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Review

Grassland Ecosystem Services: Their Economic Evaluation through a Systematic Review

by
Maria Pergola
*,
Enrica De Falco
and
Michele Cerrato
Degree Course of Agriculture, DIFARMA, Università Degli Studi di Salerno, Via Giovanni Paolo II 132, 84084 Fisciano, Italy
*
Author to whom correspondence should be addressed.
Land 2024, 13(8), 1143; https://doi.org/10.3390/land13081143
Submission received: 30 May 2024 / Revised: 11 July 2024 / Accepted: 24 July 2024 / Published: 26 July 2024
(This article belongs to the Special Issue Grassland Ecosystem Services: Research Advances and Future Directions for Sustainability II)
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Abstract

:
Grasslands provide a wide range of provision, support, regulation, and cultural ecosystem services (ESs), whose valuation methods can be grouped into three categories (ecological, sociocultural, and economic). The present manuscript aims to provide an overview of academic studies on grassland ESs and of the most used economic evaluation methods. To this end, a systematic and bibliometric review was conducted using the scientific database Scopus and the VOSviewer software. The results highlighted that China and the USA were the main countries with the highest number of publications regarding ESs provided by grasslands. The number of publications began to grow starting in 2005, thanks, perhaps, to the publication of influential documents, such as the Millennium Ecosystem Assessment, and the general increase in interest in ecological policy issues. The year 2023 had the highest number of documents in absolute (646), demonstrating the timeliness, importance, and relevance of this research topic. The most studied grassland ES has been carbon storage; however, a central role was played also by biodiversity. In this context, papers that estimated grassland ESs from an economic perspective represented only 3% of all papers that Scopus has returned. More than half of these referred to the use of equivalent coefficients to calculate the ES value of different land uses/land cover categories or, at most, of 11 types of ES. All this highlights the difficulty in estimating individual ESs provided by grasslands from an economic point of view and the greater propensity to use physical, chemical, and biological indicators. Consequently, the sustainable management of grasslands requires more studies on the economic evaluation of their ES, as well as environmental aspects in the economic accounting of governments, or to implement a support system for farms in delivering various ecosystem services.

1. Introduction

According to FAO, grasslands account for 28% of the world’s land area, 63% of which is concentrated in the Russian Federation, Australia, the United States of America (USA), Canada, China, Kazakhstan, and Brazil [1]. The definitions given of grassland are different depending on the aspects that are taken into consideration. Indeed, FAO [2] defined grassland as “land covered with grass and with less than 2% tree or shrub cover”. The International Vegetation Classification [3] and Dixon et al. [4] identified four different grassland types: tropical grasslands, Mediterranean grasslands, temperate grasslands, and semidesert grasslands. Allen et al. [5], instead, with the aim of developing a consensus on terms and definitions to ensure clear international communication regarding grasslands, defined many grazing land terms at a global level. Among these, pastureland is defined as “land (and the vegetation growing on it) devoted to the production of introduced or indigenous forage for harvest by grazing, cutting, or both” and “is synonymous with grassland when referring to an imposed grazing-land ecosystem with a vegetation broadly interpreted to include grasses, legumes, and other forbs, and at times woody species may be present”. Among other definitions, that of rangeland is also interesting, which the authors defined as “a land on which the indigenous vegetation is predominantly grasses, grass-like plants, forbs or shrubs that are grazed or have the potential to be grazed, and which is used as a natural ecosystem to produce grazing livestock and wildlife”. The latter include natural grasslands, savannas, shrublands, many deserts, steppes, tundras, alpine communities, and swamps. Table 1 reports the different types of grasslands identified by Allen et al. [5].
Referring to natural grasslands, in the world, there are 42 zonal natural grassland types [6], where the characteristic vegetation is determined by climatic and soil conditions, grazing animals, and fire. For example, in Uruguay, southern Brazil, and north-eastern Argentina, the sub-tropical climate favors the presence of “campos” [7]; in central Brazil, the tropical climate allows the presence of “cerrado” [8]; in eastern and central Argentina, the climate humid to arid ensures the presence of “pampa” (treeless grasslands on flat and fertile plains) [9]; in North America, prairies are widespread, and in south-eastern Europe, Asia, and North America are the steppes [5].
In Europe, natural grasslands cover limited areas, which according to Peeters et al. [10] are alpine and boreal tundra grasslands (beyond the tree line); rocky Pannonian grasslands (in Hungary); the Macaronesian mesophilic pasturelands of the Atlantic islands (e.g., Azores); sub-desert meadows (in Romania, Russia, and Ukraine); Mediterranean xeric grasslands (e.g., the main Mediterranean islands and Stipa grasslands in south-eastern Spain). On the contrary, in most parts of Europe, grasslands are semi-natural, which is the result of centuries or millennia of low-intensity human land use from forests as potential natural vegetation [11].
Each of these grassland types provides important provision, support, regulation, and cultural ecosystem services (ES) [12,13], namely, the benefits humans obtain from grassland functions [14]. In the past, grasslands have played an important role in people’s livelihoods as areas producing fodder for animals. Today, instead, the increasing demand for animal products is being met by the production of fodder on cropland and improved grasslands [13]. Consequently, the conversion to arable land [15] and the lack of management/increased abandonment [16,17] have led to the decline of grasslands worldwide during the last century. Anyway, grass remains the most natural forage for various herbivores, providing minerals, vitamins, and other active substances supporting living processes and providing healthy meat and milk for people [18]. It is largely recognized that grasslands have not only a local importance, for the maintenance of biodiversity, pollination, and food production but also at regional (water and erosion regulation, recreation, inspiration) and global scales (climate regulation) [13]. Nowadays, grassland plants may be used for energy production needs or as natural dyes for coloring clothes, and various medical plants can be found in pastureland, especially if natural [18]. Thus, a complete picture of the ES provided by grasslands can be found in Richter et al. [12], Bengtsson et al. [13], O’Mara [19], Erb et al. [20], Veen et al. [21], Habel et al. [22], and Dengler et al. [11].
Referring to Europe, Table 2 reports the functions and the relative ecosystem services provided by the main typologies of European grasslands.
In particular, in Nordic countries, grasslands are very heterogeneous, and their importance varies greatly from country to country. In fact, natural pasturelands are very large in Iceland and play a notable function in livestock production, while they are much less relevant in other countries [23]. In temperate regions of Europe, both permanent and temporary grasslands are used, including a varying proportion of forage legumes, playing a key role in feed production (above all for cattle and sheep) and, in some areas, in the preservation of the environment [24]. Moreover, referring to soil erosion, permanent grassland is strongly recommended, and since, in this region, we are witnessing their reduction, Huyghe et al. [24] said that this important function should be valorized. Finally, in the Mediterranean basin (a global biodiversity hotspot with an extremely high number of endemic plant species) [25], grasslands and rangelands are important ecosystems that have traditionally played a significant role in the evolution of human societies [26]. In particular, here, livestock grazing (characterized by small ruminants and beef cattle belonging to local races) affects the quantity and quality of forage, vegetation dynamics, species, and landscape diversity [27]. Thus, in this context, grassland systems are not only important for livestock production but they also perform multiple ecosystem functions [25].
Although most grasslands provide the same set of ecosystem services, their values are different [18]. Ecosystem service valuation methods can be grouped into three categories: ecological, sociocultural, and economic [28]. Of these three, as noted by Richter et al. [12], the economic evaluation of ES has been recognized as an important tool for developing strategies for the sustainable management of ecosystems. To our knowledge, there are few systematic literature review studies on the economic evaluation methods of grassland ESs. Indeed, Kang et al. [29] used 61 research studies in their evaluation of grassland ecosystem services’ value in China, including 564 value observations to establish a value transfer database and to construct a value model. Richter et al. [12] provided an overview of available methods for the economic valuation of ES through a review of 85 plot-scale methods to assess 29 different ES indicators for 21 provisioning, regulating, supporting, or cultural ESs. Liu et al. [30,31] provided a comprehensive evaluation of the economic value of grassland ESs: on one hand, via a meta-regression analysis of 69 studies; on the other hand, via a meta-analysis of 702 observations from 134 primary studies. Thus, the present manuscript aimed to provide a critical overview of academic studies on the ES supplied by grasslands and wanted to contribute to the research of the most commonly used economic evaluation methods.

2. Materials and Methods

According to the aim of the paper, a bibliometric and systematic review of the literature on grassland ecosystem services was carried out to highlight the geographical focus of the literature, as well as how academia approaches the study of grassland ESs and what the main economic evaluation methods are to assess such ESs. As stated by Merli et al. [32] and Denyer and Tranfield [33], systematic analysis enables one to implement a clear and reproducible process of selection, analysis, and reporting on research about a specific topic. As in Pergola et al. [34], the review process followed some phases: definition of the research question; choice of the software to use; collection of documents; systematic and bibliometric analysis of documents; selection of the documents; qualitative analysis and groupings; and evaluation of the documents (Figure 1).

2.1. The Science Mapping

For the bibliometric analysis science mapping was used, a technique that examines the relationships between research components [35]. In particular, co-word analysis was used to understand the existing relationships between issues by focusing on the written content of the documents analyzed. VOSviewer software (version 1.6.20) was utilized to extract and analyze the semantic contents of titles, abstracts, and keywords of publications, correlating them to citation count data and creating network maps to visualize the results [36]. As in Muley and Medithi [37], default parameters were used for the analysis and the construction of the network maps. In particular, in the network map, the font size of words states their frequency of occurrence; if two words occurred more frequently in the analyzed documents, they were nearest to each other. Only words that had a minimum of 10 occurrences were analyzed and shown. To create the maps, two units of analysis were used (“all keywords” and “author keywords”), and the full count method was adopted, meaning that each co-occurrence link had the same weight. The default “associative strength method”, with default values of attraction and repulsion, was used for the normalization of the co-occurrence matrix [35].
As detailed in Donthu et al. [35], in the network maps constructed using the VOSviewer software, each knot represented a keyword, wherein: (1) the size of the knot indicated the number of times that the keyword occurred, (2) the connection between the knots represented the co-occurrence among keywords (i.e., keywords that co-occurred or occurred together), (3) the thickness of the connection indicated the occurrence of co-occurrences among keywords (i.e., the number of times that the keywords co-occurred or occurred together), (4) the bigger the knot, the greater the occurrence of the keyword, and (5) the thicker the connection between knot, the higher the occurrence of the co-occurrences among keywords. Each cluster was highlighted with a different color.

2.2. The Systematic Review Process

In order to conduct a reliable bibliometric and systematic analysis [38], the scientific database Scopus [39,40] was utilized for the collection of the documents, [39]. The first phase of the review process was the collection of all grassland ES publications across the scientific community. Thus, the generic expression “grasslands ecosystem services” was adopted and the research criterion “Article title, Abstract, Keywords” was employed. All types of documents were utilized as units of analysis (articles, reviews, book chapters, conference papers, books, notes, editorials, short surveys, letters, data papers, and conference reviews) and in all languages. No chronological restriction was performed, and the query on the database was carried out on 14 May 2024. From 1980, Scopus returned a total of 4608 documents. Thus, a quantitative analysis of the distribution and evolution of these documents over time, sources, number of citations, and thematic areas was made.
Subsequently, to identify papers that dealt with economic evaluation methodologies of the grassland ecosystem services, the subject areas “Social Science” and “Economics, Econometrics and Finance” were selected. In addition, the strings “grasslands ecosystem services” + “economic evaluation” and “grasslands ecosystem services” + “economic assessment” were used with the research criterion “Article title, Abstract, Keywords” to avoid the risk of leaving out some documents. Consequently, Scopus returned a total of 1033 publications, of which, after removing duplicates (91 documents), 942 papers were evaluated. From the latter, through qualitative analysis (reading of the abstracts and/or full documents), the manuscripts that presented economic methodologies for evaluating the grassland ESs were selected. Therefore, a sample of 161 papers was carefully examined in order to group them into areas of economic assessment methods.

3. Results

3.1. The Descriptive and Bibliometric Analysis of the Literature on Grasslands Ecosystem Services

The first article on grassland ecosystem services dates back to 1980 and refers to the tallgrass prairie version of the ELM Grassland Model, used to evaluate the potential impact of creating a tallgrass prairie National Park in the Flint Hills region of Kansas [41]. In particular, the model was specifically developed to study the effects of levels and types of herbivory, climatic variation, and fertilization upon pastureland ecosystems [41]. In any case, up to 2004, there were only 53 documents. As early as 2005, the number of publications referring to grassland ESs began to increase, and a steady rise in research articles has been observed since then (Figure 2). At the same time, the highest number of publications was recorded in the last five years (n = 2544), representing 55% of the total documents found on Scopus.
The distribution of all documents by countries shows that more than 50% of them were from China (1129), the United States (985), Germany (455), and the United Kingdom (379), as reported in Figure 3.
Most of the published manuscripts were articles (4024; 87%), followed by review papers (240; 5%), book chapters (152; 3%), and conference papers (128; 3%). About 93% of the documents (4304) were published in journals, and the top ten source types are listed in Table 3. The latter shows that the main research areas were the assessment of ecological and environmental indicators, the ecology, the environment in total (atmosphere, lithosphere, hydrosphere, biosphere, and anthroposphere), the sustainability, and everything that concerns land.
In line with what has been said, the top ten cited articles dealt with the importance of biological diversity for the maintenance of ecosystems; the impact and consequences of biodiversity loss on the functioning of ecosystems, the provision of ecosystem services and human well-being; the importance of biological and microbial communities for ecosystem services and human well-being; the importance of investing in the protection and restoration of natural capital and the implementation of national policies on payments for ES to address devastating environmental crises and improve human well-being (Table 4).
Referring to subject areas, those most represented were environmental science and agricultural and biological sciences (Figure 4), confirming the importance of studying grassland ESs from an environmental and agricultural management perspective. At the same time, it is interesting to note that the area of social sciences occupied the third place, absorbing 7% of the research returned by Scopus.
The co-word analysis of all keywords of the 4608 collected documents aggregated the terms with a minimum number of occurrences of 10 into 5 clusters, easily visible in Figure 5 because they are highlighted by different colors (cluster 1 red; cluster 2 bright green; cluster 3 blue; cluster 4 light green; cluster 5 purple) (Figure 5). The top five most recurring words were grassland (occurrences: 1890), ecosystem service (occurrences: 1813), ecosystem services (occurrences: 1120), ecosystems (occurrences: 1105), and China (occurrences: 1059). The keyword “grassland” belonged to cluster 3, and the other most frequently occurring words were agricultural land, agriculture, and biodiversity. Instead, the keywords “ecosystem service”, “ecosystem services”, “ecosystems”, and “China” belonged to cluster 1, where the other most frequently occurring words were environmental protection, forestry, land use, land use change, and sustainable development (Figure 5).
At the same time, to better outline the situation, the unit of analysis “author keywords” was used, and the corresponding density map (Figure 6) showed that the most recurring keywords were ecosystem services (825 occurrences); grassland (288 occurrences); biodiversity (274 occurrences); climate change (234 occurrences); and ecosystem service value (203 occurrences) (Figure 6).
In this analysis, 6 clusters have been identified and the terms “ecosystem services” and “ecosystem service value” belonged to cluster 1, where the other most frequently occurring words were carbon storage, ecosystem service, land use, and land use change. The keywords “grassland” belonged to cluster 6, which includes the following most frequently occurring terms: agroecology, land management, landscape, livestock, and vegetation. The keywords “biodiversity” and “climate change” belonged to cluster 2, with the words drought, ecosystem function, functional diversity, grazing, multifunctionality, soil organic carbon, and species richness.

3.2. The Analysis of the Documents Referring to the Economic Evaluation Methodologies of the Grassland ES

To try to skim the 1033 documents that Scopus returned in reference to the economic methodologies for estimating grassland ESs, co-occurrence analysis was used. The latter, carried out considering “all keywords”, was not very significant for the purpose of the research given that it identified five very varied clusters and the following five most recurring words: ecosystem service (457 occurrences), grassland (324 occurrences), China (321 occurrences), ecosystem services (258 occurrences), ecosystems (221 occurrences) (Figure 7), namely, the same ones displayed before.
At the same time, this analysis made it possible to verify the presence of terms relating to the economic evaluation of the ES. Thus, the following keywords were identified: economics (67 occurrences); economic analysis (21 occurrences); willingness to pay (14 occurrences); payment for ecosystem services (10 occurrences); and ecological economics (11 occurrences). As shown in Figure 8, which shows the links of these economic terms, only the words “economics”, “ecological economics”, and “willingness to pay” were simultaneously linked to the two main research terms (ecosystem services and grasslands)—among other things, the most recurring terms. On the contrary, the keyword “economic analysis” was found to be linked to ecosystem services/service, ecosystems, and land use, while “payment for ecosystem services” was found to be far from all the other keywords and not linked to any other term.
This analysis was followed by the selection of manuscripts that had the economic evaluation of ESs as their subject of study. The selected sample (161 papers) was carefully studied and divided into 15 groups of adopted methodologies, as reported in Table 5.
The most widespread economic estimation methodology for grassland ES was the ESV assessment, which, for each land-use category (per unit area) assigns an equivalent coefficient, as reported by Xie et al. [139] and Costanza et al. [14]. “Indirect” estimation methods then follow, namely, the resource equivalency approach, according to which the economic estimates of ES occur through the quantification of compensation to farmers for the loss of the environmental resource [140]. This category includes the payments for ecosystem services (PESs), the agri-environment schemes (AESs), and the eco-compensations. We define these methodologies indirectly given that the estimation of the ES in these cases occurred through the quantification of the amount for the ecological compensation received by farmers who implemented measures to increase the ES or avoid their decrease.
Econometric models, useful for carrying out meta-regression analyses when there are multiple variables to consider simultaneously, were found in only 11 papers (Table 5). At the same time, the benefit transfer method, used to estimate the economic values of ES by transferring available data from studies already completed in another location and/or context, was applied in seven manuscripts, as well as the contingent valuation method. In particular, the latter was used to estimate landowner/farmer willingness to accept (WTA) monetary compensation in exchange for implementing management practices preserving grassland ecosystems or to assess the willingness to pay (WTP) for ES.
Other economic methodologies were the estimation of ES through the computation of the gross ecosystem product (based on the calculation of the functional quantity of each ecological element and then its conversion into value quantity) [141]; the energy value method (which refers to the available energy required directly and indirectly to make a service or good [142] and measures natural capital and ESV by assessing the costs of their generation from a donor-side perspective [122]); the use of focus groups to understand the perceptions and social preferences of interviewees; and the use of the net present value (Table 5).
The use of the market and shadow price method and the replacement cost approach were less frequent (only in two research in the first and in three in the second). Finally, the life cycle assessment approach, the use of the social cost, the cost theory of value and the random utility, and the cost–benefit analysis were decidedly rare.
Referring to the type of data used for the economic evaluation of grassland ES, the analysis of the 161 selected papers showed that land use/cover change (LUCC) data were the most used data (in 37% of cases), followed by direct data acquired through structured and semi-structured interviews (SSI) (14%) (Table 6).
In 57% of the documents, the four categories of ES (provisioning, regulating, supporting, cultural) were estimated and, for each category, the following ecosystem services were accounted: food production, raw material production, and water supply (as regards provisioning services); gas regulation, climate regulation, environment purification, and hydrological regulation (with regards to regulating services); soil conservation, nutrient cycle maintenance, and biodiversity (as supporting services); aesthetic landscape, regarding cultural services. In 22% of the papers, the overall value of the ES offered by the grasslands was calculated (ESV), without any differentiation in the different ES offered; in 13% a single ES was estimated, and, in 8%, there were multiple ecosystem services (Figure 9).
Biodiversity conservation (in five papers), cultural services (in four manuscripts), carbon storage (in four documents), and water conservation (in three papers) were the most estimated single ES (Figure 9).
Among the ecosystem services that have been economically estimated in combination with other ES, carbon sequestration stands out, followed by forage provision and soil protection (Figure 10).
In conclusion, the evaluation of the 161 selected papers showed that individual ESs were evaluated from an economic point of view mainly through bio-economic modeling approaches and data coming from structured and semi-structured interviews. The economic estimate of multiple ES was carried out with data from different sources depending on the ES to be estimated, while the accounting of the ESV and the main ES falling into the four macro categories of ES was carried out essentially through land use/cover change data more or less combined with environmental, climatic, and socio-economic data (Figure 11).

4. Discussion

4.1. Main Findings

As a first step, the present review attempted to provide a critical overview of academic studies on grassland ESs. Thus, it highlighted that China and the USA were both some of the countries with the highest percentage of grasslands (in terms of land area) and the main countries with the highest number of publications regarding ESs provided by grasslands, followed by Germany and the United Kingdom. The number of publications began to grow starting in 2005 thanks perhaps to the publication of the Millennium Ecosystem Assessment, an important document that aimed to evaluate the consequences of ecosystem change for human well-being and to establish the scientific basis for actions necessary to improve the conservation and sustainable use of ecosystems and their contribution to human well-being [143]. The increase in the number of publications on this topic is also the result of the general increase in interest in ecological policy issues that has occurred especially in the last two decades, which has led to state policies being more attentive to concerns about the condition of the environment and to an increase in scope to protect various valuable ecosystems. The main instrument of the European Union policy for the conservation of biodiversity is, undoubtedly, Natura 2000: an ecological network spread across the entire territory of the EU, established pursuant to Habitats Directive 92/43/EEC to guarantee the long-term maintenance of threatened or rare natural habitats and flora and fauna species at a community level.
At the same time, 55% of the documents found in Scopus referred to the last five years, in which 2023 represented the year with the highest number of documents ever (646). These data demonstrate the timeliness, importance, and relevance of this research topic.
The most represented subject areas of study were environmental science and agricultural and biological sciences (Figure 4), in which a central role was played by biodiversity as an important provision service. At the same time, however, among the most cited articles, in addition to those on the importance of biodiversity for the maintenance of ecosystems and on the impact and consequences of its loss for human well-being, not negligible were those that dealt with ES payments to address devastating environmental crises and improve human well-being.
The use of science mapping for bibliometric analysis has highlighted the correlation between the ecosystem services provided by grasslands with carbon storage, land use, and its change. This points out, on the one hand, that the most studied grassland ES in absolute has been the carbon storage, especially in quantitative terms, and how pasturelands act as an important soil carbon sink, and, on the other hand, how land use and its change over time can influence the provision of ES. However, the same analysis applied to the 1033 documents, selected for the subsequent screening to identify the most used methodology for the economic evaluation of grassland ESs, did not produce significant results. In our opinion, this was due to the fact that the science mapping was based on the analysis of the semantic contents of publications’ titles, keywords, and abstracts, which very often were unclear (especially the latter) and did not contain the searched words. Hence, there is a need in the future to use software that analyzes entire manuscripts.
Papers that estimated grassland ESs from an economic perspective represented only 3% of all papers that Scopus returned, demonstrating that economic research, as such, is secondary to the study of environmental functions. In fact, to evaluate an ecosystem function, it is necessary to establish that this function exists and what effects its variation has. Only then it can also be valorized from an economic point of view. Moreover, more than half of these publications referred to the methodology of Costanza et al. [14], namely, the use of equivalent coefficients to calculate the ecosystem service value of different land uses/land cover categories. In this case, some studies (about 20) have estimated the total ESV provided by grasslands, highlighting the difficulty in estimating individual ecosystem services provided by pasturelands from an economic point of view (especially those that do not have a market) and the greater propensity to use physical, chemical, and biological indicators. Other research (about 70) has identified and evaluated a maximum of 11 ecosystem services in precise economic terms: food production, raw material production, water supply, gas regulation, climate regulation, environment purification, hydrological regulation, soil conservation, nutrient cycle maintenance, biodiversity, aesthetic landscape, indicating that the latter is the most important ES offered by grasslands. This was also confirmed by studies that analyzed single or multiple ESs, in which carbon storage and biodiversity conservation took precedence.
As pointed out by Liu et al. [30], the evaluation methods commonly used for valuing ecosystem services are different depending on the ES to be evaluated. For example, direct market methods are usually used for services that can be directly traded in the market, and the price is used to reflect the value of the ES. The avoided or replacement cost method uses the cost of actions taken to avoid damages as a measure of the benefits provided by an ecosystem or ES. At the same time, the travel cost method is normally used to estimate the economic use values of recreational ecosystems or sites, and the stated preference methods (the choice experiment method and the contingent valuation method) are used to assess the economic value of various ecosystems and ESs based on a hypothetical scenario. The benefit transfer method is a unit value-based method used to evaluate economic values by transferring existing benefit estimates from completed research to other sites or ES. Thus, with Table 5, which shows, for each methodology identified, the different references in which the methodology is applied, we wanted to indicate to researchers who wanted to estimate grassland ESs in economic terms where to find the most appropriate methodologies to do so.

4.2. Limitations of the Study

The present results are influenced by the use of only one database extraction (Scopus), which includes only publications with CiteScore and not many other documents where the economic valuation of grassland ESs may have been estimated.

5. Conclusions

The results of the present systematic and bibliometric literature review highlighted the importance of grassland ESs and their estimation over time, above all in quantitative, ecological, and sociocultural terms. Economic evaluation studies were few compared to the total academic publications and mainly linked to the quantification of ecosystem service values per unit area for each land-use category or, referring to grasslands, of only 11 types of ES. Consequently, the sustainable management of grasslands requires more studies on the economic evaluation of their ES. Furthermore, such research can be the basis for including environmental aspects in the economic accounting of governments or to implement a support system for farms in delivering various ecosystem services.

Author Contributions

M.P.: Conceptualization, methodology, software, validation, formal analysis, investigation, data curation, writing—original draft preparation, writing—review and editing; M.C.: visualization, writing—review and editing; E.D.F.: supervision, writing—review and editing. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

No new data were created.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Data on Land Cover (Grassland). FAOSTAT. 2021. Available online: https://www.fao.org/faostat/en/#data/LC (accessed on 10 May 2024).
  2. FAO. Guidelines: Land Evaluation for Extensive Grazing, 1990. FAO Soil Bulletin 58. Available online: https://www.fao.org/4/t0412e/t0412e.pdf (accessed on 3 July 2024).
  3. Faber-Langendoen, D.; Keeler-Wolf, T.; Meidinger, D.; Josse, C.; Weakley, A.; Tart, D.; Navarro, G.; Hoagland, B.; Ponomarenko, S.; Fults, G.; et al. Classification and Description of World Formation Types; Gen. Tech. Rep. RMRS-GTR-2016; US Department of Agriculture, Forest Service, Rocky Mountain Research Station: Fort Collins, CO, USA, 2016; Volume 346, p. 222. [Google Scholar]
  4. Dixon, A.P.; Faber-Langendoen, D.; Josse, C.; Morrison, J.; Loucks, C.J. Distribution mapping of world grassland types. J. Biogeogr. 2014, 41, 2003–2019. [Google Scholar] [CrossRef]
  5. Allen, V.G.; Batello, C.; Berretta, E.J.; Hodgson, J.; Kothmann, M.; Li, X.; Mcivor, J.; Milne, J.; Morris, C.; Peeters, A.; et al. An international terminology for grazing lands and grazing animals. Grass Forage Sci. 2011, 66, 2–28. [Google Scholar] [CrossRef]
  6. Li, L.; Chen, J.; Han, X.; Zhang, W.; Shao, C. Types and Distribution of Chinese Grassland Ecosystems. In Grassland Ecosystems of China. Ecosystems of China; Springer: Singapore, 2020; Volume 2. [Google Scholar] [CrossRef]
  7. Lezama, F.; Cáceres, D.; Pañella, P.; del Pino, A. Land-Use Intensification by Overseeding Legumes on Natural Grasslands: Impacts on Plant Diversity. Rangel. Ecol. Manag. 2024, 93, 95–103. [Google Scholar] [CrossRef]
  8. Colman, C.B.; Guerra, A.; Almagro, A.; de Oliveira, R.F.; Rosa, I.M.D.; Fernandes, G.W.; Oliveira, P.T.S. Modeling the Brazilian Cerrado land use change highlights the need to account for private property sizes for biodiversity conservation. Sci. Rep. 2024, 14, 4559. [Google Scholar] [CrossRef] [PubMed]
  9. Noellemeyer, E.; Álvarez, L.; Álvarez, C.; Dillchneider, A.; Farrell, M.; Fernández, R.; Buss, E.F.; Frasier, I.; Gaggioli, C.; Gili, A.; et al. From science to practice: The AGSUS protocol for monitoring and certification of sustainable soil management and carbon sequestration. Soil Till Res. 2024, 241, 106102. [Google Scholar] [CrossRef]
  10. Peeters, A.; Beaufoy, G.; Canals, R.M.; De Vliegher, A.; Huyghe, C.; Isselstein, J.; Jones, G.; Kessler, W.; Kirilov, A.; Mosquera-Losada, M.R.; et al. Grassland term definitions and classifications adapted to the diversity of European grassland-based systems. In Grassland Science in Europe—EGF at 50: The Future of European Grasslands; Hopkins, A., Collins, R.P., Fraser, M.D., King, V.R., Lloyd, D.C., Moorby, J.M., Robson, P.R.H., Eds.; IBERS, Aberystwyth University: Gogerddan, UK, 2014; Volume 19. [Google Scholar]
  11. Dengler, J.; Janišová, M.; Török, P.; Wellstein, C. Biodiversity of palaearctic grasslands: A synthesis. Agric. Ecosyst. Environ. 2014, 182, 1–14. [Google Scholar] [CrossRef]
  12. Richter, F.; Jan, P.; El Benni, N.; Lüscher, A.; Buchmann, N.; Klaus, V.H. A guide to assess and value ecosystem services of grasslands. Ecosyst. Serv. 2021, 52, 101376. [Google Scholar] [CrossRef]
  13. Bengtsson, J.; Bullock, J.M.; Egoh, B.; Everson, C.; Everson, T.; O’Connor, T.; O’Farrell, P.J.; Smith, H.G.; Lindborg, R. Grasslands—More important for ecosystem services than you might think. Ecosphere 2019, 10, 1–20. [Google Scholar] [CrossRef]
  14. Costanza, R.; Dange, R.; Degroot, 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. [Google Scholar] [CrossRef]
  15. Egoh, B.J.; Bengtsson, R.; Lindborg, J.M.; Bullock, A.; Dixon, P.; Rouget, M. The importance of grasslands in providing ecosystem services: Opportunities for poverty alleviation. In Routledge Handbook of Ecosystem Services; Potschin, M., Haines-Young, R., Fish, R., Turner, R.K., Eds.; Routledge: London, UK; New York, NY, USA, 2016; pp. 421–441. [Google Scholar]
  16. Basso, F.; Santilocchi, R.; Postiglione, L.; Cavallero, A.; Grignani, C.; Reyneri, A.; Acutis, M.; Costa, G.; Pascal, G.; Ziliotto, U.; et al. Gestione e miglioramento di pascoli italiani. Riv. Agron. 1992, 26, 344–359. [Google Scholar]
  17. Queiroz, C.; Beilin, R.; Folke, C.; Lindborg, R. Farmland abandonment: Threat or opportunity for biodiversity conservation? Front. Ecol. Environ. 2014, 12, 288–296. [Google Scholar] [CrossRef]
  18. Bojārs, E.; Ruskule, A.; Veidemane, K.; Fammler, H.; Kuris, M.; Norvaišaite, R.; Burkhard, B. How do Grasslands Benefit Humans-Introduction to Grassland Ecosystem Services. Baltic Environmental Forum. 2017. Available online: https://vivagrass.eu/wp-content/uploads/2017/05/broshure-final-how-grasslands-benefit-human-introduction-to-grassland-ecosystem-services-download.pdf (accessed on 1 July 2024).
  19. O’Mara, F.P. The role of grasslands in food security and climate change. Ann. Bot. 2012, 110, 1263–1270. [Google Scholar] [CrossRef]
  20. Erb, K.H.; Kastner, T.; Plutzar, C.; Bais, A.L.S.; Carvalhais, N.; Fetzel, T.; Gingrich, S.; Haberl, H.; Lauk, C.; Niedertscheider, M.; et al. Unexpectedly large impact of forest management and grazing on global vegetation biomass. Nature 2018, 553, 73–76. [Google Scholar] [CrossRef]
  21. Veen, P.; Jefferson, R.; de Smidt, J.; van der Straaten, J. Grasslands in Europe of High Nature Value; KNNV Publishing: Zeist, The Netherlands, 2009. [Google Scholar]
  22. Habel, J.C.; Dengler, J.; Janišová, M.; Török, P.; Wellstein, C.; Wiezik, M. European grassland ecosystems: Threatened hotspots of biodiversity. Biodivers. Conserv. 2013, 22, 2131–2138. [Google Scholar] [CrossRef]
  23. Helgadóttir, Á.; Frankow-Lindberg, B.E.; Seppänen, M.M.; Søegaard, K.; Østrem, L. European grasslands overview: Nordic region. In Grassland Science in Europe—EGF at 50: The Future of European Grasslands; Hopkins, A., Collins, R.P., Fraser, M.D., King, V.R., Lloyd, D.C., Moorby, J.M., Robson, P.R.H., Eds.; IBERS, Aberystwyth University: Gogerddan, UK, 2014; Volume 19. [Google Scholar]
  24. Huyghe, C.; De Vliegher, A.; Goliński, P. European grasslands overview: Temperate region. In Grassland Science in Europe, EGF at 50: The Future of European Grasslands; Hopkins, A., Collins, R.P., Fraser, M.D., King, V.R., Lloyd, D.C., Moorby, J.M., Robson, P.R.H., Eds.; IBERS, Aberystwyth University: Gogerddan, UK, 2014; Volume 19. [Google Scholar]
  25. Cosentino, S.L.; Porqueddu, C.; Copani, V.; Patanè, C.; Testa, G.; Scordia, D.; Melis, R. European grasslands overview: Mediterranean region. In Grassland Science in Europe, EGF at 50: The Future of European Grasslands; Hopkins, A., Collins, R.P., Fraser, M.D., King, V.R., Lloyd, D.C., Moorby, J.M., Robson, P.R.H., Eds.; IBERS, Aberystwyth University: Gogerddan, UK, 2014; Volume 19. [Google Scholar]
  26. Jouven, M.; Lapeyronie, P.; Moulin, C.H.; Bocquier, F. Rangeland utilization in Mediterranean farming systems. Animal 2010, 4, 1746–1757. [Google Scholar] [CrossRef] [PubMed]
  27. Perevolotsky, A. Integrating landscape ecology in the conservation of Mediterranean ecosystems. The Israeli experience. Isr. J. Plant Sci. 2005, 53, 203–213. [Google Scholar] [CrossRef]
  28. Gómez-Baggethun, E.; Barton, D.N.; Berry, P.; Dunford, R.; Harrison, P.A. Concepts and methods in ecosystem services valuation. In Routledge Handbook of Ecosystem Services; Potschin, M., Haines-Young, R., Fish, R., Turner, R.K., Eds.; Routledge Handbooks Online: London, UK, 2016; pp. 99–111. [Google Scholar]
  29. Kang, B.; Shao, Q.; Xu, H.; Jiang, F.; Wei, X.; Shao, X. Research on grassland ecosystem service value in China under climate change based on meta-analysis: A case study of Qinghai province. Int. J. Clim. Change Strateg. Manag. 2020, 12, 617–637. [Google Scholar] [CrossRef]
  30. Liu, H.; Hou, L.; Kang, N.; Nan, Z.; Huang, J. A meta-regression analysis of the economic value of grassland ecosystem services in China. Ecol. Indic. 2022, 138, 108793. [Google Scholar] [CrossRef]
  31. Liu, H.; Hou, L.; Kang, N.; Nan, Z.; Huang, J. The economic value of grassland ecosystem services: A global meta-analysis. Grassl. Res. 2022, 1, 63–74. [Google Scholar] [CrossRef]
  32. Merli, R.; Preziosi, M.; Acampora, A. How do scholars approach the circular economy? A systematic literature review. J. Clean. Prod. 2018, 178, 703–722. [Google Scholar] [CrossRef]
  33. Denyer, D.; Tranfield, D. Producing a systematic review. In The Sage Handbook of Organizational Research Methods; Buchanan, D.A., Bryman, A., Eds.; Sage Publications Ltd.: Thousand Oaks, CA, USA, 2009; pp. 671–689. [Google Scholar]
  34. Pergola, M.; De Falco, E.; Belliggiano, A.; Ievoli, C. The Most Relevant Socio-Economic Aspects of Medicinal and Aromatic Plants through a Literature Review. Agriculture 2024, 14, 405. [Google Scholar] [CrossRef]
  35. Donthu, N.; Kumar, S.; Mukherjee, D.; Pandey, N.; Lim, W.M. How to conduct a bibliometric analysis: An overview and guidelines. J. Bus. Res. 2021, 133, 285–296. [Google Scholar] [CrossRef]
  36. Van Eck, N.J.; Waltman, L. Manual for VOSviewer Version 1.6.20. 2023. Available online: https://www.vosviewer.com/documentation/Manual_VOSviewer_1.6.20.pdf (accessed on 25 May 2024).
  37. Muley, A.; Medithi, S. A Quantitative Literature Analysis of the Research on Holy Basil (Tulsi). J. Scientometr. Res. 2022, 11, 30–36. [Google Scholar] [CrossRef]
  38. Sánchez, A.D.; de la Cruz Del Río Rama, M.; García, J.Á. Bibliometric analysis of publications on wine tourism in the databases Scopus and WoS. Eur. Res. Manag. Bus. Econ. 2017, 23, 8–15. [Google Scholar] [CrossRef]
  39. Aghaei Chadegani, A.; Salehi, H.; Md Yunus, M.M.; Farhadi, H.; Fooladi, M.; Farhadi, M.; Ale Ebrahim, N. A comparison between two main academic literature collections: Web of science and Scopus databases. Asian Soc. Sci. 2013, 9, 18–26. [Google Scholar] [CrossRef]
  40. Guz, A.N.; Rushchitsky, J.J. Scopus: A system for the evaluation of scientific journals. Int. Appl. Mech. 2009, 45, 351–362. [Google Scholar] [CrossRef]
  41. Parton, W.J.; Wright, R.G.; Risser, P.G. Simulated grazing responses on the proposed prairies National Park. Environ. Manag. 1980, 4, 165–170. [Google Scholar] [CrossRef]
  42. Loreau, M.; Hector, A. Partitioning selection and complementarity in biodiversity experiments. Nature 2001, 412, 72–76. [Google Scholar] [CrossRef] [PubMed]
  43. Balvanera, P.; Pfisterer, A.B.; Buchmann, N.; He, J.; Nakashizuka, T.; Raffaelli, D.G.; Schmid, B. Quantifying the evidence for biodiversity effects on ecosystem functioning and services. Ecol. Lett. 2006, 9, 1146–1156. [Google Scholar] [CrossRef]
  44. Tilman, D.; Reich, P.; Knops, J. Biodiversity and ecosystem stability in a decade-long grassland experiment. Nature 2006, 441, 629–632. [Google Scholar] [CrossRef]
  45. Wagg, C.; Bender, S.F.; Widmer, F.; van der Heijden, M.G.A. Soil biodiversity and soil community composition determine ecosystem multifunctionality. Proc. Natl. Acad. Sci. 2014, 111, 5266–5270. [Google Scholar] [CrossRef]
  46. Hoekstra, J.M.; Boucher, T.; Ricketts, T.H.; Roberts, C.S. Confronting a biome crisis: Global disparities of habitat loss and protection. Ecol. Lett. 2004, 8, 23–29. [Google Scholar] [CrossRef]
  47. Delgado-Baquerizo, M.; Maestre, F.; Reich, P.B.; Jeffries, T.C.; Gaitan, J.J.; Encinar, D.; Berdugo, M.; Campbell, C.D.; Singh, B.K. Microbial diversity drives multifunctionality in terrestrial ecosystems. Nat. Commun. 2016, 7, 10541. [Google Scholar] [CrossRef]
  48. Díaz, S.M.; Lavorel, S.; de Bello, F.; Quétier, F.; Grigulis, K.; Robson, T.M. Incorporating plant functional diversity effects in ecosystem service assessments. Proc. Natl. Acad. Sci. 2007, 104, 20684–20689. [Google Scholar] [CrossRef] [PubMed]
  49. Ouyang, Z.; Zheng, H.; Xiao, Y.; Polasky, S.; Liu, J.; Xu, W.; Wang, Q.; Zhang, L.; Xiao, Y.; Rao, E.; et al. Improvements in ecosystem services from investments in natural capital. Science 2016, 17, 1455–1459. [Google Scholar] [CrossRef] [PubMed]
  50. Isbell, F.; Calcagno, V.; Hector, A.; Connolly, J.; Harpole, W.S.; Reich, P.B.; Scherer-Lorenzen, M.; Schmid, B.; Tilman, D.; van Ruijven, J.; et al. High plant diversity is needed to maintain ecosystem services. Nature 2011, 477, 199–202. [Google Scholar] [CrossRef]
  51. Liu, J.; Li, S.; Ouyang, Z.; Tam, C.; Chen, X. Ecological and socioeconomic effects of China’s policies for ecosystem services. Proc. Natl. Acad. Sci. USA 2008, 15, 9477–9482. [Google Scholar] [CrossRef]
  52. Yang, C.; Li, J.; Jiang, S.; Tian, Y.; Li, C.; Yang, W.; Duan, H.; Wei, Z.; Huang, Y. The Impacts of Land-Use Changes on Ecosystem Service Value in the Yunnan–Kweichow Plateau, China. Sustainability 2024, 16, 1062. [Google Scholar] [CrossRef]
  53. Zhu, L.N.; Zhao, M.; Li, Y.F.; Fan, Y.; Wang, J. The Space-time Relationship between the Ecosystem Service Value and the Human Activity Intensity in Xi’an Metropolitan Area. J. Ecol. Rural. Environ. 2024, 40, 325–334. [Google Scholar]
  54. Li, J.; Hu, D.; Wang, Y.; Chu, J.; Yin, H.; Ma, M. Study of identification and simulation of ecological zoning through integration of landscape ecological risk and ecosystem service value. Sustain. Cities Soc. 2024, 107, 105442. [Google Scholar] [CrossRef]
  55. Hong, X.; Peng, Q.; Zheng, R.; Lin, W.; Fan, S.; Su, K. Evaluating the Spatial Evolution of the Eco-Economy Harmony in Anxi County, China, Based on Ecosystem Services Value. Sustainability 2024, 16, 1491. [Google Scholar] [CrossRef]
  56. Xia, F.; Huang, Y.; Dong, L. Comparison of comprehensive benefits of land-use systems under multi- and single-element governance. Land Use Policy 2024, 141, 107164. [Google Scholar] [CrossRef]
  57. You, C.; Qu, H.; Zhang, S.; Guo, L. Assessment of Uncertainties in Ecological Risk Based on the Prediction of Land Use Change and Ecosystem Service Evolution. Land 2024, 13, 535. [Google Scholar] [CrossRef]
  58. Dammag, A.Q.; Dai, J.; Cong, G.; Derhem, B.Q.; Latif, H.Z. Assessing and predicting changes of ecosystem service values in response to land use/land cover dynamics in Ibb City, Yemen: A three-decade analysis and future outlook. Int. J. Digit. Earth 2024, 17, 137–147. [Google Scholar] [CrossRef]
  59. Li, T.; Shi, D.; Jiang, S.; Li, Y.; Yu, H. Analysis of Spatial—Temporal Variation in Ecosystem Service Value in Shandong Province over the Last Two Decades. Sustainability 2024, 16, 515. [Google Scholar] [CrossRef]
  60. Zuo, Z.; Yang, Y.; Wang, R.; Li, J.; Zhang, P. Analysis of the gains and losses of ecosystem service value under land use change and zoning in Qiqihar. Front. Ecol. Evol. 2023, 11, 1192952. [Google Scholar] [CrossRef]
  61. Yin, D.; Yu, H.; Lu, Y.; Li, X. Spatial pattern evolution of territorial space and its effects on ecological response in the Yellow River Basin during 2000–2020. Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering. Chin. Soc. Agric. Eng. 2023, 39, 217–228. [Google Scholar]
  62. Bao, J.; Wang, W.; Zhao, T. Spatiotemporal Changes of Ecosystem Service Values in Response to Land Cover Dynamics in China from 1992 to 2020. Sustainability 2023, 15, 7210. [Google Scholar] [CrossRef]
  63. Jin, T.; Chen, Y.; Shu, B.; Gao, M.; Qiu, J. Spatiotemporal evolution of ecosystem service value and topographic gradient effect in the Da-Xiao Liangshan Mountains in Sichuan Province, China. J. Mt. Sci. 2023, 20, 2344–2357. [Google Scholar] [CrossRef]
  64. Chi, Y.; He, C. Impact of Land Use Change on the Spatial and Temporal Evolution of Ecosystem Service Values in South China Karst Areas. Forests 2023, 14, 893. [Google Scholar] [CrossRef]
  65. Zeng, J.; Bian, J.; Chen, W. Impact of slope farmland use change on ecosystem services value in China, 2000–2020. J. Mt. Sci. 2023, 20, 821–833. [Google Scholar] [CrossRef]
  66. Xu, X.; Peng, Y. Ecological Compensation in Zhijiang City Based on Ecosystem Service Value and Ecological Risk. Sustainability 2023, 15, 4783. [Google Scholar] [CrossRef]
  67. Xie, J.; Lu, Z.; Xiao, S.; Xiao, S.; Yan, C. Driving Force and Ecosystem Service Values Estimation in the Extreme Arid Region from 1975 to 2015: A Case Study of Alxa League, China. Chin. Geogr. Sci. 2021, 31, 1097–1107. [Google Scholar] [CrossRef]
  68. Gong, X.; Chang, C. Correlation and trade-off analysis of ecosystem service value and human activity intensity: A case study of Changsha, China. Environ. Dev. Sustain. 2023, 43, 100738. [Google Scholar] [CrossRef]
  69. Wang, H.; Niu, W.; Song, M.; Zhang, B.; Jin, Y. Construction and spatial optimization of ecological network in Shaanxi Province based on LUCC and its ESV response. Resour. Sci. 2023, 45, 1380–1395. [Google Scholar] [CrossRef]
  70. Xin, X.; Zhang, T.; He, F.; Zhang, W.; Chen, K. Assessing and simulating changes in ecosystem service value based on land use/cover change in coastal cities: A case study of Shanghai, China. Ocean Coast Manag. 2023, 239, 106591. [Google Scholar] [CrossRef]
  71. Huang, X.; Xie, Y.; Lei, F.; Cao, L.; Zeng, H. Analysis on spatio-temporal evolution and influencing factors of ecosystem service in the Changsha-Zhuzhou-Xiangtan urban agglomeration, China. Front. Environ. Sci. 2023, 11. [Google Scholar] [CrossRef]
  72. Farley, K.A.; Bremer, L.L. “Water Is Life”: Local Perceptions of Páramo Grasslands and Land Management Strategies Associated with Payment for Ecosystem Services. Ann. Assoc. Am. Geogr. 2017, 107, 371–381. [Google Scholar] [CrossRef]
  73. Byrne, A.T.; Hadrich, J.C.; Robinson, B.E.; Han, G. A factor-income approach to estimating grassland protection subsidy payments to livestock herders in Inner Mongolia, China. Land Use Policy 2020, 91, 104352. [Google Scholar] [CrossRef]
  74. Fan, S.; Zhao, C.; Zha, S. Analysis of the Impact of Policy Instruments on Payment for Grasslands Ecosystem Services (PGES) Implementation: A Case Study from Northwest China. Sustainability 2022, 14, 13779. [Google Scholar] [CrossRef]
  75. Behrendt, K.; Brown, C.; Qiao, G.; Zhang, B. Assessing the opportunity costs of Chinese herder compliance with a payment for environmental services scheme. Ecol. Econ. 2022, 193, 107313. [Google Scholar] [CrossRef]
  76. Li, A.; Wu, J.; Zhang, X.; Xue, J.; Liu, Z.; Han, X.; Huang, J. China’s new rural “separating three property rights” land reform results in grassland degradation: Evidence from Inner Mongolia. Land Use Policy 2018, 71, 170–182. [Google Scholar] [CrossRef]
  77. Bremer, L.L.; Farley, K.A.; Lopez-Carr, D.; Romero, J. Conservation and livelihood outcomes of payment for ecosystem services in the Ecuadorian Andes: What is the potential for ‘win-win’? Ecosyst. Serv. 2014, 8, 148–165. [Google Scholar] [CrossRef]
  78. Ranjan, R. Creating synergies between payments for ecosystem services, green bonds, and catastrophe insurance markets for enhanced environmental resilience. Land Use Policy 2024, 136, 106970. [Google Scholar] [CrossRef]
  79. Fan, S.; He, M.; Zhang, T.; Huo, Y.; Fan, D. Credibility measurement as a tool for conserving nature: Chinese herders’ livelihood capitals and payment for grassland ecosystem services. Land Use Policy 2022, 115, 106032. [Google Scholar] [CrossRef]
  80. Joslin, A. Dividing “Above” and “Below”: Constructing Territory for Ecosystem Service Conservation in the Ecuadorian Highlands. Ann. Assoc. Am. Geogr. 2020, 110, 1874–1890. [Google Scholar] [CrossRef]
  81. Chakrabarti, A.; Chase, L.; Strong, A.M.; Swallow, S.K. Making markets for private provision of ecosystem services: The Bobolink Project. Ecosyst. Serv. 2019, 37, 100936. [Google Scholar] [CrossRef]
  82. Russi, D.; Margue, H.; Oppermann, R.; Keenleyside, C. Result-based agri-environment measures: Market-based instruments, incentives or rewards? The case of Baden-Württemberg. Land Use Policy 2016, 54, 69–77. [Google Scholar] [CrossRef]
  83. Bremer, L.L.; Farley, K.A.; Lopez-Carr, D. What factors influence participation in payment for ecosystem services programs? An evaluation of Ecuador’s Socio Páramo program. Land Use Policy 2014, 36, 122–133. [Google Scholar] [CrossRef]
  84. Groth, M. Cost-effective Biodiversity Conservation: Procurement Auctions and Payment-by-Results. EuroChoices 2011, 10, 32–37. [Google Scholar] [CrossRef]
  85. Wätzold, F.; Drechsler, M.; Johst, K.; Mewes, M.; Sturm, A. A Novel, Spatiotemporally Explicit Ecological-Economic Modeling Procedure for the Design of Cost-Effective Agri-Environment Schemes to Conserve Biodiversity. Am. J. Agric. Econ. 2016, 98, 489–512. [Google Scholar] [CrossRef]
  86. Hecker, L.P.; Sturm, A.; Querhammer, L.; Wätzold, F. Cost-effectiveness of state-dependent versus state-independent agri-environment schemes for biodiversity conservation. Ecol. Econ. 2024, 217, 108088. [Google Scholar] [CrossRef]
  87. Ward, A.; Dargusch, P.; Thomas, S.; Liu, Y.; Fulton, E.A. A global estimate of carbon stored in the world’s mountain grasslands and shrublands, and the implications for climate policy. Glob. Environ. Chang. 2014, 28, 14–24. [Google Scholar] [CrossRef]
  88. Liu, Y.; Zhang, X.; Deng, H. Construction and Influencing Factors of Voluntary Compensation Subjects for Herders—From the Perspective of Sustainable Utilization of Grassland Resources. Sustainability 2024, 16, 2576. [Google Scholar] [CrossRef]
  89. Deng, Y.J.; Hou, M.Y.; Jia, L.; Wang, Y.Q.; Zhang, X.; Yao, S.B. Ecological compensation strategy of the old revolutionary base areas along the route of Long March based on ecosystem service value evaluation. Ying Yong Sheng Tai Xue Bao 2022, 33, 159–168. [Google Scholar] [CrossRef] [PubMed]
  90. Horák, I.; Marada, P. Erosion and the Economic Evaluation of the Conservation Grassland as an Existing Effective Tool to Reduce Erosion. Acta Univ. Agric. Silvic. Mendelianae Brun. 2023, 71, 141–153. [Google Scholar] [CrossRef]
  91. Lai, M.; Wu, S.H.; Yin, Y.H.; Pan, T. Accounting for eco-compensation in the three-river headwaters region based on ecosystem service value. Shengtai Xuebao 2015, 35, 227–236. [Google Scholar] [CrossRef]
  92. Dai, Q.-W.; Zhao, X.-Y. Discussion on Several Key Scientific Issues of Eco-compensation Mechanism in Gannan Tibetan Autonomous Prefecture. Acta Geogr. Sin. 2010, 65, 494–506. [Google Scholar] [CrossRef]
  93. Sannigrahi, S.; Bhatt, S.; Rahmat, S.; Paul, S.K.; Sen, S. Estimating global ecosystem service values and its response to land surface dynamics during 1995–2015. J. Environ. Manag. 2018, 223, 115–131. [Google Scholar] [CrossRef]
  94. Blignaut, J.; Mander, M.; Schulze, R.; Horan, M.; Dickens, C.; Pringle, C.; Mavundla, K.; Mahlangu, I.; Wilson, A.; McKenzie, M.; et al. Restoring and managing natural capital towards fostering economic development: Evidence from the Drakensberg, South Africa. Ecol. Econ. 2010, 69, 1313–1323. [Google Scholar] [CrossRef]
  95. Du, J.; Liu, F.; Zhou, Y.; Zhang, L.; Feng, C.; Wang, W. A Review of Ecosystem Services Assessment and Valuation of Protected Areas. Res. Environ. 2019, 32, 1475–1482. [Google Scholar] [CrossRef]
  96. Inoue, M. Change of Landscape and Ecosystem Services of Semi-natural Grassland in Mt. Sanbe, Shimane Prefecture, Japan. In Landscape Ecology for Sustainable Society; Hong, S.K., Nakagoshi, N., Eds.; Springer: Cham, Switzerland, 2017. [Google Scholar] [CrossRef]
  97. Zhao, X.; Yi, P.; Xia, J.; He, W.; Gao, X. Temporal and spatial analysis of the ecosystem service values in the Three Gorges Reservoir area of China based on land use change. Environ. Sci. Pollut. Res. Int. 2022, 29, 26549–26563. [Google Scholar] [CrossRef] [PubMed]
  98. Gascoigne, W.R.; Hoag, D.; Koontz, L.; Tangen, B.A.; Shaffer, T.L.; Gleason, R.A. Valuing ecosystem and economic services across land-use scenarios in the Prairie Pothole Region of the Dakotas, USA. Ecol. Econ. 2011, 70, 1715–1725. [Google Scholar] [CrossRef]
  99. Li, M.; Wang, X.; Chen, J. Assessment of Grassland Ecosystem Services and Analysis on Its Driving Factors: A Case Study in Hulunbuir Grassland. Front. Ecol. Evol. 2022, 10, 841943. [Google Scholar] [CrossRef]
  100. Mulwa, R.; Siikamaki, J.; Ndwiga, M.; Alvsilver, J. Influence of proximity to and type of foraging habitat on value of insect pollination in the tropics, with applications to Kenya. Afr. J. Agric. Resour. Econ. 2022, 17, 171–191. [Google Scholar] [CrossRef] [PubMed]
  101. Rewitzer, S.; Huber, R.; Grêt-Regamey, A.; Barkmann, J. Economic valuation of cultural ecosystem service changes to a landscape in the Swiss Alps. Ecosyst. Serv. 2017, 26, 197–208. [Google Scholar] [CrossRef]
  102. Kusi, K.K.; Khattabi, A.; Mhammdi, N. Analyzing the impact of land use change on ecosystem service value in the main watersheds of Morocco. Environ. Dev. Sustain. 2023, 25, 2688–2715. [Google Scholar] [CrossRef]
  103. Admasu, S. Assessing the impact of Land use changes on ecosystem services in the Alledighe rangeland, Ethiopia. Heliyon 2024, 10, 28798. [Google Scholar] [CrossRef]
  104. Aziz, T. Changes in land use and ecosystem services values in Pakistan, 1950–2050. Environ. Dev. 2021, 37, 100576. [Google Scholar] [CrossRef]
  105. Yi, F.; Yang, Q.; Wang, Z.; Li, Y.; Cheng, L.; Yao, B.; Lu, Q. Changes in Land Use and Ecosystem Service Values of Dunhuang Oasis from 1990 to 2030. Remote Sens. 2023, 15, 564. [Google Scholar] [CrossRef]
  106. Mendoza-González, G.; Martínez, M.L.; Lithgow, D.; Pérez-Maqueo, O.; Simonin, P.W. Land use change and its effects on the value of ecosystem services along the coast of the Gulf of Mexico. Ecol. Econ. 2012, 82, 23–32. [Google Scholar] [CrossRef]
  107. Gren, I.M.; Groth, K.H.; Sylvén, M. Economic Values of Danube Floodplains. J. Environ. Manag. 1995, 45, 333–345. [Google Scholar] [CrossRef]
  108. Hardaker, A.; Pagella, T.; Rayment, M. Ecosystem service and dis-service impacts of increasing tree cover on agricultural land by land-sparing and land-sharing in the Welsh uplands. Ecosyst. Serv. 2021, 48, 101253. [Google Scholar] [CrossRef]
  109. Jayalath, T.A.; Grala, R.K.; Grado, S.C.; Evans, D.L. Increasing provision of ecosystem services through participation in a conservation program. Ecosyst. Serv. 2021, 50, 101303. [Google Scholar] [CrossRef]
  110. Dong, J.; Ren, Z.; Zhang, X.; Liu, X. Pastoral Differentiations’ Effects on Willingness to Accept Valuation for Grassland Eco-Subsidy—Empirical Study of 410 Herder Households in Grass–Livestock Balance Sub-Policy Zones in Inner Mongolia, China. Sustainability 2023, 15, 10001. [Google Scholar] [CrossRef]
  111. Ingram, S.; Belcher, K.; Hesseln, H. Policy development to support ecosystem services on pasture systems in Saskatchewan: A case study. Land Use Policy 2023, 134, 106885. [Google Scholar] [CrossRef]
  112. Huber, R.; Finger, R. A Meta-analysis of the Willingness to Pay for Cultural Services from Grasslands in Europe. J. Agric. Econ. 2020, 71, 357–383. [Google Scholar] [CrossRef]
  113. Ning, J.; Jin, J.; Kuang, F.; Wan, X.; Zhang, C.; Guan, T. The Valuation of Grassland Ecosystem Services in Inner Mongolia of China and Its Spatial Differences. Sustainability 2019, 11, 7117. [Google Scholar] [CrossRef]
  114. Huber, R.; Le’Clec’h, S.; Buchmann, N.; Finger, R. Economic value of three grassland ecosystem services when managed at the regional and farm scale. Sci. Rep. 2022, 12, 4194. [Google Scholar] [CrossRef]
  115. Divinsky, I.; Becker, N.; Kutiel, P.B. Ecosystem service tradeoff between grazing intensity and other services—A case study in Karei-Deshe experimental cattle range in northern Israel. Ecosyst. Serv. 2017, 24, 16–27. [Google Scholar] [CrossRef]
  116. Shen, P.; Wu, L.; Huo, Z.; Zhang, J. A Study on the Spatial Pattern of the Ecological Product Value of China’s County-Level Regions Based on GEP Evaluation. Int. J. Environ. Res. Public Health 2023, 20, 3181. [Google Scholar] [CrossRef] [PubMed]
  117. Yu, F.; Li, X.B.; Wang, H.; Zhang, L.J.; Xu, W.H.; Fu, R. Accounting of gross ecosystem product based on emergy analysis and ecological land classification in China. Shengtai Xuebao 2016, 36, 1663–1675. [Google Scholar]
  118. Dolkar, P.; Xiao, Y.; Ouyang, Z.; Wang, L. Assessment of ecological conservation effect in Xishui county based on gross ecosystem product. Acta Ecol. Sin. 2020, 40, 499–509. [Google Scholar] [CrossRef]
  119. Zhao, N.; Wang, H.; Zhong, J.; Bai, Y.; Yi, S. Evaluation of the Gross Ecosystem Product and Analysis of the Transformation Path of “Two Mountains” in Hulunbuir City, China. Land 2023, 12, 63. [Google Scholar] [CrossRef]
  120. Dong, X.; Brown, M.T.; Pfahler, D.; Ingwersen, W.W.; Kang, M.; Jin, Y.; Yu, B.; Zhang, X.; Ulgiati, S. Carbon modeling and emergy evaluation of grassland management schemes in Inner Mongolia. Agr. Ecosyst. Environ. 2012, 158, 49–57. [Google Scholar] [CrossRef]
  121. Wanga, C.; Lic, X.; Yua, H.; Wang, Y. Tracing the spatial variation and value change of ecosystem services in Yellow River Delta, China. Ecol. Indic. 2019, 96, 270–277. [Google Scholar] [CrossRef]
  122. Yang, Q.; Liu, G.; Giannetti, B.F.; Agostinho, F.; Almeida, C.M.V.B.; Casazza, M. Emergy-based ecosystem services valuation and classification management applied to China’s grasslands. Ecosyst. Serv. 2020, 42, 101073. [Google Scholar] [CrossRef]
  123. Dong, X.B.; Yu, B.H.; Brown, M.T.; Zhang, Y.S.; Kang, M.Y.; Jin, Y.; Zhang, X.S.; Ulgiati, S. Environmental and economic consequences of the overexploitation of natural capital and ecosystem services in Xilinguole League, China. Energy Policy 2014, 67, 767–780. [Google Scholar] [CrossRef]
  124. Tindale, S.; Vicario-Modroño, V.; Gallardo-Cobos, R.; Hunter, E.; Miškolci, S.; Price, P.N.; Sánchez-Zamora, P.; Sonnevelt, M.; Ojo, M.; McInnes, K.; et al. Citizen perceptions and values associated with ecosystem services from European grassland landscapes. Land Use Policy 2023, 127, 106574. [Google Scholar] [CrossRef]
  125. Cebrián-Piqueras, M.A.; Karrasch, L.; Kleyer, M. Coupling stakeholder assessments of ecosystem services with biophysical ecosystem properties reveals importance of social contexts. Ecosyst. Serv. 2017, 23, 108–115. [Google Scholar] [CrossRef]
  126. Sigwela, A.; Elbakidze, M.; Powell, M.; Angelstam, P. Defining core areas of ecological infrastructure to secure rural livelihoods in South Africa. Ecosyst. Serv. 2017, 27, 272–280. [Google Scholar] [CrossRef]
  127. Shi, Q.; Chen, H.; Liang, X.; Zhang, H.; Liu, D. Cultural ecosystem services valuation and its multilevel drivers: A case study of Gaoqu Township in Shaanxi Province, China. Ecosyst. Serv. 2020, 41, 101052. [Google Scholar] [CrossRef]
  128. Yu, Y.; Li, J.; Zhou, Z.; Zeng, L.; Zhang, C. Estimation of the Value of Ecosystem Carbon Sequestration Services under Different Scenarios in the Central China (the Qinling-Daba Mountain Area). Sustainability 2020, 12, 337. [Google Scholar] [CrossRef]
  129. Peacock, R.; Bently, M.A.; Rees, P.; Blignaut, J.N. The benefits of ecological restoration exceed its cost in South Africa: An evidence-based approach. Ecosyst. Serv. 2023, 61, 101528. [Google Scholar] [CrossRef]
  130. Zang, Z.; Zou, X.Q. Connotation characterization and evaluation of ecological well-being based on ecosystem service theory. Ying Yong Sheng Tai Xue Bao 2016, 27, 1085–1094. [Google Scholar] [CrossRef]
  131. Durán, M.; Canals, R.M.; Sáez, J.L.; Ferrer, V.; Lera-López, F. Disruption of traditional land use regimes causes an economic loss of provisioning services in high-mountain grasslands. Ecosyst. Serv. 2020, 46, 101200. [Google Scholar] [CrossRef]
  132. Pei, S.; Xie, G.; Liu, C.; Zhang, C.; Li, S.; Chen, L. Dynamic Changes of Water Conservation Service of Typical Ecosystems in China within a Year Based on Data from CERN. Sustainability 2015, 7, 16513–16531. [Google Scholar] [CrossRef]
  133. Li, C.; Xu, C.; He, L.; Wang, S.T.; Chen, Y.H.; Xu, H. Purification function of coastal-terrestrial ecosystems and its evaluation: A case study of Huanghua City. J. Ecol. Rural. Environ. 2015, 31, 506–513. [Google Scholar] [CrossRef]
  134. Raviv, O.; Zemah-Shamir, S.; Izhaki, I.; Lotan, A. The effect of wildfire and land-cover changes on the economic value of ecosystem services in Mount Carmel Biosphere Reserve, Israel. Ecosyst. Serv. 2021, 49, 101291. [Google Scholar] [CrossRef]
  135. Brandão, M. Chapter 16: A Life Cycle Approach for Assessing the Impacts of Land-Use Systems on the Economy and Environment: Climate Change, Ecosystem Services, and Biodiversity. In Life Cycle Assessment; World Scientific: Singapore, 2022; pp. 285–298. [Google Scholar]
  136. Sallustio, L.; Quatrini, V.; Geneletti, D.; Corona, P.; Marchetti, M. Assessing land take by urban development and its impact on carbon storage: Findings from two case studies in Italy. Environ. Impact Assess. Rev. 2015, 54, 80–90. [Google Scholar] [CrossRef]
  137. Cai, Y.; Zhao, M.; Shi, Y.; Khan, I. Assessing restoration benefit of grassland ecosystem incorporating preference heterogeneity empirical data from Inner Mongolia Autonomous Region. Ecol. Indic. 2020, 117, 106705. [Google Scholar] [CrossRef] [PubMed]
  138. Zhang, L.; Li, J. Identifying priority areas for biodiversity conservation based on Marxan and InVEST model. Landsc. Ecol. 2022, 37, 3043–3058. [Google Scholar] [CrossRef]
  139. Xie, G.; Lu, C.; Xiao, Y. The Economic Evaluation of Grassland Ecosystem Services in Qinghai-Tibet Plateau. J. Mt. Sci. 2003, 21, 50–55. [Google Scholar]
  140. Lipton, J.; Özdemiroglu, E.; LeJeune, K.; Peers, J. Resource equivalency methods in the European Union: A ‘toolkit’ for calculating environmental liability. In Equivalency Methods for Environmental Liability; Lipton, J., Özdemiroğlu, E., Chapman, D., Peers, J., Eds.; Springer: Dordrecht, The Netherlands, 2018. [Google Scholar] [CrossRef]
  141. Ouyang, Z.Y.; Zhu, C.Q.; Yang, G.B.; Xu, W.H.; Zheng, H.; Zhang, Y.; Xiao, Y. Gross ecosystem product: Concept, accounting framework and case study. Acta Ecol. Sin. 2013, 33, 6747–6761. [Google Scholar] [CrossRef]
  142. Odum, H.T. Environmental Accounting: Emergy and Environmental Decision Making; John Wiley and Sons: New York, NY, USA, 1996. [Google Scholar]
  143. Reid, W.V.; Mooney, H.A.; Cropper, A.; Capistrano, D.; Carpenter, S.R.; Chopra, K.; Dasgupta, P.; Dietz, T.; Duraiappah, A.K.; Hassan, R.; et al. Ecosystems and Human Well-Being-Synthesis: A Report of the Millennium Ecosystem Assessment; Island Press: Washington, DC, USA, 2005. [Google Scholar]
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Figure 1. The review process scheme.
Figure 1. The review process scheme.
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Figure 2. Trends of documents on grassland ecosystem services.
Figure 2. Trends of documents on grassland ecosystem services.
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Figure 3. Distribution by countries of the publications on grassland ES (n = 4608) collected with the Scopus database in the period 1980–2024.
Figure 3. Distribution by countries of the publications on grassland ES (n = 4608) collected with the Scopus database in the period 1980–2024.
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Figure 4. Distribution of documents, collected with the Scopus database in the period 1980–2024, by subject area.
Figure 4. Distribution of documents, collected with the Scopus database in the period 1980–2024, by subject area.
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Figure 5. Co-word (all keywords co-occurrence) network map of the 4608 documents collected with the Scopus database in the period 1980–2024.
Figure 5. Co-word (all keywords co-occurrence) network map of the 4608 documents collected with the Scopus database in the period 1980–2024.
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Figure 6. Density map displaying words from author keywords of the 4608 documents collected with the Scopus database in the period 1980–2024.
Figure 6. Density map displaying words from author keywords of the 4608 documents collected with the Scopus database in the period 1980–2024.
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Figure 7. Co-word (all keywords co-occurrence) network map of the 1033 documents collected with the Scopus database in the period 1980–2024.
Figure 7. Co-word (all keywords co-occurrence) network map of the 1033 documents collected with the Scopus database in the period 1980–2024.
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Figure 8. Co-word (all keywords co-occurrence) network map of the 1033 documents collected with the Scopus database in the period 1980–2024.: focus on the keywords “economics”, “ecological economics”, “willingness to pay”, “economic analysis”, “payment for ecosystem services”.
Figure 8. Co-word (all keywords co-occurrence) network map of the 1033 documents collected with the Scopus database in the period 1980–2024.: focus on the keywords “economics”, “ecological economics”, “willingness to pay”, “economic analysis”, “payment for ecosystem services”.
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Figure 9. The individual ecosystem services estimated from an economic point of view.
Figure 9. The individual ecosystem services estimated from an economic point of view.
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Figure 10. Multiple ecosystem services estimated from an economic point of view.
Figure 10. Multiple ecosystem services estimated from an economic point of view.
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Figure 11. Types of data used to estimate the different ES grasslands from an economic point of view (ESV: ecosystem service value; 4 ES categories: provisioning, regulating, supporting, cultural services; LUCC: land use/cover change; LNPS: land cover data, net primary productivity, precipitation, and soil erosion data; LSE: land use data and socio-economic data; LCSEB: LUCC, climatic, socioeconomic, and biophysical data; BEM: bio-economic modeling approach; SSI: data from structured and semi-structured interviews; R: data from others research; MS: multi-source data; SE: Socio-economic data).
Figure 11. Types of data used to estimate the different ES grasslands from an economic point of view (ESV: ecosystem service value; 4 ES categories: provisioning, regulating, supporting, cultural services; LUCC: land use/cover change; LNPS: land cover data, net primary productivity, precipitation, and soil erosion data; LSE: land use data and socio-economic data; LCSEB: LUCC, climatic, socioeconomic, and biophysical data; BEM: bio-economic modeling approach; SSI: data from structured and semi-structured interviews; R: data from others research; MS: multi-source data; SE: Socio-economic data).
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Table 1. The main typologies of grasslands.
Table 1. The main typologies of grasslands.
Type of GrasslandsDescription
AnnualThe forage is established annually
CultivatedThe forage is established with domesticated species that receive periodic agricultural treatments
PermanentWith perennial or self-seeding annual forage species that may survive indefinitely or often due to limiting factors that prevent other uses (excessive slope, shallow soil depth, outcropping rockiness, stoniness)
TemporaryThe vegetation is composed of annual, biennial, or perennial forage species kept for only a few years
NaturalizedThe forage species are mainly introduced from other geographical places that have established themselves and have persisted for a long time in existing environmental and management conditions
Semi-naturalA managed ecosystem dominated by native or naturally occurring herbs and other herbaceous species
Table 2. The ESs provided by the main typologies of European grasslands.
Table 2. The ESs provided by the main typologies of European grasslands.
European AreaGrasslands TypeMain Functions ProvidedEcosystem Services Category
Nordic countries (Denmark, Finland, Iceland, Norway and Sweden)Naturallivestock productionProvisioning
Semi-naturalgrazing
maintaining biodiversity
maintaining landscape		Provisioning
Supporting
Cultivatedwinter fodder
milk in summer		Provisioning
Temperate regions (such as Ireland, the UK, France, the Benelux, Germany, Czech Republic, Slovakia, and Poland)Permanentfeed production
maintaining biodiversity
soil erosion control		Provisioning
Supporting
Regulating
Temporaryfeed production
maintaining biodiversity		Provisioning
Supporting
Mediterranean basinNatural/
Semi-natural		livestock production
soil erosion control
carbon sequestration
biodiversity conservation
maintaining landscape		Multiple
Table 3. The top ten contributor journals collected with the Scopus database in the period 1980–2024.
Table 3. The top ten contributor journals collected with the Scopus database in the period 1980–2024.
Source TitleNumber of Documents% of Documents
Ecological indicators1764%
Shengtai xuebao1754%
Science of the total environment1383%
Sustainability Switzerland1313%
Land1152%
Agriculture ecosystems and environment1102%
Journal of Environmental Management952%
Remote sensing712%
Plos one702%
Journal of Applied Ecology651%
Total of the top ten journals 114625%
Number of documents published in journals430493%
Number of documents related to grassland ESs4608100%
Table 4. The top ten cited manuscripts collected with the Scopus database in the period 1980–2024.
Table 4. The top ten cited manuscripts collected with the Scopus database in the period 1980–2024.
Title of the Top Ten Cited ManuscriptsJournal’s NameYear of PublicationNumber of Citations
Partitioning selection and complementarity in biodiversity experiments [42]Nature20012242
Quantifying the evidence for biodiversity effects on ecosystem functioning and services [43]Ecology letters20062006
Biodiversity and ecosystem stability in a decade-long grassland experiment [44]Nature20061611
Soil biodiversity and soil community composition determine ecosystem multifunctionality [45]Proc. Natl. Acad. Sci.20141495
Confronting a biome crisis: Global disparities of habitat loss and protection [46]Ecology letters20041321
Microbial diversity drives multifunctionality in terrestrial ecosystems [47]Nature Communications20161306
Incorporating plant functional diversity effects in ecosystem service assessments [48]Proc. Natl. Acad. Sci.20071250
Improvements in ecosystem services from investments in natural capital [49]Science20161228
High plant diversity is needed to maintain ecosystem services [50]Nature20111150
Ecological and socioeconomic effects of China’s policies for ecosystem services [51]Proc. Natl. Acad. Sci.20081125
Table 5. The groups of ES grassland economic evaluation methodologies identified, the number of publications for each group, and the relevant bibliographical references (our elaboration).
Table 5. The groups of ES grassland economic evaluation methodologies identified, the number of publications for each group, and the relevant bibliographical references (our elaboration).
MethodologiesNumber of DocumentsReferences
Ecosystem Service Value (ESV) Assessment91[52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71] *
Resource Equivalency Approach22[72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92]
Econometric Models11[12,93,94,95,96,97,98,99,100,101]
Benefit Transfer Method 7[102,103,104,105,106,107,108]
Contingent Valuation Method (CVM)7[109,110,111,112,113,114,115]
Gross Ecosystem Product (GEP) 4[116,117,118,119]
Emergy Value Method4[120,121,122]
Focus Groups, Perception/Social Preference Method 4[123,124,125,126]
Net Present Value3[127,128,129]
Replacement Cost Method3[115,130,131,132]
Market and Shadow Price Method2[133,134]
Life Cycle Assessment Approach1[135]
Social Cost1[136]
Theory of Value and Random Utility 1[137]
Cost–Benefit Analysis1[138]
* For reasons of space, only publications from the last year are reported.
Table 6. Typologies of data used for the economic analysis of grassland ES.
Table 6. Typologies of data used for the economic analysis of grassland ES.
Type of DataNumber of Documents
Land use/cover change (LUCC)60
Data from structured and semi-structured interviews (SSIs)22
Land cover data, net primary productivity, precipitation, and soil erosion data (LNPS)21
Multi-source data (MS)14
Bio-economic modeling approach (BEM)12
Data from others research (R)12
Land use data and socio-economic data (LSE)9
LUCC, climatic, socioeconomic, and biophysical data (LCSEB)7
Socio-economic data (SE)4
Total161
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Pergola, M.; De Falco, E.; Cerrato, M. Grassland Ecosystem Services: Their Economic Evaluation through a Systematic Review. Land 2024, 13, 1143. https://doi.org/10.3390/land13081143

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Pergola M, De Falco E, Cerrato M. Grassland Ecosystem Services: Their Economic Evaluation through a Systematic Review. Land. 2024; 13(8):1143. https://doi.org/10.3390/land13081143

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Pergola, Maria, Enrica De Falco, and Michele Cerrato. 2024. "Grassland Ecosystem Services: Their Economic Evaluation through a Systematic Review" Land 13, no. 8: 1143. https://doi.org/10.3390/land13081143

APA Style

Pergola, M., De Falco, E., & Cerrato, M. (2024). Grassland Ecosystem Services: Their Economic Evaluation through a Systematic Review. Land, 13(8), 1143. https://doi.org/10.3390/land13081143

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