Coupling between factor flows and Multiple Rural Ecosystem Service Values: a case study of Hunan Province, China

1 Introduction

Factor mobility and the associated restructuring of rural values stand at the core of contemporary debates on urban–rural relations and pathways to sustainable development. The cross-boundary movement of population, land, capital, and information not only reconfigures resource allocation and industrial division of labor but also profoundly reshapes the production, living, ecological, and socio-cultural functions of rural territories. Understanding how these flows interact with the multiple values provided by rural ecosystems is therefore essential for designing effective and context-sensitive rural revitalization policies.

In the early stages of research in this field, urban and rural areas were regarded as relatively separate dualistic spaces, with the focus being on labor migration and urban–rural development disparities. However, as urbanization, globalization, and informatization have advanced, urban–rural relations have gradually come to be understood as dynamic networks sustained by flows of population, capital, commodities, technology, and information. Consequently, integrated development of urban and rural areas and rural revitalization have emerged as key practical pathways for reshaping urban–rural relations (Liu, 2018). Further studies have pointed out that urban–rural interactions encompass multiple bidirectional flows and cannot be reduced to a unidirectional transfer of rural resources to cities (Tacoli, 1998). On this basis, the concept of “desakota” was introduced, transcending the dichotomous spatial understanding of urban and rural areas (McGee, 1991). Subsequent research further emphasized the crucial role of network linkages, social relations, and mobile factors in rural restructuring (Murdoch, 2000; Woods, 2009). These contributions have collectively steered urban–rural research toward the “urban–rural continuum” and the “relational paradigm,” providing a theoretical foundation for analyzing the multiple effects of factor mobility.

Factor mobility exerts a dual influence on rural development. On the one hand, population migration can alleviate rural labor surpluses, increase household remittance income, and facilitate occupational transitions. Land transfer and the consolidation of management rights contribute to agricultural scale operations and the optimization of rural spatial organization. The extension of capital, technology, information, digital platforms, and public services into rural areas can reduce transaction costs, improve infrastructure and service accessibility, and create conditions for industrial upgrading, employment expansion, and improved living standards (Ma et al., 2022; Liu S. et al., 2025; Liu Q. et al., 2025). Further research has revealed that factor mobility exerts direct, indirect, and spatial spillover effects on urban–rural integration (Fang and Iqbal, 2025; Liao et al., 2026). Conversely, the ongoing outmigration of young adults and skilled workers may diminish the agricultural labor supply, community vitality, and the foundations of cultural heritage, resulting in population aging and the depopulation of rural areas (Basnet and Khatiwada, 2025; Rozelle et al., 1999; Stockdale, 2004; Gailey, 2023; Zhang et al., 2019; Wang et al., 2022; Johnson and Lichter, 2019). Land capitalization, non-agricultural conversion, and development models focused on expanding land may enhance short-term resource allocation efficiency. Still, they can also encroach on cultivated land, compress ecological space, and exacerbate disparities in land revenue distribution, thereby eroding the productive and livelihood bases of rural areas (Feng et al., 2019; Sagaidak and Sagaidak, 2022; Fan et al., 2024). Consequently, factor mobility does not necessarily promote or inhibit rural development in a unidirectional manner. Its direction and intensity are contingent on the scale of flows, allocation efficiency, resource endowments, institutional settings, spatial accessibility, and the stage of regional development (Irwin et al., 2009; Zhao and Jiang, 2022; De Brauw and Rozelle, 2008; Faber, 2014; Johnson and Lichter, 2019). In essence, factor mobility represents a multi-scalar, dynamic, and spatially heterogeneous process of resource reorganization, rather than a simple quantitative increase or decrease in production factors.

As research into rural transition has progressed, the evaluation of rural development has gradually shifted from a single-dimensional perspective centered on agricultural production and economic growth to a more comprehensive framework that equally emphasizes multifunctionality and the value of multiple ecosystem services. Rural areas provide ecosystem services such as food supply, agricultural production, and ecological regulation, and they also fulfill multiple functions, including residential security, employment provision, social support, cultural heritage, and public service delivery. Post-productivist research suggests that rural spaces have evolved beyond their primary role as sites of agricultural production to become multifunctional systems in which production, living, ecological, and cultural functions are interwoven (Wilson, 2007; Holmes, 2006). Studies on production–living–ecological spaces further demonstrate that these functions are characterized by interdependence, mutual constraints, and dynamic adjustment (Yang et al., 2020).

The present study adopts a broad concept of “rural multiple ecosystem service values” from the perspectives of ecosystem service theory and rural territorial multifunctionality. This concept incorporates, within a unified analytical framework, both the provisioning, regulating, and cultural services directly provided by rural ecosystems and the socioeconomic wellbeing generated through the interaction of these services with rural production and living systems. This concept is not confined to the narrow notion of direct ecosystem outputs. Rather, it emphasizes the integrated linkages between ecosystem services and rural production, living, and socio-cultural functions. Accordingly, the multiple ecosystem service values of rural areas encompass five dimensions: food provision value, centred on grain production, agricultural production, and industrial development; livelihood security value, centred on household income, living consumption, and basic living conditions; ecological value, centred on carbon sequestration, biodiversity conservation, and environmental regulation; social support value, centred on education, transportation, healthcare, and community public services; and cultural heritage value, centred on intangible cultural heritage, beautiful village construction, and rural civility (Peng et al., 2023; Zhang et al., 2025). These values are not independent of one another. Instead, they form complex synergies and trade-offs through resource competition, spatial substitution, industrial restructuring, and social change (Liu S. et al., 2025; Liu Q. et al., 2025).

Land-use adjustments, including afforestation, the conversion of cropland to forest, and ecological restoration, constitute an important contextual condition affecting rural multiple ecosystem service values. These processes generally enhance vegetation cover, carbon sequestration capacity, and ecosystem stability. However, the expansion of ecological land may also alter cropland use, agricultural operations, and farmers' livelihoods, thereby affecting food supply, agricultural employment, and livelihood security (Silva Bezerra et al., 2022). In the context of ecological compensation, green industry development, and multi-actor governance coordination, ecological conservation and socio-economic development may achieve a certain degree of collaborative enhancement (Wan et al., 2021; Peng et al., 2023; Geng et al., 2024). Nonetheless, land-use adjustments are not a standalone phenomenon. Their ecological and socio-economic ramifications are frequently intertwined with the reallocation of factors such as labor, capital, technology, information, and public services. A substantial body of research has been dedicated to the coordination of production, living, and ecological spaces, the alignment of ecosystem service supply and demand, and the coupling relationships between urban–rural integration and land-use efficiency (Shen et al., 2023; Hou et al., 2023; Liu M. et al., 2024; Liu J. et al., 2024; Zhong et al., 2026). However, there remains a paucity of systematic explanations regarding the influence of factor mobility on the synergies and trade-offs among rural multiple ecosystem service values in the context of land-use adjustments.

Regarding the correlation between factor mobility and rural multiple ecosystem service values, extant studies have primarily established three interconnected explanatory pathways. Firstly, the impact of driving economic growth in rural areas can be examined. The inflow of capital, technology, information, talent, and public services into rural areas has been demonstrated to activate rural industries, improve infrastructure, expand employment opportunities, and enhance quality of life. This, in turn, facilitates the realization of multiple values, including food supply, livelihood security, ecological conservation, and socio-cultural aspects (Dou and Wang, 2023; Liu S. et al., 2025; Liu Q. et al., 2025). Secondly, the crowding-out effect must be considered. Excessive or misallocated flows of land, capital, and population may lead to the conversion of cropland for non-agricultural uses, fragmentation of ecological space, rising living costs, and weakening of local social networks. This, in turn, has the potential to undermine rural multiple ecosystem service values (Feng et al., 2019; Fan et al., 2024). Thirdly, the structural optimization effect must be considered. Moderate population outflow, withdrawal of inefficient factors, and land consolidation do not necessarily lead to rural decline. Instead, they may alleviate resource and environmental pressures, promote scale operations, optimize industrial structure, and facilitate ecological restoration, thereby forming a development pathway characterized by “factor reduction–structural optimisation–value enhancement” (Wang et al., 2022; Sagaidak and Sagaidak, 2022; Geng et al., 2024). These pathways are not mutually exclusive. Rather, they may alternate or coexist depending on regional resource endowments, development stages, institutional environments, land-use structures, and value dimensions.

In general, existing studies have room for further expansion. First, the driving, crowding-out, and structural optimization effects have been discussed separately. A unified analytical framework that explains the conditions under which these effects shift and their differentiated impacts across dimensions of rural multiple ecosystem service values remains to be developed. Secondly, extant research has predominantly emphasized economic output, ecological environment, and resource utilization, while paying insufficient attention to social support, cultural heritage, and their relationships with ecosystem services. In particular, the synergies and trade-offs among multiple values—including food supply, livelihood security, carbon sequestration, biodiversity, social support, and cultural heritage—and their dynamic evolution have not been fully revealed. Thirdly, conventional methods such as comprehensive evaluation, linear specification, and coupling coordination degree are limited in their ability to simultaneously capture potential asymmetric responses, temporal dynamics, and differentiated coupling types between factor mobility and rural multiple ecosystem service values (Qiao et al., 2023; Trivedi and Dube, 2025).

The present study makes two contributions to the extant literature. Firstly, it establishes an analytical framework linking multidimensional factor mobility with the value of multiple ecosystem services in rural areas, thereby contributing to a theoretical understanding of how factor mobility generates synergies and trade-offs within rural systems. Secondly, by combining entropy-weighted assessment, TOPSIS, elasticity analysis, Spearman's correlation, and Geodetector within a single empirical exercise, it offers a novel methodological protocol for identifying coupling patterns, temporal variations, spatial differentiation, and their driving mechanisms at the county level.

Hunan Province is located in the middle reaches of the Yangtze River basin in China and is abbreviated as “Xiang.” The region is distinguished by its substantial grain production and its provincial importance in central China. The province features diverse rural territorial types and significant urban–rural development disparities, thus making it a suitable case for studying the coupling relationship between urban–rural factor mobility and rural multiple ecosystem service values. On the one hand, some rural areas still face challenges such as the outmigration of young adults and skilled labor, insufficient supply of advanced factors including capital and technology, and the need for improved factor allocation efficiency. On the other hand, the ongoing implementation of the rural revitalization strategy, ecological protection and restoration programs, and the development of transportation and information infrastructure have created conditions for bidirectional factor flows and the enhancement of rural multiple ecosystem service values. The Dongting Lake Plain, the central Hunan hilly areas, and the mountainous regions of western and southern Hunan exhibit marked differences in resource endowments, industrial bases, ecological baselines, and urbanization levels. These variations provide a suitable context for identifying the complex coupling relationships between factor mobility and rural multiple ecosystem service values. The present study takes Hunan Province as a case study. An elasticity coefficient model of factor mobility and rural multiple ecosystem service values is constructed to identify the coupling types and their spatiotemporal evolution, and to explore the influencing factors. The findings are intended to inform the optimization of urban–rural factor allocation and the formulation of differentiated rural sustainable development policies.

2 Theoretical framework

The relationship between factor mobility and rural multiple ecosystem service values is not one of unidirectional causation. Rather, it is a dynamic coupling process shaped by the continuous interaction of material, energy, information, and human flows between urban and rural areas. To theorize this process, we draw upon and integrate three complementary strands of literature: agglomeration–diffusion theory, new economic geography, and rural restructuring within social–ecological systems.

The direction and spatial effects of factor flows can be understood through the lens of agglomeration-diffusion theory and new economic geography. In the early stages of urbanisation, capital, labor, and public resources are concentrated in cities, whilst rural areas face pressures such as population outmigration, industrial weakening, and service contraction. However, as urban–rural linkages deepen, capital flows to rural areas, technology diffuses, information permeates, and consumer demand spills over. These processes may further promote rural industrial upgrading and functional restructuring. Nevertheless, these theories pay insufficient attention to the differentiated responses across various ecosystem service values and the conditions under which factor concentration may generate crowding-out rather than synergistic effects.

Theoretical frameworks such as rural restructuring theory and social–ecological systems theory address this gap by emphasizing that the impact of factor mobility is contingent not only on its quantity, but also on the manner in which it is embedded in rural social–ecological systems. Material factors, such as capital and land, primarily alter production conditions, spatial morphology, and infrastructure. Non-material factors, including technology, information, talent, organizational capacity, and local knowledge, enhance endogenous rural development capacity through green transformation, public governance, and cultural reproduction. Social–ecological systems theory further identifies feedbacks, thresholds, and resilience among economic development, ecological conservation, and social governance. This provides a holistic perspective for explaining the synergies and trade-offs among multiple ecosystem service values-specifically, how improvements in food supply and livelihood security may be accompanied by losses in ecological protection, social support, or cultural heritage values when material factors are overly concentrated, non-material factors are insufficiently supplied, or when objectives related to resource development and ecological protection are imbalanced.

In light of these mechanisms, we posit that factor mobility significantly influences the multifarious values of ecosystem services by modifying rural resource allocation and system absorptive capacity. This process gives rise to a dynamic evolution that is shaped by feedback from changes in service values. Improvements in food supply capacity and industrial returns can enhance rural attractiveness to capital, labor, and market actors. Higher levels of livelihood security and social support can strengthen residents' willingness to settle and retain talent. Favorable carbon sequestration and biodiversity conditions can form ecological asset advantages, attracting green investment, eco-consumption, and leisure tourism demand. Revitalizing cultural heritage can help shape place branding and expand spaces for cultural consumption and social participation. Conversely, ecological degradation, inadequate public services, weakened social support networks, and the loss of cultural heritage actors can accelerate the outmigration of capital, labor, and talent, forming a negative feedback loop. Consequently, due to variations in resource endowments, development stages, locational conditions, and governance capacity, different regions exhibit distinct coupling states, including collaborative enhancement, ecologically constrained, value-lagging, or structurally differentiated outcomes. The optimization of factor allocation should not be confined to augmenting material inputs; rather, it should encompass the coordinated provision of technology, talent, information, public services, and ecological governance capacity. Such coordinated provision has the potential to facilitate the transition of multiple values from trade-offs toward synergies (see Figure 1).

3 Research methods and data sources

3.1 Overview of the study area

Hunan Province is located in the middle reaches of the Yangtze River in China. It is a major grain-producing area in central China and an important inland node of the Belt and Road Initiative, endowed with both agricultural production and ecological resource advantages (see Figure 2). The province's urbanization rate is 62.07%, which is below the national average. The rural aging rate reaches 23.7%, and the urban–rural disposable income ratio is 2.35, indicating persistent urban–rural development disparities. The forest coverage rate of 54.11% reflects the province's ecological advantage as a major forestry region, providing a solid ecological foundation for the supply of multiple ecosystem services. In recent years, the implementation of the rural revitalization strategy has deepened, accelerating factor mobility within China's rural regions. Industrial integration has gradually strengthened, while the urban–rural dual structure has shown signs of convergence. These developments present a dynamic observational window through which to study the coupling relationship between factor mobility and rural multiple ecosystem service values. The province of Hunan is characterized by a diverse array of landform types, which give rise to a distinct spatial pattern. The Changsha–Zhuzhou–Xiangtan urban agglomeration functions as the primary center of growth, while the Dongting Lake Plain is predominantly dedicated to agricultural production. The mountainous regions of western and southern Hunan act as ecological barriers, underscoring the province's varied geographical characteristics. This pronounced core-periphery differentiation gives rise to significant regional disparities in factors such as mobility, industrial development, and the realization of rural value. Consequently, Hunan serves as a noteworthy case study for the analysis of spatiotemporal heterogeneity in the coupling relationship.

3.2 Construction and measurement of the indicator system

3.2.1 Construction and measurement of the evaluation indicator system for factor mobility

The classification and definition of factors serve as the theoretical prerequisite for measuring factor mobility. In economics, population, land, and capital have long been recognized as the three primary production factors (Glahe, 1994; Groenewegen and McFarlane, 2014). In the policy context, a national policy document in 2020 was the first to list data alongside land, labor, capital, and technology, thereby establishing a “five-factor” policy framework. The transition from the conventional three-factor to the contemporary five-factor framework signifies a theoretical progression from a paradigm dominated by “material inputs” to one encompassing “material-human-technology-data” dimensions. This evolution can be interpreted as an augmentation of factor connotations, which have been extended from tangible physical forms to intangible knowledge-based forms. Nevertheless, the traditional three-factor approach is predicated on material inputs from a production standpoint and struggles to fully incorporate non-material factors that have become increasingly significant in contemporary economies, such as information and technology. Despite the five-factor policy framework's expansion in the types of factors it encompasses, its classification criteria are still predicated on economic attributes and functional characteristics, resulting in inconsistent classification logic. More importantly, both paradigms are based on the assumption of static factor allocation within closed economic systems. They cannot fully capture the distinctive characteristics of factor mobility in urban–rural interaction systems. In this context, a classification framework is needed that transcends specific numerical categories and reveals the essential differences in factor mobility.

This study draws on the extant classification of material and non-material factors in rural restructuring theory. Factors with physical forms, including capital, consumer goods, and non-agricultural consumer goods, are categorized as material factors. Non-material transfers of knowledge and skills accompany population (labor) mobility. Technology and information, being inherently knowledge-based, are thus categorized as non-material factors (Castells, 2011; Liao et al., 2026). The rational basis for this classification is the fundamental disparities in mobility mechanisms between material and non-material factors. Material factors, as represented by capital, are considerably influenced by spatial distance, transport costs, and geographical barriers, manifesting a distance-decay pattern. Non-material factors, exemplified by information and technology, are distinguished by non-rivalry and partial non-excludability. Enabled by the internet and communication technologies, they can transcend geographical space, exhibiting network spillover effects and spatial leapfrogging characteristics. In summary, this classification system represents an advancement on the numerical and nominal differences between the traditional three-factor and policy five-factor frameworks. It captures the essential characteristics of factor mobility: material factors follow a distance-decay pattern, whereas non-material factors exhibit stronger spatial transcendence and network spillover effects.

In light of the prevailing context of urban–rural development in Hunan Province, this study proposes an evaluation index system for factor mobility, categorized into two dimensions: material factors and non-material factors (see Table 1). Each indicator is standardized, with the entropy weight method employed to determine indicator weights. The linear weighted aggregation method is then utilized to calculate comprehensive scores of factor mobility at the county level. These scores indicate the relative intensity of factor mobility across diverse geographical regions (OECD, 2008; Nardo et al., 2005).

Table 1. Index system of urban–rural factor flow intensity.

3.2.2 Construction and measurement of the evaluation indicator system for the value of rural multifunctional ecosystem services

The present study has selected 15 indicators across five dimensions—food supply, livelihood security, carbon sequestration and biodiversity, social support, and cultural heritage—to construct an evaluation index system for the realization level of rural multiple ecosystem service values (see Table 2). The concept of rural multiple ecosystem service values employed in this study refers to a comprehensive evaluation framework oriented toward rural territorial systems, rather than a monetary valuation of ecosystem services in a narrow sense. While adhering to the fundamental connotations of ecosystem service supply, regulation, and culture, this study further incorporates indicators reflecting rural production development, livelihood security, social support, and cultural heritage. These indicators are intended to characterize the integrated values formed through the interaction between ecosystem services and rural socio-economic systems. The evaluation results reflect the relative development levels and structural characteristics of rural multiple ecosystem service values. The selection of indicators builds on existing frameworks that have focused on the food supply–livelihood security–biodiversity ternary structure or the economic development–social support–biodiversity–cultural four-dimensional framework. This study refines these approaches by addressing five dimensions that also closely align with the five major goals explicitly articulated in the Communist Party of China Central Committee's rural revitalization strategy document in 2018. Moreover, the present study underscores the necessity for a more comprehensive characterization of individual value dimensions. To illustrate this point, the dimension of carbon sequestration and biodiversity is considered. Previous studies have frequently employed forest coverage rate as a solitary indicator. While this study retains forest coverage rate as a means of reflecting vegetation cover and carbon sink potential, it also incorporates pesticide application intensity and annual average PM2.5 concentration. The former indicator reflects agricultural non-point source pollution pressure, while the latter reflects regional environmental quality. Thus, the two indicators together reflect ecosystem health conditions and biodiversity protection constraints. This approach enhances the comprehensiveness of the dimension's evaluation.

Table 2. Evaluation index system for Multiple Rural Ecosystem Service Values.

To measure the degree to which rural multiple ecosystem service values are realized, this study adopts the same approach as for factor mobility measurement. First, the entropy weight method is used to determine indicator weights for rural multiple ecosystem service values. Subsequently, the TOPSIS method is applied to calculate the comprehensive scores of rural multiple ecosystem service values (Zhong et al., 2026; Dou and Wang, 2023). The main principle of this method is to measure the weighted Euclidean distances between samples and the ideal solution and the negative-ideal solution. This allows for the assessment of proximity to the ideal solution and the ranking of samples, thereby generating evaluation results.

3.2.3 Indicator system development and measurement for drivers of the coupling between factor mobility and rural multifunctional ecosystem service value

To ascertain the key driving factors of spatial differentiation in coupling relationship types, this study selects five influencing factors from five dimensions: population and spatial structure, social environment, economic foundation, modern agricultural development level, and locational conditions (see Table 3). Urbanization level indicates the direction and intensity of factor agglomeration. The number of commercial service organizations measures the capacity for socialized services in rural areas. The fiscal self-sufficiency rate measures the investment capacity of local governments in rural development. The number of new agricultural business entities reflects the development level of modern agriculture and the maturity of institutional carriers for factor transformation. Distance to the municipal city center measures counties' locational advantage in terms of radiation-driven effects from central cities.

Table 3. Influencing factor index system for the coupling between factor flows and Multiple Rural Ecosystem Service Values.

3.3 Research methods

3.3.1 Elasticity coefficient model of factor mobility-value of rural multifunctional ecosystem services

Conventional approaches, such as coupling coordination degree analysis, predominantly emphasize system-level relationships between systems. These methods struggle to elucidate interactions among the internal components within systems. The underlying assumptions of these methods are predominantly linear, which hinders their ability to effectively capture the nonlinear feedback relationships frequently observed in real-world contexts. Additionally, these methods are inherently static, resulting in inadequate characterization of dynamic evolutionary processes.

To more precisely characterize the complex, dynamic, and nonlinear interactions between factor mobility and rural multiple ecosystem service values, this study constructs an elasticity coefficient model of factor mobility and rural multiple ecosystem service values (Trivedi and Dube, 2025; Wan et al., 2021). The elasticity coefficient is a quantitative metric that quantifies the magnitude and direction of coupling interactions. The magnitude of the elasticity coefficient directly reflects the degree of coupling tightness, while the sign of the elasticity coefficient reveals the nature of the coupling relationship. Furthermore, the dimensionless property of the elasticity coefficient enables cross-dimensional comparisons across different value dimensions, thereby overcoming dimensional barriers within rural value systems. The calculation formula is as follows:

ε i = Δ V i Δ E i = ( V i + 1 − V it ) ( E i + 1 − E it ) (1)

In this equation, ε i denotes the elasticity coefficient of factor flows and Multiple Rural Ecosystem Service Values for county unit i. Δ E i and Δ V i represent the changes in factor flows and Multiple Rural Ecosystem Service Values, respectively, for county unit i. E it and E i + 1 are the factor flow scores of county unit i at the beginning and end of the period, respectively. V it and V i + 1 are the corresponding scores for Multiple Rural Ecosystem Service Values.

Based on whether ε i, Δ E i and Δ V i are greater than zero, the correlation between factor flows and Multiple Rural Ecosystem Service Values is categorised into four distinct types (see Table 4) to enable regional regulation (Geng et al., 2024). The collaborative type describes a scenario in which an increase in factor flow intensity drives value enhancement, with both elements evolving in synchrony. The crowding-out type emerges when substantial inflows of specific factors, such as capital or labor, into cities or specific regions displace rural resources, leading to a decline in values rather than an increase. The inverse-increment type delineates instances where outflows of certain factors, such as population outmigration, may in fact stimulate internal structural optimization or value restructuring in rural areas, thereby achieving a form of “reducing inputs while increasing efficiency.” The lagging type refers to a diminution in certain values due to a reduced factor flow intensity.

Table 4. Types of coupling relationships between factor flows and Multiple Rural Ecosystem Service Values.

3.3.2 Geographical detector

The present study employs the geographical detector method to analyze the factors influencing the spatiotemporal variation of coupling types between factor flows and Multiple Rural Ecosystem Service Values, with a view to revealing spatial differentiation patterns and their underlying driving mechanisms. The method can handle both qualitative and quantitative data and detect interactions between pairs of factors. This facilitates the identification of the types of interactive relationships among driving factors (Liu M. et al., 2024; Liu J. et al., 2024). The calculation formula is as follows:

q = 1 − ∑ h = 2 L N h σ h 2 N σ 2 (2)

In this study, the dependent variables N and N h are used to denote the total number of county-level units in Hunan Province, and the number of units belonging to a specific coupling relationship type, respectively. The independent variable h represents different coupling relationship types. The variances of the overall coupling relationship types and those of specific types across the entire study area are denoted by σ 2 and σ h 2, respectively. The value of q ranges from 0 to 1. A larger q value indicates stronger explanatory power of the driving factor for spatial differentiation of coupling types.

3.3.3 Spearman correlation matrix

To reveal the synergies and trade-offs among the five value dimensions, this study employed Spearman's rank correlation coefficient. This method does not require normally distributed data. It calculates correlations based on ranks rather than raw values, enabling effective capture of monotonic (including nonlinear) associations and demonstrating strong robustness to outliers (Xiao et al., 2026). Spearman's rank correlation coefficients were computed among the elasticity coefficients for the five value dimensions in each county across two periods: 2013–2018 and 2018–2024. By comparing structural changes in the correlation matrices between the two periods, the stage-specific evolution of relationships among multi-dimensional values was identified.

3.4 Data sources and notes

The data on socioeconomic indicators, encompassing population, output value, fiscal revenue and expenditure, and grain production, were obtained from the Hunan Statistical Yearbook, China County Statistical Yearbook, Hunan Rural Statistical Yearbook, China Urban–Rural Construction Statistical Yearbook, as well as the statistical yearbooks and statistical bulletins on national economic and social development of the counties and districts within the study area. Data on infrastructure facilities, including primary and secondary schools and hospitals, were sourced from the Gaode Points of Interest (POI) database. Road network data was extracted from OpenStreetMap. Information on new agricultural entities and commercial service organizations was derived from the Hunan Rural Statistical Yearbook. Air quality data, including PM2.5 concentrations from 2013 to 2024, were obtained from an online database.¹ Intangible cultural heritage data were primarily sourced from the Hunan Provincial Intangible Cultural Heritage Resource Database. Scenic spot data were obtained from the development statistical bulletins of the Hunan Provincial Department of Culture and Tourism. The study period spans from 2013 to 2024. The year 2013 marks the launch of the New Urbanization Strategy. The year 2018 serves as a critical node—the release year of the Rural Revitalization Strategic Plan and a turning point in China's economic and social transformation. 2024 marks the conclusion of the first phase of the Rural Revitalization Plan. By incorporating changes in policy and institutional environments, economic and social transitions, industrial restructuring, and ecological shifts, this study analyses the mechanisms and coupling between Multiple Rural Ecosystem Service Values and factor flows (Wang et al., 2022).

4 Results

4.1 Spatio-temporal variation of the coupling between factor flows and Multiple Rural Ecosystem Service Values

From the perspective of temporal evolution, the coupling relationship between factor mobility and rural multiple ecosystem service values in Hunan Province shifts from “collaborative dominance” toward “inverse-increment and lagging dominance” (see Figure 3). From 2013 to 2018, the collaborative type was predominant across the province. The crowding-out, inverse-increment, and lagging types manifested only sporadically. This phenomenon suggests that factor mobility and rural multiple value enhancement were largely aligned, and the system maintained a relatively coordinated developmental trajectory. After 2018, the collaborative type contracted markedly. Conversely, the inverse-increment and lagging types expanded substantially, ultimately becoming the dominant types. This shift can be attributed to the increasing disparity between factor mobility and rural value growth.

The rise in the inverse-increment type indicates that, in certain regions, rural multiple values exhibited faster growth than factor mobility. The expansion of the lagging type signifies that, despite intensified factor mobility, the comprehensive transformation of values into ecological, industrial, and socio-cultural aspects remained inadequate. This transition may be associated with the implementation of the rural revitalization strategy, tightened ecological protection constraints, and the diffusion of digital factors. There has been an increasing allocation of capital, technology, and talent to rural areas. However, due to regional differences in absorptive capacity, industrial foundations, and public service conditions, the efficiency of value transformation has diverged. The continuous strengthening of institutional constraints—such as ecological conservation redlines and farmland protection—has led to an acceleration in the improvement of ecosystem service values in regions where ecological baselines are favorable, thus contributing to the inverse-increment pattern. The ongoing advancement of digital village construction has facilitated the diffusion of non-material factors, including information and technology, into rural areas. However, the uneven distribution of digital infrastructure, talent pools, and platform application capabilities has led to lagging in some regions, characterized by rapid factor entry but slow comprehensive value transformation. Overall, the policy orientation has shifted from a singular focus on factor input toward an integrated emphasis on ecological protection, digital empowerment, and urban–rural integration. This shift has driven the coupling relationship from a generally collaborative state toward multi-type differentiation.

From a spatial perspective, the coupling types between factor mobility and rural multiple ecosystem service values in Hunan Province exhibit pronounced regional differentiation and mosaic agglomeration characteristics (see Figure 3). From 2013 to 2018, the collaborative type exhibited a distribution characterized by contiguous clusters. Other types manifested predominantly as point-like or small patchy mosaics. After 2018, the lagging type expanded primarily toward northwestern Hunan, southwestern Hunan, and certain marginal mountainous regions. The inverse-increment type demonstrated concentration in specific areas within central, eastern, and southern Hunan. The collaborative type contracted, becoming dispersed into isolated patches, primarily situated in regions characterized by substantial development foundations, favorable ecological resource endowments, or enhanced urban–rural linkages. Regions dominated by the lagging type are characterized by a high proportion of mountainous and hilly terrain, weak transportation accessibility, insufficient industrial support, and notable population outmigration. Although factor mobility has increased in these areas, value transformation remains relatively delayed due to constraints in market size, talent absorption capacity, and industrial chain completeness. Regions with the inverse-increment type are likely to possess favorable ecological resources, distinctive agricultural characteristics, or potential for cultural tourism development. Rural value realization has accelerated in these areas. However, the supply of high-quality factors—including capital, technology, and market access—has yet to be adequately matched. Regions exhibiting the collaborative type have initiated a virtuous cycle of factor agglomeration, industrial development, ecological conservation, and rural value enhancement.

In light of the aforementioned spatiotemporal differentiation, Hunan Province can be broadly categorized into three distinct regions. The lagging region, predominant in northwestern Hunan, southwestern Hunan, and certain marginal mountainous areas, should be the primary focus of efforts to enhance infrastructure, public services, and talent-absorption capacity. Conversely, the inverse-increment region, which is situated in parts of central, eastern, and southern Hunan, necessitates a more targeted allocation of capital, technology, and market factors. Localized collaborative areas should document their experiences in urban–rural integration, industrial linkage, and ecological value transformation to develop replicable models. The distinction between these three regions provides a clear spatial basis for further identification of influencing factors such as factor mobility, ecological endowments, industrial foundations, and public service conditions.

4.2 Dynamic transition of coupling types

The preceding analysis reveals the overall evolution of the four coupling types in Hunan Province. However, further identification is required of their spatial expansion, contraction, and agglomeration directions. To this end, this study employs the centroid migration model to track the displacement trajectories of the spatial centroids of each type during 2013–2018 and 2018–2024, thereby characterizing their spatial adjustment features (see Figure 4). Figure 4 should be understood primarily as a descriptive spatial visualization of the directions and temporal succession of the identified migration sequences. Rather than independently measuring migration intensity or establishing causal relationships, it complements the quantitative analysis by illustrating the spatial distribution and temporal organization of the different coupling types.

From the perspective of migration directions, the centroids of the four coupling types have all shifted to varying degrees, with notable differences in their trajectories. The centroid of the collaborative type has shifted from the south-central part of the province toward the southeast. This indicates that areas where factor mobility and rural multiple ecosystem service values are relatively coordinated are agglomerating toward the central-southern Hunan transitional zone and the southeastern direction. The centroid of the crowding-out type exhibited a distinct tendency to migrate toward the northeast. This observation suggests that regions encountering incongruities between factor agglomeration and rural value enhancement are extending toward northeastern Hunan, where urban−rural linkages are more pronounced and industrial factor agglomeration is comparatively elevated. The centroid of the inverse-increment type exhibited a general shift westward and a slight northward movement. This suggests that regions where rural multiple values exhibit faster growth than factor mobility are progressively concentrating in western Hunan and adjacent areas. The centroid of the lagging type exhibited a pronounced tendency to migrate southeastward. This phenomenon can be interpreted as an aggregation of regions where factor mobility growth exceeds value transformation, or where rural value enhancement lags behind other areas. These regions are concentrated in the southeastern region of Hunan and other marginal areas.

Regarding migration distance, the lagging type shows the greatest displacement, indicating the most substantial spatial restructuring among the four types examined. The crowding-out and collaborative types exhibit moderate migration distances, suggesting that both have undergone some reorganization and directional shifts. The inverse-increment type, conversely, exhibits the shortest centroid displacement, indicating that its spatial distribution remains relatively stable overall. In conclusion, the centroid migration trajectories further corroborate the deepening spatial differentiation of coupling types that was identified earlier. Differentiated policy measures must be adopted accordingly: the collaborative type should prioritize experience diffusion; the inverse-increment type should strengthen the supply of high-quality factors; the crowding-out type should optimize factor allocation structures; and the lagging type should focus on enhancing the value transformation capacity of infrastructure, public services, and distinctive industries.

4.3 Heterogeneity of coupling relationships across dimensions: dynamics of synergies and trade‑offs in ecosystem service values

The theoretical framework classifies rural values into two categories, based on the types of driving factors. The primary drivers of production and livelihood security are material factors, characterized by scale inputs. These factors exhibit a linear input–output response logic, reflecting the efficiency frontier of factor allocation. In contrast, carbon sequestration and biodiversity, social support, and cultural heritage are more dependent on the sustained supply of non-material factors. The manifestation of coupling states in these factors indicates the system's adaptability and vulnerability to structural factor imbalances, following nonlinear response logic. The response pathways of these two categories differ fundamentally and thus require separate examination. Moreover, the five value dimensions do not operate in isolation. Their synergies or trade-offs need to be verified through cross-dimensional correlation analysis. Accordingly, this section sequentially examines the evolutionary characteristics of coupling types for food supply and livelihood security, carbon sequestration and biodiversity, and social support and cultural heritage. Spearman correlation matrices are employed to validate the synergies and trade-offs among the five value dimensions, providing a value-level micro-explanation for the overall transition of coupling types.

4.3.1 Marginal attenuation of food supply and livelihood security services under excessive material factor concentration

During the period under study, both the food supply and livelihood security services were significantly influenced by factor mobility. However, their response mechanisms differed. The overall conclusion of this study is that factor mobility provided strong support for agricultural production and improvements in residents' living conditions during the period 2013–2018. Following 2018, however, the collaborative type contracted, while the lagging and crowding-out types gradually expanded. This shift indicates that the allocation of material factors—dominated by capital, land, and infrastructure investment—exhibited weakened transformative effects on rural service values. Rural development thus faces a practical need to transition from scale-oriented factor expansion toward structural optimization.

The food supply demonstrates a marginal attenuation pattern, characterized by “increasing factor inputs-diminishing value transformation” (see Figure 5A). During the period 2013–2018, the collaborative and inverse-increment types predominated. This finding suggests that, within the context of the conventional agricultural production system, factor inputs, encompassing land, labor, and capital, effectively promoted agricultural output and enhanced food supply values. Following 2018, the collaborative type declined, whilst the lagging type expanded rapidly and became dominant. This shift indicates a declining alignment between factor inputs and agricultural production needs. There has been an increase in the concentration of capital and land around urban peripheries and non-agricultural sectors, resulting in increased agricultural operation costs and reduced agricultural production space. Concurrently, there has been inadequate integration of technology, facilities, and capital with dispersed, small-scale farming systems, thereby constraining the transformation of new factor inputs into stable supply capacity.

The livelihood security service evinces characteristics of both “factor agglomeration-cost crowding-out” and “service provision-response lag” (see Figure 5B). During the period 2013–2018, the livelihood security service was predominantly of the collaborative and inverse-increment types. This finding suggests that infrastructure construction, increased public investment, and strengthened urban−rural linkages have had a beneficial effect on residents' living conditions. After 2018, the collaborative type contracted substantially, while crowding-out and lagging types increased. This suggests that material factor agglomeration failed to enhance rural residents' livelihood security levels simultaneously. In the Changsha–Zhuzhou–Xiangtan region and surrounding areas where factor agglomeration accelerated, inflows of land, capital, and population can drive economic development. However, they may also push up housing, employment, and daily living costs. Meanwhile, the expansion of public services such as education and healthcare has lagged, resulting in a crowding-out effect of factor agglomeration on quality-of-life improvements. In peripheral mountainous areas and traditional agricultural regions, the outmigration of high-quality resources and weak basic service facilities constrain improvements in livelihood security values. In summary, the hypothesis that the continuous enhancement of rural service values can be sustained by relying solely on large-scale material factor inputs and spatial agglomeration is not supported by the evidence.

4.3.2 Differentiation of value responses under insufficient non-material factor supply

In comparison with fundamental services such as food provision and livelihood security, carbon sequestration and biodiversity are, respectively, associated with rural ecological security, social resilience, and cultural continuity. During the study period, the coupling relationships among these three service types shifted from synergy to divergence. However, the evolutionary directions of these relationships differed. This variation reflects the differing degrees of dependence of various rural values on non-material factors, including technology, information, talent, and organizational capacity.

Carbon sequestration and biodiversity exhibit strong ecological resilience and policy dependence (see Figure 6A). During 2013–2018, the collaborative, inverse-increment, and localized lagging types were interspersed. After 2018, the inverse-increment type expanded significantly and became dominant. This indicates that policies—including ecological civilization construction, ecological conservation redlines, and ecological compensation—have promoted forest protection, ecological restoration, and environmental governance. As a result, regions with favorable ecological baselines have achieved improvements in carbon sink and biodiversity values despite limited growth in factor inflows. However, the persistence of localized lagging types suggests that the ecological monitoring technologies, professional talent, and market-based transformation mechanisms required for ecological value realization still need further improvement.

Social support and cultural heritage exhibit pronounced response lags (see Figures 6B,C). After 2018, the collaborative type of social support services contracted significantly, while the lagging type expanded rapidly and became dominant. This indicates that improvements in public services, grassroots governance, and social security have not kept pace with factor mobility. The underlying reasons for this phenomenon are attributable to the characteristics inherent in the concept of social support. These characteristics include protracted investment cycles, a dispersed population of service recipients, and a high degree of reliance on skilled personnel. The scarcity of teachers, healthcare workers, social workers, and grassroots governance talent impedes efforts to enhance these values. Cultural heritage services have been shown to exhibit a pattern of collaborative contraction and coexistence of inverse-increment and lagging types. This suggests that the protection of traditional villages and the construction of cultural facilities have contributed to the realization of some cultural values. However, the loss of cultural inheritors, the outmigration of young people, and the weakening of cultural organizations impede the revitalization of cultural resources and intergenerational continuity. The findings indicate that inadequate provision of non-material factors significantly constrains efforts to strengthen social support and cultural heritage values. Furthermore, it constitutes a critical bottleneck affecting the collaborative development of rural multiple ecosystem services.

4.3.3 Evolution of the correlation structure among the five value dimensions

In the period 2013–2018, the five categories of ecosystem service values in rural Hunan Province demonstrated robust coupling characteristics (see Table 5). Food provision, livelihood security, and social support exhibited notable positive correlations with each other, with correlation coefficients of 0.776, 0.669, and 0.697, respectively. This suggests that, under factor mobility, agricultural production, living standard improvement, and social service enhancement progressed in a highly synchronized manner. The findings of this study demonstrate a significant negative correlation between carbon sequestration and biodiversity on the one hand, and food supply, livelihood security, and social support on the other. The correlation coefficients for these relationships were −0.801, −0.791, and −0.625, respectively. These results reflect the encroachment of production expansion, living construction, and social development on ecological space and ecological functions. Despite improvements in food supply and livelihood security, these developments have come at the expense of ecological values to a certain extent. The findings of the study demonstrated a weak positive correlation between cultural heritage and food supply (ρ = 0.251), as well as a weak negative correlation with carbon sequestration and biodiversity (ρ = −0.298). However, the study's results did not reveal a significant correlation between cultural heritage and livelihood security or social support. These findings imply that cultural heritage exhibits limited linkages with other service values.

Table 5. Spearman correlation coefficient matrix of elasticity coefficients of the five value dimensions for different years.

**p < 0.01 (two‑tailed); *p < 0.05 (two‑tailed).

The period between 2018 and 2024 saw a weakening of the overall coupling relationships among the five service values (see Table 5). The correlation between food supply and livelihood security, as well as between food supply and social support, remained positive, but the correlation coefficients declined to 0.197 and 0.389, respectively. The correlation coefficient between livelihood security and social support decreased from 0.697 to 0.328, indicating a marked weakening of the collaborative relationships among these three dimensions. The findings of this study demonstrate a persistent and substantial negative relationship between carbon sequestration and biodiversity on the one hand, and food supply, livelihood security, and social support on the other. The magnitude of these correlations ranges from −0.353 to −0.473, with the highest correlation coefficient observed in food supply. However, the overall effect of these trade-offs has diminished, suggesting a shift toward a less pronounced interplay between ecological protection and production-living development. The relationship between cultural heritage and carbon sequestration, as well as biodiversity, shifted from a weak negative correlation to a significant positive correlation (ρ = 0.286). However, its correlations with food supply, livelihood security, and social support remained non-significant. This finding suggests that cultural heritage and ecological protection may be beginning to exhibit collaborative potential. Nevertheless, cultural heritage has not yet formed stable linkages with food supply, livelihood security, or social support.

In summary, the rural multiple ecosystem service values in Hunan Province transitioned from an early-stage strongly coupled pattern—characterized by synergies among food supply, livelihood security, and social support, alongside ecological constraints—to a later-stage pattern of weakened linkages and divergent responses. The positive linkages among food supply, livelihood security, and social support weakened, and the trade-offs between carbon sequestration and biodiversity, and between production and living services, became less pronounced. Furthermore, cultural heritage gradually exhibited collaborative potential with ecological values. The responses of different service values to factor mobility diverged more substantially, and the synergies and trade-offs within the system underwent structural restructuring.

4.4 Identification and analysis of influencing factors in the spatial differentiation of coupling types

The preceding analysis revealed the spatial differentiation pattern of coupling types and identified three typical regions. However, the key factors driving this pattern remain unclear. To address this, this section applies the geographical detector model. Five influencing factors are selected for investigation: urbanization level (X1), number of commercial service organizations (X2), fiscal self-sufficiency rate (X3), number of new agricultural entities (X4), and distance to the prefecture-level city center (X5). With coupling type as the dependent variable, the explanatory power of each factor on the spatial differentiation of coupling types is identified.

4.4.1 Single-factor detection analysis

The results of the single-factor detection analysis (see Table 6) indicate that urbanization level (X2) and the number of new agricultural entities (X4) are significant factors in the spatial differentiation of coupling types. Among these factors, X4 exhibited the strongest explanatory power (q = 0.18), suggesting that the development level of agricultural operators directly affects the efficiency with which factor inflows are transformed into Multiple Rural Ecosystem Service Values. Counties with more new agricultural entities tend to have stronger industrial absorption capacity, enabling factor flows to translate more effectively into Multiple Rural Ecosystem Service Values. collaborative or inverse enhancement types predominantly characterize such counties. In contrast, counties with fewer such entities are mostly lagging. X2 ranked second in explanatory power (q = 0.15), indicating that in the Changzhou Zhuanghe Tan core area, where urbanization is relatively high, factors are highly concentrated. Still, value transformation lags, leading to crowding-out types. Meanwhile, in the western marginal areas where urbanization is lower, factor attractiveness is insufficient, and lagging types are more common. X1, X3, and X5 did not pass the significance test. X3 approached marginal significance (p = 0.12), possibly due to the substantial inter-county fiscal transfers within Hunan, which may influence the fiscal self-sufficiency rate. The non-significance of X1 may be related to improvements in transportation infrastructure in recent years, which have weakened the barrier effect of geographical distance.

Table 6. Single-factor detection results.

4.4.2 Interaction detection analysis

The interaction detection results (see Figure 7) demonstrate that the interaction between X2 and X4 yielded the highest q value (0.60), exhibiting two-way enhancement. This suggests that the collaborative combination of urbanization level and the number of new agricultural entities is the strongest driving force explaining spatial differentiation in coupling types. Urbanization provides the pull force for factor agglomeration, while new agricultural entities serve as carriers for value transformation. Their joint explanatory power exceeds the sum of their individual effects. Other interaction pairs—such as X1 ∩ X3 (q = 0.54) and X3 ∩ X4 (q = 0.59)—also showed relatively strong enhancement effects. These interactions imply that the combined effects of location conditions and fiscal capacity, as well as those of fiscal capacity and agricultural operators, also play important roles in shaping coupling patterns in local areas.

In summary, urbanization level and the number of new agricultural business entities are the primary factors explaining spatial differentiation of coupling types. For the crowding-out type region in Changsha–Zhuzhou–Xiangtan, high urbanization is accompanied by excessive factor concentration. However, the development of new agricultural business entities lags, making it difficult for factor inflows to translate into value enhancement. For the lagging-type region in western Hunan, low urbanization and a scarcity of new agricultural business entities impose dual constraints, resulting in weak factor attraction and limited transformation capacity. These findings provide quantitative attribution for the spatial differentiation of the three typical regions and offer a basis for region-specific regulation.

5 Discussion

5.1 Nonlinear effects of factor mobility on the value of rural multifunctional ecosystem services and their theoretical explanation

The present study identifies four coupling states: collaborative, crowding-out, inverse-increment, and lagging. There is an overall shift from a dominance of collaborative and inverse-increment types to a dominance of crowding-out and lagging types. This finding suggests that the relationship between factor mobility and rural multiple ecosystem service values does not simply show stable linear promotion. This result is consistent with the dual effects of factor mobility revealed in extant studies. The inflow of capital, technology, information, and public services has been shown to improve production conditions, employment opportunities, and livelihood wellbeing (Dou and Wang, 2023; Liu S. et al., 2025; Liu Q. et al., 2025). Conversely, excessive agglomeration or misallocation of land, capital, and population has been shown to lead to farmland conversion for non-agricultural uses, fragmentation of ecological space, and weakening of local social networks (Feng et al., 2019; Fan et al., 2024; An et al., 2024).

In comparison with extant studies that primarily examine the effects of rural development from a single perspective, such as labor transfer, land transfer, or capital flow to rural areas, this study reveals the differentiated responses of factor mobility across multiple value dimensions, including food supply, livelihood security, ecological value, social support, and cultural heritage. The agglomeration of material factors has been shown to enhance agricultural production and ensure livelihood security. However, when considered within the context of ecological carrying capacity constraints, this process may also result in the displacement of carbon sequestration, biodiversity, and cultural heritage values. The inadequate supply of technology, talent, information, and organizational capacity significantly constrains the ongoing enhancement of ecological, social, and cultural values. This finding is consistent with the principles of social–ecological systems theory concerning feedbacks, thresholds, and trade-offs, as synthesized in the coupled human and natural systems framework (Liu et al., 2021). It indicates that coupling effects are contingent on factor structures and their congruence with local ecological carrying capacity, industrial foundations, and governance capacity. The elasticity coefficient model utilized in this study offers methodological support for capturing asymmetric and phased responses.

5.2 The “Central Crowding-Out-Peripheral Lag” pattern and comparison with related studies

This study finds that the coupling relationship between factor mobility and rural multiple ecosystem service values in Hunan Province shows a spatial pattern of “central crowding-out and peripheral lagging.” The crowding-out type is mainly concentrated in the Changsha–Zhuzhou–Xiangtan core region, while the lagging type is primarily distributed in the western and southern peripheral areas of Hunan. This pattern aligns with agglomeration–diffusion theory and new economic geography: in the early stages of urbanization, capital, labor, and public resources tend to agglomerate toward regional cores. As core areas gain stronger factor absorption capacity through market size, transportation conditions, and industrial foundations, sustained agglomeration may raise resource and environmental costs through land development, industrial expansion, and population attraction, thereby reducing the ecological and socio-cultural spaces of surrounding rural areas.

The crowding-out characteristics of the Changsha–Zhuzhou-Xiangtan region demonstrate that high urbanization and intensive factor agglomeration do not necessarily result in corresponding improvements in rural multiple values. This finding is consistent with existing studies showing that land capitalization and construction expansion may encroach on cultivated land and compress ecological space (Feng et al., 2019; Fan et al., 2024; Chen et al., 2024). The present study further reveals that the impacts of factor agglomeration in core regions are not limited to ecological value constraints. Social support and cultural heritage values may also be affected by population outmigration, rising living costs, and the weakening of local social networks. The lagging type predominantly characterizes the peripheral regions of western and southern Hunan. These regions generally possess favorable forest resources and ecological baselines. However, such ecological advantages have not automatically translated into comprehensive values including food supply, livelihood security, social support, and cultural heritage. This result does not contradict studies that treat ecological resource endowments as foundations for rural development. Rather, it further demonstrates that resource endowments are a necessary but not sufficient condition for value realization. Due to the relatively insufficient supply of advanced factors—including talent, technology, digital information, market channels, and public services—peripheral areas struggle to effectively embed ecological assets into green industries, ecological compensation mechanisms, cultural tourism, and community governance systems. This ultimately results in a situation characterized by “sound ecological foundations but lagging value transformation.”

The geographical detector results further indicate that urbanization level and the number of new agricultural business entities are the core driving factors of spatial differentiation in coupling types. Their interaction exhibits the strongest explanatory power. This finding is consistent with existing studies that emphasize the role of urbanization and new agricultural business entities in promoting land transfer, scale operation, and industrial integration (Jin et al., 2024; Ma et al., 2022). This study further shows that the development of business entities must be coordinated with ecological constraints, public services, and local governance to achieve collaborative enhancement of multiple values. Concentration of material factors and insufficient supply of non-material factors tend to reinforce crowding-out or lagging effects.

5.3 Research implications: from a factor-input orientation to systematic coordinated allocation

The findings of this study have two implications for rural revitalization and ecological governance. First, rural development policies should not simply regard the introduction of capital to rural areas, land consolidation, or industrial projects as sufficient conditions for value enhancement. Instead, attention should be paid to the structure, speed, and local absorptive capacity of factor flows. In highly urbanized areas in particular, a factor agglomeration model dominated by construction expansion and land development should be avoided. Efforts should strengthen cultivated land protection, ecological space control, and rural public service provision. These measures can reduce the crowding-out effects of economic growth on ecological and socio-cultural functions.

Secondly, there is a need to shift policy emphasis from expanding material factors to the coordinated provision of both material and non-material factors. It is vital to recognize the crucial role of technology diffusion, talent return, digital information services, social organization participation, and grassroots governance improvement in enhancing ecological, social support, and cultural heritage values. In regions experiencing economic underperformance, such as the western and southern regions of Hunan, there is a need to address deficiencies in market connectivity, technical services, public infrastructure, and the cultivation of business entities. These efforts should focus on transforming ecological asset value. In regions experiencing overcrowding, such as Changsha–Zhuzhou–Xiangtan, greater attention should be given to ecological boundaries and the social costs of factor agglomeration. It is essential to regulate urban–rural factor flows, ensuring they do not become a source of siphoning agglomeration but rather contribute to complementary circulation. In regions where inverse-increment occurs (for example, in certain areas of central, eastern, and southern Hunan), there is a need for targeted allocation of capital, technology, and market factors. For regions where synergies occur at a local level, it is crucial to synthesize experiences in urban–rural integration, industrial linkage, and ecological value transformation to formulate replicable development models.

Thirdly, the present study incorporates the driving effect, crowding-out effect, and structural optimization effect within a unified analytical framework. This demonstrates that the three effects are not mutually exclusive. Instead, they alternate depending on regional development stages, resource endowments, land-use structures, and governance conditions. This recognition challenges the conventional single evaluation paradigm that equates rural development solely with economic growth or ecological protection. It provides a comprehensive perspective for understanding the dynamic synergies and trade-offs among multiple rural ecosystem service functions.

5.4 Research limitations and future directions

Several limitations exist in this study. First, rural multiple ecosystem service values include components such as social support and cultural heritage that are difficult to quantify fully. The current indicator system reflects overall regional differences, yet it only partly characterizes micro-level value, including local identity, social mutual assistance, indigenous knowledge, and cultural practices. Future research could integrate multiple data sources, such as household surveys, in-depth interviews, participatory observation, and online texts, to improve the accuracy of socio-cultural value measurement.

Secondly, the present study identifies coupling types and their spatial differentiation based on regional-scale data. It remains challenging to fully reveal behavioral differences among villages, households, and new agricultural business entities. Future research could further conduct longitudinal surveys at the village or household scale. Such investigations could examine how land transfer, labor migration, capital entry, and technology adoption influence multiple values through the decision-making processes of different actors.

Thirdly, the primary function of the elasticity coefficient model is to reveal the dynamic associations and type differences between factor mobility and rural multiple values. However, further investigation is required to identify the causal factors. Potential future research could involve integrating spatial econometric models, quasi-natural experiments, multi-level models, or comparative case studies. These approaches could further examine the direct, indirect, and spatial spillover effects of urbanization, ecological compensation, public service provision, and the development of new agricultural business entities on different value dimensions.

Finally, Hunan Province is notable for its diverse territorial types, including urban agglomerations, major grain-producing areas, and ecological mountainous regions. This diversity renders it a highly representative case for the present study. However, the findings still require validation in other provinces, river basins, or regions at different development stages. Future cross-regional comparative studies could further identify the common mechanisms and differentiated pathways through which factor mobility promotes the collaborative enhancement of rural multiple values. These mechanisms and pathways may vary under different resource endowments, institutional environments, and urban–rural relationship conditions.

6 Conclusion

The present study focuses on Hunan Province, examining the coupling relationship between factor mobility and rural multiple ecosystem service values, along with their spatiotemporal differentiation. The results reveal a significant nonlinear association between the two, with their coupling states exhibiting clear phase transition characteristics. Overall, as factor mobility intensity increases, its promoting effect on rural multiple values does not continuously strengthen. In some areas, a mismatch has emerged between factor agglomeration and ecosystem service value enhancement.

The coupling relationship demonstrates marked core-periphery differentiation in spatial dynamics. In the core region, which is delineated by Changsha–Zhuzhou–Xiangtan, factor agglomeration predominantly exerts a crowding-out effect on rural multiple values. Conversely, in the peripheral areas of western and southern Hunan, characterized by stronger ecological functions, value enhancement lags behind factor mobility. Urbanization level, the emergence of new agricultural business entities, and their interaction are pivotal factors in shaping this spatial differentiation.

The extant research indicates that rural value enhancement cannot rely solely on one-way agglomeration of material capital and labor. Instead, coordinated allocation of production factors, ecological resources, and socio-cultural resources should be prioritized. Accordingly, factor allocation should be optimized based on coupling types in different regions, and the supply of non-material factors—including talent, technology, information, and public services—should be strengthened. These measures could foster the coordinated realization of rural economic development, ecological protection, and socio-cultural functions.

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Footnotes

Edited by: Mengcheng Wang, Huazhong Agricultural University, China

Reviewed by: Jianchao Peng, Nanjing Agricultural University, China

Han Hu, Hunan University, China

Footnotes

https://weijing-rs.github.io/product.html.