Spatial Characteristics and Influencing Factors in Supply–Demand Matching of Rural Social Values: A Case Study of Yangzhong City, Jiangsu Province

Abstract

The spatial mismatch between the supply and demand of rural values is a key cause of the rural identity crisis. Promoting a shift in rural value research from a resource-oriented to a subject-perception-oriented approach is a crucial pathway to addressing this crisis. From the perspective of subjective perception, this study introduces the concept of rural social values (RSVs). Taking Yangzhong City, Jiangsu Province, as a case study, the SolVES model, comparative analysis, and structural equation modeling (SEM) were employed to investigate the spatial matching between RSV supply and demand. The main findings are as follows: (1) RSV supply exhibits a complex pattern characterized by “ecological baseline constraints” and “urban–rural boundary differentiation”; (2) RSV demand is shaped by both the collective expectations of subjects and the actual supply level of rural areas; (3) RSV supply–demand matching exhibits a complex situation coexisting with universal deficits, structural surpluses, and regional misalignments; (4) geographical environment and socio-economic conditions are key factors influencing the spatial matching between supply and demand at the village level, while the impact of individual characteristics is overshadowed by the overall village environment. This study not only provides a new theoretical perspective for understanding the rural value identity crisis, but also offers practical references for rural spatial governance in Yangzhong City and similar regions.

1. Introduction

The implementation of the Rural Revitalization Strategy in China has significantly improved production, living, and ecological conditions in the countryside [1,2,3]. However, under the long-term influence of urban-centrism, the enhancement of material conditions has not been concurrently translated into a broad public recognition of rural values [4,5]. It is still often narrowly perceived as either agricultural economic output or esthetic landscape functions. Such an identity crisis has become a major obstacle constraining integrated urban–rural development and comprehensive rural revitalization.

From a practical perspective, the underlying cause of the rural value identity crisis is that people’s developmental needs have not been effectively met within rural spaces [6]. On the demand side, urban residents’ imagination of the countryside is often confined to pastoral imagery, which is detached from authentic rural life [7]. Meanwhile, rural residents’ low satisfaction with public services and development opportunities starkly contrasts with their higher-level needs for ecological justice and cultural dignity. On the supply side, local government practices frequently prioritize economic and efficiency-oriented industrial development, which has consequently resulted in the continuous marginalization of localized traditional farming techniques, ecological wisdom, and living culture [4].

Regarding the issue of the rural value identity crisis, academic research has conducted in-depth exploration from resource-oriented perspectives, such as spatial production [8,9,10,11], rural construction practices [12,13,14], rural resilience [15,16], and rural transformation [17,18]. However, within the context of ecological civilizations, the countryside is perceived as a survival culture co-shaped by the natural environment and human history [19]. Defining rural value only from the object-centric perspective of “what the countryside has” can easily lead to the neglect of differentiated perceptions and value expectations formed by multiple stakeholders, such as the government, market, and villagers, based on their diverse standpoints. The disregard for subjects’ perceptions is precisely the underlying cause of the supply–demand mismatch. Consequently, promoting a paradigm shift in rural value research from a resource-oriented to a subject-perception-oriented approach is of significant importance for accurately identifying and alleviating supply–demand contradictions.

To advance the paradigm shift, this study introduces the concept of rural social values (RSVs). The concept does not represent a specific social, economic, or ecological value. Instead, it inherits the theoretical core of the social values of ecosystem services [20,21], emphasizing that value originates from the subjective recognition and meaning attribution of stakeholders with respect to the benefits provided by the rural territorial system [22], thereby reflecting its human-oriented and socially constructed nature. Based on this, RSV supply can be understood as the benefits actually perceived and obtained by multiple stakeholders within specific rural spaces. The focus of its measurement is not the objectively existing resource entities but the stakeholders’ sense of value acquisition, which reflects the actual supply effectiveness of value. Correspondingly, RSV demand refers to the value expectations and preferences held by stakeholders for specific rural spaces, representing their sense of value demand about what values are expected and where. Grounding both supply and demand analyses in subjective perception, it helps transform the abstract issue of supply–demand mismatch into an observable and comparable spatial coupling relationship between the sense of acquisition and the sense of demand.

This subjective perception-based analytical framework for RSV supply–demand matching is particularly applicable to rural areas within developed regions experiencing rapid urbanization and intense urban–rural interactions. In such areas, the value demands of stakeholders are more complexly intertwined, rendering supply–demand mismatches more pronounced. Yangzhong City in Jiangsu Province serves as a typical case of such an area. With its high level of integrated urban–rural development, the city’s rural territorial system simultaneously supports multiple functions, including rural residents’ livelihoods, urban residents’ recreation, and regional ecological conservation. This has resulted in significantly differentiated perceptions and expectations of RSVs among various stakeholders such as the government, villagers, and tourists, thus offering an ideal case study context for examining the spatial matching mechanism of RSV supply and demand.

In summary, taking Yangzhong City as a case study, this study constructs an analytical framework for RSV supply–demand matching from a perceptual perspective, with the aim of achieving the following objectives: (1) analyze the spatial differentiation characteristics of RSV supply; (2) characterize the spatial pattern of RSV demand; and (3) assess the spatial features of RSV supply–demand matching and identify the key factors influencing the matching degree. By promoting the shift towards a subject-perception-oriented approach in rural value research, this study aims to provide a scientific basis for addressing the rural value identity crisis and advancing high-quality rural revitalization.

2. Materials and Methods

2.1. Theoretical Framework Construction

2.1.1. The Connotation and Formation Logic of RSV

From the perspective of human perception, this study defines rural social values (RSVs) as collective perception and consensus on rural benefits, which are co-constructed by multiple stakeholders through continuous interaction with the natural and humanistic environment, as well as through social negotiation and contestation within the rural socio-ecological system. In the formation of RSV, its core components include stakeholders, value sources, and actors (Figure 1). Value sources are the carriers of RSV, referring to non-human elements within the rural territorial system [22,23], such as farmland, residential dwellings, and cultural heritage, that can elicit value perception by subjects. Stakeholders are those who demand RSV, encompassing all groups who currently perceive, have previously perceived, or may potentially perceive RSV. These groups include both local groups—such as villagers and village collectives—and external groups—such as tourists and investors. Actors broadly refer to all elements participating in the RSV production process, encompassing both human actors with subjective agency and non-human actors such as land and technology [24].

Transformational relationships exist among actors, stakeholders, and value sources. Specifically, on the RSV supply side, stakeholders and value sources participate in value production in concert, thereby becoming actors; on the RSV demand side, when actors perceive value sources, their roles correspondingly transform into those of stakeholders. Based on these transformational relationships, the three categories of elements collectively constitute a dynamic value perception–action feedback system. Within this system, actors serve as the driving force for its evolution, continually shaping and altering the state of value sources through various practices [25]. The perceived value sources, in turn, supply value to stakeholders. This value perception prompts stakeholders to form evaluations and feedback, which then propels them to initiate new practical actions.

2.1.2. The Spatial Pattern of RSV Supply–Demand Matching

Both the supply and demand of RSV have spatial attributes. Value sources are fixed in specific geographical spaces, which gives the sense of value acquisition that stakeholders derive from them a corresponding spatial location. At the same time, the sense of value demand formed by stakeholders based on their own value appeals also exists in specific spatial contexts. The spatial coupling between the sense of acquisition and the sense of demand forms the spatial pattern of RSV supply–demand matching (Figure 2). This pattern can be categorized into three basic types: supply–demand balance, supply deficit, and supply surplus [26,27]. Supply–demand balance means that within a specific spatial unit, there is a close match between the type and intensity of the stakeholders’ sense of acquisition and their sense of demand. A supply deficit refers to a situation where stakeholders have a strong sense of demand but a low sense of actual acquisition. A supply surplus occurs when, within a specific spatial unit, the stakeholders’ sense of value acquisition exceeds their sense of value demand for that space, which often appears as idle resources.

2.2. Study Area

Yangzhong City (119°42′ E–119°58′ E, 32° N–32°19′ N) in Jiangsu Province is situated in the middle and lower reaches of the Yangtze River. It is a typical river-island county unit composed of four main islands: Taiping, Zhongxin, Xisha, and Leigong. The terrain is low and flat, with elevations ranging between 4.0 and 4.5 m, and it comprises a total area of 332 km², of which 228 km² is land. Yangzhong administers four towns, two subdistricts, one provincial-level high-tech industrial development zone, and one economic development zone, with a resident population of approximately 316,500 in 2024 [28].

This study focuses on the rural territory of Yangzhong City, covering the administrative villages and rural communities under Xinba Town, Youfang Town, Baqiao Town, Xilaiqiao Town, and Xinglong Subdistrict, as well as the non-urban core administrative villages of Sanmao Subdistrict, totaling 66 village-level units (including Leigong Island) (Figure 3). This study excludes the Yangtze River water bodies and the highly urbanized urban core. This exclusion of the water bodies is due to the “Ten-Year Fishing Ban” policy, which has disconnected their functions from rural production and daily life.

Yangzhong City serves as an ideal case study for this study due to two key characteristics. First, its distinctive river-island pattern forms a clearly bounded and relatively independent socio-ecological system, where rural production, living, and ecological spaces are closely interwoven, and human–land interactions are explicit [29]. This setting facilitates the analysis of the generation, flow, and perception of RSV under conditions where interfering variables are relatively controlled. Second, its high level of integrated urban–rural development has prompted a shift in the value demands of multiple stakeholders from basic survival needs towards quality-of-life and developmental aspirations. This not only fosters a diverse and complex structure of the sense of value demand but also results in differentiation in the sense of value acquisition among different stakeholders within the same space [30,31]. Consequently, Yangzhong provides a suitable context for investigating the spatial matching mechanism of RSV supply and demand in the context of highly integrated urban–rural development.

2.3. Methods

2.3.1. Classification and Assessment System of RSV

RSV not only originates from agricultural production but is also embedded in multiple dimensions, such as ecological maintenance and lived experience [32,33]. Simultaneously, as the countryside is a continuously evolving socio-ecological system, its social values possess both historical accumulation and ongoing expansion with societal development [34]. Accordingly, this study constructs a classification and assessment system for RSV based on four dimensions: Production Value, Living Value, Ecological Value, and Future Value. Building on the theoretical framework of Production–Living–Ecological (PLE) functions, this system establishes Future Value as an independent dimension, aiming to emphasize the importance of sustainable development within RSV.

Production Value refers to the sense of efficacy and identity stakeholders derive from experiencing rural production functions. It is specifically reflected in the sense of reliability regarding material supply, recognition of resource-use efficiency, and the experiential appreciation of agricultural development. Living Value represents the sense of belonging and well-being that stakeholders gain from engaging with rural living functions. It manifests specifically as satisfaction with residential comfort, security regarding employment opportunities, and the physical and mental restoration obtained in the rural environment. Ecological Value embodies the sense of resonance and appreciation stakeholders hold for rural ecological functions. It specifically stems from confidence in ecological security, trust in ecological products, the esthetic enjoyment of natural landscapes, and the willingness to conserve biodiversity. Future Value is intrinsically tied to the sustainability of rural PLE functions, as it represents stakeholders’ conviction that current production, living, and ecological functions can be sustained for future generations. For instance, if villagers widely support the preservation of a traditional agricultural landscape and their motivation stems from regarding it as a vital heritage to be passed on to future generations, rather than valuing it solely for its immediate tourism or production benefits, then they are affirming the Future Value embodied in this landscape.

Building upon the Production–Living–Ecological–Future classification system, this study further refined the second-level value types and their corresponding evaluation indicators, drawing on relevant research [22,35,36], as detailed in

Table 1. Classification and assessment system of rural social values.

Level-1 Values	Level-2 Values	Perceptual Indicators	Indicator Description
Production Value	Livelihood Security Value	Perception of Food Security	Perception of the stability of land output and agricultural product supply
	Resource Utilization Value	Perception of Land Efficacy	Perception of farmland quality and utilization efficiency
		Sense of Gain from Resource Development	Perception of fairness in benefit distribution from agricultural land and mineral resource development
	Agricultural Experience Value	Sense of Participation in Agro-processing	Sense of achievement from participating in the extension of the agricultural industry chain
		Perception of Agricultural Economic Returns	Satisfaction with market returns from agricultural products
Living Value	Residential Experience Value	Perception of Housing Comfort	Perception of housing quality and environmental adaptation
		Perception of Facility Convenience	Perception of accessibility to infrastructure (e.g., transportation, water conservancy) and public services (e.g., education, healthcare, elderly care)
	Employment Value	Perception of Employment Opportunities	Perception of the availability of local employment opportunities
	Cultural Identity Value	Perception of traditional Inheritance	Perception of the degree of traditional cultural inheritance
		Enjoyment of Cultural Tourism Experiences	Evaluation of rural cultural tourism development and satisfaction with related activities
	Therapeutic Value	Perception of Environmental Comfort	Perception of the comfort level of the rural humanistic and natural environment
		Experience of Physical and Mental Restoration	Perception of the therapeutic effects of the rural environment on physical and mental health
	Education Value	Sense of Gain in Education Opportunities	Perception of access to education (including basic education, production skills training, and nature appreciation)
Ecological Value	Ecosystem Regulation Value	Perception of Climate Regulation	Perception of the climate regulation effects provided by the countryside
		Perception of Hydrological Security	Perception of flood control and drought resistance capabilities
	Ecosystem Provision Value	Perception of Water Purity	Perception of the water quality purity in rivers, lakes, etc.
		Recognition of Eco-friendly Food	Perception of the ecological attributes of agricultural products
	Ecosystem Support Value	Appreciation of Ecological Landscapes	Esthetic pleasure derived from natural landscapes
		Perception of Species Richness	Awareness level of biodiversity
Future Value	Future Value	Contemporary Identity	Identification with contemporary rural production and living modes

.

2.3.2. Assessment Methods for RSV Supply and Demand

The SolVES (Social Values for Ecosystem Services) model was employed to quantify the Supply Index (SI) and Demand Index (DI) for the 20 RSV perceptual indicators. The quantification procedure [37,38] was as follows: (1) Based on respondent-marked data obtained from a Public Participation GIS (PPGIS) survey, a weighted kernel density analysis was conducted separately for the value sources and demand points corresponding to each perceptual indicator. The weight was the total score of the corresponding indicator for each point, resulting in a kernel density surface for each indicator. (2) The kernel density surfaces of each indicator were standardized to generate kernel density SI and DI layers with value ranges of 0–10, and the maximum SI and DI for each layer were identified. (3) The maximum SI (or DI) was multiplied by a 0–1 logical layer index, and the result was standardized again to finally generate SI and DI layers ranging from 0 to 10. The Maximum Supply Index (M-SI) and Maximum Demand Index (M-DI) for each indicator were then determined.

The SolVES model relies on its embedded MaxEnt model for statistical analysis and result generation [39]. Model accuracy was evaluated using the Area Under the Receiver Operating Characteristic Curve (AUC), with the following interpretation: 0.7 ≤ AUC < 0.8 indicates acceptable accuracy, 0.8 ≤ AUC < 0.9 indicates good accuracy, and 0.9 ≤ AUC ≤ 1.0 indicates excellent accuracy [40].

The SolVES model operates on the premise that the spatial patterns of RSV supply and demand are influenced by environmental variables. Following the principles of regional representativeness, data accessibility, relevance, and non-redundancy and building upon relevant research [41,42,43,44], as well as the specific context of Yangzhong City, five environmental variables were selected as model inputs: current land use, transport accessibility, river connectivity, distance to towns, and farmland fragmentation. Among these, transport accessibility was calculated using the OD cost matrix method; distance to towns was derived using the Euclidean distance tool in ArcGIS 10.2; river connectivity was represented by the connectivity index, and farmland fragmentation was indicated by patch density, both of which were computed using Fragstats 4.2.

Following the calculation of the SI and DI for all 20 indicators, these computed results were aggregated using an equal-weight averaging method (all indicators assigned a weight of 1). This process generated raster-scale SI and DI values for the 4 Level-1 and 12 Level-2 value categories. Subsequently, the Zonal Statistics tool in ArcGIS was employed to calculate the average SI and DI for each category within every administrative village. This procedure enabled a comprehensive assessment of RSV supply and demand at the village level.

2.3.3. Analytical Methods for Spatial Characteristics of Supply–Demand Matching

A comparative analysis was employed to examine the spatial characteristics of RSV supply–demand matching. The spatial pattern of RSV matching at the village scale was characterized by constructing a Supply–Demand Ratio (SDR) index. Based on the quantitative relationship between supply and demand, three fundamental types were identified: supply deficit, supply surplus, and supply–demand balance. The SDR was calculated using Equation (1) [26]:

$$\mathrm{SDR}={\displaystyle \frac{\mathrm{SI} - \mathrm{DI}}{(\mathrm{M}\mathrm{SI} - \mathrm{MDI})∕2}}\left\{\begin{array}{c}> 0, \mathrm{s}\mathrm{u}\mathrm{r}\mathrm{p}\mathrm{l}\mathrm{u}\mathrm{s} \\ = 0, \mathrm{b}\mathrm{a}\mathrm{l}\mathrm{a}\mathrm{n}\mathrm{c}\mathrm{e}\\ < 0, \mathrm{d}\mathrm{e}\mathrm{f}\mathrm{i}\mathrm{c}\mathrm{i}\mathrm{t}\end{array}\right.$$

(1)

In the equation, SDR represents the RSV Supply–Demand Ratio; SI and DI denote the Supply Index and Demand Index, respectively, for a specific value category within a given spatial unit; MSI and MDI refer to the Maximum Supply Index and Maximum Demand Index, respectively, for that value category across the entire study area.

The matching status is determined as follows: when SDR > 0, this indicates a supply surplus; when SDR < 0, this indicates a supply deficit; when SDR = 0, this indicates a complete supply–demand balance.

2.3.4. Analytical Methods for Influencing Factors of Spatial Supply–Demand Matching

Structural equation modeling (SEM), which is capable of effectively handling complex causal pathways among multiple variables, is widely applied in research on human behavior and perception [45,46]. This study employed SEM to examine the driving effects of individual characteristics, village socio-economic conditions, and geographical environment on the spatial matching of RSV supply and demand, using the SDR of Production, Living, Ecological, and Future Values for each village as the observed variables.

Individual characteristics were measured using age and annual household income as observed variables. Age reflects differences in value demands corresponding to an individual’s life stage, while income serves as a key economic factor that elevates demand levels. However, the spatial matching of RSV supply and demand at the village scale represents the aggregate relationship between total supply and demand within a spatial unit, where the independent effects of micro-level individual characteristics may be overwhelmed by macro-level environmental variables [47]. Accordingly, hypothesis (H1) is proposed: individual characteristics have no significant effect on the spatial matching of RSV supply and demand.

The observed variables for village socio-economic conditions consisted of the level of agricultural mechanization, gross output value of the primary industry, and gross output value of the secondary industry. The level of agricultural mechanization reflects the modernization of agricultural production, while the output values of the primary and secondary industries collectively characterize the rural industrial structure. According to supply–demand balance theory [48], the development of socio-economic conditions enhances RSV supply while simultaneously triggering an upgrade in the demand structure [49]. If demand growth consistently outpaces supply growth, it may lead to a development-driven mismatch, resulting in a decrease in the SDR. Based on this theoretical framework and the context of Yangzhong City with its economic prosperity and rapidly upgrading demands, hypothesis (H2) is proposed: village socio-economic conditions have a significant negative effect on the spatial matching of RSV supply and demand.

The observed variables for the village geographical environment consisted of distance to towns, river connectivity, and NDVI. Distance to towns is a key indicator of rural locational advantage; greater distances typically imply higher costs for accessing information, capital, public services, and other essential elements [50]. In regions with well-developed water systems, such as Yangzhong City, river connectivity reflects local ecological regulation capacities and landscape coherence [22]. NDVI directly represents the regional vegetation coverage and ecological baseline condition. Generally, while a favorable geographical environment supports the supply of specific RSV, it may also conflict with demands for agglomeration and high-intensity industrial development, thereby constraining the overall spatial supply–demand matching. Accordingly, hypothesis (H3) is proposed: the village geographical environment has a significant negative effect on the spatial matching of RSV supply and demand.

Building on the selected variables and theoretical derivation above, this study developed a theoretical model with hypothesized paths, as shown in Figure 4.

2.4. Data Sources and Processing

2.4.1. Survey Data

Built on the premise that RSV essentially constitutes a collective perception of rural benefits by the public, capturing this perception necessitates engaging diverse social groups. This study achieves this objective by spatially integrating individual perception data. Specifically, a Public Participation GIS (PPGIS) survey [51] was employed to collect data on RSV supply perceptions and demand preferences from various stakeholders in Yangzhong City (survey conducted in 2025). These data were then spatially integrated and quantitatively assessed using ArcGIS and the SolVES model. The PPGIS approach quantifies preferences and perceptions by instructing respondents to score the importance or perceived intensity of each indicator [52,53,54]. The questionnaire modules and corresponding data types are listed in

Table 2. Questionnaire modules and data types.

Questionnaire Module	Data Types
Demographic Characteristics	Gender, age, education level, annual household income, occupation, place of residence
Supply Perception	RSV supply level perception scores, spatial locations of value sources
Demand Preference	RSV demand preference importance scores, spatial locations of demand points

.

The survey targeted diverse stakeholder types, including town and village cadres, enterprise managers, cooperative leaders, village group leaders, Party group leaders, local notables, general villagers, Yangzhong residents, and non-local tourists. A total of 381 valid samples were collected. The sample structure is shown in

Table 3. Sample structure of respondents.

Characteristic	Category	Number of Respondents	Percentage (%)
Gender	Male	192	50.39
	Female	189	49.61
Age	Under 20	62	16.27
	20–29	77	20.21
	30–39	59	15.49
	40–49	22	5.77
	50–59	62	16.27
	60 and above	99	25.98
Education Level	Below high school	152	39.90
	High school	82	21.52
	Associate degree	45	11.81
	Bachelor’s degree	80	21.00
	Graduate degree	22	5.77
Annual Household Income (CNY)	Under 10,000	29	7.61
	10,000–49,999	38	9.97
	50,000–99,999	54	14.17
	100,000–149,999	110	28.87
	150,000–199,999	83	21.78
	200,000 and above	67	17.59
Place of Residence	Yangzhong urban area	63	16.54
	Yangzhong rural area	278	72.97
	Other areas in Jiangsu	25	6.56
	Outside Jiangsu	15	3.94

.

The survey employed a percentage-based scoring system, requiring respondents to allocate 100 points across the 20 RSV perceptual indicators. In the supply perception section, respondents were asked to assign higher scores to indicators they perceived as having higher supply levels; in the demand preference section, they assigned higher scores to indicators that they considered important or urgently needed. Both the supply and demand sections were constrained to a total of 100 points. Furthermore, for each indicator scoring above zero, respondents were required to mark 1–3 of the most representative spatial points on a digital map.

The survey was conducted in two phases. The first phase was a pilot survey, carried out from October to November 2024, in which 60 respondents were randomly invited to complete the questionnaire in Baqiao Town, Yangzhong City, and provide suggestions for improvement. Over 90% of respondents found the questionnaire content easy to understand. The second phase was a formal survey conducted from March to April 2025, covering all 66 administrative villages in the study area and the main urban area of Yangzhong City. Survey points were set at village committees, enterprises, public activity spaces, and urban public areas, with research assistants providing explanations at each location.

A total of 500 questionnaires were distributed during the formal survey, with 423 returned and 381 deemed valid, resulting in a response rate of 84.60% and a valid questionnaire rate of 90.07%.

2.4.2. Geographical Environment Data

The geographical environment data used in this study and their sources are listed in

Table 4. Sources and description of geographical environment data.

Data Category	Data Type	Source and Processing Description
Administrative Boundaries	Vector	Extracted from 2020 Land Use Data of Yangzhong City
Land Use	Raster (10 m)	Original vector data sourced from the Yangzhong Natural Resources Department (2020), converted to raster using ArcGIS 10.2
Digital Elevation Model (DEM)	Raster (10 m)	91 Weitu Assistant (Enterprise Edition) (2021)
Normalized Difference Vegetation Index (NDVI)	Raster (30 m)	National Science and Technology Resource Sharing Service Platform (2022) http://www.nesdc.org.cn/sdo/detail?id=60f68d757e28174f0e7d8d49 (accessed on 12 August 2025)
Transportation Network	Vector	Open Street Map (2025)

. All spatial data were uniformly projected to the CGCS2000_3_Degree_GK_ Zone_40 coordinate system, and raster data were resampled to a 10 m resolution and spatially aligned.

2.4.3. Socio-Economic Data

Socio-economic data were primarily sourced from the township-level statistical departments and village committees of Yangzhong City. The statistical indicators included the crop cultivation area, mechanized farming area, per capita disposable income of rural residents, and the gross output value of the primary and secondary industries for each administrative village.

2.5. Analytical Workflow

(1)

Analysis of RSV Supply and Demand Spatial Characteristics: Using the SolVES model, the SI and DI of each perceptual indicator were quantified at the raster scale (10 m × 10 m). These values were then aggregated stepwise using the equal-weight averaging method to derive the raster-scale SI and DI for both Level-2 and Level-1 values. Subsequently, the Zonal Statistics tool in ArcGIS was employed to calculate the supply and demand levels for both Level-2 and Level-1 values within each administrative village, thereby revealing their spatial differentiation characteristics.

(2)

Analysis of the Spatial Characteristics of RSV Supply–Demand Matching: Based on the village-scale supply and demand assessment results, the SDR was calculated to classify the matching type for each value category in every village. This process helped elucidate the overall spatial pattern and differentiation rules of RSV supply–demand matching.

(3)

Identifying the Influencing Mechanism of RSV Supply–Demand Matching: Using the SDR of the four Level-1 values as the dependent variable and individual characteristics, socio-economic conditions, and geographical environment as independent variables, a structural equation model (SEM) was constructed. Through model fitting and hypothesis testing, the key factors influencing the spatial matching of RSV supply and demand, along with their action paths and effect strengths, were identified (Figure 5).

3. Results

3.1. Spatial Characteristics of RSV Supply and Demand

3.1.1. Spatial Characteristics of RSV Supply

The spatial distribution of the Supply Index (SI) for Level-2 values (Figure 6) reveals that low-SI areas for Livelihood Security, Resource Utilization, and Agricultural Experience Values form a ring around the main urban area of Yangzhong City, while their high-SI areas exhibit a spreading, planar pattern in traditional agricultural zones farther from towns. High-SI areas for Residential Experience, Employment, and Education Values form contiguous clusters in Xinba Town, which is located in the northern part of the study area. High-SI areas for Cultural Identity and Therapeutic Values are primarily located at the northern and southern ends of the city, but the Maximum Supply Index (M-SI) for these values is mainly concentrated in Xinba Town. High-SI areas for Ecosystem Regulation Value are clustered in Xilaiqiao Town, which is located in the southern part of the study area, whereas those for Ecosystem Provision Value are predominantly distributed in villages with excellent water quality and specialized agricultural areas. The spatial pattern of Ecosystem Support Value shows some similarity to the Level-2 values under Production Value; however, the transition between its high-SI and low-SI areas is sharper, and the spatial demarcation is more distinct.

At the Level-1 value dimension (Figure 7), high-SI areas for Production Value are mainly distributed within agricultural production zones, with the SI decreasing as the distance to towns decreases. This pattern reflects the squeezing effect of urbanization on agricultural production. Conversely, the spatial distribution of Living Value exhibits a pattern opposite to that of Production Value, with its high-SI areas clustered in towns and their surrounding areas; the SI decreases with increasing distance from towns. High-SI areas for Ecological Value are concentrated in Xilaiqiao Town, where the natural context of being surrounded by rivers constitutes its advantage in ecological supply. A similar ecological highland exists in the northern part of the city. Together, they form a “twin-peak” pattern in Ecological Value supply. Meanwhile, the periphery of the main urban area, affected by construction land expansion, exhibits severe fragmentation of ecological spaces, forming distinct SI depressions. The SI of Future Value is generally low across the entire study area, indicating a potential gap between the current rural development model and stakeholders’ expectations for sustainability.

3.1.2. Spatial Characteristics of RSV Demand

Regarding the spatial distribution of the Demand Index (DI) for Level-2 values (Figure 8), high-DI areas for Livelihood Security and Agricultural Experience Values are distributed in villages far from the main urban area, indicating that stakeholders associate the fulfillment of basic survival and agricultural production needs with traditional farming areas. Conversely, high-DI areas for Resource Utilization Value are clustered on the periphery of the main urban area and in the economic and technological development zone. Residential Experience, Employment, and Education Values share a similar spatial distribution pattern for their high-DI areas, primarily clustered around the periphery of the main urban area and in Xinba Town, with medium-DI areas present around Baqiao Village and Limin Village. High-DI areas for Cultural Identity and Therapeutic Values are found in villages with more pronounced “rural character” that are distant from towns. Medium-DI and high-DI areas for Ecosystem Regulation Value are mainly located along the Yangtze River, reflecting stakeholders’ emphasis on the critical ecological functions maintained by riverside villages. High-DI areas for Ecosystem Provision, Ecosystem Support, and Future Values are clustered in the town centers and surrounding villages of Xinba, Youfang, Baqiao, and Xilaiqiao towns. Among these, the spatial extent of high-DI areas for Ecosystem Support and Future Values is smaller than that for Ecosystem Provision Value.

At the Level-1 value dimension (Figure 9), high-DI areas for Production Value form a relatively large cluster in the border area between Baqiao Town and Youfang Town. In contrast, no cluster of comparable size is found in Xinba Town, which is one of the core industrial areas of Yangzhong City. High-DI areas for Living Value are concentrated in towns, surrounded by medium-DI areas, while the periphery of the main urban area and the economic development zone predominantly exhibit a low DI. High-DI areas for Ecological Value are mainly distributed along the Yangtze River, with medium-DI areas predominating inland; the extent of low-DI areas around the main urban area is significantly larger than in other regions. The distribution of high-DI areas for Future Value is similar to that of Living Value, but they are more numerous. Characteristic pastoral villages such as Limin Village, Xinzhi Village, and Yongsheng Village have also become hotspots for Future Value demand.

3.2. Spatial Characteristics of RSV Supply–Demand Matching

3.2.1. Spatial Characteristics of Supply–Demand Matching for Level-2 Values

The spatial pattern of supply–demand matching for Level-2 values can be categorized into four types: benign matching, universal deficit, polarized pattern, and structural differentiation (Figure 10). Resource Utilization, Residential Experience, Ecosystem Support, Employment, and Education Values generally fall under the benign matching pattern. Among these, a deficit belt extends along the main urban–economic development corridor for the first three value types, while their surrounding villages mainly exhibit balance or surplus. Only three villages show an absolute deficit in Employment and Education Values, with the entire study area approaching a supply–demand balance. Therapeutic Value, Ecosystem Regulation Value, and Ecosystem Provision Value exhibit a universal supply deficit. Livelihood Security Value displays a polarized pattern, forming three supply surplus clusters at the northern and southern ends of the city and in the western part of Youfang Town, whereas the industrial corridor from Xinba Town to Baqiao Town constitutes a contiguous supply deficit zone. Agricultural Experience Value and Cultural Identity Value demonstrate a structural differentiation pattern. The former forms a large, contiguous supply surplus belt adjacent to urban areas, with small deficit clusters appearing in the central industrial–agricultural transition zone. The deficit and balanced areas of the latter are distributed along the urban sprawl, with deficit intensity increasing towards the northern and southern edges of the study area, while surplus areas shrink towards the economic development zone and the periphery of the main urban area.

3.2.2. Spatial Characteristics of Supply–Demand Matching for Level-1 Values

The supply–demand matching of Level-1 values shows significant spatial differentiation (Figure 11). Production Value and Living Value show a generally high degree of supply–demand matching overall. However, the junction area of the main urban periphery, the economic development zone, Baqiao Town, and Xilaiqiao Town is both a supply deficit area for Production Value and a supply surplus area for Living Value. Ecological Value exhibits a universal supply deficit crisis. For Future Value, large, clustered supply deficit areas form around towns, while the surrounding villages are predominantly characterized by supply surplus or balance.

3.3. Influencing Factors of RSV Supply–Demand Matching

All fit indices for the structural equation model exceeded the evaluation criteria (

Table 5. Fit indices of the structural equation model.

Fit Index	x2	DF	x2/DF	IFI	GFI	CFI	RMSEA
Criterion	/	/	<5	>0.9	>0.9	>0.9	<0.08
Measured Value	60.919	37.000	1.646	0.973	0.966	0.972	0.048

), confirming the model’s reliability for hypothesis testing.

Figure 12 shows the hypothesis testing results. The influence of individual characteristics on the spatial matching of RSV supply and demand was not statistically significant (p > 0.05), supporting H1. This suggests that at the village scale, the independent effect of individual characteristics on spatial matching may be overshadowed by macro-level factors. Conversely, the influence of village socio-economic conditions on RSV spatial matching was also not significant, thus not supporting H2. This result indicates a potential dual effect of village economic development on RSV supply and demand: While improved socio-economic conditions can provide economic and technical support for RSV supply, they may also trigger an expansion in the scale and an upgrade in the structure of stakeholders’ demands. The counterbalancing of these dual effects could result in a statistically non-significant net effect. In contrast, the village geographical environment demonstrated a significant negative effect (β = −0.598, p < 0.001), supporting H3. This implies that at the current stage of development in Yangzhong City, villages with superior geographical environmental endowment may face greater constraints in RSV supply–demand matching.

4. Discussion

4.1. Causes of the Spatial Characteristics of RSV Supply, Demand, and Supply–Demand Matching

The spatial patterns of RSV supply, demand, and their matching in Yangzhong City exhibit significant spatial heterogeneity, which stems from its unique natural geographical conditions and the process of integrated urban–rural development [22,55,56].

On the supply side, the spatial distribution of SI is shaped by both natural and human factors. High-SI areas for Ecological Value are concentrated in Xilaiqiao Town, surrounded by rivers, and in the northern river-adjacent zones, highlighting the decisive role of ecological baseline conditions in its supply capacity. The spatial distributions of Production and Living Value SI exhibit a regular pattern of variation correlated with distance from urban areas. High-supply areas for Production Value were located in agricultural zones distant from towns, reflecting the significant weakening of agricultural production functions in highly urbanized areas. Living Value, in contrast, demonstrated the opposite trend, forming distinct high-SI areas in towns and their surroundings, which reflects the agglomeration effects of public services and employment opportunities in urbanized areas. The observed spatial patterns may be attributed to the dependence of Ecological Value supply on the integrity and connectivity of natural systems, while the divergence between Living and Production values stems from land use competition between urban and rural areas. Specifically, land in peri-urban areas tends to be allocated to more economically rewarding residential functions, thereby marginalizing agricultural production. Furthermore, the generally low supply level of Future Value across the study area may be explained by the fact that current sustainability initiatives in rural development have not yet been fully translated into tangible and perceptible value provisions.

On the demand side, the spatial distribution of DI reflects the collective expectations of the public regarding the functions of rural areas. High-DI areas for Production Value are distributed in traditional agricultural zones, indicating that the public generally expects these areas to maintain their production function. High-DI areas for Ecological Value are distributed along the Yangtze River, stemming from the public’s strong awareness of the need to protect critical ecological functional zones. The concentration of high demand for Living and Future Values in towns and characteristic pastoral villages suggests that these areas are perceived by the public as crucial spaces for achieving high-quality living and sustainable development. Furthermore, the formation of the RSV demand spatial pattern is also influenced by the actual level of supply. Specifically, when the quantity and quality of RSV supply in a given area reach a certain threshold, it can stimulate demand from stakeholders; conversely, areas that fail to meet this threshold may be excluded from their demand considerations.

Regarding the matching of supply and demand, the phenomenon of imbalance warrants particular attention. For instance, the universal supply deficit of Ecological Value indicates that the actual supply fails to meet public expectations. The supply surplus of Agricultural Experience Value observed in peri-urban areas likely stems from the failure or inadequacy of translating agricultural production and agritourism functions into perceived supply. Furthermore, the spatial dislocation between Production and Living Value reveals the difficult trade-offs in functional transition within rural territories. The high level of urban–rural integration in Yangzhong has not resolved these contradictions. Instead, it has intensified the dynamism and complexity of supply–demand spatial matching through accelerated factor mobility and rapidly upgrading demands. This complexity further underscores that, against the backdrop of urban–rural integration, rural territorial systems are undergoing a structural shift from single-function provision to multifunctional and composite offerings. Meanwhile, the mismatches between supply and demand can be largely attributed to spatiotemporal lags and cognitive disparities.

4.2. Mechanisms of RSV Spatial Supply–Demand Matching and Alleviation of the Identity Crisis

This study argues that the rural value identity crisis stems not from an absolute shortage of material resources but from a failure to achieve effective matching between the stakeholders’ sense of value acquisition and sense of value demand within rural spaces. The empirical study of Yangzhong City demonstrates that this supply–demand mismatch is primarily attributed to the combined effect of the village’s geographical environment and socio-economic conditions.

The structural equation model reveals that the influence of individual characteristics on RSV supply–demand spatial matching is not significant, indicating that at the village scale, individual attributes and preferences are overshadowed by macro-level environmental factors. Therefore, enhancing rural value identity should not focus solely on meeting fragmented individual needs but must prioritize shaping the collective value perception of stakeholders. This finding further corroborates the human-oriented and socially constructed nature of value emphasized in this study. It also demonstrates that rural value research must extend beyond objective resources to focus on subjective perception, particularly the collective needs at the village level.

The non-significant net effect of village socio-economic conditions on RSV spatial matching indicates a complex supply–demand relationship during rural development. Specifically, the sense of value acquisition and the sense of value demand among stakeholders are not simply correlated. In practice, while improved economic conditions help enhance RSV supply capacity, they also drive stakeholders to develop higher-level and more diversified value demands. When supply upgrades fail to keep pace with these demand shifts, a development-driven mismatch emerges [57]. This explains why the value identity crisis persists despite material improvements. Therefore, resolving this crisis requires a shift from a supply-centric approach to one that is guided by demand perception. This entails understanding stakeholders’ diversified and upgraded value demands to steer supply-side optimization.

The significant negative effect of a village’s geographical environment reveals a practical contradiction between ecological conservation and comprehensive development in current rural governance. Villages with superior ecological baselines often implement strict development restrictions to meet conservation needs. While these measures ensure the supply of Ecological Value and satisfy society’s collective demand for “green mountains and clear waters” [58,59], they may simultaneously suppress local residents’ sense of acquisition regarding production convenience and livelihood improvements [60]. This imbalance in local value supply, caused by the simplification of spatial functions, becomes an important cause of value identity crises among specific groups. In response, rural spatial governance should transition from single-function zoning to “composite value coordination” [61,62], exploring spatial utilization models that protect the ecological baseline while balancing production, living, and ecological values.

In summary, addressing the rural value identity crisis demands a paradigm shift: Regarding the scale of policy intervention, there should be a shift from the individual to the collective; regarding the logic of value supply, a transition from undifferentiated input to a precise response based on demand perception is needed; regarding the mode of spatial governance, there should be a shift from functional segregation to the synergistic coexistence of multiple values. Through these transformations, the supply of rural values can be better aligned with public perceptions and expectations, thereby establishing a solid and widespread rural value identity.

5. Conclusions

Based on the subjective perception perspective and taking Yangzhong City, Jiangsu Province, as a case study, this research conducted an empirical analysis of the spatial characteristics and influencing factors of RSV supply–demand matching. The main conclusions are as follows:

• RSV supply exhibits a composite spatial pattern characterized by “ecological baseline constraints” and “urban–rural boundary differentiation”. The supply of Ecological Value is primarily determined by natural conditions, while the supply of Production and Living Values forms a complementary distribution along the urban–rural boundary, reflecting the reality of functional competition for land. The supply of Future Value remains generally weak throughout the study area.
• The RSV demand pattern is shaped by both collective functional expectations and the actual level of supply. Public expectations for maintaining production functions in traditional agricultural zones, willingness to protect ecological functions along the Yangtze River, and recognition of the leading role of towns constitute the foundation of the RSV demand spatial pattern. Moreover, when both the quantity and quality of RSV supply in a specific location reach a certain threshold, that area can transform into a stable demand hotspot.
• RSV supply–demand matching displays a complex situation characterized by the coexistence of universal deficits, structural surpluses, and regional misalignments. Ecological Value faces a universal supply shortage, Agricultural Experience Value exhibits localized supply surplus in peri-urban areas, and the spatial misalignment between Production and Living Values reveals the trade-offs in decision-making during rural functional transitions.
• Geographical environment and socio-economic conditions are key factors influencing village-level RSV supply–demand matching. The geographical environment demonstrates a significant negative effect. Socio-economic conditions show no significant net effect due to their dual role in enhancing supply while simultaneously stimulating demand. The influence of micro-level individual characteristics is overshadowed by the overall village environment.

Based on the above conclusions, this study contends that rural governance strategies should move beyond conventional physical–spatial planning toward precision interventions capable of responding to and shaping public value perceptions. First, categorized governance should be implemented by optimizing territorial spatial functions according to specific types of RSV supply–demand mismatches in villages, such as ecological deficit or production deficit. Second, the “supply creates demand” mechanism should be utilized to enhance public value recognition and future confidence by providing high-quality rural social values, particularly through the development of esthetic ecological landscapes and distinctive cultural experiences. Such an approach would stimulate endogenous rural vitality and foster synergistic improvements in both quality of life and sustainability.

This study provides a new analytical perspective for shifting rural value research from an object-based resource theory to a subject-perception-oriented approach, offering both a theoretical basis and practical insights for rural governance and the enhancement of public identification with rural areas in Yangzhong City and similar regions. However, it should be noted that this study primarily relies on cross-sectional data, which limits the ability to dynamically capture the evolution of supply–demand matching. Future studies could incorporate time-series data to further reveal the dynamic evolutionary mechanism of RSV supply–demand matching.