Promoting the optimization of ecosystem services supply-flow-demand by integrating urban-rural metacoupling interactions

Abstract

Clarifying how to systematically manage ecosystem services is crucial for addressing urban-rural conflicts and promoting urban-rural sustainable development, while ecosystem service optimization offers an effective approach. The optimization of ecosystem services encompasses not only the traditional supply and demand but also the entire process, including supply, flow, and demand, especially for the complex urban-rural socio-ecological systems. The metacoupling framework can offer a feasible theoretical approach for advancing the spatial optimization of ecosystem service supply-flow-demand and adaptively explain the interaction relationships between urban and rural regions. This study first explored urban-rural patterns and the complex characteristics of socio-ecological systems to understand the metacoupling mechanisms, including intracoupling, pericoupling, and telecoupling, between urban areas, urban-rural fringes, and rural areas. Furthermore, by integrating the spatial relationships of ecosystem services supply-flow-demand with the entire process assessment approaches, we interpreted varied urban-rural metacoupling interactions of ecosystem services nexus. Finally, a metacoupling-based ecosystem services supply-flow-demand optimization framework was developed, which considers reducing supply-demand trade-offs, balancing supply-demand deficits, and improving flow efficiency. The metacoupling framework for urban-rural ecosystem services optimization constructed in this study provides a new research perspective and adaptive theoretical approach for the comprehensive and sustainable management of urban, urban-rural fringe, rural ecosystem services.

Keywords

Ecosystem services urban-rural metacoupling optimization

Introduction

Ecosystem services are the foundation for human survival and development. The supply, demand, and flow of ecosystem services are three basic elements for understanding how ecosystem services link natural ecosystems with human social systems (Bagstad et al., 2014). In recent years, with rapid socio-economic development, there have been significant changes in landscape patterns and ecological processes, which have severely impacted the spatial patterns and relational mechanisms of the supply, demand, and flow of ecosystem services (Costanza et al., 2017). Urban expansion has caused severe degradation of ecosystem service supply. The urbanization process, along with the decline of ecosystem services and the increasing demands of people, can lead to an exacerbation of the imbalance between the supply and demand of ecosystem services (Burkhard et al., 2012). In addition to the imbalance in supply and demand within the same spatial area, there is often also a spatial mismatch between the supply of ecosystem services in a region and the demands of human society (Shen and Wang, 2021). This mismatch needs to be addressed by linking supply and demand through ecosystem service flow. To clarify the interactions and flow relationships between natural ecosystems and human social systems, it is necessary to consider not only the supply and demand of ecosystem services but also the flow process (Wang et al., 2022a). Thus, the integrated management of the entire process of ecosystem service supply, flow, and demand would help promote the sustainable development of landscapes and regions.

Many countries in the world are facing issues of social, economic, and environmental disparities between urban and rural areas, particularly in developing countries. Due to limited access to basic infrastructure services in small towns and rural areas, such as electricity and transportation, economic development in these regions often falls behind that of urban areas. Meanwhile, urban areas, due to the high density of human activities, often experience severe environmental degradation (Ferré et al., 2012). A strict division of conventional rural-urban binary is generally not adequate to make different populations aware and act upon the education levels, economic gaps and environmental management. A position along the urban-rural transitional landscape and within the hierarchical settlement system would offer us a more nuanced perspective for understanding the spatial relationships between humans and nature (Liu et al., 2024). Many resources, like food, water, raw materials, etc., consumed in urban areas are often heavily reliant on the surrounding suburbs and rural areas (Gebre and Gebremedhin, 2019). For example, the supply and demand of food between urban and rural areas are closely interconnected. Rural areas provide food to urban areas to improve livelihoods, while cities rely on the surrounding suburbs and rural areas for resources and ecosystem services. A detailed analysis of the food system within the urban-rural continuum is more conducive to accurately capturing the spatial information and formation mechanisms of food insecurity. The issue of food security needs to be addressed at a macro scale, encompassing the urban-rural fringe in which food systems operate. Many developing countries around the world, such as China, India, Brazil, and Indonesia, as well as developed countries in Europe like the Netherlands and Germany, are focusing on strengthening the connections of ecosystem services between urban and rural areas (Tefft et al., 2017). Therefore, advancing the integration of ecosystem services for urban and rural areas is of great practical significance for coordinating ecological environments and ensuring sustainable development (Allawi and Al-Jazaeri, 2023; Fang et al., 2024b).

As the rapid increase in urban population and built-up land may lead to severe ecosystem service supply-demand deficits and blockages in service flow (Xu et al., 2021), it is urgent to clarify the supply-demand relationship and flow mechanisms of ecosystem services across urban-rural continuum. Usually, urban-rural ecosystem services' spatial flow between human urban-rural systems and natural coupled systems is based on a metacoupling process (Liu et al., 2016). The metacoupling framework enables research on the connections between different locations, multiple coupled systems, and various scales (Liu, 2023). It has been applied in global food trade, long-distance water transfer, tourism, species migration, and other fields (Sun et al., 2018). The flow of ecosystem services is the result of the exchange of materials, energy, and information between the supply area of ecosystem service, such as natural habitats or rural areas with high natural quality, and the beneficiary area of ecosystem service, urban centers or some densely populated suburbs (Kleemann et al., 2020). Thus, a metacoupling framework is a feasibility framework that can be used to explain the entire-process mechanism and management of ecosystem services supply-flow-demand of urban-rural regions.

To achieve effective governance and manage the urban-rural ecosystem services, spatial optimization provides a means and tool worth advocating (Guerry et al., 2015). There are usually several bases for optimization: based solely on ecosystem service supply (Zhang et al., 2023b); based on the relationship between ecosystem service supply and demand (Li et al., 2023a); based on the trade-off between ecosystem service supply and demand (Feng et al., 2021); and based on the integration of ecosystem service supply, demand, and flow (Fang et al., 2024a). The optimization of ecosystem service flow helps promote coordinated development between regions, especially between urban and rural areas. Additionally, optimizing the entire process of supply-flow-demand can alleviate spatial inequalities in ecosystem services and reduce the resource consumption associated with the flow process (Zhai et al., 2021). Specifically, optimizing the direction, volume, and efficiency of the flow can enhance the utilization efficiency of ecosystem services, thus alleviating supply-demand imbalances. Improving the efficiency of ecosystem service flow also facilitates reducing the economic costs of the flow process, promoting the improvement of both ecological and socio-economic systems (Fang et al., 2024a). Optimization based on the entire process of ecosystem service supply-flow-demand is a promising development direction, and it also aligns well with the metacoupling framework. This typically requires considering both the optimization of pericoupling ecosystem services within the regional ecosystem and the telecoupling optimization between internal and external regions. By combining multi-objective analysis, and scenario simulation (Luan et al., 2024; Naidoo et al., 2008; Tang et al., 2024), urban-rural metacoupling-based ecosystem services optimization should not only optimize the traditional ecosystem services supply and demand but also can combine the flow paths and modes.

For urban and rural areas, during the process of implementing ecosystem service supply-flow-demand metacoupling assessment and optimization, it is possible to quantitatively assess and analyze the coupling mechanisms of ecosystem service supply, flow, and demand based on understanding of the urban-rural socio-ecological complex system. In many countries around the world that face urban-rural contradictions, the optimization and regulation strategies proposed by extension research are conducive to promoting the integration of urban and rural areas as well as landscape sustainability (Figure 1). The process needs to address the following two key issues: (1) How to understand the urban-rural ecosystem services “supply-flow-demand” relationships and related metacoupling mechanisms? (2) How to carry out the entire process optimization based on ecosystem service “supply-flow-demand” nexus framework to promote urban-rural socio-ecological sustainability?

Figure 1.

The framework for the metacoupling mechanisms of urban-rural ecosystem service supply-flow-demand interactions and optimization.

Urban-rural metacoupling mechanisms

Urban-rural patterns and socio-ecological characteristics

Since the concept of urban and rural areas emerged, the exact boundary between them has been a subject of controversy (Woods and Heley, 2017). For a long time, the division between urban and rural areas has predominantly been binary, but this approach usually overlooks the interactions and connections between the two and fails to reflect the ecosystem service capacity provided by regions of different scales. In the early 21st century, the concept of a continuous continuum from rural to urban areas began to take shape to address the limitations of the binary approach (Cattaneo et al., 2021). Unlike the urban-rural dichotomy, the urban-rural continuum does not distinguish between urban and rural areas from a simple binary perspective. According to the degree of urbanization or ruralization of a region, there exists a transitional zone between urban and rural areas, commonly referred to as the urban-rural fringe or suburban region (Simon, 2008) (Figure 2). Due to its location between urbanized areas and rural regions, this special zone often incorporates both urban and rural characteristics, resulting in complexity and heterogeneity across multiple dimensions such as ecological environment, social structure, and economic activities (Gonçalves et al., 2017; Vizzari and Sigura, 2015). The urban-rural fringe is often characterized by a complex population structure, high population mobility, diverse economic activities, intense land-use conflicts, uneven development of infrastructure and public services, and various social conflicts (Kontgis et al., 2014). It is a transitional area where urban and rural landscapes are intermingled and urban and rural functions overlap, making it a key area for achieving urban-rural integration and sustainable development (Wang et al., 2023b).

Figure 2.

Urbanization characteristics and classification for an urban-fringe-rural area.

When analyzing urban and rural ecosystem service issues, it is essential to understand the spatial patterns of urban-rural areas. Typically, there are several common approaches to studying the urban-rural gradient. Firstly, linear transects (unidirectional) are established along the urban-rural gradient, where sampling points are arranged according to the pattern of the gradient. This type of urban-rural gradient research is mainly suitable for simulating the distribution or diversity of natural vegetation (Vakhlamova et al., 2014) and obtaining on-site water quality parameters (Kunz et al., 2023), etc. Another approach involves representing the urbanization gradient by expanding outward from the city center along a concentric circle buffer or concentric ring-shaped expansion (omnidirectional). This type of urbanization gradient research is primarily used to explore the patterns of urban land expansion (Wang et al., 2022b) and the spatial scaling laws of ecological and environmental indicators (Sun et al., 2023). The third approach involves the division of urban and rural areas or the identification of urban and rural landscapes. This method is often utilized to identify and contrast the differences in indicators, such as vegetation phenology (Ji et al., 2023), ecosystem services (Fang et al., 2024a) across various urban and rural zones, to explore the patterns of urban-rural spatial heterogeneity, and most importantly, to underpin the sustainable planning and management of urban and rural regions. Such urban-rural identification and classification can also provide a zonal foundation for the quantitative assessment and spatial optimization of the supply, demand, and flow of urban and rural ecosystem services.

Due to the complex characteristics of urban-rural transitional patterns, delineating their spatial extent is often relatively difficult (Xu et al., 2020). Currently, the main identification methods can be divided into two types (Figure 2): (1) Rurality index-based method: this method calculates a rurality index based on a set of physical or socio-economic indicators, typically at the administrative unit scale. Clustering analysis is then used to classify areas based on the index values, and a threshold is set for urban-rural classification (Dická et al., 2019; Gonçalves et al., 2017); (2) Land use pattern-based method: this method detects inflection points or critical values based on land use patterns, primarily at the grid unit scale. Techniques such as wavelet transform and clustering analysis are often used to delineate urban-rural boundaries (Feng et al., 2020; Peng et al., 2020). In addition, using full-resolution network models for urban-rural classification is also a promising approach. This model, based on convolutional neural networks, utilize convolutional kernels to consider the relationships between neighboring pixels. It is capable of integrating multi-resolution data while maintaining high-resolution features (Wang et al., 2024).

Urban-rural metacoupling interactions

The urban areas, urban-rural fringe zones, and rural areas are all complex social-ecological systems. The interactions of social and ecological activities, both within these urban-rural areas and across regional boundaries, often exist between human social systems and natural ecosystems. While metacoupling theory and framework reflects the interactions and feedback mechanisms between human social and natural coupled systems at multiple scales, both nearby and distant (Liu, 2017; Tromboni et al., 2021). The concept of metacoupling extends from “telecoupling” and includes intracoupling, pericoupling, and telecoupling. Among them, “telecoupling” is the most commonly mentioned concept, and its theoretical framework was proposed in 2013. It mainly includes three human-nature coupling systems: the sending system, receiving system, and spillover system, with each coupling system connected through flows. The concept of coupling systems and flows in the metacoupling framework align with the core concepts of supply area, benefit area, and flow in the ecosystem services supply-flow-demand assessment. Therefore, the metacoupling framework is highly applicable at macro scales such as landscapes and regions (Schröter et al., 2018). For example, the metacoupling interactions within and between the three systems: urban, urban-rural fringe, and rural areas can fully reflect the three forms of full-process coupling (Figure 3).

Figure 3.

Metacoupling types, framework, and urban-rural interactions diagram. (a) Metacoupling types and human-nature relationships. (b) Telecoupling framework. (c) Urban-rural metacoupling interactions.

Metacoupling interactions for urban-rural ecosystem services supply-flow-demand

Ecosystem services supply-flow-demand nexus and assessment

Ecosystem service supply refers to the quantity of products and services provided by natural ecosystems within a specific temporal and spatial scale (Burkhard et al., 2012). Identifying ecosystem service supply areas and quantitatively assessing their supply potential can provide spatial information and a fundamental basis for ecosystem service management (Shi et al., 2020); Ecosystem service demand refers to the quantity of products and services consumed and utilized by human social systems within a specific temporal and spatial scale (Burkhard et al., 2012). Ecosystem service demand is primarily influenced by factors such as socio-economic conditions, policy-making, and cultural values (Villamagna et al., 2013). On the basis of ecosystem service supply assessments, further clarifying the spatial distribution and demographic structure of ecosystem service demand will help explore the balance between ecosystem service supply and demand (Wu et al., 2023); Ecosystem service flow refers to the process through which ecosystem services, with or without a carrier, are transmitted from the supply area to the benefit area along a certain direction and path, driven by natural or human factors (Bagstad et al., 2013). When exploring the balance relationships between ecosystem service supply and demand, there is a phenomenon where the ecosystem service supply provided by a region cannot meet the human demands within the same space. It is necessary to clarify the entire process of ecosystem service generation, flow, and use, and identify the flow path of ecosystem services from the providing area to the demand or benefit area, i.e., connecting natural ecosystem and human social system in the form of flow (Figure 4).

Figure 4.

The spatial relationships of ecosystem service supply, flow, and demand.

The biophysical models commonly used for assessing the supply of ecosystem services include InVEST (Integrated Valuation of Ecosystem Services and Tradeoffs) and SolVES (Social Values for Ecosystem Services), which have been applied in various countries and regions worldwide (Goldstein et al., 2012). Ecological process models, such as the SWAT hydrological model, are primarily used to assess water quantity and flood regulation. While these models have broad applicability, they are sensitive to data parameters (Rahman et al., 2022). Other methods, such as the ecological footprint approach, are simple and user-friendly but lack a deep understanding of ecological processes when evaluating the supply of ecosystem services (Mancini et al., 2018). For ecosystem service demand, questionnaire surveys are mostly used to investigate cultural service and payment intention (Sun et al., 2024). The monetary value method mainly calculates the demand of ecosystem services based on parameters such as unit price and area, and is only used to estimate services with market value (De Groot et al., 2012). The empirical statistical method mainly uses population, income and other indicators to reflect the demand. The method is simple, but the data stability is often influenced by the spatial scale (Xu et al., 2022) (Figure 5).

Figure 5.

The assessment methods for ecosystem services supply, demand, and flow.

Before assessing the flow of ecosystem services, it is essential to understand the carrier medium and the flow paths. Typically, food supply service flows through trade networks, which are influenced by the means of consumption, market, policies, and other factors. Water supply, water purification, and soil conservation services mainly follow local hydrological flow paths, affected by factors such as the position of upstream and downstream areas, elevation, and river network distribution (Li et al., 2017). Climate regulation and air purification services are mainly mediated by atmospheric flow and circulation, influenced by local climatic factors, topographical conditions, and other factors. Habitat quality service mainly flows through local habitat corridors, usually influenced by landscape connectivity, human disturbances, and other factors. Leisure and recreation services mainly follow local tourism and transportation ways, influenced by factors such as population density, transportation distance, and location of attractions (Figure 5). The assessment of ecosystem service flow primarily includes three categories:

(1) Actual use-based method: this category of method emphasizes the “utility” of ecosystem services and does not consider the intermediate processes of service flow. Instead, it reveals the dynamic changes in the beneficiaries' access to ecosystem services through various data indicators, typically based on actual usage as the flow (Vallecillo et al., 2019). This type of method includes using source-sink flow ratios to reflect the spatial flow of carbon sequestration services (Li et al., 2017), as well as the black/gray box theory to reflect the distribution of ecosystem services from production to consumption. In addition, methods using flow indicators can measure the temporal and spatial changes of a specific indicator on both the supply and demand sides of ecosystem services, thereby determining the extent of service utility acquisition (Xu et al., 2019).

(2) Spatial connection method: this category is mainly based on the spatial connectivity of ecosystem services, similarly without considering the intermediate processes of service flow. It quantifies the flow by calculating the area ratio between the beneficiary and supply areas of ecosystem services. While these methods are useful for obtaining characteristic information about the flow on a large scale, they often overlook the complex processes between ecosystems and human society (Schirpke et al., 2019; Serna-Chavez et al., 2014).

(3) Ecological flow process method: the third category emphasizes the process of ecosystem service delivery, using various geographic information tools to visually simulate the transfer paths and process of ecosystem service supply and demand, in order to establish spatially distributed models. Currently, this mainly involves hydrological models such as SWAT and HSPF (Hydrological Simulation Program FORTRAN), which calculate water flow based on parameters like slope and flow direction (Chen et al., 2020; Lin et al., 2021). Additionally, the Artificial Intelligence for Ecosystem Services (ARIES) project reflects information on sources, sinks, and beneficiaries through conditional probability tables and uses the SPANs (Service Path Attribution Networks) algorithm to simulate the flow paths of ecosystem services from supply points to beneficiary points (Bagstad et al., 2014; Johnson et al., 2012).

Metacoupling interactions for urban-rural ecosystem services supply-flow-demand

Today, ecosystem service research focused on urbanized areas, watersheds, croplands, forests, and parks has become relatively established (Magerl et al., 2022; Palomo et al., 2013). Most of these studies involve the quantitative or qualitative analysis of the ecosystem service supply and demand relationships within administrative regions or watershed units, or the total ecosystem service flow between regions (Kleemann et al., 2020). However, the ecosystem service supply and demand relationships and flow mechanisms within regions, such as urban, urban-rural fringe, and rural areas with different levels of urbanization, are also worth exploring further.

Understanding the supply-flow-demand mechanisms of ecosystem services is helpful for explaining the spatial interactions between urbanized human systems and ruralized natural ecosystems. Exploring the ecosystem services supply and demand across rural-urban gradients or comparing their differences between different urban, urban-rural fringe, and rural areas facilitates an understanding of how urbanization processes and effects affect ecosystem services’ patterns and sustainability (Herrero-Jáuregui et al., 2019; Theodorou et al., 2021). Existing studies mainly consider public perception and evaluation into the decision-making process when evaluating urban-rural ecosystem service demand and paying attention to the differences in preferences of different ecosystem service indicators between urban and rural residents (Rodríguez-Morales et al., 2020; Zhou et al., 2018). However, the heterogeneity characteristics and flow processes of the supply and demand of various ecosystem services between regions with different urbanization levels also should be emphasized (Metzger et al., 2021). Since promoting the smooth flow of urban and rural ecosystem services can effectively alleviate the problem of supply and demand deficit, and the coordinated and sustainable development of urban and rural ecosystem services pays more attention to the difference and flow between urban and rural areas, it is necessary to further explore the coupling interactions of the entire process of supply-flow-demand in the complex urban-rural system.

It is challenging to fully understand and reflect the ecosystem service issues at regional or landscape levels (Wu et al., 2023). Fortunately, the metacoupling framework provides a comprehensive and appropriate set of analytical tools for the assessment and optimization of ecosystem services supply-flow-demand. In this context, the ecosystem service supply area is regarded as the sending system, and the ecosystem service demand area is regarded as the receiving system (Kleemann et al., 2020). The flow between ecosystem service supply and demand areas represents the flow between the sending and receiving systems, while other areas that influence and feedback the ecosystem service supply-demand flow process are considered spillover systems. Based on the spatial relationship between the ecosystem service providing and demand areas, they can be classified into intracoupling, pericoupling (including directional flow and omnidirectional flow), and telecoupling (Tromboni et al., 2021). Based on the proximity of different ecosystem service supply and demand flows, urban-rural coupling ways can be further classified into six types: urban-urban coupling, urban-fringe coupling, urban-rural coupling, fringe-fringe coupling, fringe-rural coupling, and rural-rural coupling. Among them, some ecosystem services between urban and rural areas are directional, such as the flow of food and water resources, while others are bidirectional, such as the flow of air and carbon (Figure 6).

Figure 6.

The urban-rural ecosystem service supply-flow-demand diagram based on metacoupling framework.

Optimizing the entire process of ecosystem services by integrating urban-rural metacoupling

Optimizing the entire process of ecosystem services supply-flow-demand

The spatial optimization of the magnitude and paths of ecosystem service flow in urban-rural coupling systems is necessary to effectively address issues such as the inequitable distribution of ecosystem service resources and the smooth flow between urban and rural areas. Unlike most research that focused solely on the optimization of providing areas, metacoupling optimization involves not only the ecosystem services providing areas, but also the demand areas and their associated flows. By identifying the hot spots of ecosystem services supply-demand deficits, as well as the flow size and flow paths of ecosystem services under different urban, rural-urban, and rural metacoupling types, and combining optimization algorithms or models, it is feasible to reduce ecosystem service supply-demand deficits and enhance flow efficiency (Fang et al., 2024b; Schirpke et al., 2019). Among them, in the multi-objective optimization models, the constraints and objectives can be set regarding land use area, economic investment, ecosystem service synergies or trade-offs.

For certain ecosystem services, the physical providing service indicators such as food flow and water resource flow, the optimization scenarios were often designed to minimize the cost of flow paths (Fang et al., 2024a; Wang et al., 2023a). This would be helpful for reducing disruptions or losses in the flow process, while also protecting and restoring critical ecosystem service flow paths, including transport corridors, greenways, waterways, etc. For the regulating service indicators such as climate regulation and water purification, in addition to the fundamental optimization of land use patterns, optimization can also be achieved by incorporating the construction and improvement of ecological corridors such as green spaces, rivers, and wetlands (Long et al., 2024). This promotes the connectivity of these corridors and enhances the quality of natural habitats, allowing natural processes, such as the water cycle, to flow smoothly. Simultaneously, in the detailed design, it is essential to comprehensively consider the water flow paths and air flow directions to improve the efficiency of these flows (Harvey et al., 2019). In the process of optimizing the flow of cultural services, such as the recreational and tourism service, in addition to the flow direction and flow quantity, issues such as flow speed and flow boundaries also should be involved. To enhance residents' accessibility of cultural services, by considering the respective velocities to service destinations or attractions and the reachable boundaries, different transportation modes as well as transportation routes also should be adjusted and designed (Fan et al., 2025).

Taking urban-rural food ecosystem service flow optimization as an example, food flow is a complex process from food surplus areas (e.g., rural areas with extensive distributions of croplands) to food deficit areas (e.g., urban areas with highly concentrated populations and socio-economic activities), involving many stages such as production, harvesting, processing, storage, transportation, and sales. Therefore, the optimization of food flow can be considered from three aspects: supply, demand, and flow sides (Garnett, 2013). The optimization of food supply and demand mainly involves the adjustments to factors such as the cropping structure in production areas and the dietary structure of residents in consumption areas (Singh and Singh, 2017; Springmann et al., 2023; Wang et al., 2022c). This process requires a deep understanding of local agricultural practices, ecological conservation policies, and consumer preferences. It is worth noting that these factors often vary significantly between regions. Compared to the complex process of supply and demand optimization, flow-based optimization focuses on the efficiency and effectiveness of the food supply chain. Flow optimization can ensure the food is transferred from surplus areas to deficit areas in a more sustainable and cost-effective manner and is often considered a more efficient perspective compared to supply-demand optimization (Karakoc and Konar, 2024; Robinson et al., 2016). This type of optimization approach is highly targeted for material flows, such as food flow and water flow, which depend on artificial carriers and physical pathways. The optimization objectives for food flow can be to enhance the overall efficiency of the food supply chain, reduce transport costs, and minimize the accompanying environmental impacts. The optimization approach involves two common aspects: the optimization of flow paths and the optimization of transportation modes. The optimization of flow paths mainly involves determining the optimal transportation routes from production areas to consumption areas, thereby minimizing transportation costs and duration (Prakash and Mitchell, 2018). The optimization of transportation modes primarily focuses on improving overall transportation efficiency and the related costs. Specifically, it is necessary to consider the most adaptive transportation mode for each segment of the transport process, while balancing factors such as transportation costs, time, reliability, and environmental impacts. Additionally, the transfer and transshipment processes between different transportation modes should be considered, aiming to reduce waiting costs and duration (Mazaraki et al., 2020; Zuo et al., 2023).

Promoting ecosystem services optimization with adapting urban-rural metacoupling mechanisms

Sustainable urban-rural development implies the coordination and integration of urban areas, urban-rural fringes, and rural areas, encompassing multiple dimensions of socio-economic and ecological balance and improvement (He et al., 2024). Specifically, the entire process spatial optimization of ecosystem service supply, flow, and demand often involves multiple urban-rural intracoupling, pericoupling, and telecoupling ways (Fang et al., 2024b). In the future, constructing urban-rural ecological security patterns based on the identification of hotspots for ecosystem service supply-flow-demand would be a promising and expandable direction. For urban-rural socio-ecological complexed systems, it is possible to go beyond the traditional “source-to-source” optimization focused solely on ecosystem service supply areas by integrating ecosystem service demand areas and flow pathways to achieve comprehensive “source-flow-sink” optimization.

The management of urban-rural metacoupled systems is mostly implemented at the regional or landscape level (Li et al., 2023b). Especially when it comes to the metacoupling management of ecosystem service supply, demand, and flow, the regional or administrative scale is the appropriate scale for simulating the entire process, scenario simulation, and policy management (Zhang et al., 2023a). When implementing the entire process optimization of urban-rural ecosystem services, in addition to focusing on the supply-demand balance and flow mechanisms, it is also necessary to consider the trade-offs between different ecosystem service supply and demand indicators. Designing multiple alternative scenarios with optimized goals, such as reducing the urban-rural ecosystem service supply-demand deficit, lowering flow costs, and minimizing trade-offs, would contribute to achieving more efficient multi-dimensional management of ecosystem services (Figure 7).

Figure 7.

The framework for urban-rural ecosystem services supply-flow-demand optimization.

In addition, ecosystem services are closely linked to the well-being of urban and rural residents. In the process of optimizing ecosystem services, it is also essential to consider the needs and preferences of stakeholders, including institutions and groups, as well as the well-being and perspectives of different urban and rural populations and individuals (Hölting et al., 2020; Sun et al., 2024). For example, divergent recreational ecosystem service demand and landscape preferences were detected across urban-rural residents’ genders, ages, income groups, and other socio-economic factors. Specifically, the needs of vulnerable groups, including elderly and low-income residents should be given priority attention (Riechers et al., 2018). Therefore, the optimization and management of ecosystems in urban and rural areas require adaptive, context-specific approaches, taking into account diverse factors, such as local policies, natural geographical features, socio-cultural traditions, economic development conditions, and the needs of urban and rural residents (Gebre and Gebremedhin, 2019). More importantly, the developed optimization scenarios for urban and rural ecosystem services supply-demand-flow need to be translated into practical and actionable strategies (Figure 7). This requires further integration of ecosystem service optimization strategies with urban-rural land use planning, such as the adjustments to landscape structures, which involve not only the compositions of the landscape but also its spatial configurations. Specifically, the regulation of croplands, forests, and urban lands must strictly comply with requirements such as the ecological conservation redline, the limit of arable land, and urban growth boundary (Asante-Yeboah et al., 2024; Assis et al., 2023). To enhance the efficiency of the metacoupling relationships for urban and rural areas, bridging the ecosystem services flow path, such as developing or reconnecting key trade transport routes, river corridors and networks, designing efficient long-distance greenways and pathways from key provisioning areas to the benefiting areas (e.g., urban centers) would be feasible solutions, especially on the regional level (Sun et al., 2024). In addition, policies related to ecological compensation (Wang et al., 2023b), the construction of ecological infrastructure, and the design of ecological practice projects need to be closely integrated to support the coordinated and sustainable development of the urban-rural socio-ecological system.

However, the optimization framework for the urban-rural ecosystem services supply-flow-demand, based on a metacoupling perspective, that we proposed has several limitations. On the one hand, the proposed research process and framework do not account for scale effects. Typically, the problems such as ecosystem service supply-demand deficits, trade-offs, and flow blockages vary at different scales (Zeng et al., 2024). The key issues related to ecosystem services are often influenced by changes in scale. Considering scale effects is essential to more accurately connect and support the practical management of ecosystem services through theoretical assessments (Norton et al., 2016). On the other hand, the optimization of ecosystem services in different countries, regions, and landscapes is a complex issue, and it is difficult to fully convey the knowledge and information through general theories and frameworks. When assessing and managing ecosystem services, it is crucial to fully consider the local characteristics of the region or landscape (Rodríguez-Robayo et al., 2020), including social and environmental parameters, to implement sustainable optimization that is tailored to the specific context.

Conclusions

Conducting comprehensive ecosystem service assessment and entire process optimization from the perspective of promoting urban-rural integration can effectively enhance the socio-ecological sustainability of urban and rural regions. Ecosystem service flow can effectively connect supply and demand, helping us understand the coupling mechanisms of the entire process from supply to flow to demand. The metacoupling framework provides an adaptive theoretical approach for understanding ecosystem services supply-flow-demand relationships and mechanisms. In light of this, this study focuses on two key issues: first, it interpreted the urban-rural socio-ecological system coupling relationships and their ecosystem service supply-flow-demand relationships; second, it developed a metacoupling methodological framework for spatial optimization of ecosystem services based on the entire supply-flow-demand process. Among them, for urban-rural complex systems, we focus on incorporating urban-rural fringe as a key and important area for management. This study provided a comprehensive understanding and exploration of the urban-rural ecosystem services supply-flow-demand relationships and their metacoupling spatial optimization. It not only helps promote a balanced supply and demand of ecosystem services and effective flow between urban, urban-rural fringe, and rural regions, but also further supports the integration of ecosystem service management into practical decision-making.

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the National Natural Science Foundation of China: (Grant No. 42271113), National Key Research and Development Program of China: (Grant No. 2022YFD2001105), and Young Elite Scientist Sponsorship Program by Cast: (Grant No. 2021QNRC001).

ORCID iD

Xiao Sun

Author biographies

Xiao Sun is currently an associate professor at the School of Ecology and Environment, Renmin University of China. Her research interests include ecosystem services, urban-rural landscape sustainability, and multi-scale spatial optimization.

Jing Sun is currently a professor at the Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, and has long been focused on human-environment coupling and metacoupling systems.

Yuanyuan Yang is a professor at the School of Public Administration, Renmin University of China. Her research mainly focuses on land use change and sustainable urban-rural development.

Qiao Jianmin is a professor at Shandong Normal University, China. His research interests focus on landscape ecology, land system design, and land use changes.

References

Allawi

Al-Jazaeri

HMJ

(2023) A new approach towards the sustainability of urban-rural integration: The development strategy for central villages in the Abbasiya District of Iraq using GIS techniques. Regional Sustainability 4(1): 28–43.

Asante-Yeboah

Koo

Sieber

, et al. (2024) Designing mosaic landscapes for sustainable outcome: Evaluating land-use options on ecosystem service provisioning in southwestern Ghana. Journal of Environmental Management 353: 120127.

Assis

Hohlenwerger

Metzger

, et al. (2023) Linking landscape structure and ecosystem service flow. Ecosystem Services 62: 101535.

Bagstad

Johnson

Voigt

, et al. (2013) Spatial dynamics of ecosystem service flows: A comprehensive approach to quantifying actual services. Ecosystem Services 4: 117–125.

Bagstad

Villa

Batker

, et al. (2014) From theoretical to actual ecosystem services: Mapping beneficiaries and spatial flows in ecosystem service assessments. Ecology and Society 19(2): 64.

Burkhard

Kroll

Nedkov

, et al. (2012) Mapping ecosystem service supply, demand and budgets. Ecological Indicators 21: 17–29.

Cattaneo

Nelson

McMenomy

(2021) Global mapping of urban–rural catchment areas reveals unequal access to services. Proceedings of the National Academy of Sciences 118(2): e2011990118.

Chen

Yang

, et al. (2020) Quantifying water provision service supply, demand and spatial flow for land use optimization: A case study in the YanHe watershed. Ecosystem Services 43: 101117.

Costanza

De Groot

Braat

, et al. (2017) Twenty years of ecosystem services: How far have we come and how far do we still need to go? Ecosystem Services 28: 1–16.

10.

De Groot

Brander

Van Der Ploeg

, et al. (2012) Global estimates of the value of ecosystems and their services in monetary units. Ecosystem Services 1(1): 50–61.

11.

Dická

Gessert

Sninčák

(2019) Rural and non-rural municipalities in the Slovak Republic. Journal of Maps 15: 84–93.

12.

Fan

Yang

(2025) Spatiotemporal assessment of recreation ecosystem service flow from green spaces in Zhengzhou’s main urban area. Humanities and Social Sciences Communications 12: 97.

13.

Fang

Sun

, et al. (2024a) Advancing the optimization of urban–rural ecosystem service supply-demand mismatches and trade-offs. Landscape Ecology 39(2): 32.

14.

Fang

Sun

Zheng

, et al. (2024b) Optimizing the ecosystem service flow of grain provision across metacoupling systems will improve transmission efficiency. Applied Geography 172: 103420.

15.

Feng

Zhao

Duan

, et al. (2021) Coupling trade-offs and supply-demand of ecosystem services (ES): A new opportunity for ES management. Geography and Sustainability 2(4): 275–280.

16.

Feng

Peng

(2020) Using DMSP/OLS nighttime light data and K–means method to identify urban–rural fringe of megacities. Habitat International 103: 102227.

17.

Ferré

Ferreira

Lanjouw

(2012) Is there a metropolitan bias? The relationship between poverty and city size in a selection of developing countries. World Bank Economic Review 26(3): 351–382.

18.

Garnett

(2013) Food sustainability: Problems, perspectives and solutions. Proceedings of the Nutrition Society 72(1): 29–39.

19.

Gebre

Gebremedhin

(2019) The mutual benefits of promoting rural-urban interdependence through linked ecosystem services. Global Ecology and Conservation 20: e00707.

20.

Goldstein

Caldarone

Duarte

, et al. (2012) Integrating ecosystem-service tradeoffs into land-use decisions. Proceedings of the National Academy of Sciences of the United States of America 109(19): 7565–7570.

21.

Gonçalves

Gomes

Ezequiel

, et al. (2017) Differentiating peri-urban areas: A transdisciplinary approach towards a typology. Land Use Policy 63: 331–341.

22.

Guerry

Polasky

Lubchenco

, et al. (2015) Natural capital and ecosystem services informing decisions: From promise to practice. Proceedings of the National Academy of Sciences 112(24): 7348–7355.

23.

Harvey

Gomez-Velez

Schmadel

, et al. (2019) How hydrologic connectivity regulates water quality in river corridors. Journal of the American Water Resources Association 55(2): 369–381.

24.

Wen

Fang

, et al. (2024) Impacts of urban-rural integration on landscape patterns and their implications for landscape sustainability: The case of Changsha, China. Landscape Ecology 39(7): 129.

25.

Herrero-Jáuregui

Arnaiz-Schmitz

Herrera

, et al. (2019) Aligning landscape structure with ecosystem services along an urban-rural gradient. Trade-offs and transitions towards cultural services. Landscape Ecology 34: 1525–1545.

26.

Hölting

Komossa

Filyushkina

, et al. (2020) Including stakeholders’ perspectives on ecosystem services in multifunctionality assessments. Ecosystems and People 16(1): 354–368.

27.

Zhan

, et al. (2023) Urban-rural gradient in vegetation phenology changes of over 1500 cities across China jointly regulated by urbanization and climate change. ISPRS Journal of Photogrammetry and Remote Sensing 205: 367–384.

28.

Johnson

Bagstad

Snapp

, et al. (2012) Service Path Attribution Networks (SPANs): A network flow approach to ecosystem service assessment. International Journal of Agricultural and Environmental Information Systems 3(2): 54–71.

29.

Karakoc

Konar

(2024) Optimization of national grain imports to balance risk and return: A portfolio theory approach. Environmental Research: Food Systems 1(1): 011001.

30.

Kleemann

Schröter

Bagstad

, et al. (2020) Quantifying interregional flows of multiple ecosystem services – A case study for Germany. Global Environmental Change 61: 102051.

31.

Kontgis

Schneider

Fox

, et al. (2014) Monitoring peri-urbanization in the greater Ho Chi Minh City metropolitan area. Applied Geography 53: 377–388.

32.

Kunz

Schneider

Anthony

, et al. (2023) Microplastics in rivers along an urban-rural gradient in an urban agglomeration: Correlation with land use, potential sources and pathways. Environmental Pollution 321: 121096.

33.

Liu

, et al. (2017) Evaluating regional water security through a freshwater ecosystem service flow model: A case study in Beijing-Tianjian-Hebei region, China. Ecological Indicators 81: 159–170.

34.

Wang

, et al. (2023a) Identifying the optimization potential for urban GI by linking supply and demand of ecosystem services under multi-scenarios a case study of Tianjin, China. Ecological Indicators 154: 110570.

35.

Jia

, et al. (2023b) Transboundary flows in the metacoupled Anthropocene: Typology, methods, and governance for global sustainability. Ecology and Society 28(3): 19.

36.

Lin

Huang

Prell

, et al. (2021) Changes in supply and demand mediate the effects of land-use change on freshwater ecosystem services flows. Science of the Total Environment 763: 143012.

37.

Liu

Wang

, et al. (2024) Multidimensional spatial inequality in China and its relationship with economic growth. Humanities and Social Sciences Communications 11(1): 1–13.

38.

Liu

Yang

(2016) Framing ecosystem services in the telecoupled Anthropocene. Frontiers in Ecology and the Environment 14(1): 27–36.

39.

Liu

(2017) Integration across a metacoupled world. Ecology and Society 22(4): 29.

40.

Liu

(2023) Leveraging the metacoupling framework for sustainability science and global sustainable development. National Science Review 10(7): nwad090.

41.

Long

Gao

, et al. (2024) Optimization of ecological network to improve water conservation services in the Nianchu River Basin. Journal of Environmental Management 372: 123368.

42.

Luan

Liu

Zhang

, et al. (2024) Multi-objective land use optimization based on integrated NSGA–II–PLUS model: Comprehensive consideration of economic development and ecosystem services value enhancement. Journal of Cleaner Production 434: 140306.

43.

Magerl

Matej

Kaufmann

, et al. (2022) Forest carbon sink in the U.S. (1870–2012) driven by substitution of forest ecosystem service flows. Resources, Conservation and Recycling 176: 105927.

44.

Mancini

Galli

Coscieme

, et al. (2018) Exploring ecosystem services assessment through Ecological Footprint accounting. Ecosystem Services 30: 228–235.

45.

Mazaraki

Matsiuk

Ilchenko

, et al. (2020) Development of a multimodal (railroad-water) chain of grain supply by the agent-based simulation method. Eastern-European Journal of Enterprise Technologies 6: 14–22.

46.

Metzger

Villarreal-Rosas

Suárez-Castro

, et al. (2021) Considering landscape-level processes in ecosystem service assessments. Science of The Total Environment 796: 149028.

47.

Naidoo

Balmford

Costanza

, et al. (2008) Global mapping of ecosystem services and conservation priorities. Proceedings of the National Academy of Sciences 105(28): 9495–9500.

48.

Norton

Greene

Scholefield

, et al. (2016) The importance of scale in the development of ecosystem service indicators? Ecological Indicators 61: 130–140.

49.

Palomo

Martín-López

Potschin

, et al. (2013) National Parks, buffer zones and surrounding lands: Mapping ecosystem service flows. Ecosystem Services 4: 104–116.

50.

Peng

Liu

Blaschke

, et al. (2020) Integrating land development size, pattern, and density to identify urban–rural fringe in a metropolitan region. Landscape Ecology 35: 2045–2059.

51.

Prakash

Mitchell

(2018) Estimating freight movements using Dijkstra’s algorithm. In: 40th Australasian Transport Research Forum (ATRF), Darwin, Australia, 30 October–1 November.

52.

Rahman

Pham

Jadoon

, et al. (2022) Comparison of machine learning and process-based SWAT model in simulating streamflow in the Upper Indus Basin. Applied Water Science 12(8): 178.

53.

Riechers

Barkmann

Tscharntke

(2018) Diverging perceptions by social groups on cultural ecosystem services provided by urban green. Landscape and Urban Planning 175: 161–168.

54.

Robinson

Shirazi

Liu

, et al. (2016) Network optimization of food flows in the U.S. In: 2016 IEEE International Conference on Big Data (Big Data), Washington, DC, 5–8 December, 2190–2198. New York: IEEE.

55.

Rodríguez-Morales

Roces-Díaz

Kelemen

, et al. (2020) Perception of ecosystem services and disservices on a peri-urban communal forest: Are landowners’ and visitors’ perspectives dissimilar? Ecosystem Services 43: 1–7.

56.

Rodríguez-Robayo

Perevochtchikova

Ávila-Foucat

, et al. (2020) Influence of local context variables on the outcomes of payments for ecosystem services. Evidence from San Antonio del Barrio, Oaxaca, Mexico. Environment, Development and Sustainability 22: 2839–2860.

57.

Schirpke

Candiago

Vigl

, et al. (2019) Integrating supply, flow and demand to enhance the understanding of interactions among multiple ecosystem services. Science of the Total Environment 651: 928–941.

58.

Schröter

Koellner

Alkemade

, et al. (2018) Interregional flows of ecosystem services: Concepts, typology and four cases. Ecosystem Services 31: 231–241.

59.

Serna-Chavez

Schulp

CJE

van Bodegom

, et al. (2014) A quantitative framework for assessing spatial flows of ecosystem services. Ecological Indicators 39: 24–33.

60.

Shen

Wang

(2021) Allocating and mapping ecosystem service demands with spatial flow from built-up areas to natural spaces. Science of the Total Environment 798: 149330.

61.

Shi

Zhou

, et al. (2020) Identification of ecosystem services supply and demand areas and simulation of ecosystem service flows in Shanghai. Ecological Indicators 115: 106418.

62.

Simon

(2008) Urban environments: Issues on the peri-urban fringe. Annual Review of Environment and Resources 33: 167–185.

63.

Singh

(2017) Traditional agriculture: A climate-smart approach for sustainable food production. Energy, Ecology and Environment 2: 296–316.

64.

Springmann

Van Dingenen

Vandyck

, et al. (2023) The global and regional air quality impacts of dietary change. Nature Communications 14(1): 6227.

65.

Sun

Mooney

, et al. (2018) Importing food damages domestic environment: Evidence from global soybean trade. Proceedings of the National Academy of Sciences of the United States of America 115(21): 5415–5419.

66.

Sun

Liu

Liao

, et al. (2024) Understanding recreational ecosystem service supply-demand mismatch and social groups’ preferences: Implications for urban–rural planning. Landscape and Urban Planning 241: 104903.

67.

Sun

Fang

(2023) Spatial scaling of land use/land cover and ecosystem services across urban hierarchical levels: Patterns and relationships. Landscape Ecology 38: 753–777.

68.

Tang

Halike

Yao

, et al. (2024) Ecosystem service valuation and multi-scenario simulation in the Ebinur Lake Basin using a coupled GMOP-PLUS model. Scientific Reports 14(1): 5071.

69.

Tefft

Jonasova

Adjao

, et al. (2017) Food systems for an urbanizing world. Report. @ World Bank, Washington, DC and Food and Agriculture Organization of the United Nations. http://hdl.handle.net/10986/32502.

70.

Theodorou

Baltz

Paxton

, et al. (2021) Urbanization is associated with shifts in bumblebee body size, with cascading effects on pollination. Evolutionary Applications 14: 53–68.

71.

Tromboni

Liu

Ziaco

, et al. (2021) Macrosystems as metacoupled human and natural systems. Frontiers in Ecology and the Environment 19(1): 20–29.

72.

Vakhlamova

Rusterholz

Kanibolotskaya

, et al. (2014) Changes in plant diversity along an urban-rural gradient in an expanding city in Kazakhstan, Western Siberia. Landscape and Urban Planning 132: 111–120.

73.

Vallecillo

La Notte

Zulian

, et al. (2019) Ecosystem services accounts: Valuing the actual flow of nature-based recreation from ecosystems to people. Ecological Modelling 392: 196–211.

74.

Villamagna

Angermeier

Bennett

(2013) Capacity, pressure, demand, and flow: A conceptual framework for analyzing ecosystem service provision and delivery. Ecological Complexity 15: 114–121.

75.

Vizzari

Sigura

(2015) Landscape sequences along the urban–rural–natural gradient: A novel geospatial approach for identification and analysis. Landscape and Urban Planning 140: 42–55.

76.

Wang

Sun

Liu

, et al. (2024) A novel full-resolution convolutional neural network for urban-fringe-rural identification: A case study of urban agglomeration region. Landscape and Urban Planning 249: 105122.

77.

Wang

Zheng

, et al. (2023a) A comprehensive framework for quantifying ecosystem service flow focusing on social-ecological processes. Transactions in Earth, Environment, and Sustainability 1(1): 20–34.

78.

Wang

, et al. (2023b) Identifying and tracking the urban–rural fringe evolution in the urban–rural transformation period: Evidence from a rapidly urbanized rust belt city in China. Ecological Indicators 146: 109856.

79.

Wang

Zheng

Chen

, et al. (2022a) Systematic review of ecosystem services flow measurement: Main concepts, methods, applications and future directions. Ecosystem Services 58: 101479.

80.

Wang

Bai

Alatalo

, et al. (2022b) Impacts of urbanization at city cluster scale on ecosystem services along an urban–rural gradient: A case study of Central Yunnan City Cluster, China. Environmental Science and Pollution Research 29: 88852–88865.

81.

Wang

Yin

Wang

, et al. (2022c) Integrating crop redistribution and improved management towards meeting China’s food demand with lower environmental costs. Nature Food 3(12): 1031–1039.

82.

Woods

Heley

(2017) Conceptualisation of rural-urban relations and synergies. ROBUST Project Report, Deliverable 1.1. University of Aberystwyth, Wales.

83.

Fan

, et al. (2023) Assessment of ecosystem service flow and optimization of spatial pattern of supply and demand matching in Pearl River Delta, China. Ecological Indicators 153: 110452.

84.

Jiang

Huang

, et al. (2020) Ecosystem services response to rural-urban transitions in coastal and island cities: A comparison between Shenzhen and Hong Kong, China. Journal of Cleaner Production 260: 121033.

85.

Yang

Zhuang

, et al. (2021) Spatial gradient differences of ecosystem services supply and demand in the Pearl River Delta region. Journal of Cleaner Production 279: 1–12.

86.

Fan

Wei

, et al. (2019) Evaluation and simulation of the impact of land use change on ecosystem services based on a carbon flow model: A case study of the Manas River Basin of Xinjiang, China. Science of the Total Environment 652: 117–133.

87.

Peng

Dong

, et al. (2022) Spatial correlation between the changes of ecosystem service supply and demand: An ecological zoning approach. Landscape and Urban Planning 217: 104258.

88.

Zeng

Jiang

Bai

, et al. (2024) Understanding scale effects and differentiation mechanisms of ecosystem services tradeoffs and synergies relationship: A case study of the Lishui River Basin, China. Ecological Indicators 167: 112648.

89.

Zhai

Zhang

Zhao

(2021) How to optimize ecological compensation to alleviate environmental injustice in different cities in the Yellow River Basin? A case of integrating ecosystem service supply, demand and flow. Sustainable Cities and Society 75: 103341.

90.

Zhang

Huang

, et al. (2023a) Understanding ecosystem service flows through the metacoupling framework. Ecological Indicators 151: 110303.

91.

Zhang

Shen

Liu

, et al. (2023b) Integrating ecosystem services conservation into the optimization of urban planning policies in eco-fragile areas: A scenario-based case study. Cities 134: 104200.

92.

Zhou

Koomen

Leeuwen

(2018) Residents’ preferences for cultural services of the landscape along the urban-rural gradient. Urban Forestry & Urban Greening 29: 131–141.

93.

Zuo

Wen

Clarke

, et al. (2023) Cropland displacement contributed 60% of the increase in carbon emissions of grain transport in China over 1990–2015. Nature Food 4(3): 223–235.