The general structure ofthis study is illustrated in Figure 1.Land 2024, 13, x FOR PEER REVIEW 3 of 18 Qinghai province has a fragile ecosystem in Western China. In the past 20 years, Qinghai province has mainly implemented ecological projects, such as the NFPP and the project of returning farmland to forest. Land use has changed in the province, and the ecosystem has achieved remarkable benefits [21]. These projects aim to establish a sustain-able landscape and functional forest ecosystem [35]. Several studies have reported the progress and capital operation of EE [20] and analyzed the effectiveness of EE in terms of ecological and social benefits in the province [22]. However, most of these studies have focused on ch racter zing the impact of EE on resource protectio and one or two ESs [21,36]. From a scientific perspect ve, there is still uncertainty egarding the optimal level of intensity at which EE should be implemented to uphold the essential functions of ESs. The primary objectives of this study were to: (1) model future land-use patterns un-der various intensities of EE implementation, (2) evaluate the impacts of different EE im-plementation intensities on ESs and assess the tradeoffs among them, and (3) propose an optimal EE implementation intensity for Qinghai province in the near future. We formu-lated the following hypotheses: (1) an increase in EE imple entation intensity would lead to signific nt improvemen in most ESs due to an associated i cr as in vegetation cov-erage and (2) the optimal EE implementation intensity for Qi ghai province would be exhibited in a scenario with a low i plementation intensity that effectively balances all the major ESs. 2. Materials and Methods We first designed f ur land-use scenarios under different EE implementation inten-sities using the PLUS model. Then, we used the integrated valuation of ecosystem services and tradeoffs (InVE T) model to quantify t e impact of EE implementation intensities on ESs. Next, we conducted a correlation analysis to determine whether there was a tradeoff between each pair of ESs. For those ESs that had a tradeoff relationship, the PPF curve between the ESs was drawn. Finally, we determined the optimal implementation intensity by calculating the tradeoff intensity index based on the PPF curve. The general structure of this study is illustrated in Figure 1. Figure 1. The technical framework of this study. Figure 1. The technical framework of this study.Land 2024, 13, 848 4 of 182.1. Study AreaQinghai province is located in the northeastern Tibetan Plateau and the northwesternpart of China at geographic coordinates 89◦35’~103◦04’ E and 31◦36’~39◦19’ N (Figure 2).The total surface area is 69,650,000 km2 (Ministry of Civil Affairs of the People’s Republicof China). The region belongs to a typical plateau continental climate zone, with an annualaverage precipitation between 50 mm and 450 mm and an annual average temperaturebetween −5.1 and 9.0 ◦C [37]. The terrain is mainly dominated by high mountains in thenorth, valleys and basins in the central and western regions, and plateaus in the south [38].The average elevation is greater than 3000 m. Qinghai province is a key freshwater supplyregion in China, is also called the “Chinese water tower”, and is the source of the Yangtze,Yellow, and Lancang rivers [39]. The vegetation types in Qinghai province are abundantand diverse. The vertical and horizontal zonal distribution is particularly obvious in theQilian mountains [40]. Among the forest resources in Qinghai province, natural forestresources account for 95.82% of the total forest area, and the area of planted forests is verysmall [41]. The core goal of EE in Qinghai province is to protect the forest ecosystem.Land 2024, 13, x FOR PEER REVIEW 4 of 18 2.1. Study Area Qinghai province is located in the northeastern Tibetan Plateau and the northwestern part of China at geographic coordinates 89°35’~103°04’ E and 31°36’~39°19’ N (Figure 2). The total surface area is 69,650,000 km2 (Ministry of Civil Affairs of the People’s Republic of China). The region belongs to a typical plateau continental climate zone, with an annual average precipitation between 50 mm and 450 mm and an annual average temperature between −5.1 and 9.0 °C [37]. The terrain is mainly dominated by high mountains in the north, valleys and basins in the central and western regions, and plateaus in the south [38]. The average elevation is greater than 3000 m. Qinghai province is a key freshwater supply region in China, is also called the “Chinese water tower”, and is the source of the Yangtze, Yellow, and Lancang rivers [39]. The vegetation types in Qinghai province are abundant and diverse. The vertical and horizontal zonal distribution is particularly obvi-ous in the Qilian mountains [40]. Among the forest resources in Qinghai province, natural forest resources account for 95.82% of the total forest area, and the area of planted forests is very small [41]. The core goal of EE in Qinghai province is to protect the forest ecosys-tem. Figure 2. Geographical location of Qinghai province and distribution of land-use types in 2020. 2.2. Data Collections Our study collected multi-source data, such as land-use, meteorological, DEM (Dig-ital Elevation Model), soil, population density, and GDP (Gross Domestic Product) data, to run the models. For land-use data in 2010 and 2020, we used ENVI 5.3 software to iden-tify the LULC types by a maximum-likelihood algorithm based on the supervised classi-fication. Qinghai province exhibited all 11 land classification types, namely farmland, for-est, natural shrub, sparse woodland, natural grassland, water, snow, built-up land, bare land, desertification land, and wetland (Figure 1 and Table S7). Since the similar spectral characteristics of natural and planted forests in remote sensing interpretation made them difficult to distinguish, the forest type included natural forests and plantations. By verify-ing with ground testing samples, the overall interpretation accuracy was not less than 85%. To reduce the error caused by climate factors, we used the average climate data across 40 years to evaluate ESs. The data sources, data processing, and related detailed description of the other data required for this study are available in Table S1 and Table S3. All layers were resampled to a 1 km resolution, and the projected coordinate system was unified to the WGS 1984 UTM Zone 47N for consistent analysis. Figure 2. Geographical locati n of Qinghai province and d stributi n of land-use types in 2020.2.2. Data CollectionsOur study collected multi-source data, such as land-use, meteorological, DEM (DigitalElevation Model), soil, population density, and GDP (Gross Domestic Product) data, torun the models. For land-use data in 2010 and 2020, we used ENVI 5.3 software toidentify the LULC types by a maximum-likelihood algorithm based on the supervisedclassification. Qinghai province exhibited all 11 land classification types, namely farmland,forest, natural shrub, sparse woodland, natural grassland, water, snow, built-up land, bareland, desertification land, and wetland (Figure 1 and Table S7). Since the similar spectralcharacteristics of natural and planted forests in remote sensing interpretation made themdifficult to distinguish, the forest type included natural forests and plantations. By verifyingwith ground testing samples, the overall interpretation accuracy was not less than 85%. Toreduce the error caused by climate factors, we used the average climate data across 40 yearsto evaluate ESs. The data sources, data processing, and related detailed description ofthe other data required for this study are available in Tables S1 and S3. All layers wereresampled to a 1 km resolution, and the projected coordinate system was unified to theWGS 1984 UTM Zone 47N for consistent analysis.2.3. Land-Use Simulation2.3.1. PLUS ModelWe used the PLUS model to predict the land-use pattern in Qinghai province in2030 [42]. The PLUS model integrates a rule-mining framework based on a land expansionanalysis strategy (LEAS) and a cellular automata (CA) model based on multi-type randomLand 2024, 13, 848 5 of 18patch seeds (CARS) [43] to analyze landscape dynamics. This approach accurately simulatesthe intrinsic nonlinear relationships of land-use patch-level changes to improve the previousmodels [30]. Compared with other CA models, the simulation results of the PLUS modelare more accurate, and the landscape pattern is more realistic [42].The PLUS model extracts the expansion parts of each land-use type in the two periodsand uses the random forest algorithm to obtain the suitability probability of each land-usetype in LEAS, and then predicts the future landscape pattern in CARS [31]. Based onthe conditions in Qinghai province and data accessibility, we calculated the suitabilityprobability of each land-use type in the LEAS module by using 13 driving factors andland-use expansion data. The driving factors included 6 natural factors (temperature,precipitation, elevation, slope, terrain undulation, and soil type), 2 socioeconomic factors(GDP and population density), and 5 accessibility factors (the proximity to open water,roads, railways, rivers, and lakes). The CARS module operation requires two parameters,including the transition matrix and neighborhood weights. The transition matrix includesvalues of 0 (one land-use type cannot be transformed into another), and 1 (one land-usetype can be transformed). The neighborhood weight indicates the expansion intensityof different land-use types, and its value range is 0–1. The closer the weight is to 1, thestronger the expansion intensity of the land-use type. In this study, the land-use transitionmatrix and the change ratios of land-use types from 2010 to 2020 were used to represent thetransition matrix and the neighborhood weights of the corresponding land-use types from2020 to 2030. We thus set the transition matrix shown in Table S4 and the neighborhoodweights in Table S5.2.3.2. PLUS Model ValidationTo validate the PLUS model, we first simulated the spatial distribution of land use in2020 based on that of 2010, and compared the simulation results with factual data fromthe same year. The Kappa coefficient was relatively reliable because it considered theproportion of cells that were correctly classified due to chance [44]. The figure of merit(FOM) was able to measure the performance of the model in simulating the changing partof the land use and provided a more precise indication of the model’s ability [44]. Thus, weselected the Kappa coefficient and FOM to validate the accuracy of the simulation results.The closer the Kappa coefficient was to 1, and the greater the FOM value was above 0.15,the higher the simulation accuracy was.2.3.3. EE Implementation Intensity ScenariosCombined with the development status, related government documents [35], and theFourteenth Five-Year Plan of Qinghai province, we set four intensity scenarios of EE imple-mentation in 2030 based on the historical pattern of land-use changes in 2010–2020 (Table S8).The four scenarios were as follows: (1) natural implementation scenario (NIS): the land usechanged at a constant rate consistent with that of 2010–2020; (2) low-intensity implementationscenario (LIS): the forests were planted by increasing the transition probability of farmland,bare land, and desertification land shifting to forest and natural shrub by 10%, and protectedby decreasing the transition probability of forest and natural shrub shifting to farmland, bareland, and desertification land by 10%; (3) medium-intensity implementation scenario (MIS):the transition probability increased by 30% in the planting of forests; and (4) high-intensityimplementation scenario (HIS): the transition probability increased by 50% in the plantingof forests. The transition probability setting for the MIS and HIS was consistent with theLIS for the protection of forests. Based on the ‘Qinghai Province Water Pollution Preventionand Control Work Plan’, in recent years, the government has emphasized the priority ofnatural protection and restoration, and highlighted the protection of glaciers, snow, and lakes.Therefore, we also limited the expansion of open water and snow and kept the built-up landunchanged. The land demand amounts under four scenarios were shown in Table S6.Land 2024, 13, 848 6 of 182.3.4. Land-Use Type ChangesWe calculated the rate of land-use change to quantify the various land-type changesunder four scenarios [45]. The calculation formula is as follows:∆Ti = Tti − Tt−1iri =Tti − Tt−1iTt−1iwhere ∆Ti is the change to areas of land of type i. Tti is the area of land for type i in year t.Tt−1i is the area of land of type i in the previous year t. ri is the area’s rate of change forland type i.2.4. Quantifying Ecosystem Services2.4.1. Ecosystem Service Selection and EvaluationWe selected ESs based on the ES classification framework [46], the Common Inter-national Classification of Ecosystem Services (CICES) [47], the questionnaire of Qinghairesidents [21], and the environmental conditions in the study area. Although Qinghaiprovince provided extensive ESs, soil erosion and water resource shortages were majorenvironmental problems. Thus, we considered water-related ESs, including soil retention(SR), water yield (WY), and water purification (WP). Moreover, the Three-River HeadwatersNational Park in Qinghai province has many species and a large amount of wildlife; thus, itis necessary to evaluate the habitat quality (HQ). Of course, it is also necessary to evaluatecarbon storage (CS). Therefore, we finally selected five ESs, namely SR, WY, WP, HQ, andCS (more detailed information on the ES selection can be found in Yan, Wang, Li, Wang, Jin,Jiang, Yang and Wang [21]).The InVEST model is a comprehensive model that simulates the changes in the qualityand value of ESs under different land-use scenarios, aiming to provide a scientific basis fordecision makers [48]. The model has been widely used to assess natural capital and ESs, andhas achieved good effects in the United States [49], China [50], and other countries [29,51].It includes many modules for evaluation. In this study, we selected the sediment deliveryratio module (for SR), the WY module (for WY), the nutrient delivery ratio module (for WP),the habitat quality module (for HQ), and the carbon storage and sequestration module (forCS) to evaluate the ESs in 2020 and 2030 under different scenarios in Qinghai province. Adetailed description of the quantitative ESs process is shown in Supplementary InformationA. The relevant input parameters and settings were shown in Tables S1 and S2.2.4.2. InVEST Model ValidationBased on the yearly hydrological monitoring data of sediment export and WY fromtwenty-three hydrological stations, the sediment delivery ratio module and WY modulewere validated. For the nutrient delivery ratio module, when setting the nitrogen loadvalue, we referred to many parameters from the literature [52,53] and the InVEST userguide (Table S2) to reduce the module’s error. We simulated the suitable habitats of twenty-two species, including seven birds, thirteen other instances of wildlife, and two endangeredplants, using the maximum entropy model to validate the HQ module. Since we used theobserved data in the process of assessing carbon storage, it did not need to be verified.2.4.3. Ecosystem Services Tradeoff AnalysisCorrelation analysis (Pearson’s r) was used to estimate the relationships among ESsunder different EE implementation scenarios. Based on the specific resolution requirements,we randomly generated 65,000 points in the study area to obtain sufficient data samples. Anegative correlation between ESs (p < 0.05) was considered a tradeoff relationship, while apositive correlation was regarded as a synergistic relationship.Land 2024, 13, 848 7 of 182.5. Determining an Optimal EE Implementation Intensity2.5.1. Production Possibility Frontier CurveThe PPF curve can quantitatively describe the degree of tradeoff between the twooutputs [33]. If ES combination points are below the curve, it indicates that resourceutilization is insufficient and land use has optimization potential. If ES combination pointsare on the curve, land use is in an optimal configuration. If the points are above the curve,then ESs exceed the optimal allocation, but this situation is difficult to achieve with existingresources and technical conditions.We first estimated the five ESs in Qinghai province and normalized the ESs to a rangeof 0–1. Second, the five normalized layers were superimposed to obtain the total ES valuefor each grid. We arranged the total ES values in ascending order, and selected the gridswith larger ES values. Two tradeoff ESs values corresponding to these selected grids wereextracted to fit the PPF curve.2.5.2. Tradeoff Intensity IndexIf the tradeoff direction is selected to optimize the combination of the two services, theoptimal state can be viewed as an equilibrium state. Only one optimal state can be attainedwithin a specified set of conditions; that is, only one PPF curve can be obtained [54]. If Prepresents the point corresponding to the mean value of two tradeoff ESs, its coordinateis (x0, y0) . We assume that the expression of the PPF curve is y = f (x), and use pointP(x0, y0) as the center and IPQ as the radius to make a circle. The circle has only oneintersection point Q(x , y) with the PPF curve. The shortest distance (IPQ) from the meanvalue of the two tradeoff ESs (P(x0, y0) ) to the PPF curve (Q(x , y)) represents the tradeoffintensity index (Figure 3) [33].Land 2024, 13, x FOR PEER REVIEW 7 of 18 2.4.3. Ecosystem Services Tradeoff Analysis Correlation analysis (Pearson’s 𝑟) was used to estimate the relationships among ESs under different EE implementation scenarios. Based on the specific resolution require-ments, we randomly generated 65,000 points in the study area to obtain sufficient data samples. A negative correlation between ESs (𝑝 < 0.05) was considered a tradeoff relation-ship, while a positive correlation was regarded as a synergistic relationship. 2.5. Determining an Optimal EE Implementation Intensity 2.5.1. Production Possibility Frontier Curve The PPF curve can quantitatively describe the degree of tradeoff between the two outputs [33]. If ES combination points are below the curve, it indicates that resource utili-zation is insufficient and land use has optimization potential. If ES combination points are on the curve, land use is in an optimal configuration. If the points are above the curve, then ESs exceed the optimal allocation, but this situation is difficult to achieve with exist-ing resources and technical conditions. We first estimated the five ESs in Qinghai province and normalized the ESs to a range of 0–1. Second, the five normalized layers were superimposed t obtain the total ES value for each grid. We arranged the total ES values in ascending order, and selected the grids with larger ES values. Two tr deoff ESs values corresp nding to these selected grids were extracted to fit the PPF curve. 2.5.2. Tradeoff Intensity Ind x If the tradeoff direction is selected to optimize the combination of the two services, the optimal state can be viewed as an equilibrium state. Only one optimal state can be attaine withi a sp cified set of conditions; that is, only one PPF curve can be obtained [54]. If 𝑃 r presents the point corr spondi g to t mean value of two tradeoff ESs, its coordinate is ሺ𝑥଴, 𝑦଴ሻ. We assume that the expression of the PPF curve is y = 𝑓ሺxሻ, and use poin 𝑃ሺ𝑥଴, 𝑦଴ሻ as the center a d 𝐼௉ொ as the radiu to make a circle. The circle has only one intersection point 𝑄ሺ𝑥, yሻ with the PPF curve. The shortest distance (𝐼௉ொ) from the mean value of the t o tradeoff ESs (𝑃ሺ𝑥଴, 𝑦଴ሻ) to the PPF curve (𝑄ሺ𝑥, yሻ) represents the tradeoff intensity index (Figure 3) [33]. Figure 3. The definition of tradeoff intensity index, where 𝐼௉ொ is the tradeoff intensity index. The larger the 𝐼௉ொvalue is, the stronger the tradeoff intensity and the more insufficient the resource uti-lization of the two ESs. The lower the 𝐼௉ொ value is, the weaker the tradeoff intensity and the better the optimal configuration of the two ESs. 3. Results 3.1. Spatiotemporal Changes in Future Land Use Under all land-use scenarios, forest, natural shrub, and sparse woodland signifi-cantly increased (Figure 4), while farmland, bare land, and desertification land signifi-cantly decreased from 2020 to 2030. Under the NIS, LIS, MIS, and HIS scenarios, natural Figure 3. The definition of tradeoff intensity index, where IPQ is the tradeoff intensity index. Thelarger the IPQ value is, the stronger the tradeoff intensity and the more insufficient the resourceutilization of the two ESs. The lower the IPQ value is, the weaker the tradeoff intensity and the betterthe optimal configuration of the two ESs.3. Results3.1. Spatiotemporal Changes in Future Land UseUnder all land-use scenarios, forest, natural shrub, and sparse woodland significantlyincreased (Figure 4), while farmland, bare land, and desertification land significantlydecreased from 2020 to 2030. Under the NIS, LIS, MIS, and HIS scenarios, natural shrubsincreased the most; from 2020 to 2030, they increased by 13.34%, 14.22%, 15.57%, and16.93%, respectively (Figure 5). Natural shrubs were mainly transferred from naturalgrass and, ba e l nd, and farmland and ere mainly distributed in th central and westernregions. The forest area increased from 2020 to 2030 with rates of 6.71%, 7.09%, 7.42%,Land 2024, 13, 848 8 of 18and 7.79%, respectively, for the four scenarios. Forest was mainly converted from naturalshrub, farmland, sparse woodland, and bare land and was mainly located in the centraland eastern regions. Compared with 2020, the area of desertification land consistentlydecreased, with rates of 6.89%, 6.93%, 6.98%, and 7.04%, respectively. The area of bare landalso decreased by 1.54%, 1.82%, 2.24%, and 2.67%, respectively.Land 2024, 13, x FOR PEER REVIEW 8 of 18 shrubs increased the most; from 2020 to 2030, they increased by 13.34%, 14.22%, 15.57%, and 16.93%, respectively (Figure 5). Natural shrubs were mainly transferred from natural grassland, bare land, and farmland and were mainly distributed in the central and western regions. The forest area increased from 2020 to 2030 with rates of 6.71%, 7.09%, 7.42%, and 7.79%, respectively, for the four scenarios. Forest was mainly t from natural shrub, farmland, sparse wo dland, and bare l ainly located in the central and eastern egions. Compared with 20 , the area of desertification land consistently de-creased, with rates of 6.89%, 6.93%, 6.98%, and 7.04%, respecti l . area of bare land also decreased by 1.54%, 1.82%, 2.24%, and 2.67 , respectively. Figure 4. Spatial distribution of land use in 2030 and the changes from 2020 to 2030 under differentscenarios. The pie chart shows the proportion of different land-use types and land-use changes.Land 2024, 13, 848 9 of 18Land 2024, 13, x FOR PEER REVIEW 9 of 18 Figure 4. Spatial distribution of land use in 2030 and the changes from 2020 to 2030 under different scenarios. The pie chart shows the proportion of different land-use types and land-use changes. Figure 5. Chord diagram of the main land-use type transfers from 2020 to 2030 under different sce-narios in Qinghai province. The start of the arrow represents 2020, and the end represents 2030. The number shows the change in areas (kmଶ) of land-use type. 3.2. Model Validation For the PLUS model, the calculated Kappa coefficient between simulated and ob-served land use in 2020 was 0.844, and the FOM was 0.385, indicating that the PLUS model had high reliability. For the InVEST model, strong linear relationships were exhibited be-tween the observed data and the predicted data for the sediment export and WY, with R2 values of 0.790 and 0.932 (p < 0.001) (Figure 6). For HQ, the results revealed that 60.10% of the appropriate habitats replicated by the two models were congruous. Figure 5. Chord diagram of the main land-use type transfers from 2020 to 2030 under differentscenarios in Qinghai province. The start of the arrow represents 2020, and the end represents 2030.The number shows the change in areas (km2) of land-use type.3.2. Model ValidationFor the PLUS model, the calculated Kappa coefficient between simulated and observedland use in 2020 was 0.844 and the FOM was 0.385, indicating that the PLUS odel hadhigh reliability. For the InVEST model, strong linear relationships were exhibited betweenthe observed data and the predicted data for the sediment export and WY, with R2 valuesof 0.790 and 0.932 (p < 0.001) (Figure 6). For HQ, the results revealed that 60.10% of theappropriate habitats replicated by the two models were congruous.Land 2024, 13, 848 10 of 18 , , x FOR PEER REVIEW 10 of 0 2 4 6 8 100246810Predicted (108 kg per year)Observed (108 kg per year)y=1.22x+2.75 × 107 R2=0.790NRMSE=0.226 MAE=1.12 × 109y=1.19x-4.66 × 107 R2=0.932NRMSE=0.067 MAE=1.34 × 108Sediment exportWater yield0 5 10 15 2005101520Predicted (108 m3 per year)Observed (108 m3 per year) Figure 6. InVEST model validation (sediment export and water yield in 2020). 3.3. Ecosystem Service Changes under Different Scenarios A high provision of SR was found in the eastern and southern regions. This was mainly because of the flat slope and larger areas of forest, natural grassland, and natural shrub (Figure 7). Northwestern regions were mainly dominated by bare land, causing se-rious soil loss, and thus had a low amount of SR. Under the NIS, LIS, MIS, and HIS sce-narios, the SR increased from 2020 to 2030 by 0.05%, 0.06%, 0.07%, and 0.08%, respectively. SR changes were directly related to the natural shrub and forest areas (Figure S1). As the EE implementation intensity increased, vegetation coverage increased, leading to a signif-icant increase in SR. The distribution trend in WY was consistent with that of precipitation. The WY with high provision was mainly located in the southern and northeastern regions. Areas with lower value were mainly located in the northwestern and central regions. The WY de-creased by 2.59%, 2.91%, 3.15%, and 3.46%, respectively, for the four scenarios. Due to the increase in vegetation coverage, evapotranspiration significantly increased in the eastern region, and, thus, the WY decreased. We used the nitrogen export to represent the capacity of WP. The higher the nitrogen export value, the worse the WP capacity was. Due to the lower nitrogen export in the central region, the WP in the central regions was higher than that of the other regions. This was mainly because farmland and built-up land were located in the eastern region, result-ing in an increase in nitrogen export. For the NIS, LIS, MIS, and HIS, the nitrogen export decreased by 1.13%, 1.19%, 1.24%, and 1.35%, respectively. Due to the enhancement of WP capacity caused by the increase in the vegetation coverage and the reduction of the pollu-tant load caused by the decrease in farmland, WP significantly improved. HQ was higher in the eastern, southern, and central regions because these regions had a higher natural shrub coverage and a lower degree of habitat fragmentation. The lower HQ areas were distributed in the regions with bare land, which has a fragile eco-logical environment. The mean HQ index was 0.568 in 2020, and predicted to be 0.574, 0.575, 0.576, and 0.577 under the NIS, LIS, MIS, and HIS in 2030, respectively. As the nat-ural shrub and forest areas increased, better habitat conditions were provided for animals and plants, and thus the HQ index also increased. i r . I l li ti (s i t rt t r i l i ).3.3. Ecosystem Service Changes under Different ScenariosA high provision of SR was found in the eastern and southern regions. This was mainlybecause of the flat slope and larger areas of forest, natural grassland, and natural shrub(Figure 7). Northwestern regions were mainly dominated by bare land, causing serioussoil loss, and thus had a low amount of SR. Under the NIS, LIS, MIS, and HIS scenarios,the SR increased from 2020 to 2030 by 0.05%, 0.06%, 0.07%, and 0.08%, respectively. SRchanges were directly related to the natural shrub and forest areas (Figure S1). As the EEimplementation intensity increased, vegetation coverage increased, leading to a significantincrease in SR.The distribution trend in WY was consistent with that of precipitation. The WYwith high provision was mainly located in the southern and northeastern regions. Areaswith lower value were mainly located in the northwestern and central regions. The WYdecreased by 2.59%, 2.91%, 3.15%, and 3.46%, respectively, for the four scenarios. Dueto the increase in vegetation coverage, evapotranspiration significantly increased in theeastern region, and, thus, the WY decreased.We used the nitrogen export to represent the capacity of WP. The higher the nitrogenexport value, the worse the WP capacity was. Due to the lower nitrogen export in thecentral region, the WP in the central regions was higher than that of the other regions.This was mainly because farmland and built-up land were located in the eastern region,resulting in an increase in nitrogen export. For the NIS, LIS, MIS, and HIS, the nitrogenexport decreased by 1.13%, 1.19%, 1.24%, and 1.35%, respectively. Due to the enhancementof WP capacity caused by the increase in the vegetation coverage and the reduction of thepollutant load caused by the decrease in farmland, WP significantly improved.HQ was higher in the eastern, southern, and central regions because these regions hada higher natural shrub coverage and a lower degree of habitat fragmentation. The lowerHQ areas were distributed in the regions with bare land, which has a fragile ecologicalenvironment. The mean HQ index was 0.568 in 2020, and predicted to be 0.574, 0.575, 0.576,and 0.577 under the NIS, LIS, MIS, and HIS in 2030, respectively. As the natural shrub andforest areas increased, better habitat conditions were provided for animals and plants, andthus the HQ index also increased.Land 2024, 13, 848 11 of 18Land 2024, 13, x FOR PEER REVIEW 11 of 18 The distribution trend of CS was higher in the eastern region than in the northwestern region because of the higher vegetation coverage. Compared to 2020, CS increased under the NIS, LIS, MIS, and HIS in 2030 by 1.32%, 1.40%, 1.53%, and 1.65%, respectively. Figure 7. Cont.Land 2024, 13, 848 12 of 18Land 2024, 13, x FOR PEER REVIEW 12 of 18 Figure 7. ES spatial distribution in 2020 and under different scenarios in 2030. 3.4. Tradeoffs among Ecosystem Services There were some significant tradeoffs and synergies among the five ESs across the different scenarios (Table S9). Compared to 2020, when SR showed an increasing trend due to the expansion of forests and shrubs in all scenarios for 2030, HQ and CS also showed an increasing trend, while the WY and nitrogen export showed the opposite. Due to the inverse relationship between nitrogen export and WP, the relationships between WP and the other ESs were opposite to the relationships between nitrogen export and the other ESs. Therefore, WY showed a significantly negative correlation with SR, HQ, CS, and WP (p < 0.01) under all scenarios, indicating WY had tradeoff relationships with the other four ESs. There were significant positive correlations between SR, CS, WP, and HQ (p < 0.01). 3.5. PPF Curves among Ecosystem Service Tradeoffs To determine the optimal EE implementation intensity, we fitted the PPF curves of the two ESs in the different scenarios based on their tradeoff relationships (Figure 8). The simulated value corresponding to the grid with the larger value was consistent under all scenarios, and, thus, the PPF curves coincided completely. Four PPF curves presented a downward-convex curve between two tradeoff ESs. The PPF curve between WY and SR was divided into two phases: a small tradeoff phase where the curve changed slowly for the SR in the range of 0–0.8 and a large tradeoff phase where the curve changed rapidly for the SR in the range of 0.8–1. Similar relationships were also observed between WY and WP, HQ, and CS. When the WY was in the range of 0.4–1, the curves for WP, HQ, and CS showed a slow downward trend. When the WY was in the range of 0–0.4, the values of the other three ESs were at their maximum, and the curve showed a vertical line, indicat-ing the tradeoffs increased significantly. Figure 7. ES spatial distribution in 2020 and under different scenarios in 2030.The distribution trend of CS was higher in the eastern region than in the northwesternregion because of the high r vegetation coverage. Compared to 2020, CS increased underthe NIS, LIS, MIS, and HIS in 2030 by 1.32%, 1.40%, 1.53%, and 1.65%, respectively.3.4. Tradeoffs among Ecosystem ServicesThere were some significant tradeoffs and synergies among the five ESs across thedifferent scenarios (Table S9). Compared to 2020, when SR showed an increasing trend dueto the expansion of forests and shrubs in all scenarios for 2030, HQ and CS also showedan increasing trend, while the WY and nitrog n export showed the opposit . Due to theinverse relationship between nitrogen export nd WP, the relati nships bet een WP andthe other ESs were opposite to the relationships between nitrogen export and the otherESs. Therefore, WY showed a significantly negative correlation with SR, HQ, CS, and WP(p < 0.01) under all scenarios, indicating WY had tradeoff relationships with the other fourESs. There were significant positive correlations between SR, CS, WP, and HQ (p < 0.01).3.5. PPF Curves among Ecosyste Service TradeoffsTo determine the l EE implementa ion intensity, we fitted the PPF curves ofthe two ESs in the differ arios based on their t adeoff relationship (Figure 8). Thesimulated value corresponding to the grid with the larger value was consistent under allscenarios, and, thus, the PPF curves coincided completely. Four PPF curves presented adownward-convex curve between two tradeoff ESs. The PPF curve between WY and SRwas divided into two phases: a small tradeoff phase where the curve changed slowly forthe SR in the range of 0–0.8 and a large tradeoff phase where the curve changed rapidlyfor the SR in the range of 0.8–1. Similar relationships were also observed between WY andWP, HQ, and CS. When the WY was in the range of 0.4–1, the curves for WP, HQ, and CSshowed a slow downward trend. When the WY was in the range of 0–0.4, the values of theth r three ESs were at their maximum, and the curve showed a vertical line, indicatingthe tradeoffs increased significantly.The tradeoff intensity index among the two ESs was different in different scenarios(Table 1). Only for WY versus HQ did the HIS have the lowest tradeoff intensity index(0.4235). Between WY and SR, WP, and CS, the LIS had the lowest tradeoff intensity index,with values of 0.8138, 0.6695, and 0.7175, respectively. This result indicated that under theLIS, the ES combination points were nearest to the PPF curve, showing an optimal effectof the tradeoff relationship among ESs. Therefore, the LIS was the optimal scenario of EEimplementation in Qinghai province.Land 2024, 13, 848 13 of 18Land 2024, 13, x FOR PEER REVIEW 13 of 18 Figure 8. PPF curves between two tradeoff ESs under different scenarios in 2030. The tradeoff intensity index among the two ESs was different in different scenarios (Table 1). Only for WY versus HQ did the HIS have the lowest tradeoff intensity index (0.4235). Between WY and SR, WP, and CS, the LIS had the lowest tradeoff intensity index, with values of 0.8138, 0.6695, and 0.7175, respectively. This result indicated that under the LIS, the ES combination points were nearest to the PPF curve, showing an optimal effect of the tradeoff relationship among ESs. Therefore, the LIS was the optimal scenario of EE implementation in Qinghai province. Table 1. Tradeoff intensity index among ESs under different scenarios in 2030. Tradeoff Intensity Index 2030 (NIS) 2030 (LIS) 2030 (MIS) 2030 (HIS) WY versus SR 0.8149 0.8138 0.8166 0.8182 WY versus WP 0.6725 s0.6695 0.6710 0.6737 WY versus HQ 0.4256 0.4249 0.4242 0.4235 WY versus CS 0.7182 0.7175 0.7209 0.7242 4. Discussion 4.1. Impact of EE Implementation Intensities on ESs Land-use change is the most important factor influencing ES changes [7], and EE is the dominant element in the land-use change of Qinghai province [21]. In this study, we simulated land-use changes from 2020 to 2030 under four EE implementation intensities and evaluated their impacts on ESs in Qinghai province, China. We found that land-use changes under different scenarios had obvious similarities, but there were also some Figure 8. PPF curves between two tradeoff ESs under different scenarios in 2030.Table 1. Tradeoff intensity index among ESs under different scenarios in 2030.TradeoffIntensity Index 2030 (NIS) 2030 (LIS) 2030 (MIS) 2030 (HIS)WY versus SR 0.8149 0.8138 0.8166 0.8182WY versus WP 0.6725 s0.6695 0.6710 0.6737WY versus HQ 0.4256 0.4249 0.4242 0.4235WY versus CS 0.7182 0.7175 0.7209 0.72424. Discussion4.1. Impact of EE Implementation Intensities on ESsLand-use change is the most important factor influencing ES changes [7], and EE isthe dominant element in the land-use change of Qinghai province [21]. In this study, wesimulated land-use changes from 2020 to 2030 under four EE implementation intensitiesand evaluated their impacts on ESs in Qinghai province, China. We found that land-use changes under different scenarios had obvious similarities, but there were also somedifferences. EE implementation promoted conversions from farmland, bare land, anddesertification land to natural shrubs and forests. The higher the EE implementationintensity, the greater the conversion rate. The expansion of natural shrubs and forests wasalso predicted in the central region of Qinghai province under the four scenarios. Suitableclimate conditions with more prec pitation and flatter terrain would promote an i crease invegetat on cov rage.The results of our simulations revealed that the increase in vegetation coverage with EEimplemen ation intensity contributed to maintaining SR, increasing CS, a improving HQ.How ver, it was not conducive to forming runoff, and, thus, it reduced the WY. FarmlandLand 2024, 13, 848 14 of 18was the major contributor to the nitrogen export, and a decline in farmland would reducethe nitrogen export and thus increase the WP. Many regions have also implemented EE inthe past 20 years, such as the forest region of northeastern China [55] and southwesternChina [22], indicating that EE can promote the renewed growth of vegetation and enhanceecological function. In general, EE is an effective way to improve the ESs and humanwell-being.Although EE has provided significant ecological benefits, decision-makers also need tocarefully consider the negative impacts caused by EE implementation, such as a reductionin WY [21]. Climate change is the main reason for WY changes [49,56]. However, in regionswith less precipitation, such as Qinghai province, the impact of expanding vegetation coveron WY is a serious concern. The results of our simulations showed that the five ESs hadcomplex tradeoff relationships under different EE implementation intensities. The tradeoffsbetween WY and the other ESs (i.e., SR, HQ, CS, and WP) were more obvious with higherEE implementation intensities. Our findings are consistent with previous studies [21,27,57].Due to the complex tradeoff relationship among ESs, it is almost impossible for variousESs to increase or decrease simultaneously in the implementation of EE. Policy-makerscannot strengthen some ESs and ignore other ESs, especially those that human beingscan benefit directly from. To ensure the long-term sustainability of EE, the relationshipbetween economic development and ecological protection must be balanced. By setting upscenarios with different implementation intensities and conducting simulations, land-usechanges at various intensities and their impacts on ESs can be predicted. This scientificprediction and quantitative assessment method helps find an optimal balance betweenecological protection and economic development. Therefore, the balance between variousESs must be comprehensively considered in EE planning and decision-making to betterguide ecological construction.4.2. Determining the Optimal EE Implementation Scenario Based on PPFThe win-win situation of multifunctional ecosystems via the implementation of theappropriate EE intensity is a necessary condition for sustainable development [17]. Previousstudies only explored the impact of land-use change driven by EE on various ESs throughscenario simulation to find a suitable land-use pattern [27,58]. This method had a certaindegree of subjectivity. Our study calculated the tradeoff intensity index to select an optimalEE intensity by combining the scenario simulation with PPF curves, which can providequantitative guidance for the selection of the optimal implementation intensity. Thiscomprehensive approach helps policy-makers better understand the impact of differentpolicy decisions on ecosystems and land use, thereby formulating more scientific andsustainable development strategies.The four fitted PPF curves show that the implementation of different intensities of EEleads to various ESs having different change rates and even changing directions in Qinghaiprovince. The MIS and the HIS had a higher tradeoff intensity index, indicating that thetradeoffs between the provisioning services and the regulating services were significant,consistent with the findings of Ma et al. [58]. However, under the LIS, even though thefunction of the ES was not fully developed, the appropriate intensity increases promoteda relatively good balance among the ESs. Thus, we chose the LIS with a lower tradeoffintensity index as the optimal scenario for the balanced development of multiple ecosystemfunctions. It is worth noting that ecosystems are dynamic and may exhibit different servicedemands and tradeoffs at different temporal and spatial scales. By regularly evaluatingand adjusting the PPF curve, we can promptly respond to changes in social needs, andimprove the flexibility and adaptability of ecological management.4.3. Limitations and Future ResearchThe current study developed a framework for determining the optimal EE implemen-tation intensity to help policy-makers enact appropriate land management. However, someuncertainties and limitations remain. First, different ESs, including supplying, supporting,Land 2024, 13, 848 15 of 18cultural, and regulating services, have different tradeoff relationships. Our study mainlyevaluated the impacts of EE implementation on five ESs and explored the relationshipbetween sets of two ESs. Thus, integrating diverse ESs into a framework and consideringinteractions among ESs in practical EE applications to guide land management should befurther researched and explored. Secondly, the PPF should contain as many production-possibility data points as possible. We only simulated land-use changes under four EEimplementation intensities based on the development planning of Qinghai province in2030. Dozens or even hundreds of scenarios should be designed to analyze the ES tradeoffsto obtain the optimal scenario. Therefore, it is necessary to gradually establish a set offrameworks combined with more scenario analyses based on regional practical situationsto fit a perfect PPF curve to provide critical knowledge for achieving optimal land-useallocation. Third, due to the complexity and uncertainties of EE investment, our methodsdid not consider the cost of increasing the vegetation cover by tree or shrub plantation inplateau and arid regions. This limitation should be considered in future studies.5. ConclusionsIn this study, we applied a methodology using the PLUS model, the InVEST model,and the PPF curve to model the land-use changes and analyze the ES tradeoffs under fourEE implementation intensities in Qinghai province from 2020 to 2030. We found that withthe increase in EE implementation intensity, WY had significant tradeoff relationships withSR, HQ, CS, and WP. Based on the tradeoff intensity index, under all EE implementationscenarios, the low-intensity implementation scenario could be the optimal EE implementa-tion intensity (406.68 km2 expansions of forest and shrub) to achieve a balance betweenregional economic development and the sustainable provision of ESs. We suggest that thelocal government should comprehensively consider the changes in ESs and their trade-offs according to regional ecological goals and geographical factors when formulating EEpolicies to realize a sustainable supply of ESs. Our study proposed a novel framework toeffectively determine the investment in EE and the return in ESs for policymakers.Supplementary Materials: The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/land13060848/s1: Table S1: Data requirements for the InVESTmodels; Table S2: Key parameters used in biophysical table of modules; Table S3: Data requirementsfor the PLUS models; Table S4: Transition matrix for CARS module operation in the PLUS model;Table S5: Neighborhood weights for CARS module operation in the PLUS model; Table S6: Landdemand amounts under four scenarios; Table S7: LULC classes used in the maps for Qinghai province;Table S8: Land-use composition in Qinghai province in 2010 and 2020; Table S9: Pearson correlationamong five ESs under different implementation intensity scenarios; Figure S1: Spatial changes ofthe five ESs under different scenarios from 2020 to 2030; Supplement S1: InVEST model processes;Supplement S2: Data requirements; Supplement S3: LULC classes and composition; Supplement S4:Ecosystem services changes; Supplement S5: Ecosystem services tradeoffs. References [48,59–69] arecited in the supplementary materials.Author Contributions: Conceptualization, K.Y. and W.W.; methodology, K.Y., B.Z. and J.J. (JiaxinJin); software, K.Y., B.Z. and R.W.; validation, J.J. (Jiaxin Jin), J.J. (Jiang Jiang), W.D. and T.W.; formalanalysis, K.Y.; investigation, K.Y.; resources, W.W.; data curation, K.Y., X.W., J.J. (Jiaxin Jin) andT.W.; writing—original draft preparation, K.Y.; writing—review and editing, X.W., J.J. (Jiaxin Jin), J.J.(Jiang Jiang), W.D., R.W., H.Y., T.W. and W.W.; visualization, K.Y.; supervision, Y.L. and W.W.; projectadministration, Y.L. and W.W.; funding acquisition, W.W. All authors have read and agreed to thepublished version of the manuscript.Funding: This research was funded by the National Key Research and Development Program ofChina (No. 2021YFD2200404), the Research and Innovation Project of Northwest Surveying andPlanning Institute of National Forestry and Grassland Administration (No. XBJ-KJCX-2021-16), theChina Scholarship Council (No. 202208320373), the Postgraduate Research and Practice InnovationProgram of Jiangsu Province (No. KYCX21_0869), and the Priority Academic Program Developmentof Jiangsu Higher Education Institutions (PAPD).Land 2024, 13, 848 16 of 18Data Availability Statement: The original contributions presented in this study are included in thearticle; further inquiries can be directed to the corresponding author.Acknowledgments: Special thanks to Shuiqiang Yu, Yanrong Yang, Leying Zhang, and Cheng Hufor their useful discussions and comments. We thank the anonymous reviewers and academic editorfor the invaluable suggestions.Conflicts of Interest: The authors declare no conflicts of interest.References1. Costanza, R.; d’Arge, R.; de Groot, R.; Farber, S.; Grasso, M.; Hannon, B.; Limburg, K.; Naeem, S.; O’Neill, R.V.; Paruelo, J.; et al.The value of the world’s ecosystem services and natural capital. Nature 1997, 387, 253–260. [CrossRef]2. Daily, G.C.; Söderqvist, T.; Aniyar, S.; Arrow, K.; Dasgupta, P.; Ehrlich, P.R.; Folke, C.; Jansson, A.; Jansson, B.-O.; Kautsky, N.;et al. The value of nature and the nature of value. Science 2000, 289, 395–396. [CrossRef]3. Niu, L.; Shao, Q.; Ning, J.; Huang, H. Ecological changes and the tradeoff and synergy of ecosystem services in western China. J.Geogr. Sci. 2022, 32, 1059–1075. [CrossRef]4. Ouyang, Z.; Song, C.; Zheng, H.; Polasky, S.; Xiao, Y.; Bateman, I.J.; Liu, J.; Ruckelshaus, M.; Shi, F.; Xiao, Y.; et al. Using grossecosystem product (GEP) to value nature in decision making. 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ecological engineering
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multi-scenario simulation
production possibility frontier
tradeoff intensity
Effects of the Implementation Intensity of Ecological Engineering on Ecosystem Service Tradeoffs in Qinghai Province, China
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