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Review

The Progress and Prospects in the Scenario Simulation Research on the Sustainability of Regional Ecosystem Services Based on a “Safe Operating Space”

by
Xiuquan Zhang
1 and
Xuening Fang
1,2,*
1
School of Environmental and Geographical Sciences, Shanghai Normal University, Shanghai 200234, China
2
Yangtze River Delta Urban Wetland Ecosystem National Field Scientific Observation and Research Station, Shanghai 200234, China
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(14), 11249; https://doi.org/10.3390/su151411249
Submission received: 16 May 2023 / Revised: 29 June 2023 / Accepted: 10 July 2023 / Published: 19 July 2023
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Abstract

:
Integrating scenarios and models to assess the sustainability of future regional ecosystem services is at the forefront of ecosystem service science. However, there are a lack of comprehensive reviews on this topic. Therefore, this study provides a systematic review of the research progress considering two aspects: ecosystem service scenario simulation and sustainability assessments based on the concept of a “safe operating space.” We found that (1) a number of studies have already started to explore methods for evaluating the sustainability of future ecosystem services; (2) in terms of scenario construction methods, most existing studies have adopted the global classical scenario downscaling approach, while less consideration has been given to the important socio-economic-environmental characteristics of a region itself, which affect the credibility and policy relevance of scenarios; and (3) in terms of sustainability simulation evaluation, most existing studies are qualitative comparisons of the sustainability of ecosystem services within different scenarios, while quantitative methods are lacking. We proposed an approach that combined participatory scenario construction and a regional safe operating space to address the above identified challenges. Successfully implementing this research approach would provide decision makers with more accurate and practical early warning information regarding the sustainability of future ecosystem services.

1. Introduction

Ecosystem services (ES) refer to the various benefits (such as food provisioning, air purification, and water retention) that humans obtain from ecosystems, serving as a crucial foundation for human well-being [1,2]. ES sustainability is the ability of ecosystems to provide services without compromising their integrity while promoting socioeconomic development [3,4,5]. Research has indicated that 12 out of the 17 United Nations Sustainable Development Goals (SDGs) are closely related to the sustainability of ES [6]. Many of China’s ecological civilization policies, such as ecological red lines, ecological security barriers, and ecological compensation systems, are based on the concept of ES sustainability. However, over the past few decades, the environmental changes caused by human activities have significantly altered the structure and functioning of ecosystems, posing a severe threat to their sustainability [7]. Therefore, a timely and accurate assessment of the impacts of human activities on the sustainability of ES is a prerequisite for formulating effective ecological conservation strategies and a necessary requirement for achieving sustainable development goals and ecological civilization.
Early research was primarily focused on evaluating the spatiotemporal patterns and driving factors of historical and current ES, providing a solid foundation for understanding the mechanisms of ES changes [8,9,10]. In recent years, researchers have recognized the need for ecosystem management to consider the long-term effects of our current behaviors on future ES. It is essential to understand questions such as: what are the potential socioeconomic development pathways in the future? How do different pathways affect the sustainability of ES? Are there alternative pathways that can achieve both economic development and ES protection? [11]. Therefore, predicting future socioeconomic development pathways and assessing their impacts on the sustainability of ES are of great significance in assisting decision makers with formulating relevant policies and avoiding irreversible ecosystem degradation.
Predictive models are one of the most commonly used tools for assessing the sustainability of future ES. However, due to the complex characteristics of social–ecological systems, such as their adaptability, nonlinearity, and interactive feedback, future changes in ES are fundamentally uncertain and difficult to predict. Scenarios refer to a series of logically consistent hypothetical event sequences constructed based on causal processes [12]. Unlike predictive models, scenario analysis is a structured approach to analyzing the key uncertainties in a system, providing a means for addressing the limitations of predictive models in analyzing future uncertainties. The integration of scenarios (such as the Shared Socioeconomic Pathways (SSPs)) and models (such as the InVEST model) for assessing the sustainability of future ES has become a frontier in ES science. However, there are a lack of comprehensive reviews on this topic. Therefore, this study will provide a systematic summary of the research progress considering two aspects: ES scenario simulation and sustainability assessments based on the concept of a “safe operating space.” Additionally, we will propose effective approaches to integrating them for landscape sustainability.

2. Latest Advances in Scenario Simulation Research

Scenario simulation, as a great method for assessing the future sustainability of ES, has drawn attention from scientists and practitioners for several decades. Scenario construction refers to a rational description of the future based on a series of key drivers and causal relationships [1]. It can generally be divided into four stages: scoping, defining scenarios, scenario specification, and scenario exploration. Traditional scenario construction often presents a narrative with a clear storyline [12]. Qualitative storylines help to illustrate complex variables that are difficult to quantify in the social–ecological system and are more readily accepted by stakeholders, thus expanding the impact of the research. However, using only qualitative descriptions often raises questions about the scientific rigor of the scenarios [13].
Recent research has found that integrating quantitative models into the scenario exploration stage can greatly enhance the internal consistency and credibility of these scenarios [11]. On the one hand, qualitative storylines can describe the potential future trends of key drivers, providing a reasonable background for inputting into models. On the other hand, quantitative models can provide important data support for qualitative scenarios. For example, quantitative models can translate socioeconomic scenarios into predictions of future ES, which can then be further integrated into a storyline as scientific evidence. Therefore, combining qualitative scenarios with quantitative models in scenario simulation research can greatly enhance its scientific rigor, policy relevance, and practical applicability.
An important goal of scenario simulation research is to assist decision makers with exploring future possibilities and understanding the impacts of their current decisions in the face of key uncertainties, thereby guiding them to make more informed decisions [14]. Therefore, under the guidance of researchers, it is crucial to involve decision makers and other stakeholders in the scenario construction process, in order to jointly explore alternative future scenarios through participatory scenario simulation research. Stakeholder involvement is beneficial for integrating different types of knowledge, such as scientific knowledge, perspectives, expectations, and desires. It also facilitates social learning and collective action, thereby better achieving the desired objectives [15]. In recent years, participatory scenario simulation research, as an applied research method, has been increasingly applied in environmental and ecosystem management decision-making research and practice [16,17].

3. The Application Progress of Scenario Simulation Methods in the Assessment of Ecosystem Service Sustainability

While scenario simulation approaches have been well established, their application in the field of ES is a recent development. The application of scenario simulation methods in the assessment of ES sustainability began in the early 21st century, with the most notable being the Millennium Ecosystem Assessment (MEA) and its future scenario simulation research. The MEA pioneered the integration of qualitative scenarios and quantitative models to explore the consequences of future ecosystem functioning, ES, and human well-being within four scenarios: Global Orchestration, Order from Strength, Adapting Mosaic, and Technology Garden [1]. Since then, future ES scenario simulations have been prevalent at global and national scales [18]. For example, Watson et al. [19] investigated the changes in the global ES values under different climate emission scenarios, the IPBES explored the application of ES scenarios and models for decision making at a global scale (IPBES, 2019), and Nelson et al. [20] examined the impacts of future climate change scenarios on the ES and human well-being in the United States. However, there is still a relative lack of comprehensive future ES scenario simulation research at the regional and landscape scales, and further improvements are needed in existing technologies and methods.
Regarding scenario drivers, current regional-scale scenario simulation research mostly focuses on direct drivers such as climate change [21], land-use change [22], and their impacts on future ES. For instance, a recent review found that over 90% of 52 aquatic ES scenario simulation studies only considered one or two easily quantifiable direct drivers, with less consideration of indirect drivers such as population, policy, and economy [23]. The interdisciplinary nature and difficulty of their quantification may be significant reasons for neglecting these indirect driving factors. However, research has suggested that narrative scenarios enriched with indirect driving factors are equally important. In many cases, the degradation of ecosystem services caused by indirect factors may outweigh the influence of direct drivers such as climate change [24]. Additionally, narrative scenarios with rich plots can interact strongly with stakeholders, enhancing the credibility and policy relevance of these scenarios and promoting the generation of new ideas and social learning [11]. Therefore, while quantitative scenarios based on direct drivers play a crucial role in exploring the future changes in ES, their practical value can be further enhanced by linking them with indirect socioeconomic drivers such as policy, market demands, and human preferences.
In terms of scenario-setting methods, the downscaled application of global typical scenarios to specific study areas is a classic approach in regional-scale future ES scenario simulation research. For example, Zhang et al. [25] explored the impact of future urban expansion on ecosystem services by downscaling the global “Shared Socioeconomic Pathways” (SSPs) scenarios to regional scale. Hernández-Blanco et al. [26] investigated the changes in the ES values in Latin America and the Caribbean by downscaling four global scenarios from the “Great Transition Initiative.” Downscaling the global typical scenario framework facilitates comparability across different regional case studies, but this method often overlooks the consideration of regionally important social–economic–environmental conditions. Diversified scenarios based on regional key features have important value in attracting residents and decision makers’ participation [27]. Recently, many studies have begun to set scenarios based on regional characteristics. For example, Fu et al. [28] simulated the future changes in the ES in the Altai region of China by setting land-change scenarios based on the existing literature records. Liu et al. [29] constructed scenarios based on local policy features to simulate the future ES conditions in the Bohai Sea region. These studies are beneficial explorations for constructing scenario simulation methods that align with regional characteristics, but further research is needed on how to incorporate stakeholders’ perspectives into scenario construction and how to integrate regional key features into global typical scenarios.
In terms of sustainable simulation assessments, there are currently several models (Table 1) available for simulating the future changes in ES under different scenarios, such as the INVEST model, TESSA model, IMAGE model, and ARIES model, among others. However, it is still unclear how to determine the sustainability of these ES changes under different scenarios. Often, existing studies have qualitatively compared the trends of future ES changes under different scenarios [25,26,27,28], and researchers tend to believe that a decrease in ES quantity indicates unsustainability [30]. In fact, based on the sustainability theory [3], regional socio-economic development inevitably leads to a certain degree of reduction in ES, but its ecological impacts need to be controlled within a certain range. Determining the reasonable range for the sustainable use of ES is crucial for a quantitative evaluation of ES sustainability. The “Planetary Boundaries” concept, defining a “Safe Operating Space,” provides a good framework for determining the sustainable utilization range of ES.

4. The Latest Research Developments on the “Safe Operating Space” Concept

In addition to ES scenario simulation, another concept that is closely related to the sustainability evaluation of future ES is the “safe operating space” concept. The concept of the “Safe Operating Space” originates from the “Planetary Boundaries” theory (Figure 1) [31,32]. This theory suggests that the Earth’s life support systems have thresholds or boundaries, and if degradation exceeds these boundaries, unexpected and irreversible catastrophes may occur. The space within these boundaries where human development can safely operate is defined as the “Safe Operating Space” [33]. Early research in this field was primarily focused on defining indicators and threshold methods for these “Planetary Boundaries” [34]. In recent years, the concept of the Safe Operating Space has started to be integrated with the socio-economic dimension and has gradually been applied in regional environmental sustainability assessment studies.
Regarding its integration with the socio-economic dimension, the early stages of the Safe Operating Space concept primarily considered the Earth’s biophysical state and did not account for the socio-economic development of human society. However, sustainable development requires considering both human well-being and ecological dimensions. In this context, researchers have proposed the concept of a “Safe and Just Operating Space”, which improves human well-being while avoiding exceeding ecological thresholds [34]. Within this space, both the sustainable use of natural resources within planetary boundaries and the realization of human well-being can be ensured. However, studies have shown that, globally, most countries can only meet basic well-being needs such as food, electricity, and poverty eradication within these planetary boundaries [35]. To achieve a higher quality of life, resource consumption would need to be 2–6 times the planetary boundary values. Research has also found that, for developing countries to achieve the well-being levels of developed countries within the planetary boundaries, developed countries would need to reduce their ecological footprint by 40–50% [36]. Therefore, ensuring global well-being within the planetary boundaries presents significant challenges and requires the coordination and rational allocation of the “Safe and Just Operating Space” in different regions.
In terms of its regional-scale applications, the concept of the “Safe Operating Space” was initially developed at the global scale, but recent research has shown that the regional scale is a more critical level for studying and implementing this concept. On the one hand, the regional scale is crucial for decision-making and implementation processes, and on the other hand, regional-scale sustainable development issues are of greater concern to stakeholders [37]. Currently, there are two approaches to applying the Safe Operating Space concept at the regional scale: (1) allocating the global “Planetary Boundaries” top-down to countries and regions [37]. However, this approach requires coordination and communication among different countries and regional stakeholders, which can present challenges in terms of practical applications. (2) Determining the Safe Operating Space from the bottom-up based on the characteristics of regional ecosystem dynamics [38]. The underlying assumption of this approach is that only when most regions are globally operating within the Safe Operating Space can global human development be ensured within the Planetary Boundaries. Compared to the first approach, this method is more operationally feasible and has been widely applied in quantitative assessments of environmental sustainability. For example, Hossain et al. [39] used system dynamics to simulate whether the agricultural system in the coastal regions of Bangladesh exceeds the regional Safe Operating Space under different scenarios of climate change and sea-level rise. Cooper and Dearing [40] predicted the probability of the Indian fisheries system to operate within the Safe Operating Space under climate change conditions based on a constructed fisheries socio-ecological coupling system model.
In China, research on the Safe Operating Space is still in its early stages. Existing research progress is mainly focused on introducing the concepts of “Planetary Boundaries” and the Safe Operating Space and distinguishing them from other related concepts. For example, some researchers have reviewed the concept of “Planetary Boundaries” and compared it with environmental carrying capacity and ecological red lines. Some researchers have explored the complementarity of the concepts of “Planetary Boundaries” and the ecological footprint in the field of sustainable assessment. Some researchers have analyzed the relationship between ecological vulnerability and the Safe Operating Space and discussed its application in ecological risk research. However, empirical research in this area is still lacking. Currently, only a few studies have attempted to evaluate environmental sustainability by integrating the Safe Operating Space concept with ecological footprint analyses, and there is limited research on applying the Safe Operating Space method to assess the sustainability of regional ecosystem services.

5. Knowledge Gaps in the Simulation of Regional Future Ecosystem Service Sustainability Scenarios Based on the “Safe Operating Space” Concept

Based on the above progress, this study believes that previous research has made positive explorations in assessing the sustainability of future regional ES through the integration of scenarios and models, laying a solid foundation for related studies. However, due to the interdisciplinary and complex nature of this field, there are still some aspects that need to be improved. (1) In terms of scenario construction methods, existing research has focused more on easily quantifiable direct drivers, such as climate change and land-use change, while giving less consideration to underlying indirect drivers, such as economics, society, and policies. (2) In terms of ecosystem service simulation, together, terrestrial ecosystems and freshwater ecosystems form closely connected landscape mosaics, providing important ES such as freshwater supply, food supply, flood regulation, surface water purification, groundwater purification, soil conservation, climate regulation, terrestrial aesthetics and recreation, lake recreation, and more. Existing single ES models have difficulty simulating the interrelations between multiple types of ecosystems and their associated ES. (3) In terms of the simulation and evaluation of ES sustainability, existing research has mostly relied on qualitative comparisons of ES sustainability under different scenarios, lacking quantitative methods. The “Safe Operating Space” approach is an advanced quantitative method for assessing environmental sustainability. However, this method is currently applied more at the global and national scales, with limited application in the assessment of ES sustainability at the regional scale.

6. Future Research Prospects

Based on the aforementioned limitations, we propose comprehensive approaches to conducting scenario-based simulations for assessing the sustainability of future regional ES. This idea integrates methods such as structural equation modeling, stakeholder interviews, bibliometrics, multi-model coupling, and the concept of a “Safe Operating Space”. The approaches we propose are applied only to supporting services (e.g., habitat quality), provisioning services (e.g., water yield), and regulating services (e.g., water purification). The research approach involves the following steps: (1) understanding the historical dynamics and spatial variations in regional ES, elucidating the mechanisms through which indirect and direct drivers influence the dynamics of ES, (2) collaboratively constructing future socio-economic development scenarios with stakeholders, and (3) using multiple ecosystem process models to simulate and assess the sustainability of ecosystem service provision under different development scenarios. This approach aims to provide scientific evidence for regional ecosystem management.
In terms of scenario construction methods, a “participatory” approach can be adopted (Figure 2). This involves involving stakeholders in the process to combine top-down typical global scenarios with bottom-up regional socio-economic-environmental characteristics. In other words, within the constraints of the input requirements for ecosystem process models, scenarios are constructed by integrating prototype drivers from typical global scenarios, important regional socio-economic processes, and environmental conditions, as well as stakeholder perspectives. Firstly, based on the selected combination of ecosystem process models, the direct drivers (such as rainfall, temperature, and land-use changes) and potential indirect drivers (such as population, economy, dietary structure, and land policies) required for the model’s operation are determined, and they serve as the candidate variables of interest for future scenario construction. Secondly, through a bibliometric analysis, the important prototype drivers of typical scenarios related to current and future changes in ES globally or in other regions are identified. These prototype drivers, such as market changes, social value changes, technological changes, inequality, and system collapse, can expand the range of socio-economic-environmental changes in the study area and compensate for ideas about the future changes in ES that have not yet emerged in the study area. Next, stakeholders such as managers, residents, and experts are selected using the snowball sampling method for interviews. Finally, based on the identified most important uncertainty factors, a scenario matrix is constructed to determine the themes and storyline of each quadrant in the matrix, and qualitative descriptions of the scenario narratives are provided.
In terms of ES simulation, an approach that combines multiple models through integrated coupling can be employed. For example, a combination of spatially explicit terrestrial ecosystem process models (e.g., Agro-IBIS), land hydrological pathway models (e.g., THMB), and lake water quality models (e.g., WQM) can be used to simulate terrestrial and freshwater eco-hydrological processes [11]. The corresponding indicators of the simulated ecosystem processes determined by the above three linked models can be used to indicate the important types of ES in the region. Most of the sub-models of InVEST can also be used as simulation models for this study, except for the sub-model on cultural services. We acknowledge that the choice of simulation model, the needs of the local population, and the scale of analysis can affect the results and applicability of these methods [41]. We suggest that, when using these approaches, users should explicitly consider the choice of simulation model, the needs of the population, and the scale of the analysis.
In terms of sustainability simulation and assessment, the “safe operating space” concept is an advanced quantitative method for evaluating environmental sustainability. However, this method is currently mostly applied at the global and national scales, with limited applications in the assessment of regional-scale ES sustainability. In the future, a bottom-up regionalized approach based on the “safe operating space” concept could be used for a quantitative evaluation of ES sustainability. The method for discerning the safe operating spaces of ES under different types of trends is as follows: after converting qualitative scenario narratives into quantitative model inputs, firstly, ES changes under different scenarios are simulated based on localized ES models.
Then, the future trends of different ES are classified (Figure 3), which can be divided into four categories based on the study by Dearing et al. (2014): linear trends, nonlinear trends, thresholds, and early warning signals [38]. For different trend types, the following methods are used to determine whether ES are sustainable (whether they are within the safe operating space): (1) linear trends: environmental constraints are used, which means that, when a certain environmental variable exceeds or falls below a certain value, it will have a significant negative impact. For example, exceeding a certain standard of PM2.5 in the air will have a significant impact on human health. (2) Nonlinear trends: the normal range of variation is used to determine whether a trend is within a safe range. Nonlinear trends usually have a normal fluctuation range, and ecosystem service changes within this range are considered to be safe. (3) Threshold trends: some ecosystem processes are prone to transitioning between different states once they exceed a certain environmental threshold, and these state transitions may have serious consequences for ecosystems and society. (4) Early warning signals: before a system undergoes a state transition, there are often statistical changes, such as an increased variability, increased skewness, and enhanced system autocorrelation. These statistical indicators often indicate a decrease in the system’s stability, a loss of resilience, or the beginning of a relatively rapid state transition.

7. Conclusions

The sustainability of regional ES can contribute to the achievement of sustainable development goals and provide a scientific basis for ecological civilization construction. Early studies were primarily focused on evaluating the spatial and temporal patterns and drivers of past and present ES. In recent years, scientists have recognized the importance of considering the impact of future socioeconomic development on the sustainability of ES in ecosystem management. Evaluating the sustainability of future ES in a region has become a frontier and hotspot in ES science. Scenario simulation is a cutting-edge approach to assessing the future changes in ES. The challenge lies in how to construct socioeconomic development scenarios that are consistent with regional characteristics and quantitatively assess the sustainability of ES under different scenarios. This study proposed an innovative approach that combined “participatory” scenario construction with the concept of a regional “safe operating space” to address these challenges. On the one hand, considering the limitations of ES modeling, a “participatory” approach involving stakeholders can be used to integrate typical global scenarios with regional socioeconomic and environmental characteristics, thereby constructing future scenarios for the socioeconomic development in a region. On the other hand, based on the regional “safe operating space” concept, the trends of ES changes under different scenarios can be classified into four categories: linear trends, nonlinear trends, threshold trends, and early warning signals. Quantitative evaluations of their sustainability can be conducted using methods such as environmental constraints, normal variation range, threshold detection, and statistical indicators of variability. This study presented a new approach that combined “participatory” scenario simulation with the regional “safe operating space” to incorporate regional socioeconomic and environmental conditions into the scenario construction process and quantitatively assess the sustainability of ES under different scenarios. It has significant practical implications for implementing “ecological civilization” construction and promoting sustainable development.

Author Contributions

Conceptualization, X.Z. and X.F.; methodology, X.Z.; formal analysis, X.Z.; investigation, X.Z.; writing—original draft preparation, X.Z. and X.F.; writing—review and editing, X.Z. and X.F.; supervision, X.F.; funding acquisition, X.F. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the National Natural Science Foundation of China (No. 42201101).

Acknowledgments

We thank Zhen Zhong for his assistance with the translation of the manuscript.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Reid, W.; Mooney, H.; Cropper, A.; Capistrano, D.; Carpenter, S.; Chopra, K. Millennium Ecosystem Assessment. In Ecosystems and Human Well-Being: Synthesis; Island Press: Washington, DC, USA, 2005. [Google Scholar]
  2. Fang, X.; Zhou, B.; Tu, X.; Ma, Q.; Wu, J. What Kind of a Science is Sustainability Science?” An Evidence-Based Reexamination. Sustainability 2018, 10, 1478. [Google Scholar] [CrossRef] [Green Version]
  3. Wu, J. Landscape sustainability science: Ecosystem services and human well-being in changing landscapes. Landsc. Ecol. 2013, 28, 999–1023. [Google Scholar] [CrossRef]
  4. Zheng, H.; Wang, L.; Peng, W.; Zhang, C.; Li, C.; Robinson, B.E.; Wu, X.; Kong, L.; Li, R.; Xiao, Y.; et al. Realizing the values of natural capital for inclusive, sustainable development Informing China’s new ecological development strategy. Proc. Natl. Acad. Sci. USA 2019, 116, 8623–8628. [Google Scholar] [CrossRef] [Green Version]
  5. Fang, X.; Zhao, W.; Fu, B.; Ding, J. Landscape service capability, landscape service flow and landscape service demand: A new framework for landscape services and its use for landscape sustainability assessment. Prog. Phys. Geogr. 2015, 39, 817–836. [Google Scholar] [CrossRef]
  6. Wood, S.L.R.; Jones, S.K.; Johnson, J.A.; Brauman, K.A.; Chaplin-Kramer, R.; Fremier, A.; Girvetz, E.; Gordon, L.J.; Kappel, C.V.; Mandle, L.; et al. Distilling the role of ecosystem services in the Sustainable Development Goals. Ecosyst. Serv. 2018, 29, 70–82. [Google Scholar] [CrossRef] [Green Version]
  7. Fang, X.; Ma, Q.; Wu, L.; Liu, X. Distributional environmental justice of residential walking space: The lens of urban ecosystem services supply and demand. J. Environ. Manag. 2023, 329, 117050. [Google Scholar] [CrossRef] [PubMed]
  8. Costanza, R.; de Groot, R.; Sutton, P.; van der Ploeg, S.; Anderson, S.J.; Kubiszewski, I.; Farber, S.; Turner, R.K. Changes in the global value of ecosystem services. Glob. Environ. Chang. 2014, 26, 152–158. [Google Scholar] [CrossRef]
  9. Zhong, Z.; Fang, X.; Zhang, Y.; Shu, X.; Guo, D. Mapping Ecosystem Service Supply-Demand Bundles for an Integrated Analysis of Tradeoffs in an Urban Agglomeration of China. Land 2022, 11, 1558. [Google Scholar] [CrossRef]
  10. Fang, X.; Wu, J. Causes of overgrazing in Inner Mongolian grasslands: Searching for deep leverage points of intervention. Ecol. Soc. 2022, 27, 8. [Google Scholar] [CrossRef]
  11. Carpenter, S.; Booth, E.; Gillon, S.; Kucharik, C.; Loheide, S.; Mase, A.; Motew, M.; Qiu, J.; Rissman, A.; Seifert, J.; et al. Plausible futures of a social-ecological system: Yahara watershed, Wisconsin, USA. Ecol. Soc. 2015, 20, 10. [Google Scholar] [CrossRef] [Green Version]
  12. Peterson, G.; Cumming, G.; Carpenter, S. Scenario Planning: A Tool for Conservation in an Uncertain World. Conserv. Biol. 2003, 17, 358–366. [Google Scholar] [CrossRef] [Green Version]
  13. Cork, S.; Peterson, G.; Bennett, E.; Petschel-Held, G.; Zurek, M. Synthesis of the Storylines. Ecol. Soc. 2006, 11, 11. [Google Scholar] [CrossRef] [Green Version]
  14. Mahmoud, M.; Liu, Y.; Hartmann, H.; Stewart, S.; Wagener, T.; Semmens, D.; Stewart, R.; Gupta, H.; Dominguez, D.; Dominguez, F.; et al. A formal framework for scenario development in support of environmental decision-making. Environ. Modell. Softw. 2009, 24, 798–808. [Google Scholar] [CrossRef]
  15. Oteros-Rozas, E.; Martín-López, B.; Daw, T.; Bohensky, E.; Butler, J.; Hill, R.; Martin-Ortega, J.; Quinlan, A.; Ravera, F.; Ruiz-Mallén, I.; et al. Participatory scenario planning in place-based social-ecological research: Insights and experiences from 23 case studies. Ecol. Soc. 2015, 20, 32. [Google Scholar] [CrossRef]
  16. Iwaniec, D.; Cook, E.; Davidson, M.; Blasquez, M.; Georgescu, M.; Krayenhoff, E.; Middel, A.; Sampson, D.; Grimm, N. The co-production of sustainable future scenarios. Landsc. Urban Plan. 2020, 197, 103744. [Google Scholar] [CrossRef]
  17. Thorn, J.P.R.; Klein, J.A.; Steger, C.; Hopping, K.A.; Capitani, C.; Tucker, C.M.; Nolin, A.W.; Reid, R.S.; Seidl, R.; Chitale, V.S.; et al. A systematic review of participatory scenario planning to envision mountain social-ecological systems futures. Ecol. Soc. 2020, 25, 6. [Google Scholar] [CrossRef]
  18. Booth, E.G.; Qiu, J.; Carpenter, S.R.; Schatz, J.; Chen, X.; Kucharik, C.J.; Loheide, S.P.; Motew, M.M.; Seifert, J.M.; Turner, M.G. From qualitative to quantitative environmental scenarios: Translating storylines into biophysical modeling inputs at the watershed scale. Environ. Modell. Softw. 2016, 85, 80–97. [Google Scholar] [CrossRef] [Green Version]
  19. Watson, L.; Straatsma, M.W.; Wanders, N.; Verstegen, J.A.; de Jong, S.M.; Karssenberg, D. Global ecosystem service values in climate class transitions. Environ. Res. Lett. 2020, 15, 024008. [Google Scholar] [CrossRef]
  20. Nelson, E.; Kareiva, P.; Ruckelshaus, M.; Arkema, K.; Geller, G.; Girvetz, E.; Goodrich, D.; Matzek, V.; Pinsky, M.; Reid, W.; et al. Climate change’s impact on key ecosystem services and the human well-being they support in the US. Front. Ecol. Environ. 2013, 11, 483–893. [Google Scholar] [CrossRef] [Green Version]
  21. Bangash, R.F.; Passuello, A.; Sanchez-Canales, M.; Terrado, M.; López, A.; Elorza, F.J.; Ziv, G.; Acuña, V.; Schuhmacher, M. Ecosystem services in Mediterranean river basin: Climate change impact on water provisioning and erosion control. Sci. Total Environ. 2013, 458–460, 246–255. [Google Scholar] [CrossRef]
  22. Yang, S.; Zhao, W.; Liu, Y.; Wang, S.; Wang, J.; Zhai, R. Influence of land use change on the ecosystem service trade-offs in the ecological restoration area: Dynamics and scenarios in the Yanhe watershed, China. Sci. Total Environ. 2018, 644, 556–566. [Google Scholar] [CrossRef] [PubMed]
  23. March, H.; Therond, O.; Leenhardt, D. Water futures: Reviewing water-scenario analyses through an original interpretative framework. Ecol. Econ. 2012, 82, 126–137. [Google Scholar] [CrossRef]
  24. Alexander, P.; Rounsevell, M.D.A.; Dislich, C.; Dodson, J.R.; Engström, K.; Moran, D. Drivers for global agricultural land use change: The nexus of diet, population, yield and bioenergy. Glob. Environ. Chang. 2015, 35, 138–147. [Google Scholar] [CrossRef] [Green Version]
  25. Zhang, D.; Huang, Q.; He, C.; Wu, J. Impacts of urban expansion on ecosystem services in the Beijing-Tianjin-Hebei urban agglomeration, China: A scenario analysis based on the Shared Socioeconomic Pathways. Resour. Conserv. Recycl. 2017, 125, 115–130. [Google Scholar] [CrossRef]
  26. Hernández-Blanco, M.; Costanza, R.; Anderson, S.; Kubiszewski, I.; Sutton, P. Future scenarios for the value of ecosystem services in Latin America and the Caribbean to 2050. Curr. Res. Environ. Sustain. 2020, 2, 100008. [Google Scholar] [CrossRef]
  27. Wardropper, C.B.; Gillon, S.; Mase, A.S.; McKinney, E.A.; Carpenter, S.R.; Rissman, A.R. Local perspectives and global archetypes in scenario development. Ecol. Soc. 2016, 21, 12. [Google Scholar] [CrossRef] [Green Version]
  28. Fu, Q.; Hou, Y.; Wang, B.; Bi, X.; Li, B.; Zhang, X. Scenario analysis of ecosystem service changes and interactions in the mountain-oasis-desert system: A case study in the Altay Prefecture, China. Sci. Rep. 2018, 8, 12939. [Google Scholar] [CrossRef] [Green Version]
  29. Liu, Y.; Hou, X.; Li, X.; Song, B.; Wang, C. Assessing and predicting changes in ecosystem service values based on land use/cover change in the Bohai Rim coastal zone. Ecol. Indic. 2020, 111, 106004. [Google Scholar] [CrossRef]
  30. Abson, D.J.; von Wehrden, H.; Baumgärtner, S.; Fischer, J.; Hanspach, J.; Härdtle, W.; Heinrichs, H.; Klein, A.M.; Lang, D.J.; Martens, P.; et al. Ecosystem services as a boundary object for sustainability. Ecol. Econ. 2014, 103, 29–37. [Google Scholar] [CrossRef]
  31. Rockström, J.; Steffen, W.; Noone, K.; Persson, Å.; Chapin Iii, F.S.; Lambin, E.F.; Lenton, T.; Scheffer, M.; Folke, C.; Schellnhuber, H.; et al. A safe operating space for humanity. Nature 2009, 461, 472–475. [Google Scholar] [CrossRef]
  32. Fang, X.; Wu, J.; He, C. Assessing human-environment system sustainability based on Regional Safe and Just Operating Space: The case of the Inner Mongolia Grassland. Environ. Sci. Policy 2021, 116, 276–286. [Google Scholar] [CrossRef]
  33. Steffen, W.; Richardson, K.; Rockström, J.; Cornell, S.; Fetzer, I.; Bennett, E.; Biggs, R.; Carpenter, S.; Vries, W.; de Wit, C.; et al. Planetary Boundaries: Guiding Human Development on a Changing Planet. Science 2015, 347, 1259855. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  34. Raworth, K. A Safe and Just Space for Humanity: Can We Live within the Doughnut; Oxfam: Oxford, UK, 2012; Volume 8. [Google Scholar]
  35. O’Neill, D.; Fanning, A.; Lamb, W.; Steinberger, J. A good life for all within planetary boundaries. Nat. Sustain. 2018, 1, 88–95. [Google Scholar] [CrossRef] [Green Version]
  36. Hickel, J. Is it possible to achieve a good life for all within planetary boundaries? Third World Q. 2018, 40, 1–17. [Google Scholar] [CrossRef]
  37. Häyhä, T.; Lucas, P.L.; van Vuuren, D.P.; Cornell, S.E.; Hoff, H. From Planetary Boundaries to national fair shares of the global safe operating space—How can the scales be bridged? Glob. Environ. Chang. 2016, 40, 60–72. [Google Scholar] [CrossRef] [Green Version]
  38. Dearing, J.A.; Wang, R.; Zhang, K.; Dyke, J.G.; Haberl, H.; Hossain, M.S.; Langdon, P.G.; Lenton, T.M.; Raworth, K.; Brown, S.; et al. Safe and just operating spaces for regional social-ecological systems. Glob. Environ. Chang. 2014, 28, 227–238. [Google Scholar] [CrossRef] [Green Version]
  39. Hossain, M.S.; Dearing, J.A.; Eigenbrod, F.; Johnson, F.A. Operationalizing safe operating space for regional social-ecological systems. Sci. Total Environ. 2017, 584–585, 673–682. [Google Scholar] [CrossRef]
  40. Cooper, G.S.; Dearing, J.A. Modelling future safe and just operating spaces in regional social-ecological systems. Sci. Total Environ. 2019, 651, 2105–2117. [Google Scholar] [CrossRef]
  41. Zhou, Z.; Liu, D.; Sun, Y.; He, J. Predicting joint effects of multiple land consolidation strategies on ecosystem service interactions. Environ. Sci. Pollut. Res. 2022, 29, 37234–37247. [Google Scholar] [CrossRef]
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Figure 1. The “safe operating space” defined by planetary boundaries, which was derived from Rockström et al. (2009) [31].
Figure 1. The “safe operating space” defined by planetary boundaries, which was derived from Rockström et al. (2009) [31].
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Figure 2. Steps of “participatory” future scenario building.
Figure 2. Steps of “participatory” future scenario building.
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Figure 3. Schematic diagram of safe operational spatial identification methods for different ecosystem service change trend types, which was derived from Dearing et al. (2014) [38].
Figure 3. Schematic diagram of safe operational spatial identification methods for different ecosystem service change trend types, which was derived from Dearing et al. (2014) [38].
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Table
Table 1. The main models of ecosystem service simulation and their characteristics (Modified from IPBES website: https://www.ipbes.net/, accessed on 1 June 2023).
Table 1. The main models of ecosystem service simulation and their characteristics (Modified from IPBES website: https://www.ipbes.net/, accessed on 1 June 2023).
ModelsModel TypeSpatial and Temporal ExtentEase of UseCommunity of Practice
IMAGEProcessGlobal, dynamicDifficultSmall
EcoPath with
EcoSim		ProcessRegional, dynamicMediumLarge
ARIESExpertRegional, dynamicDifficultSmall
InVESTProcess and
correlative		Regional, staticMediumLarge
TESSAExpertLocal, staticEasySmall
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Zhang, X.; Fang, X. The Progress and Prospects in the Scenario Simulation Research on the Sustainability of Regional Ecosystem Services Based on a “Safe Operating Space”. Sustainability 2023, 15, 11249. https://doi.org/10.3390/su151411249

AMA Style

Zhang X, Fang X. The Progress and Prospects in the Scenario Simulation Research on the Sustainability of Regional Ecosystem Services Based on a “Safe Operating Space”. Sustainability. 2023; 15(14):11249. https://doi.org/10.3390/su151411249

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Zhang, Xiuquan, and Xuening Fang. 2023. "The Progress and Prospects in the Scenario Simulation Research on the Sustainability of Regional Ecosystem Services Based on a “Safe Operating Space”" Sustainability 15, no. 14: 11249. https://doi.org/10.3390/su151411249

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