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Article

Polarization or Equilibrium: Spatial and Temporal Patterns and Divergent Characteristics of Rural Restructuring in Unevenly Developed Regions

by
Lin Shao
1,*,
Bochuan Zhou
2,†,
Yeyang Li
1,
Qiaoli Huang
1 and
Xuening Fang
1
1
School of Environmental and Geographical Sciences, Shanghai Normal University, Shanghai 200234, China
2
School of Finance and Business, Shanghai Normal University, Shanghai 200234, China
*
Author to whom correspondence should be addressed.
†
Co-first author of this work.
Sustainability 2025, 17(13), 5989; https://doi.org/10.3390/su17135989
Submission received: 29 April 2025 / Revised: 30 May 2025 / Accepted: 24 June 2025 / Published: 30 June 2025
(This article belongs to the Special Issue Nature-Based Solutions for Landscape Sustainability Challenges)
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Abstract

Rural areas are experiencing significant changes in socio-economic and spatial patterns, and research on the characteristics of rural restructuring is conducive to the planning of rural revitalization. However, few studies have focused on the changes in regional development imbalances in the process of rural restructuring. This study aims to explore whether rural restructuring mitigates or exacerbates existing regional disparities, and to assess the degree of coordination among economic, social, and spatial restructuring dimensions. In this study, the evolution of spatio-temporal patterns and divergence characteristics of unevenly developed regions in the process of rural restructuring from 2010 to 2020 were investigated by using the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) model and the coupled coordination model. We found the following: (1) The level of rural development has increased significantly and the overall pattern has not changed. Meanwhile, the degree of regional imbalance has deepened, evolving from a low level of disequilibrium to a pattern of high levels but more pronounced spatial polarization. (2) The impacts of different dimensions of rural restructuring on regional imbalance are not consistent, and the social and spatial dimensions are significantly more unbalanced than the economic dimension. (3) The analysis of the driving mechanism shows that there are significant spatial and temporal differences between a variety of driving factors, the strength of their role, positive and negative have evolved in stages, and the transition from a government-led to a market-driven trend is gradually obvious. In the future, rural planning should pay more attention to resource inputs in the social and spatial dimensions, and improve the equilibrium of the social and spatial dimensions, which is more conducive to mitigating the trend of regional polarization.

1. Introduction

As industrialization and urbanization progress, the socio-economic structures and spatial configurations of many rural areas are being reconstructed to varied degrees of restructuring. The migration of substantial populations and the redirection of production factors—including land, financial resources, data, and technological—toward urban areas and industries outside of agriculture [1,2,3] have caused notable changes in the structures, traits, and dynamics of regional urban–rural relationships [4,5]. Concurrently, the government’s phased rural construction policies and strategies have continuously strengthened local investments in agriculture and rural areas in order to promote the transition and growth of rural production [6,7]. In this context, the dynamic interaction and collision of urban and rural growth motorists, coupled with the persistent demands for both internal and external development, alongside the advent of modern communication technologies and technical empowerment, have resulted in drastic changes in the various elements, functions, structures, and organizational relationships within current rural areas [8], including transformations in land use functions [9] and the spatial allocation of public service facilities [10]. The characteristics of traditional villages are evolving, with rural settlements shifting from “homogeneous and isomorphic” to “heterogeneous and diverse.” This increasing differentiation and diversification have attracted significant attention from governments and society. Therefore, accurately understanding and recognizing the changes in rural development, along with their restructuring patterns, holds great significance for the effective execution of the rural vitalization strategy and realizing sustainable development in rural areas of China.
Research on rural restructuring has emerged in response to significant changes and restructuring processes occurring in rural areas of developed countries and regions, such as Western Europe, North America, and Australia, amidst the waves of urbanization and counter-urbanization [11,12,13]. This phase of research primarily investigates the interactive relationship in urban–rural areas during the development process [14], shifts in employment structures [15], economic restructuring [16], rural industrial transformation [17], changes in local government management models [18], and agricultural policy [19]. Market dynamics and economic structural transformation have also been noted as key drivers in rural construction [20]. With the introduction of research findings related to “social relevance” from political economy theories, many scholars have combined GIS spatial analysis methods with socio-economic factors to conduct visual analyses of the rural restructuring process. This approach facilitates an examination of various development models for rural restructuring across different regions [21] while progressively building a multi-scale research system encompassing macro-national, meso-region level, and micro-village community levels, while also reflecting concerns about cumulative spatial inequality and the Matthew effect in rural development [22]. The research dimensions have expanded beyond material aspects to include social and cultural representations [23,24], transitioning from a unidimensional, static object space to a multidimensional, dynamic subject space.
Following the rural vitalization strategy proposed in China, research on rural development and restructuring has gained momentum and become a hotspot, because it is closely related to rural revitalization. At the regional scale, attention is primarily directed towards the spatial configurations and evolutionary patterns of rural settlements across different types of areas [25,26], organizational differentiation and functional evolution in rural areas [27,28], the transformation processes in rural development [29,30], the arrangement and optimization of rural settlements [31], and the allocation of government resources in poverty-stricken or transitional rural regions [32].The multi-scale interactions shaping rural sustainable development have been emphasized, especially regarding land use restructuring and institutional factors [20], mechanisms and models for spatial restructuring in typical rural areas [33], and the optimization and regulation pathways of rural restructuring [34]. At the micro-level, studies have focused on specific types of rural areas such as suburban countryside [35], the eco-cultural tourism countryside [36], and ethnic rural settlements [37]. These studies examine the restructuring pathways, characteristic effects, driving factors, and mechanisms of representative villages, conducting qualitative research from three dimensions—rural economy, society, and spatial restructuring—while also incorporating assessments of coupling coordination between urbanization and land use efficiency in different ecological and regional contexts [38,39,40].
However, existing studies have largely overlooked the internal structural imbalances within regions undergoing rural restructuring. In many areas, uneven development has been shaped by historical, geographical, and socio-economic differences, leading to distinct intra-regional disparities [41,42,43]. Given the dynamic and complex nature of rural territorial systems, it is crucial to analyze the spatio-temporal evolution and differentiation patterns of rural restructuring in regions with longstanding imbalances. Such analysis offers valuable decision-making support for promoting regionally coordinated development and implementing targeted rural revitalization strategies.
Building upon existing research on rural restructuring, this study is conceptually grounded in the framework of socio-economic spatial differentiation and regional development theory. Prior studies have documented that rural areas undergoing industrialization and urbanization experience complex changes not only in economic activities but also in social structures and spatial organization. However, the processes underlying these transformations are uneven across regions due to varying geographic, historical, and socio-economic contexts.
Therefore, this study aims to systematically investigate the spatio-temporal patterns and internal differentiation of rural restructuring in a region historically characterized by developmental imbalance. Specifically, it seeks to achieve the following: (1) examine whether rural restructuring has intensified or alleviated intra-regional disparities; (2) evaluate the degree of coordination among economic, social, and spatial restructuring dimensions; and (3) identify the dominant driving forces of rural restructuring across different intra-provincial regions, and analyze their functional patterns and temporal evolution. Based on the existing literature and the observed unbalanced development, the following hypotheses are proposed: H1: Rural restructuring has tended to reinforce rather than reduce regional disparities due to uneven resource allocation and historical development trajectories. H2: The restructuring processes across economic, social, and spatial dimensions are uncoordinated and display varying degrees of coordination. H3: The driving effects of different factors on rural restructuring exhibit significant spatio-temporal differentiation, with their influence varying across regions and evolving in stages over time.

2. Materials and Methods

2.1. Study Area

This research takes Jiangsu Province as a case study, which is located in the eastern part of China’s coastal area (Figure 1). With its superior natural conditions and long history of agricultural civilization, it has always been at the forefront of China in terms of economic aggregate, urbanization, industrialization levels, and agricultural and rural economic development. As an important province in China’s economic landscape, Jiangsu is characterized by significant internal differences. There are disparities among southern Jiangsu, middle Jiangsu, and northern Jiangsu in aspects such as natural ecological environment, resource distribution, socio-economic foundation, industrial structure, urban–rural construction, and folk culture, which have led to notable variations in the development processes, modes, and functions of their respective rural areas. In the 1980s, two township enterprise development models emerged in Jiangsu Province: the “Southern Model” and the “Gengche Model.” These models are very different and have each become national examples for exploring paths of regional economic transformation and development.
Regional disparities in natural conditions and socio-economic development within Jiangsu have led to divergent rural development models, which continue to shape rural reconstruction and county-level development in the province. Consequently, interpreting the spatial and temporal variations in rural development levels across different regions of Jiangsu Province, evaluating the rural restructuring patterns in various periods, and exploring the coupling coordination relationship of restructuring intensity in Jiangsu Province holds substantial theoretical and practical importance. In selecting research units for this study, 29 central urban areas in 13 cities in Jiangsu Province were excluded to focus on rural areas, resulting in the inclusion of 66 county-level administrative areas.

2.2. Data

The data required for a comprehensive assessment of the phases of rural development and the features of restructuring patterns in this research are as follows: The statistical yearbooks of 13 cities in Jiangsu Province, the national economy’s statistical bulletin, and the statistical yearbooks of Jiangsu Province’s rural areas for the years 2010, 2015, and 2020 are the sources of socio-economic data. In addition, the collection of data for this study has been facilitated by conducting surveys and making visits to the statistical bureaus of various cities and municipalities, the Bureau of Transportation and Communications, and the Bureau of Natural Resources. The Jiangsu Province summary of land use status surveys has been used to derive the area data for urban construction land, rural residential land, and ecological land for the relevant years. The Chinese Academy of Sciences’ Institute of Geography Science and Resources provides the multi-temporal remote sensing dataset on land use in China.

2.3. Methods

2.3.1. Constructing the Indicator System

Establishing a scientific evaluation index system is essential for Jiangsu’s rural development level and restructuring pattern features to be systematically researched and analyzed. To accurately and comprehensively reflect the evolution and features of rural regional system restructuring, this paper draws on existing research related to rural restructuring, rural development, and evaluation indicators for rural revitalization [44,45,46]. Based on the principles of comprehensiveness, scientific validity, significance, and representativeness, the system focuses on three dimensions: economic–social–spatial. This approach aims to design evaluation indicators rationally and select them scientifically. This study seeks to build a holistic and complete evaluation index system that accurately reflects the rural development level in Jiangsu Province and its restructuring characteristics (Table 1), enabling a precise assessment of the effectiveness of rural restructuring and development stages.
In the context of urbanization and industrialization in Jiangsu Province, the chosen indicators account for various factors, including the evolution of the rural economic structure, changes in rural population, improvements in living standards, infrastructure development (such as transportation and healthcare), and shifts in land use types between urban and rural areas.
The rural economy’s development can be gauged through indicators such as economic development levels, economic structure, farmers’ income, and agricultural output value. Therefore, this study includes four indicators to reflect the urban and rural economic development levels: regional per capita economic output [47], per capita net income of rural households [48,49], Engel’s coefficient for permanent rural residents [50,51], and agricultural labor productivity [25,47].
Social development encompasses aspects such as healthcare, transportation, communication, and education. Four indicators—road network density [52,53], communication infrastructure [54,55], rural electrification level [56,57], and medical and health conditions [58,59]—have been chosen for this study in order to describe the degree of social development.
Spatial serves as a crucial role for regional socio-economic growth, with the efficiency and development levels of “production-living-ecological” spaces reflecting the capacity for regional restructuring and transformation. This study includes four indicators to quantify the development and utilization levels of these spaces: agricultural land use efficiency [51,57], rural per capita housing area [60,61], the second and third industry land use efficiency [62], and regional heterogeneity [63,64].

2.3.2. Entropy-Weighted TOPSIS Method

To objectively evaluate rural development levels across multiple indicators, this study employs the entropy-weighted TOPSIS method—a well-established multi-criteria decision-making approach. Traditional TOPSIS determines each option’s relative closeness to an ideal solution, but its results may be biased by subjectively assigned weights. To mitigate this, the entropy method is integrated to derive indicator weights based on the inherent information in the data.
The calculation process involves the following: (1) construction of an evaluation index matrix and standardization of the raw indicator matrix to ensure comparability across different units and attributes; (2) entropy-based weight determination, where weights are calculated based on the variability and distribution of indicator values across regions, reducing subjectivity; (3) calculation of the normalized weighted matrix; (4) identification of the optimal solution and the worst solution; (5) calculation of the Euclidean distance of each scheme from the optimal and worst solutions; (6) computation of the closeness degree to rank rural development levels, where a higher value indicates better performance.

2.3.3. Measurement of Rural Restructuring Intensity Index

The term ‘rural restructuring’ describes the positive and qualitative transformation that occurs within the rural territorial system in relation to a specific point in time, or the process of transformational development that gradually emerges from the accumulation of quantitative changes. To determine if a research unit has undergone restructuring, the development status at the end of the study period (T2) is compared with that at the beginning (T1). Using the rural development level index evaluation system, changes in selected indicators serve as new indicators reflecting the restructuring intensity.
Utilizing the TOPSIS model supported by the entropy method, we are able to calculate the rural economic restructuring intensity index Rx, social restructuring intensity index Ry, spatial restructuring intensity index Rz, and the comprehensive restructuring intensity index RC for the study area and counties during the periods 2010–2015, 2015–2020, and 2010–2020, with RC = 1/3 (Rx + Ry + Rz).
For positive indices, if T2/T1 > 1, restructuring has occurred, and the intensity index of each dimension is calculated according to its ratio. If T2/T1 ≤ 1, restructuring has not occurred, and the intensity is recorded as 0. For negative indices, if T1/T2 > 1, restructuring has occurred, and the intensity index is calculated accordingly; if T1/T2 ≤ 1, restructuring has not occurred, and the index value is marked as 0 [65].

2.3.4. Coupling Coordination Degree Model

Measuring the coupling coordination development process of rural economy–society–space restructuring is a key focus of this study. The coupling coordination degree model, which is widely used to assess the interaction between socio-economic development and ecological environment evolution, is a useful tool for evaluating the overall coordination within a region. A rural economic–social–spatial coupling coordination model was constructed on the basis of existing modeling methods [66]. This model quantitatively assesses the strength of the interactions among the economy–society–space system, and its specific formula is as follows:
C = R x × R y × R z R x + R y + R z / 3 3 1 3
In the above formula, C is the degree of coupling. The value of C indicates the coupling degree of the system, and higher value indicates the better condition of coupling developed by the three systems.
The strength of the mutual coupling effect among the three systems of economy–society–space can be reflected by the coupling degree model, and the coupling degree alone cannot measure the coordination level of each other. Consequently, it is imperative to combine the coupling degree with the coordination degree, and the coordination degree model is used to calculate the coordination degree level D of the rural economy–society–space system based on the above calculation process.
D = C × R C
In the above formula, D is the coordination degree, and RRI(c) is the rural comprehensive restructuring intensity.

2.3.5. Driver Analysis Method and Variable Setting

In order to reveal the driving mechanism of rural restructuring, the spatio-temporal geographically weighted regression model (GTWR) is applied, which introduces the time dimension on the basis of the traditional geographically weighted regression (GWR), which can simultaneously portray the non-stationarity of the regression coefficients in both time and space, and is suitable for interpreting the spatial and temporal heterogeneity of geographic phenomena.
In terms of driving factors, referring to related studies and taking into account data availability, nine driving variables covering both endogenous and exogenous sources are selected, including location and transportation, resource endowment, agricultural modernization, population and employment structure, urbanization, industrialization, marketization, government inputs, and market investments (see Table 2). The GTWR model was constructed with the “rural development level index” as the dependent variable to analyze the spatial and temporal driving characteristics and evolution patterns under the three time nodes of 2010, 2015 and 2020.
In order to ensure the robustness of the model, the OLS method was used to test for multicollinearity, and the VIF values of the nine variables were all less than 5, indicating that there is no serious covariance, which meets the requirements for the use of the GTWR model.

3. Results

3.1. Spatial-Temporal Evolution of Rural Development Level in Jiangsu Province

Based on the index evaluation system for rural development level set up in the previous section, we apply the TOPSIS comprehensive evaluation model to appraise the rural development levels in Jiangsu Province for the years 2010, 2015, and 2020. Figure 2 displays the distribution pattern of the evaluation outcomes. The results demonstrate a notable enhancement in the rural development levels in Jiangsu Province between 2010 and 2020, accompanied by a deepening degree of discrete development. From 2010 to 2020, the mean value of rural comprehensive development in Jiangsu Province ascended from 0.143 to 0.324, reflecting an overall improvement. However, the standard deviation expanded from 0.098 to 0.133, indicating greater data dispersion and volatility. The skewness coefficient decreased from 1.471 to 1.228, and the peak value fell from 1.720 to 1.308, suggesting that the data distribution is approaching a normal distribution, with reduced kurtosis indicating a gradual flattening.
In terms of dimensions, the mean values for economic–social–spatial development have all increased. Notably, the mean value for the degree of economic development rose from 0.132 to 0.455, representing the largest increase, and the average annual growth rate (AGR) was 13.16%. Following this was the average level of social development, which increased from 0.125 to 0.233 with an AGR of 6.45%. The increase in spatial utilization was comparatively modest, rising from 0.172 to 0.283, and the AGR was 5.11%. The varying development speeds of the economic–social–spatial system in the rural areas of Jiangsu Province indicate that each dimension impacts rural regional development differently. Additionally, the dispersion of each dimension’s index data is gradually widening, suggesting that the heterogeneity of rural economy–society–space in Jiangsu Province is deepening. Among the dimensions, spatial utilization exhibits the highest dispersion, while economic development shows the least dispersion.

3.2. Spatial–Temporal Differentiation of Rural Development in Counties of Jiangsu Province

This article introduces the partition boundaries of southern, central, and northern Jiangsu for further spatio-temporal differentiation analysis. It uses the provincial average ±1 and 0.5 standard deviation of various indices as critical values to delineate six categories of rural development levels (see Table 3 and Figure 3 and Figure 4), referring to classification standards proposed in previous research [62]. The research findings indicate that from 2010 to 2020, the rural development pattern in Jiangsu Province, characterized by superior development in the south, good development in the central region, and weaker development in the north, has not undergone significant changes. Overall, there has been a progressive transition from a comparatively low degree of heterogeneous development to a more profound and advanced stage of heterogeneous development (Figure 3 and Figure 4).
From an economic development perspective, the overall spatial pattern of “strong south, moderate center, weak north” has remained largely unchanged, with growing regional disparities. High-value counties remain concentrated along Taihu Lake and the Yangtze River in southern Jiangsu, while low-value counties have increased from 9 in 2010 to 17 in 2020, expanding beyond the central belt in northern Jiangsu. This persistent imbalance reflects long-standing disparities in industrial agglomeration and infrastructure investment. Southern Jiangsu, integrated into the Yangtze River Delta urban agglomeration, benefits from stronger economic spillovers, better transportation, and higher levels of private and foreign capital. In contrast, northern Jiangsu, with its agricultural base and limited industrial diversification, continues to face challenges in attracting investment and skilled labor. The expansion of low-value counties likely stems from an increasingly uneven distribution of fiscal resources and development opportunities.
The social development level in Jiangsu’s rural areas remains comparatively low, with minimal changes in its spatial pattern. High-value counties declined slightly from 26 to 23 and are mainly located in the Taihu Plain and coastal areas near the Yangtze River estuary. In contrast, low-value counties increased from 26 to 28, gradually expanding from north to south, while medium–low-value areas remain scattered. Compared with economic and spatial development, social progress lags behind, partly due to increasing pressure on rural public services caused by population mobility. These demographic shifts have strained transportation, healthcare, and communication systems, particularly in areas that were previously leading. The slight decline in high-value counties and the expansion of low-value regions indicate a growing mismatch between infrastructure capacity and rising demand, widening regional disparities in social service provision.
The pattern of rural spatial utilization in Jiangsu Province exhibits distinct regional disparities. Southern and central regions show higher mean values, while northern Jiangsu lags behind. High-value counties, accounting for 12.12% of the total, are mainly distributed in strips along the southern coast of the Yangtze River estuary and within urban core influence zones, where land development benefits from industrial agglomeration, better infrastructure, and stronger spatial governance. The number of high-value and medium–high-value counties increased from 10 to 12 (18.18%), reflecting expanding spatial efficiency around urban centers. Medium–low-value areas, comprising 28.79%, are mainly located in the northern Yili Mountain region and along the Yangtze River in central Jiangsu. In contrast, low-value areas dominate the spatial pattern (39.39%) and are concentrated in the Xuhuai, Lixiahe, and Coastal Plains, where weaker industrial bases and insufficient infrastructure investment constrain land use efficiency. These variations indicate that economic structure and institutional capacity remain key factors shaping spatial utilization outcomes.

3.3. Pattern Characteristics of Rural Restructuring Intensity in Each County of Jiangsu Province

In order to further understand the changes in the rural restructuring degree in Jiangsu Province in each period, this paper measures the economic, social, spatial, and comprehensive restructuring intensity of rural areas in Jiangsu Province in 2010–2015, 2015–2020, and 2010–2020 in the research areas, which are Rx, Ry, Rz and RC, respectively, and the results are detailed in Table 4.
It is discovered that the restructuring intensity of each dimension is generally high in rural Jiangsu Province, with the mean value of the economic restructuring index reaching 0.801, the mean value of the social restructuring index being 0.277, and the mean value of the spatial restructuring index being 0.222 from 2010 to 2020. It indicates that the rural economic sub-system, focused on industrial growth and economic enhancement, is the most active in rural development. In terms of each period, the mean value of Jiangsu Province’s restructuring intensity index for each dimension exhibits the characteristics of increasing first and then slowing down. For the two five-year study periods, the composite restructuring intensity mean values were 0.248 and 0.187, which reflected that the rural restructuring in Jiangsu Province in the second five-year period was less intense than that in the first five-year period, and the overall rate of restructuring has slowed down. In different dimensions, the mean values of economic restructuring intensity are higher than those of social and spatial restructuring in all time periods, with Rx mean > Ry mean > Rz mean in both 2010–2015 and 2015–2020.
In accordance with the criterion of determining the boundary value in the previous section, the rural spatial distribution restructuring intensity index is further drafted (Figure 5), which we can use to analyze the characteristics of the pattern evolution of the rural restructuring intensity in Jiangsu Province, with a specific focus on the sub-dimensional restructuring index.
(1) The intensity of economic reconstruction in rural Jiangsu Province is high, while the degree of divergence is relatively low. From 2010 to 2015, 48.48% of the counties had economic restructuring index values higher than the average value, and the percentage rose to 66.67% from 2015 to 2020. These areas above the average value are concentrated in northern Jiangsu, and mainly near urban centers. Meanwhile the areas where the reconstruction intensity is less than the average value are roughly concentrated in the Taihu Plain and the Yangtze River coast in southern Jiangsu. Throughout 2010–2020, the areas with higher-than-average reconstruction intensity are concentrated in northern Jiangsu, and are mainly concentrated around the urban center of the city.
(2) The overall development of social restructuring patterns in rural Jiangsu Province is relatively smooth, but the divergence is relatively obvious. The value of the standard deviation of the index of the intensity of social restructuring increased from 0.054 to 0.055 between the two periods, which is the highest degree of dispersion among the three types of restructuring. These higher-than-average counties were distributed in a ‘U’ shape, including the central and western regions of middle and northern Jiangsu gradually evolving to both sides. The counties exhibiting below-average restructuring intensity are primarily located in vast areas of southern Jiangsu, along the Yangtze River in middle Jiangsu, and the suburbs of Xuzhou City in northern Jiangsu.
(3) The intensity of spatial restructuring in Jiangsu Province is low, and the degree of divergence shows clear stages. The standard deviation value decreases from 0.075 to 0.036. From 2010 to 2015, the counties had a significant divergence of spatial restructuring, which ranked first in each dimension. The districts with high values were predominantly situated along the eastern coastal area of Jiangsu, whereas those with medium-to-high values were concentrated primarily in the plains adjacent to the Yangtze River. From 2015 to 2020, the balance of spatial restructuring intensity in counties has been significantly improved. The trend of high spatial restructuring intensity in eastern middle Jiangsu, the Taihu Lake coast of southern Jiangsu, and Nanjing has been further strengthened. Meanwhile, the counties of the medium–low-value areas gradually extend southward, and are scattered in the central–southern parts of northern Jiangsu, the northern bank of the Yangtze River in middle Jiangsu, and in the southwest of Southern Jiangsu.

3.4. Analysis on the Coupling Coordination Pattern of Rural Restructuring in Various Counties of Jiangsu Province

3.4.1. Analysis on the Coupling Degree of Rural Restructuring in Each County of Jiangsu Province

Utilizing an analysis of the spatial–temporal evolution of the rural economic–social–spatial restructuring pattern in Jiangsu Province, the coupling association model was employed to compute the coupling degree of economic–social–spatial restructuring in each period and to analyze the coupling level and changes (Figure 6). It is found that the coupling level of economic–social–spatial reconfiguration of rural areas in Jiangsu Province is generally high and maintains a certain positive development trend during 2010 to 2020. From 2010 to 2015, the mean value of the coupling was 0.906, with a standard deviation of 0.026 and a coefficient of variation of 0.028. Counties exhibiting higher coupling degrees were predominantly located along the Yangtze River, in the eastern part of middle Jiangsu, and in the east–central part of northern Jiangsu. From 2015 to 2020, the coupling degree averaged 0.951, with a standard deviation of 0.018 and a variation coefficient of 0.019. In this period, the overall coupling degree of Jiangsu Province significantly increased, and the degree of dispersion between counties (districts) decreased. The counties with high coupling degree were mainly distributed along the Taihu Lake in southern Jiangsu, the southern part of the Ningzhenyang hilly area and the Lixiahe Plain, the eastern coastal plain, and Xuzhou suburban counties in middle and northern Jiangsu.

3.4.2. Analysis of Coordination Degree of Rural Restructuring in Counties (Districts) of Jiangsu Province

The high degree of coupling and its development reflects the strong interdependence and correlation of the economy, social development, and spatial utilization in Jiangsu Province’s rural areas. However, a high coupling degree does not mean that the development effect is always positive, and it cannot distinguish the low or high restructuring intensity from the coupling level or objectively reflect the good or bad coordination of the elements within the research object. Hence, it is imperative to further measure the coordinated development degree of rural economic–social–spatial restructuring in each period by the coordination degree model. The outcomes of this assessment are presented in Figure 7. The classification of the grades mainly refers to the criteria for classifying the grades of the degree of harmonization D of the classification criteria in existing studies [67], in order to classify them into five grade types.
The research revealed significant spatial disparities in the development level of rural reconstruction coordination in Jiangsu Province, and the coupled coordination degree of restructuring intensity shows a slow downward trend. From 2010 to 2015, Jiangsu Province’s average coordination degree for rural restructuring intensity was 0.473, accompanied by a standard deviation of 0.036 and a coefficient of variation of 0.077. The proportion of barely coordinated counties was 16.67%, which were primarily located in the eastern and southern parts of northern Jiangsu and scattered in the suburbs of cities in middle Jiangsu. The proportion of proximate imbalance counties was 83.33%, which were mainly distributed in the vast areas of southern Jiangsu, the central–north parts of middle Jiangsu, and the western–northern parts of northern Jiangsu. From 2015 to 2020, the mean value of the overall coordination degree decreased to 0.421, the standard deviation was 0.023, and the coefficient of variation was 0.055. Considering that the development of this stage has entered a relatively stable period, the economic growth rate has gradually slowed down; combined with the comprehensive impact of the COVID-19 epidemic at the end of the study, the economic–social–spatial restructuring intensity in Jiangsu Province’s rural areas has shown a declining trend compared with the previous stage, which has led to a phased decrease in the coordination degree. Only one county is barely coordinated, and the number of proximate imbalance counties further increased to 87.88%, while there is mild imbalance counties appeared. From 2010 to 2020, Jiangsu Province exhibited an average coordination degree of 0.594 for rural restructuring intensity, with a standard deviation of 0.051 and a coefficient of variation of 0.085. On the whole, this indicates a level of coordination. The areas with a higher degree of coordination were still concentrated along the Yangtze River, the coastal plains of eastern Jiangsu, and the coastal areas of Hongze Lake.

3.5. Drivers of Rural Reconstruction in Jiangsu Province

The dominant dynamics of rural restructuring differ in different periods. Different regions in different periods of natural resources, the impact of urbanization and industrialization, and the role of government regulation are not the same, leading to differences in the dominant driving force of rural restructuring in a region at different stages. Based on the temporal and spatial heterogeneity of the regression coefficients of the selected driving factors of rural restructuring in Jiangsu Province, the dominant driving forces and modes of rural restructuring in different regions of the province are summarized. The dominant driving force is mainly based on the strength of the role of each driving factor in different districts and counties on the transformation and development of rural restructuring, i.e., the size of the regression coefficient values in different time nodes are compared.
This study shows that there are obvious spatial and temporal differences in the driving force of each of the nine driving factors on rural restructuring at the three time nodes, and the strength of their roles, positive and negative, have all evolved in stages (Figure 8). It can be seen that during the period of 2010–2020, the transformation and development of rural restructuring in Jiangsu Province was affected by the combined effect of many driving forces. The interactive game of many driving factors makes the rural territorial system pattern undergo a series of restructuring. At the same time, the effectiveness of the dominant driving factors shows obvious alternation, which can basically characterize the coupling and coordination status of the dynamic changes in the rural restructuring pattern in various dimensions.
By combing the effectiveness of the driving factors, it is easy to find that the positive driving effect of government rural financial input, agricultural land productivity, the proportion of secondary and tertiary industries, and arable land resource endowment on rural restructuring in Jiangsu Province is more significant. Among them, the positive driving effect of the government’s rural financial input is the strongest in the early stage of the study, and it becomes the dominant driving force leading the development of rural restructuring in the three regions of southern Jiangsu, central Jiangsu, and northern Jiangsu. Then the positive driving effect of the transportation and road conditions, and the government’s rural financial input, is gradually weakened; the proportion of the secondary and tertiary industries and the degree of non-agriculturalization of the rural labor force’s employment have a gradual enhancement of the positive driving effect of the rural reconstruction and urbanization. The positive driving effect of urbanization, arable land resource endowment, agricultural modernization, and social fixed investment in primary production on rural reconstruction shows an “inverted U-shape” trend. By the middle of the study, under the marketization and industrialization, the proportion of secondary and tertiary industries has gradually become the dominant driving force of rural reconstruction in southern and central Jiangsu, and urbanization and agricultural land productivity have become the dominant driving force of rural reconstruction in northern Jiangsu. Until the end of 2020, the proportion of secondary and tertiary industries and the non-agricultural employment of the rural labor force will be the dominant driving forces of rural reconstruction in southern Jiangsu, and the proportion of secondary and tertiary industries and the land productivity have become the dominant driving force of rural reconstruction in central Jiangsu. The share of secondary and tertiary industries and land productivity are the dominant drivers of rural reconstruction in the central Jiangsu region; the non-farming employment of the rural labor force and the density of transportation road networks become the dominant drivers of rural reconstruction in the northern region. Over time, the dominant driving force of rural restructuring in the three major regions shifted from the government’s rural financial investment to other driving factors, indicating the existence of various driving forces in the process of rural restructuring in each region within Jiangsu Province, which gradually shifted from the government’s support and investment power to the driving force of a more open market economy under urbanization and industrialization.

4. Discussion

4.1. Deepening Intra-Regional Imbalances in the Process of Rural Reconfiguration

In the process of rural restructuring, regional development dynamics have evolved from a low level of imbalance to a high level, but with a more pronounced spatial polarization. While the overall pattern of rural development being excellent in the south, good in the middle, and weak in the north has not changed significantly, the degree of imbalance between regions has deepened significantly. Further research found that there is a certain degree of mismatch between the level of rural development and the intensity of rural reconstruction in Jiangsu Province during 2010–2020 (Figure 3 and Figure 4). For example, in 2010, the Taihu Lake coast of the southern region of Jiangsu Province was an area with a high level of economic development, but the intensity of economic reconstruction was less than the average value in 2010–2020. In 2010, the average value of social development in northern Jiangsu was lower, but in the period of 2010–2020, it had a higher intensity of social reconstruction, which is a mismatch reflecting that the region with a weaker development foundation has been developed fast in the past ten years, but due to its poor development foundation, the development gap between regions is still widening.
The reason for the above phenomenon is, on the one hand, the Matthew effect of development. Because of the virtue of the previous advantages and the relatively well-constructed cooperation mechanism which promotes the optimal allocation of resources in the region, those groups with existing advantages tend to accumulate more [22]. On the other hand, it reflects the structural imbalance in the allocation of public infrastructures and affairs among different regions [5]. Because of the financial constraints, local governments tend to bet on the strong, concentrating resources in areas with better existing conditions while neglecting areas in greater need [32]. Thus, although the northern part of Jiangsu Province has benefited from the local common wealth and comprehensive modernization promotion policies, and has gained rapid economic growth and improved public service levels [10], as a whole, the imbalance within the region in the process of rural reconstruction is deepening.

4.2. Multidimensional Sources of Heterogeneity in the Deepening of Regional Imbalances

We found that the impact of rural restructuring on regional development and equilibrium degree is different in different dimensional perspectives. In this study, we analyzed the impact of the rural restructuring process on regional development and spatial equilibrium degree from three dimensions: economic, social, and spatial. The results show that the high level of development and large increase in the economic dimension indicate that the rural economy of Jiangsu Province maintains a rapid growth trend at a high level, which is the most important driving force for rural development. The findings are consistent with the existing studies [9], which emphasize that economic development is the dominant driving force for the development of urbanization in Jiangsu. However, our study further compares development level and rural restructuring performance across economic, social, and spatial dimensions, and finds that regional imbalances are more pronounced in the social and spatial dimensions. This adds more new insights than previous studies that focused mainly on economic disparities. For example, while existing studies [9] have mainly pointed to overall economic imbalances between regions, our findings suggest that the economic dimension now exhibits the highest level of equilibrium among the three dimensions, while the social and spatial dimensions are increasingly different.
The social and spatial dimensions have a lower level of development than the economic dimension, with the social dimension having the lowest level of development; the spatial dimension, on the other hand, is mainly characterized by a low rate of growth, with an average annual growth rate of only 5.11 per cent. In terms of dispersion, the regional imbalance of the social and spatial dimensions is significantly higher than that of the economic dimension and continues to rise, with the spatial dimension having a relatively high degree of imbalance in the first stage and the social dimension having the highest degree of imbalance in the second stage. This reflects the lagging progress of social rural restructuring, especially in economically underdeveloped rural areas, where the imbalance between social development and economic development, such as infrastructure and healthcare, is significant [38]. The improvement of land use efficiency and development levels remains insufficient, with uneven changes in rural land use patterns and spatial morphology across counties within the province [68].
While previous studies usually analyze these dimensions separately, our coupled coordination analysis reveals the interactions and mismatches among the three dimensions, and the findings help to explain why structural adjustment of the rural economy sometimes fails to reduce regional disparities.

4.3. Spatio-Temporal Differentiation of Driving Forces in Rural Restructuring

The driving forces behind rural changes in Jiangsu Province show clear differences over time and across regions. These differences appear not only in the changes in regression coefficients across different places and time periods, but also in how the main forces behind the changes shift and work together at different stages of development.
Over time, rural changes in Jiangsu have gone through different stages, each with different main drivers. At first, government fiscal investments in rural areas were very important everywhere, especially in infrastructure and public services. This matches earlier studies that showed how government help is very important in the early steps of rural growth in China [4]. But unlike those studies that say policy support stays the most important, our study finds a clear change. By the middle of the study period, changes in the economy—especially growth in industry and services—became stronger than government fiscal investments in central and southern parts of Jiangsu. This shows that the market and changes in the economy are becoming more important. This matches what Zhang et al. found [69], as they pointed out that local economic strength is now playing a bigger role in rural changes.
Across regions, the south, middle, and north of Jiangsu show different paths in rural reconstruction. Southern Jiangsu, with its strong economic foundation and earlier urban–rural integration process, has quickly moved toward a market-based economy. This shift is led by non-agricultural employment and industrial upgrading. In contrast, northern Jiangsu still depends a lot on government investment and land resources. It also has restricted labor mobility and a lower urbanization rate. Middle Jiangsu is in between. Here, government policy, business growth, and land use all play a part. This study used a detailed look at the differences across regions to improve past research, which mostly used a simple north–south comparison. It also suggests that different areas need different types of policies.
Also, the way the main forces work together has changed over time. At first, government investment led the changes. Later, its effects gradually weakened with industrial structure, employment shifts, and transportation infrastructure becoming the primary drivers. This shows that rural restructuring is a multi-scale, multi-actor process shaped by institutional arrangements and structural forces [20]. Previous studies often saw rural changes as mostly driven by government or markets alone. But our study shows that the process is more complex. It changes over time and depends on multiple driving forces, including urbanization and social fixed investment, which exhibit an inverted U-shaped impact. This new understanding can help create better and more flexible plans for rural growth, thereby enriching the current understanding of the rural reconstructing process.

4.4. Limitations and Implications

This study has several limitations. First, the construction of the evaluation index system is limited by data availability, and some indicators are difficult to include quantitatively, which makes it hard to comprehensively reflect the complex interaction between economic, social, and spatial elements in rural reconstruction. Second, the empirical analysis focuses on Jiangsu Province, a typical eastern plains region, and its findings may have applicability bias when extended to regions with large differences in ecological environment or socio-economic conditions.
In addition, this paper adopts a quantitative measurement method to analyze the process of rural reconstruction, but it is still insufficient in reflecting the deep cultural identity of villages, institutional arrangements, and villagers’ subjective perceptions. The roles of local government, how villagers feel and react to changes in space, and the effects of social rules and personal networks have not been clearly shown. Based on this, future research can use qualitative research methods such as in-depth interviews, participatory observation, and field surveys to combine quantitative and qualitative analysis. It can reveal more comprehensively the dynamic mechanisms in the process of rural reconstruction. At the same time, future research can investigate more samples with different geographic locations and socio-economic condition.
In contrast to previous studies that focused primarily on the economic dimensions of rural reconfiguration [9], the results of this study indicate that the imbalance of social and spatial dimensions is the main reason for the deepening of regional imbalance in Jiangsu Province, and it is worth noting that the imbalance between the socio-spatial dimension and the economic dimension in the process of rural restructuring makes the restructuring intensity of the coupling coordination show a certain trend of a slow decrease. Low coupling coordination may lead to inefficient resource allocation [39], hindering the improvement of the quality of rural restructuring. In addition, the apparent differences in the development level of coupled coordination across villages reflect their differences in their ability to promote rural restructuring and policy concerns [40]. This also implies that low coupling coordination may exacerbate regional development imbalances, ultimately affecting the sustainable development of the region and the achievement of the goal of high-quality rural revitalization. The implication for rural planning is that it should go beyond mere economic growth and pay more attention to the enhancement of social and spatial dimensions to improve regional equilibrium by optimizing resource allocation.
Based on the findings, rural planning should not only focus on economic growth, but also on the enhancement of social and spatial dimensions to promote coordinated regional development. Firstly, the structure of rural financial inputs should be optimized, and the supply capacity of social public services should be improved, especially in the northern part of Jiangsu Province, to increase the efficiency of resource allocation and narrow the regional gap. At the same time, village agglomeration should be guided through planning to enhance the efficiency of arable land resource utilization, and the spatial structure of villages should be improved by strengthening the management of spatial order. On this basis, regional differentiated development strategies should be formulated according to local conditions. Southern Jiangsu has entered a development stage dominated by non-agricultural employment and industrial structure transformation, and should focus on promoting the development of secondary and tertiary industries and the transfer of labor force for employment. Central Jiangsu should strengthen the improvement of land productivity and the optimization of industrial structure, and northern Jiangsu needs to accelerate the improvement of transportation road networks and infrastructure construction, and promote the process of agricultural modernization and urbanization. As the leading force in rural restructuring shifts from government financial input to market-oriention and industrialization, a smooth transition from government-led to market-driven should be gradually achieved to stimulate the potential for endogenous development in rural areas, improve the market mechanism, and promote the participation of pluralistic main bodies, especially private capital, so as to build a more sustainable, balanced, and resilient new pattern of rural development.

5. Conclusions

This study combines the TOPSIS model and the coupled coordination model to explore the impact of rural restructuring on the level of regional development and the degree of imbalance during the period 2010–2020. Our results show that the overall level of rural development is found to have increased significantly, with the overall pattern remaining unchanged but the degree of discrete development deepening, showing a further deepening of heterogeneous development from the original lower level to a higher level. The relatively high degree of coupling reflects the existence of a certain degree of dependence between the economic–social–spatial aspects of the countryside, but the impact of different dimensions of rural restructuring on regional equilibrium is not consistent. The development and reconstruction equilibrium of the economic dimension is significantly higher than that of the social and spatial dimensions, and the imbalance of development in different dimensions makes regional harmonization show an increasing and then slowing development. Our findings suggest that improving the balance of the social and spatial dimensions is more conducive to avoiding spatial heterogeneity in the region, and that the identified low- and medium-value zones can help policymakers more accurately determine where to focus their resource inputs.

Author Contributions

Conceptualization, L.S.; Methodology, L.S. and B.Z.; Formal analysis, B.Z. and Y.L.; Investigation, Q.H. and Y.L.; Writing—original draft preparation, L.S., Y.L. and Q.H.; Writing—review and editing, L.S., B.Z. and X.F.; Supervision, L.S.; Funding acquisition, X.F. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by General Research Fund of Shanghai Normal University, grant number KF202340.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.

Acknowledgments

The authors wish to thank in advance the editor and reviewers for their contribution in the submission and revision phases, most importantly, Rui Zhou for his comments on an earlier version of this paper.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviation is used in this manuscript:
TOPSISTechnique for Order Preference by Similarity to Ideal Solution

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Figure 1. Location of Jiangsu Province, in the eastern part of China’s coastal area.
Figure 1. Location of Jiangsu Province, in the eastern part of China’s coastal area.
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Figure 2. Statistics of rural development level in Jiangsu Province in 2010, 2015, and 2020.
Figure 2. Statistics of rural development level in Jiangsu Province in 2010, 2015, and 2020.
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Figure 3. Mean value of rural development level in northern, central, and southern Jiangsu Province, 2010, 2015, and 2020.
Figure 3. Mean value of rural development level in northern, central, and southern Jiangsu Province, 2010, 2015, and 2020.
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Figure 4. Spatial pattern of rural development level of each dimension in counties of Jiangsu Province in 2010, 2015, and 2020.
Figure 4. Spatial pattern of rural development level of each dimension in counties of Jiangsu Province in 2010, 2015, and 2020.
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Figure 5. Spatial pattern of the rural reconfiguration intensity in the counties of Jiangsu Province, 2010–2015, 2015–2020, and 2010–2020.
Figure 5. Spatial pattern of the rural reconfiguration intensity in the counties of Jiangsu Province, 2010–2015, 2015–2020, and 2010–2020.
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Figure 6. Spatial pattern of coupling degree of economic–social–spatial restructuring strength in rural Jiangsu Province, 2010–2020.
Figure 6. Spatial pattern of coupling degree of economic–social–spatial restructuring strength in rural Jiangsu Province, 2010–2020.
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Figure 7. Spatial pattern of coordination degree of economic–social–spatial restructuring intensity in Jiangsu Province’ rural areas from 2010 to 2020.
Figure 7. Spatial pattern of coordination degree of economic–social–spatial restructuring intensity in Jiangsu Province’ rural areas from 2010 to 2020.
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Figure 8. Spatial and temporal differentiation of regression coefficients for drivers of rural restructuring in Jiangsu Province in 2010, 2015, and 2020.
Figure 8. Spatial and temporal differentiation of regression coefficients for drivers of rural restructuring in Jiangsu Province in 2010, 2015, and 2020.
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Table 1. System of indicators for evaluating the level of rural development.
Table 1. System of indicators for evaluating the level of rural development.
DimensionIndicator LayerCalculation Method and UnitAttribute
Economic
development		Regional per capita economic aggregateGross regional product/resident population (yuan)+
Level of farmers’ incomePer capita net income of rural residents (yuan)+
Engel coefficient of rural permanent residentsConsumption expenditure on food as a proportion of total consumption expenditure by rural permanent residents (%)−
Agricultural labor productivityGross output value of agriculture, forestry, animal husbandry, and fishery/total number of people employed in agriculture, forestry, animal husbandry, and fishery (yuan/person)+
Social
development		Medical and health facilitiesNumber of beds in medical institutions/resident population × 1000 (beds/per thousand people)+
Level of communication facilities(Number of fixed and mobile telephone households + Internet broadband subscribers)/Total population+
Level of rural electrificationRural electricity consumption/rural resident population (kwh/person)+
Road network densityTotal regional road mileage/county land area (km/km2)+
Spatial
utilization		Agricultural land use efficiencyGrain yield/grain sown area (tons/hectare)+
Rural housing per capitaPer capita housing area of peasant families (square meters)+
Land use efficiency in the second and third industriesValue added of the second and third industries/(urban construction land + other construction land such as industry, mining and transport) (billion yuan/km2)+
Regional heterogeneityUrban industrial and mining land area/county land area (%)+
Table 2. Independent variable selection and calculation method.
Table 2. Independent variable selection and calculation method.
Driving Force TypeCriterion LevelIndicator LevelCalculation Method
Endogenous DriversLocation ConditionsTransportation ConditionTotal road mileage/Total area (km2)
Natural Resource EndowmentArable Land Resource EndowmentArable land area/Rural agricultural workers (hm2/person)
UrbanizationUrban Development LevelUrban population/Total population (%)
Exogenous DriversIndustrialization and MarketizationIndustrial StructureSecondary and tertiary industries output/Regional GDP (%)
Non-agricultural Employment in Rural AreasRural non-agricultural workers/Rural population (%)
Agricultural ModernizationLevel of Agricultural MechanizationTotal power of agricultural machinery/Arable land area (kw/hm2)
Land ProductivityAgricultural output/Arable land area (104 yuan/hm2)
External InvestmentGovernment Investment in Rural AreasAgri./Forestry/Water expenditure/Rural population (104 yuan/person)
Fixed-asset Investment in Primary SectorTotal investment in primary industry (billion yuan)
Table 3. The grading standards for the evaluation of rural development level in the study area.
Table 3. The grading standards for the evaluation of rural development level in the study area.
Grade of Value AreaStatistical Standard
I Lowest(0, mean value − 1 standard deviation]
II Lower(mean value − 1 standard deviation, mean value − 0.5 standard deviation]
III Medium–low(mean value − 0.5 standard deviation, mean value]
IV Medium–high(mean value, mean value + 0.5 standard deviation]
V Higher(mean value + 0.5 standard deviation, mean value + 1 standard deviation]
VI Highest(mean value + 1 standard deviation, 1]
Table 4. Statistics on the intensity index of rural restructuring in Jiangsu Province by time period.
Table 4. Statistics on the intensity index of rural restructuring in Jiangsu Province by time period.
Time Period2010–20152015–20202010–2020
CategoryEcon.Soc.Spat.Comp.Econ.Soc.Spat.Comp.Econ.Soc.Spat.Comp.
Mean value0.3930.1830.1690.2480.2550.1700.1370.1870.8010.2770.2220.434
Standard deviation0.0310.0540.0750.0350.0230.0550.0360.0220.0480.1010.1180.057
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Shao, L.; Zhou, B.; Li, Y.; Huang, Q.; Fang, X. Polarization or Equilibrium: Spatial and Temporal Patterns and Divergent Characteristics of Rural Restructuring in Unevenly Developed Regions. Sustainability 2025, 17, 5989. https://doi.org/10.3390/su17135989

AMA Style

Shao L, Zhou B, Li Y, Huang Q, Fang X. Polarization or Equilibrium: Spatial and Temporal Patterns and Divergent Characteristics of Rural Restructuring in Unevenly Developed Regions. Sustainability. 2025; 17(13):5989. https://doi.org/10.3390/su17135989

Chicago/Turabian Style

Shao, Lin, Bochuan Zhou, Yeyang Li, Qiaoli Huang, and Xuening Fang. 2025. "Polarization or Equilibrium: Spatial and Temporal Patterns and Divergent Characteristics of Rural Restructuring in Unevenly Developed Regions" Sustainability 17, no. 13: 5989. https://doi.org/10.3390/su17135989

APA Style

Shao, L., Zhou, B., Li, Y., Huang, Q., & Fang, X. (2025). Polarization or Equilibrium: Spatial and Temporal Patterns and Divergent Characteristics of Rural Restructuring in Unevenly Developed Regions. Sustainability, 17(13), 5989. https://doi.org/10.3390/su17135989

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