Next Article in Journal
A Digital Framework for the Implementation of the Circular Economy in the Construction Sector: Expert Opinions
Previous Article in Journal
Heritage Resilience and Identity: Lesson from Trabocchi Coast about Climate Change Adaptation Strategies
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Sustainability
Volume 16
Issue 14
10.3390/su16145850
sustainability-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

Rural Network Resilience: A New Tool for Exploring the Mechanisms and Pathways of Rural Sustainable Development

by
Chao Yu
1,
Zhiyuan Zhou
1 and
Junbo Gao
2,*
1
School of Geographic Sciences, Xinyang Normal University, Xinyang 464000, China
2
School of Tourism, Xinyang Normal University, Xinyang 464000, China
*
Author to whom correspondence should be addressed.
Sustainability 2024, 16(14), 5850; https://doi.org/10.3390/su16145850
Submission received: 20 June 2024 / Revised: 7 July 2024 / Accepted: 8 July 2024 / Published: 9 July 2024
Browse Figures
Versions Notes

Abstract

:
Enhancing rural resilience is crucial due to the significant vulnerabilities faced by rural areas, such as weak economic foundations, scarce resources, and deficient infrastructure. This paper aims to provide a comprehensive review of rural network resilience (RNR) to underscore its importance in understanding the mechanisms and pathways of rural sustainable development. To establish the theoretical foundations of RNR, we trace the evolution of resilience concepts and their applications across disciplines. The proposed analytical framework integrates evaluation methods from network science to assess RNR’s structural characteristics and outlines simulation techniques for predicting resilience under various risk scenarios. Furthermore, the paper examines the key influencing factors that shape RNR within complex rural regional systems, exploring the intricate evolutionary mechanisms involved. To bridge existing research gaps, a synergistic development perspective is introduced, emphasizing the interconnected, multi-layered nature of rural networks across societal, economic, and ecological domains. Additionally, a county-level analytical framework tailored for county-level rural resilience analysis is presented to offer strategic guidance for enhancing RNR and driving sustainable rural revitalization. This transdisciplinary synthesis propels RNR as an emerging field with significant policy implications.

1. Introduction

An important aspect of comprehensively transforming the understanding of modern economic and social life is connecting the focus on rural development with “networks” [1]. This is crucial as networks enable us to encompass both the “internal” and “external” aspects within the same analytical framework, thereby offering a means to delve into the evolutionary mechanisms of complex and open rural regional systems.
While villages are less developed than cities and rural networks are not as robust as urban networks, similar to urban networks, rural networks also encompass the circulation and complementarity of vertical networks, as well as the competition and cooperation of horizontal networks [2]. When constructing rural networks with counties as units, they can serve as independent growth nodes, participating in production and consumption outside the county [3]. Additionally, they can act as carriers for rural development by analyzing the internal structure and layout of counties, examining the contribution of county development to regional growth, and evaluating the current status and evolutionary trends of county industrial clusters [4], social relations [3], and ecological landscapes [5]. This analysis facilitates the identification of implementation paths for the coordinated development of various subsystems of rural regional systems during the network construction process at different scales. Therefore, building rural networks serves as an effective means to provide robust support for sustainable rural development.
However, it is important to note that rural areas with a significant share of agricultural production are particularly exposed to risks, given their weak economic foundation, scarce social resources, and backward infrastructure, making them even more vulnerable than cities [6]. The purpose of network construction goes beyond promoting orderly element flow within and outside the region; it also crucially aims at enhancing the region’s ability to respond to acute shocks and chronic pressures from both internal and external sources by relying on network resilience [7]. The outbreak of the COVID-19 pandemic in the spring of 2020 starkly revealed the vulnerabilities of rural areas in terms of medical resources, information dissemination, economic pressures, social support systems, and an aging population, underscoring the pressing need to strengthen rural resilience [8,9].
Enhancing rural network resilience (RNR) is a fundamental prerequisite for sustaining the structure and function of rural networks and promoting sustainable development. Without the continuous generation and timely release of network resilience, it is difficult to sustain a healthy network structure and function [10,11]. Regional sustainable development will naturally lack a driving force in the absence of this resilient network infrastructure. In rural areas, the distribution of social relations, resource allocation, economic activities, and information dissemination is more uneven compared to cities. This disparity may lead to some nodes having a higher degree in rural networks, exhibiting significant “scale-free” characteristics [12]. The random robustness and attack vulnerability displayed by scale-free networks fully reflect the stability and adjustment capabilities of rural networks in responding to random disturbances and intentional attacks. This resilience enables enhancements in RNR to promote sustainable development and further advance rural revitalization [13,14].
This paper is intended to provide a comprehensive review of RNR, emphasizing its significance in understanding the mechanisms and pathways of rural sustainable development. The proposed analytical framework for RNR is structured as follows: (1) Introducing the concept of rural network resilience entails reviewing the history of resilience development, analyzing the relationship between regional and network resilience, and delving into the connotation of rural network resilience. From the perspective of synergistic development, the importance of multidimensional network synergy for sustainable rural development is emphasized. (2) We summarize the evaluation methods of rural network structural characteristics and the correlation between its quantitative results and resilience by considering the basic components of the network—nodes and connecting lines, as well as the network as a whole, combined with the network science methodology system. Armed with this analysis, we further investigate the potential changes in the resilience of rural networks under shocks. (3) To address the complexity of rural territorial systems, we start by analyzing the specific economic, social, and ecological environments that are intrinsic to rural areas. Through this analysis, we identify the key factors that influence the resilience of rural networks and then proceed to outline the primary strategies for uncovering their evolutionary mechanisms. (4) Based on the background of China’s rural development, a rural network construction method specifically designed for county units is proposed in this study. The analytical framework of rural network resilience is subsequently formulated, drawing from the geographic research paradigm that focuses on the evolution of the “Production-Living-Ecological” spaces. Moreover, recommendations for utilizing this analytical framework are provided to enhance its effectiveness and applicability in rural settings.

2. Theoretical Foundations and Contextual Connections of Rural Network Resilience Research

2.1. Connotation and Extension of the Concept of Resilience

Since the resilience concept was first introduced into the field of ecology in the 1970s [15], this concept, which first appeared in the field of engineering, has been continuously extended to other domains [16]. Precisely because the original connotation of “resilience” is closely associated with the process of being affected by external disturbances and recovering to the original state, it has gradually become a hot topic across various disciplines as an important means of coping with natural disasters and social risks, further promoting the evolution and extension of its connotation [17]. Particularly in social–ecological systems, the resilience concept has been widely applied in areas such as economic development, infrastructure construction, disaster prevention, and mitigation, thereby extending to highly relevant concepts such as economic resilience [18], social resilience [19], ecological resilience [20], urban resilience [21], rural resilience [22,23], and network resilience [24].
Research on resilience across different disciplines has unveiled a plethora of terminologies that are continually evolving, thereby complicating the delineation of resilience research to a certain extent. The Resilience Alliance, after scrutinizing research spanning the last four decades, has elucidated the fundamental characteristics of resilience [17]. These include resilience as the process of sustaining a state while undergoing change, adaptability as the process of reacting to deliberate changes, and transformation as the process of transitioning to a novel developmental trajectory or even forging a new one. This underscores the dynamic nature of resilience, contrary to viewing it as a static concept.
Resilience research offers valuable insights in several key areas. Firstly, it is crucial to understand that resilience goes beyond mere recovery and adaptation; it also involves transformation. This becomes particularly evident in fragile rural territorial systems, where when risks surpass their inherent resilience, it signifies the activation of resilience to propel the system towards a new phase of self-adaptation through transformation and growth. Secondly, resilience research must take into account various dimensions, including temporal and spatial changes. And lastly, it is essential for resilience research to have a comprehensive understanding of the system characteristics of the research object. These observations underscore the complexity and importance of resilience in addressing challenges and fostering sustainability in different contexts.

2.2. Relationship between Network Resilience and Regional Resilience

The network perspective is a significant contribution to the investigation of regional resilience [25]. To begin with, it introduces innovative research concepts that offer insights into how the structure impacts the performance (resilience) of a system by evaluating the structural features of component interactions [26]. Furthermore, a unified analytical framework has been developed, enabling the description of complex systems using nodes and edges, and facilitating the examination of regional development dynamics by observing changes in the attributes of nodes and edges [24]. Lastly, by establishing viable comparisons, a wide range of network methods have been utilized, illustrating the prevalence of networks and enabling the comparison of structural distinctions between networks, even when data are drawn from diverse domains [27].
The relationship between network resilience and regional resilience is effectively demonstrated through the measurement of network structural properties (Table 1), a validation supported by scholars across multiple domains. For resilience, differences in node degree distributions and correlations within regional knowledge networks have been identified as mechanisms that can enhance the performance of clusters in specific technological fields, while maintaining regional resilience [28]. In regional inventor networks, the implementation of cluster policies can optimize the efficiency of local inventor networks by enhancing the small world of the network [29]. For adaptability, formal clusters within regional business networks exhibit superior performance compared to informal clusters in terms of adaptability [30]. The demonstrated potential of clusters to master and recover from crises underscores their adaptive nature [31]. The spatial distribution of facilities and road network connectivity significantly influences social service mobility within regional transportation networks, especially concerning vulnerable communities [27]. Networks with redundant pathways and hubs exhibit higher levels of robustness [32]. For transformation, the presence of key nodes in regional social networks triggers socio-spatial differentiation, leading to the formation of hierarchies [33]. However, it is important to point out that tightly connected networks and hierarchies are not mutually exclusive but rather coexisting phenomena. Special nodes, such as stores, play crucial roles in sustaining regional development during crises [34]. The “core-edge” pattern observed in regional social networks enhances the integrative capacity of the core while increasing connectivity with the periphery, thereby promoting regional expansion and resilience [35,36].
Based on the above analysis, rural network resilience can be conceptualized as a fusion of network resilience and regional resilience. Li’s review article on rural resilience provides a schematic diagram illustrating rural resilience mechanisms [23]. While the text does not systematically examine the concepts and implications of networked resilience, it does illustrate the schematic using networked language. Drawing from Li’s research findings and insights from empirical studies on rural development, we have synthesized the morphological evolution process of RNR (Figure 1). Our analysis indicates that the evolution of RNR manifests primarily in three dimensions: first, a shift in nodes from more to fewer and weaker to stronger; second, a transition in edges from sparse to dense and thin to thick; and third, a transformation in network structure from fewer centers, fewer intermediaries, and poor connectivity to more centers, more intermediaries, and better connectivity. The assessment of enhanced resilience can be inferred from the extent of structural modifications and the duration of functional recovery pre-shock, during the shock, and post-shock. These transformations lend themselves to quantitative measurement using indicators akin to network structural characteristics described in Table 1.
To summarize, by networking the interrelationships of the elements in the region and exploring the evolutionary mechanism of regional resilience, researchers can find a solid theoretical foundation, a variety of methodological choices, and widespread empirical validation. Dissecting the dynamic changes in the network’s structural characteristics allows a comprehensive understanding of how resilience can be enhanced. This approach integrates the evolutionary perspective of geography, making the study of regional network resilience a significant branch of resilience research. Furthermore, it serves as a valuable reference for the systemic investigation of RNR.

2.3. Synergistic Development Perspective in Rural Networks Resilience Research

The generation of the network is self-organized, and the network structure also presents order characteristics in time, space, and function [37,38,39,40]. Synergistic development emphasizes the self-organization of the system and the orderly structure presented in time, space, and function, and focuses on exploring the manifestation and formation mechanism of the orderly structure in the non-equilibrium state [41,42,43]. In the language of networking, the rural development problem can be transformed into the problem of uneven distribution of resources and poor circulation of factors caused by the structural defects of social, economic, and ecological sub-networks, as well as the problems of an insufficient interconnection and a low superimposed effect among sub-networks. The intuitive consequence is that there is no strong support for resilience, regardless of whether it suffers from a single risk or multiple risk shocks. Therefore, the key to solving the above problems is to pay attention to both the synergy of single-layer network elements and the synergy of multi-layer network subsystems. Guiding the synergistic development of rural networks is, therefore, an important way to enhance RNR. A significant increase in the resilience of rural networks is an important manifestation of the realization of synergistic development.
The relationship between network resilience and synergistic development in single-layer networks can be explored through quantitative measures of network structure. In contrast, for multi-layer networks, one can discern insights by comparing network structures, layouts, and trends. By examining relationships among networks at social, economic, and ecological levels (Table 2), several key dynamics emerge. Firstly, social networks play a crucial role in fostering social capital, which acts as a driving force for the emergence of economic networks [44]. Conversely, the development of economic networks reinforces social networks [45,46]. Social networks, underpinned by trust, institutions, and norms, direct local residents in adapting to ecological network transformations [47]. Simultaneously, ecological network restoration efforts are anticipated to incentivize enhanced collaboration within social networks [48,49]. Secondly, while the ecological network context predominantly limits the rapid diffusion of economic networks, it is essential for global economic networks to align with ecological network structures [50]. Additionally, maintaining a robust ecological network structure facilitates the establishment of new economic networks regionally [51].
To summarize, the consistency in theoretical connotations supports our research on RNR from a synergistic development perspective. At the same time, the results of the empirical research also emphasize that we must link network types to the social, economic, and ecological contexts that exist in a given area to effectively understand the connotation of synergistic development.

3. Evaluation Methods and Simulation Prediction of Rural Network Resilience

3.1. Evaluation Methods

The resilience evaluation of rural networks often commences with analyzing the fundamental structure of the network, drawing insights from the research accomplishments in graph theory and complex network science [52,53,54]. This evaluation typically involves assessing the characteristics and distribution features of nodes and edges, as well as examining the local (module) and overall properties of the resultant network. Indicators are developed based on the concept of “degree” to investigate node attributes such as centrality and mediation [55]. Furthermore, the analysis is extended to encompass degree distribution [56], degree correlation [57], hierarchy [58,59], and homogeneity [60]. Similarly, for network edges, indicators based on “flow” are formulated to assess characteristics like length and density [61], which are further expanded to include connectivity [62], stability [63], and redundancy [64], among others. Researchers also consider the clustering characteristics of nodes and edges within local or global networks to determine the presence of typical modules or small-world structures that may introduce imbalances into the network [65,66]. Through expanding the analysis to three-dimensional models and from single-layer to multi-layer approaches [67,68], scholars have advanced the understanding of complex, open, and authentic rural regional systems, offering viable methodologies for comprehending and scrutinizing these intricate networks.
The evaluation of RNR needs to consider both static and dynamic measurements simultaneously, as resilience is a process concept that encompasses static structural features and dynamic evolutionary potential. Zhou and Hou linked the independence, synergy, correlation, interactivity, and stability in static resilience evolution to various elements of network structure measurement, such as degree distribution, structural holes, mediation, clustering coefficients, and k-cores [69,70]. Furthermore, they associated the buffering, self-organization, and resilience in dynamic resilience evolution with the relative size, structural entropy, and network efficiency of the largest components in network evolution measurement. This approach allows for the construction of a resilient relationship between regional systems and complex networks. Ultimately, Zhou and Hou’s framework offers a standardized and practical research solution for analyzing RNR, providing a convenient methodology for researchers in this field.

3.2. Simulation Prediction

Predicting and simulating the evolution process of a network serves as a vital method for revealing network structural vulnerabilities and establishing early warning and emergency strategies [58]. Quantitative indicators can be employed to assess network structure characteristics, enabling the correlation of different risk shocks with alterations in network structure. Furthermore, it allows for an examination of how changes in network performance affect resilience. The research focuses on two main components: risk and network. The predictive simulation considers potential changes in the network and the potential impact that it could encounter. The analysis is conducted from both perspectives concurrently (Table 3).
The focus shifts from potential changes in the network to assessing whether the altered network can maintain strong resilience. Random node deletion has minimal impact on RNR due to the clustering effect that bolsters the network structure. Conversely, intentional attacks on critical nodes can significantly compromise resilience [14,58,69]. The addition of nodes or adjustment of attributes, however, can strengthen rural networks by mitigating structural vulnerabilities or introducing new functionalities [71]. In terms of connections, boosting node participation rates, enhancing connections, and increasing network redundancy are proven methods to augment RNR [72,73].
Subsequently, attention turns to the potential ramifications on the network, emphasizing the ability of the impaired network to swiftly recoup resilience. Changes in the geographical environment, such as gradual environmental degradation or sudden natural disasters, deeply affect vulnerable rural regions. Environmental degradation leads to diminished biodiversity and hampers agricultural productivity, necessitating the development of environmental restoration strategies and production recovery plans [74]. Tailored policies that consider spatial conditions present a rational approach to fortifying RNR. For instance, in scenarios involving unexpected earthquakes and landslides, it is critical to account for factors like resource accessibility, functional status, and response time in the affected areas when devising targeted solutions [75].
However, there is a significant gap between the prediction and simulation of RNR and the actual situation, with several key reasons contributing to this disparity. Firstly, the continuous evolution of the network results in different changes occurring during the early, middle, and late stages of risk shocks. Secondly, the risks faced by the network are not isolated scenarios but rather complex situations that have cascading effects on various levels, such as society, economy, and ecology. Additionally, the mapping of nodes and edges in the network to entities and relationships in the real world may present challenges when implementing operations such as addition, deletion, and modification. As a result, further research is needed to delve into the stages of network evolution, the variety of risk shocks, and the practicality of network adjustments to bridge this gap effectively.

4. Influencing Factors and Evolutionary Mechanisms of Rural Network Resilience

4.1. Influencing Factors

Resilience, as a key element in understanding the evolution of complex systems, is influenced by various factors both internal and external to the system. These factors encompass core attributes as well as application scenarios, thus highlighting the intricate nature of resilience. It is essential to align each reference frame with the particular rural development challenges encountered, requiring a connection between the network type and the unique economic, social, cultural, and environmental circumstances prevalent in specific rural settings [1]. Hence, while the network structure is significant, it is equally crucial to consider the development conditions of the region depicted by the network, as these conditions play a pivotal role in shaping regional resilience.
The relationship between network structural features and regional resilience has been widely verified across different fields (Table 1). The network is anchored in specific regions with varying developmental conditions. Managers at the social level face the enduring dilemma of balancing short-term flexibility against long-term stability [74]. Effective policy-making for enhancing regional resilience necessitates a strategic focus on diagnosing preexisting issues and implementing targeted interventions to address specific gaps in the network, rather than resorting to short-sighted measures that unconditionally boost network density [28]. On an economic front, facilitating the transformation of agricultural production through intelligent strategies to adapt to global climate change is proving to be a fruitful approach in elevating crop yield, economic gains, and the overall welfare of farmers [72]. While these strategies may not entirely alleviate the adverse effects of climate challenges, adopters still enjoy a superior quality of life compared to non-adopters. Moreover, at the ecological level, augmenting biodiversity emerges as the most effective strategy for enhancing functional response diversity. Functional diversity, essential for assessing forest resilience to landscape-scale disturbances, underscores the importance of response traits in anticipating and responding to future disruptions [71]. Consequently, the dynamics of regional social systems, transformations in economic development, and ecological modifications all play pivotal roles in shaping regional resilience.

4.2. Evolutionary Mechanisms

In his systematic analysis of the concept of resilience, Folke emphasizes that the release of resilience is not isolated but necessitates the active engagement of participants, organizations, and institutions across various scales and sources [17]. This involves the integration and synthesis of diverse experiences and knowledge, the ability to learn and adapt through change, the capacity to turn crises into opportunities, and the facilitation of innovative pathways aligned with resilience principles. Furthermore, the interconnectedness between human society and the ecosystem underscores the importance of fostering the harmonious functioning and sustainable evolution of the social ecosystem. Consequently, the evolution mechanism of regional resilience is characterized by intricate interactions at different levels and scales, encompassing the collective efforts of individual actors as well as the strategic interventions of larger organizations. Given the complexity of its operational mechanisms, employing diverse research methods tailored to the specific research objectives and content becomes imperative.
Scholars studying the evolution mechanism of RNR generally employ three key strategies. Firstly, a narrative approach is used to vividly depict the process of network formation and resilience release, elucidating the underlying mechanisms through a logical chain. For example, Huang analyzed the transformation of villages in the Yangtze River Delta, highlighting the influence of both bottom-up and top-down measures on rural development [76]. Similarly, Peth and Sakdapolrak compared rural development in Thailand and Germany, demonstrating how cross-border migration can enhance family adaptability by fostering cross-regional connections [77]. Secondly, the use of predictive simulation allows researchers to compare network structure changes in various scenarios, thereby revealing insights into the relationship between resilience inflection points and development stages. For instance, Li et al. found that deliberate attacks on critical nodes could trigger network collapse at the early stages of a risk shock [58]. Finally, scholars select proxy variables and focus on key indicators reflecting regional development performance, such as price fluctuations [74], recovery time [75], and biodiversity [71]. By utilizing common non-linear econometric models and incorporating network structure characteristics as independent variables, researchers aim to analyze the impact of these factors on the targeted proxy variables.
Overall, the network itself is a complex language. Mapping the network to rural regional systems further increases the difficulty of constructing the correlation function between network characteristics and regional background. Therefore, scholars should be guided to include as many factors that affect regional resilience as possible in their research. This will involve attempting to promote the transformation from quantitative to qualitative changes, to find breakthroughs in promoting regional sustainable development.

5. An Analytical Framework for Rural Network Resilience at the County Level

5.1. Rural Network Construction Methodology

This paper presents a framework for analyzing rural network resilience with a focus on county units, but it is essential to note that this framework can be applied beyond just county units. Counties, serving as the primary driver of rural revitalization in China, not only lend themselves well to cross-scale and cross-level research but also provide a foundation for extending the network’s scope towards urban–rural integration [78,79,80]. Scholars conducting research in this area have the flexibility to substitute the county unit with other geographic units and adjust the hierarchical structure from “County-Town-Village” accordingly [81,82]. Therefore, for ease of comprehension, this paper has formulated an analytical framework within the context of China’s rural sustainable development.
To effectively study rural network resilience, it is essential to establish a cohesive rural network. By considering the geographical aspect and emphasizing the spatial arrangement of the rural regional system encompassing “Production Space—Living Space—Ecological Space” [57,83], we can develop three interconnected network levels. These levels consist of the industrial-related network (IRN), the social network (SN), and the landscape connectivity network (LCN). Each network level is interdependent, reflecting the intrinsic connections between the different spaces within the rural environment (Table 4).
Node attributes can be obtained through county statistical data to enable additional research to suit specific research requirements. In the realm of connectivity information, interpersonal communication is an essential aspect that necessitates data collection through on-the-ground investigations. Meanwhile, industrial collaboration can be established by leveraging county industrial project listings, and landscape connectivity can be assessed via the integration of remote sensing images with GIS spatial analysis. In terms of interpersonal networks, the evaluation of connectivity status can be facilitated by adopting the social capital construction approach, which involves gathering data on connections between rural residents and individuals in surrounding towns and villages. These connections may include interactions such as familial relationships, visits, job-related movements, and residential changes. The strength of these connections can be gauged based on the frequency of interactions. Given the challenge in determining the directionality of connectivity in practical scenarios, as well as the prevalent reciprocity in such networks, it is noteworthy that the networks under consideration are all undirected.
In practice, the biggest obstacle to realizing the rural network construction method that fully reflects the intrinsic characteristics and interconnections of the “Production Space—Living Space—Ecological Space” lies in the problem of data matching. The data from remote sensing images used to obtain global land use data is readily available, whereas data for SN and IRN are typically derived from a sampling survey, making it challenging to acquire comprehensive data. Consequently, the data from the three sub-networks cannot be effectively matched. This disparity between data sources poses a significant challenge to implementing the theoretical model of rural network construction.
In light of the challenges outlined above, alternative networks are being explored to potentially replace SN and IRN. One promising option is the transportation network, given that roads are constructed specifically to facilitate economic and social exchanges. These road connections traversing various villages can be leveraged to establish village networks that, to some extent, mirror interpersonal and industrial relationships. Building upon this notion, we can then derive rural networks based on road infrastructure and ecological land coverage, translating these relationships into mathematical representations (Figure 2).

5.2. Tips for Using the Analytical Framework

This article presents a study on RNR characteristics within the geography research paradigm of “Pattern-Process-Mechanism-Optimization” to facilitate a holistic examination at the county-level scale, a framework for analyzing RNR is established, following the sequence of “Concept Definition-Framework Construction-Resilience Measurement-Mechanism Analysis-System Simulation” (Figure 3). The study focuses on society, economy, and ecology as the primary network components, analyzing the risks they encounter. The methodology employed emphasizes a multidimensional analysis across different levels, scales, and periods. Throughout the research process, particular attention is given to ensuring consistency in data, and methods, and establishing linking points between pre- and post-analysis stages.
When using the analytical framework for understanding the complexity of the rural regional system and the specificity of the resilience of the rural network, several key points should be considered:
(1)
The concept of synergistic development is essential. Coordinated efforts among the subsystems of the rural regional system play a crucial role in promoting sustainable development in rural areas. Additionally, the rural network is characterized by its multi-layered structure, incorporating various functional attributes within a quantitative measure of network structure to illustrate the interconnectedness of different subsystems. Therefore, a multi-dimensional approach to promoting synergistic development within rural networks is necessary.
(2)
It is important to note that rural networks are not confined to the traditional “Production Space—Living Space—Ecological Space” framework. Different academic disciplines offer diverse research paradigms for studying sustainable rural development, influencing the choice of network construction based on specific research requirements.
(3)
An analysis of rural network resilience must account for potential risks. Addressing vulnerabilities is fundamental to strengthening the resilience of rural networks and mitigating the impacts of risk events on sustainable rural development. The evaluation of resilience evolution within rural networks should focus on enhancing their ability to withstand risks collectively, rather than solely on individual core nodes.
(4)
Enhancing rural network resilience should be tailored to each stage of local development. This process should not solely prioritize creating exemplary villages or flagship projects but should be approached as a comprehensive, long-term initiative. Considering that some rural areas may not yet recognize the significance of rural network resilience, a gradual and systematic approach to planning resilience enhancement projects over the short, medium, and long-term is advisable to ensure effective progress in enhancing rural network resilience according to local developmental stages.

6. Discussion

Compared with other fields, research on rural resilience research is still in its infancy, with few studies directly aggregating RNR. The continuous extension of the concept of resilience has provided a solid theoretical foundation and rich research tools for rural network resilience research. However, in this emerging field, the important role of the synergistic development perspective in studying RNR has not yet been emphasized. Consequently, some issues need to be discussed in depth.

6.1. Why Emphasize Synergistic Development Perspective?

Promoting the research on RNR towards multi-level and cross-scale transformation is a key step towards further approaching the real world. However, current research focuses more on networks formed at independent levels such as society, economy, and ecology, lacking exploration of the interaction between different systems, which is precisely the basic requirement for collaborative development.
This paper constructs a framework to analyze the RNR by exploring the synergistic relationship among the three subsystems of economy, society, and ecology from the geographic research paradigm of the “Production-Living-Ecological” spaces. The fundamental aim here is to address the challenging question of whether these subsystems are truly synergistic and to what extent they have developed synergistically at a theoretical level. Presently, it is evident that there will be uneven development among the subsystems, with economic priorities likely dominating in underdeveloped villages for a relatively long period. However, the crux of synergistic development lies in establishing order, which arises from the mutual constraints imposed by the subsystems. Highlighting the perspective of synergistic development underscores the importance of understanding the performance and mechanisms involved in creating an orderly structure amidst disequilibrium. Thus, it is imperative that the research direction on RNR shifts focus from solely examining its structural characteristics and evolutionary patterns to investigating the interactive ordering characteristics and constraints within the system.

6.2. Why Emphasize Multi-Layer Network Coupling?

The multi-level and cross-scale research, guided by the perspective of collaborative development, has set forth higher requirements for the research methods of RNR. Despite the presence of systematic evaluation methods for the coupling process of multi-layer networks, its applicability in the study of RNR has not yet been explored. Hence, questions arise such as how to integrate the status of nodes in different networks, whether the superposition of connections in different networks entails a sum of weights or a formation of multiple paths, how to distinguish the contributions of different networks to resilience improvement, and how to evaluate the amplification effect engendered by collaborative development.
In our analytical framework of RNR, we aim to establish a multi-layer network relationship by conducting multi-dimensional comparisons (including structure, layout, and trend analysis) and categorizing villages based on hierarchical levels and spatial status. Through this approach, we endeavor to fully realize the potential value of each node within the network. Particularly noteworthy is the heightened importance attributed to certain villages with characteristics such as small populations, remote locations, and poor economies, but possessing exceptional ecological value, as revealed in the results of our multi-layer network coupling analysis. This analytical shift from the conventional “Elite Capture” strategy, which prioritizes the development of “Star Villages” to a more holistic perspective underscores the significance of uncovering the unique roles and statuses of all villages within the network. This transformation in approach not only reflects a more nuanced understanding of rural development dynamics but also underscores the critical role of utilizing network perspectives in promoting rural sustainable development.

6.3. Why Emphasize the Practical Significance and Application Value of Rural Network Resilience Enhancement?

Improving the practical significance and application value of research on RNR is crucial. This is because the value of RNR extends beyond providing stable support for rural development. It also involves identifying development shortcomings and formulating response plans for risk shocks. While current research uses various methods for prediction and simulation, there are shortcomings in the evaluation approach.
One notable shortcoming is the failure to consider the diversity of rural development. The evaluation results tend to focus on the overall situation, neglecting the spatial differences in the multifunctional transformation of rural development. Previous discussions on multi-layer network coupling have highlighted that each network node holds the potential to serve as a key node, a potential stemming from the diversity inherent in rural development.
Another critical oversight is the lack of consideration for the diverse nature of risk shocks that rural areas face in reality. These risks range from phased economic recessions, and cumulative social contradictions, to sudden natural disasters. Neglecting to account for these diverse risks in scenario simulations hinders the accurate understanding of the transmission paths and mechanisms of risks within rural networks.
The last one is the neglect of local practicality. The theoretical simulations are crucial for the process, but practical experimentation is equally essential. Without a robust guarantee mechanism, enhancing RNR effectively is unattainable. Even when specific feasible conditions exist, it is vital to consider the progression and continuity of the enhancement pathway, avoiding excessive overreach.
Consequently, the analytical framework for RNR incorporates optimization ideas, synergistic measures, and guarantee mechanisms in the final enhancement path. The primary objective is to consistently blend the contextual realities of local development with the quantifiable results of theoretical analysis, maximizing the application value of RNR enhancement.

7. Conclusions and Inspirations

7.1. Conclusions

Enhancing rural network resilience (RNR) is a fundamental prerequisite for promoting sustainable development and rural revitalization. This paper has provided a comprehensive review of RNR, synthesizing theoretical foundations, evaluation methods, simulation techniques, influencing factors, and evolutionary mechanisms.
(1)
In the context of sustainable rural development, networked language offers valuable tools for analyzing the mechanism and path towards progress. However, to effectively study rural network resilience—a concept resulting from the amalgamation of network resilience and regional resilience—it is imperative to redefine the research object, content, and objectives. Among them, the basic requirements for considering the above issues are cross-time, cross-scale, and cross-space; the basic idea is to consider rural sustainable development in a holistic manner; and the basic goal is to make the improvement measures practicable and feasible.
(2)
The synergistic development perspective is essential in advancing the transformation of rural network resilience research across different time frames, scales, and levels. This perspective not only guides the quantification of RNR characteristics and the exploration of strategies for enhancement but also introduces novel concepts for comprehensive rural development strategy transformation. At its foundation lies the recognition of the existing disparities in rural development, while at its core lies the emphasis on order and constraints.
(3)
Considering the difficulty of obtaining data and information in rural areas, the current method of constructing a rural network, which takes administrative villages as nodes and connects them with roads and ecological coverage, is a relatively feasible way to face the county scale (a large area that usually contains hundreds of villages). The complexity of the rural territorial system determines that the construction of a rural network needs to consider the research needs and objectives.
(4)
The analytical framework provides a standardized and operable technical route for the systematic study of RNR. When using it, one should always adhere to the theory of synergistic development, fully tap the burgeoning potential of multifunctional transformation and development of the countryside, and consistently consider the realistic background of rural development.

7.2. Inspirations

The introduction of resilience into the study of rural sustainable development faces several challenges despite a basic consensus on its core attributes due to its relatively abstract formulation. One of the most crucial difficulties is defining the research object and conceptual connotation of RNR. This paper, adopting a synergistic development perspective, addresses the challenges of rural development and extends them to the study of RNR. The focus lies on economic, social, and ecological macro-levels, with selected network linkages pertaining to simple road and land associations. However, the empirical study reveals a broad and subtle array of sources for rural networks. For instance, economic linkages encompass employment relationships, production cooperation, and cross-regional investments, raising the question of which is the most representative. Scholars may make different choices based on their research needs, risking potential deviations from the core attributes of resilience.
To address the challenges mentioned above, it is proposed in this paper that early-stage research on RNR should include an examination of the “5Ws” framework. The “5Ws” encompass a set of crucial questions in RNR research, namely “whose resilience”, “what resilience”, “when resilience”, “where resilience”, and “why resilience”. These questions serve to not only identify the focal point of the research but also elucidate the multifaceted nature of resilience. Moreover, they highlight the significance of spatial and temporal considerations, thus enabling researchers to better delineate the scope of their study and gauge the relevance of their findings in the context of RNR. By integrating these key questions into the research process, scholars can enhance their understanding and analysis of the resilience dynamics within rural networks.

Author Contributions

Conceptualization, C.Y. and J.G.; methodology, C.Y.; resources, C.Y. and Z.Z.; writing—original draft preparation, C.Y. and Z.Z.; writing—review and editing, C.Y. and J.G.; visualization, C.Y. and Z.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China (42371225); the Key R&D and Promotion Project (Soft Science Research) in Henan Province, China (242400410100, 222400410203); and the Social Science Planning Program in Henan Province, China (2023BSH021).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

As a review article, the references cited in the article are all from publicly published papers.

Acknowledgments

The authors gratefully acknowledge all funding and the Nanhu Scholars Program for Young Scholars of XYNU for their support in this research. We would like to express our thanks to the editor and all anonymous reviewers for their insightful comments and suggestions.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Murdoch, J. Networks—A New Paradigm of Rural Development? J. Rural Stud. 2000, 16, 407–419. [Google Scholar] [CrossRef]
  2. Camagni, R.; Diappi, L.; Stabilini, S. City Networks: An Analysis of the Lombardy Region in Terms of Communication Flows; Aldershot: Avebury, UK, 1994. [Google Scholar]
  3. Chen, X.; Zhou, J.; Xing, L.; Wang, H.; Lu, J. Spatiotemporal Evolution and Driving Factors of the Coupling Coordination between County Land Urbanization and Grain Production: The Case of Jiangsu Province, China. Front. Ecol. Evol. 2023, 11, 1179551. [Google Scholar] [CrossRef]
  4. Zeng, Y.; Guo, H.; Yao, Y.; Huang, L. The Formation of Agricultural E-Commerce Clusters: A Case from China. Growth Chang. 2019, 50, 1356–1374. [Google Scholar] [CrossRef]
  5. Liu, S.; Xia, Y.; Ji, Y.; Lai, W.; Li, J.; Yin, Y.; Qi, J.; Chang, Y.; Sun, H. Balancing Urban Expansion and Ecological Connectivity through Ecological Network Optimization—A Case Study of ChangSha County. Land 2023, 12, 1379. [Google Scholar] [CrossRef]
  6. Yang, R.; Wen, Q.; Wang, C.; Du, G.; Li, B.; Qu, Y.; Li, H.; Xu, J.; He, Y.; Ma, L.; et al. Discussions and Thoughts of the Path to China’s Rural Revitalization in the New Era: Notes of the Young Rural Geography Scholars. J. Nat. Resour. 2019, 34, 890–910. (In Chinese) [Google Scholar] [CrossRef]
  7. Philip, L.; Wilson, R.; Currie, M.; Duffy, P. Rural Transformations, Rural Futures: Introduction to Theme Section. Scott. Geogr. J. 2024, 140, 7–11. [Google Scholar] [CrossRef]
  8. Yu, J.; Zhang, J.; Zhou, M.; Cai, W. Impact of COVID-19 on the Comprehensive Resilience of Rural Areas—A Case Study of Jilin Province of China. Sustainability 2023, 15, 3152. [Google Scholar] [CrossRef]
  9. Gajić, T.; Đoković, F.; Blešić, I.; Petrović, M.D.; Radovanović, M.M.; Vukolić, D.; Mandarić, M.; Dašić, G.; Syromiatnikova, J.A.; Mićović, A. Pandemic Boosts Prospects for Recovery of Rural Tourism in Serbia. Land 2023, 12, 624. [Google Scholar] [CrossRef]
  10. Hutton, N.S. Sustaining Resilience: Modeling Nonprofit Collaboration in Recovery. Prof. Geogr. 2018, 70, 655–665. [Google Scholar] [CrossRef]
  11. Flood, K.; Mahon, M.; McDonagh, J. Everyday Resilience: Rural Communities as Agents of Change in Peatland Social-Ecological Systems. J. Rural Stud. 2022, 96, 316–331. [Google Scholar] [CrossRef]
  12. Kocur-Bera, K. A Safe Space of Rural Areas in the Context of the Occurrence of Extreme Weather Events—A Case Study Covering a Part of the Euroregion Baltic. Land Use Policy 2018, 71, 518–529. [Google Scholar] [CrossRef]
  13. Shrestha, J.K.; Pudasaini, P.; Mussone, L. Rural Road Network Performance and Pre-Disaster Planning: An Assessment Methodology Considering Redundancy. Transp. Plan. Technol. 2021, 44, 726–743. [Google Scholar] [CrossRef]
  14. Wang, D.; Ji, X.; Jiang, D.; Liu, P. Importance Assessment and Conservation Strategy for Rural Landscape Patches in Huang-Huai Plain Based on Network Robustness Analysis. Ecol. Inform. 2022, 69, 101630. [Google Scholar] [CrossRef]
  15. Holling, C.S. Resilience and Stability of Ecological Systems. Annu. Rev. Ecol. Syst. 1973, 4, 1–23. [Google Scholar] [CrossRef]
  16. Folke, C. Resilience: The Emergence of a Perspective for Social–Ecological Systems Analyses. Glob. Environ. Chang. 2006, 16, 253–267. [Google Scholar] [CrossRef]
  17. Folke, C. Resilience (Republished). Ecol. Soc. 2016, 21, 44. [Google Scholar] [CrossRef]
  18. Martin, R. Regional Economic Resilience, Hysteresis and Recessionary Shocks. J. Econ. Geogr. 2012, 12, 1–32. [Google Scholar] [CrossRef]
  19. Keck, M.; Sakdapolrak, P. What Is Social Resilience? Lessons Learned and Ways Forward. Erdkunde 2013, 67, 5–19. [Google Scholar] [CrossRef]
  20. Adger, W.N. Social and Ecological Resilience: Are They Related? Prog. Hum. Geogr. 2000, 24, 347–364. [Google Scholar] [CrossRef]
  21. Meerow, S.; Newell, J.P. Urban Resilience for Whom, What, When, Where, and Why? Urban Geogr. 2019, 40, 309–329. [Google Scholar] [CrossRef]
  22. McManus, P.; Walmsley, J.; Argent, N.; Baum, S.; Bourke, L.; Martin, J.; Pritchard, B.; Sorensen, T. Rural Community and Rural Resilience: What Is Important to Farmers in Keeping Their Country Towns Alive? J. Rural Stud. 2012, 28, 20–29. [Google Scholar] [CrossRef]
  23. Li, Y. A Systematic Review of Rural Resilience. China Agric. Econ. Rev. 2022, 15, 66–77. [Google Scholar] [CrossRef]
  24. Zhang, X.; Miller-Hooks, E.; Denny, K. Assessing the Role of Network Topology in Transportation Network Resilience. J. Transp. Geogr. 2015, 46, 35–45. [Google Scholar] [CrossRef]
  25. Janssen, M.A.; Bodin, O.; Anderies, J.M.; Elmqvist, T.; Ernstson, H.; McAllister, R.R.J.; Olsson, P.; Ryan, P. Toward a Network Perspective of the Study of Resilience in Social-Ecological Systems. Ecol. Soc. 2006, 11, 15. [Google Scholar] [CrossRef]
  26. Huggins, R.; Thompson, P. A Network-Based View of Regional Growth. J. Econ. Geogr. 2014, 14, 511–545. [Google Scholar] [CrossRef]
  27. Li, Z.; Yan, W. Service Flow Changes in Multilayer Networks: A Framework for Measuring Urban Disaster Resilience Based on Availability to Critical Facilities. Landsc. Urban Plan. 2024, 244, 104996. [Google Scholar] [CrossRef]
  28. Crespo, J.; Suire, R.; Vicente, J. Lock-in or Lock-out? How Structural Properties of Knowledge Networks Affect Regional Resilience. J. Econ. Geogr. 2014, 14, 199–219. [Google Scholar] [CrossRef]
  29. Alain N’Ghauran, K.; Autant-Bernard, C. Effects of Cluster Policies on Regional Inventor Networks: Evidence from France. Reg. Stud. 2022, 56, 1903–1918. [Google Scholar] [CrossRef]
  30. Wrobel, M. “One for All and All for One”: Cluster, Employment, and the Global Economic Crisis. Evidence from the German Mechanical Engineering Industry. Pap. Reg. Sci. 2015, 94, 273–294. [Google Scholar] [CrossRef]
  31. Duenas, M.; Campi, M. Regional Resilience during COVID-19: Evidence from Colombian Exporting Clusters. Reg. Stud. 2024, 1–17. [Google Scholar] [CrossRef]
  32. Auerbach, J.; Kim, H. Measuring Robustness and Coverage of Transportation Networks with Multiple Routes and Hubs. Ann. Am. Assoc. Geogr. 2022, 112, 1741–1760. [Google Scholar] [CrossRef]
  33. Parsons, L. Giving as a Total Social Phenomenon: Towards a Human Geography of Reciprocity. Geogr. J. 2019, 185, 142–155. [Google Scholar] [CrossRef]
  34. Morse, C. The Multifunctionality of Country Stores: Insights on Resilience from Rural Vermont. Geogr. Rev. 2018, 108, 457–475. [Google Scholar] [CrossRef]
  35. Wyss, R.; Luthe, T.; Abegg, B. Building Resilience to Climate Change—The Role of Cooperation in Alpine Tourism Networks. Local Environ. 2015, 20, 908–922. [Google Scholar] [CrossRef]
  36. Faiyetole, A.A.; Adewumi, V.A. Urban Expansion and Transportation Interaction: Evidence from Akure, Southwestern Nigeria. Environ. Plan. B Urban Anal. City Sci. 2024, 51, 57–74. [Google Scholar] [CrossRef]
  37. Li, Y.; Chen, N.; Sullivan, A.; Chen, S.; Qin, X. Changing Collaborative Networks and Transitions in Rural Sustainable Development: Qualitative Lessons from Three Villages in China. Ecol. Soc. 2023, 28, 2. [Google Scholar] [CrossRef]
  38. Zhu, X.; Chen, Q.; Chen, X.; Cheng, X.; Wang, Y. How Industry Integration Improves the Eco-Efficiency of the Textile Industry: The Case of Qun-Yi Village in China. J. Clean. Prod. 2024, 438, 140490. [Google Scholar] [CrossRef]
  39. Liu, X.; Ma, F.; Guo, T.; Ding, Z. Spatial Pattern of China’s Rural Digital Economy Based on Subjective-Objective Evaluation: Evidence from 2085 Counties. PLoS ONE 2024, 19, e0292249. [Google Scholar] [CrossRef] [PubMed]
  40. Li, C.; Wei, W.; Duan, Y.; Feng, Z.; Feng, X.; Zhu, C.; An, P. Exploration of the Regional Correlation and Network Structure Characteristics of Land Prices: A Case Study of Hebei, China. Front. Environ. Sci. 2023, 10, 1056042. [Google Scholar] [CrossRef]
  41. Gao, J.; Sun, J.; Yu, C.; Ma, Z. Theoretical connotation and quantitative measurement of county synergy development capability. Geogr. Res. 2023, 42, 1465–1479. (In Chinese) [Google Scholar]
  42. Wang, J.-L.; Liu, B.; Zhou, T. The Category Identification and Transformation Mechanism of Rural Regional Function Based on SOFM Model: A Case Study of Central Plains Urban Agglomeration, China. Ecol. Indic. 2023, 147, 109926. [Google Scholar] [CrossRef]
  43. Tan, X.; Wang, Z.; An, Y.; Wang, W. Types and Optimization Paths Between Poverty Alleviation Effectiveness and Rural Revitalization: A Case Study of Hunan Province, China. Chin. Geogr. Sci. 2023, 33, 966–982. [Google Scholar] [CrossRef]
  44. Woodhouse, A. Social Capital and Economic Development in Regional Australia: A Case Study. J. Rural Stud. 2006, 22, 83–94. [Google Scholar] [CrossRef]
  45. Hendrickson, M.K.; Massengale, S.H.; Cantrell, R. “No Money Exchanged Hands, No Bartering Took Place. But It’s Still Local Produce”: Understanding Local Food Systems in Rural Areas in the US Heartland. J. Rural Stud. 2020, 78, 480–490. [Google Scholar] [CrossRef]
  46. Chen, Z.; Barcus, H.R. The Rise of Home-Returning Women’s Entrepreneurship in China’s Rural Development: Producing the Enterprising Self through Empowerment, Cooperation, and Networking. J. Rural Stud. 2024, 105, 103156. [Google Scholar] [CrossRef]
  47. Wang, W.; Zhao, X.; Li, H.; Zhang, Q. Will Social Capital Affect Farmers’ Choices of Climate Change Adaptation Strategies? Evidences from Rural Households in the Qinghai-Tibetan Plateau, China. J. Rural Stud. 2021, 83, 127–137. [Google Scholar] [CrossRef]
  48. Xiao, M. Environmental Communication on Twitter: The Impact of Source, Bandwagon Support, and Message Valence on Target Audiences. Sustainability 2023, 15, 14732. [Google Scholar] [CrossRef]
  49. Xu, Z.; Li, Y.; Wang, C.; Shan, J. Social Capital and Environmentally Friendly Behaviors. Environ. Sci. Policy 2024, 151, 103612. [Google Scholar] [CrossRef]
  50. Yang, Y.; Zhou, X.; Xu, J.; Wang, H.; Liu, L.; Cao, W. Coupling Mutual Information into Ecological Networks to Analyze the Sustainability of Water-Energy Nexus: A Case Study of Yangtze River Economic Belt. J. Clean. Prod. 2024, 448, 141705. [Google Scholar] [CrossRef]
  51. Vigano, F.; England, L.; Comunian, R. Connecting Craft, Design and the Wood Industry in South Tyrol: From Clusters to Creative Ecosystem. J. Rural Stud. 2023, 104, 103149. [Google Scholar] [CrossRef]
  52. Xu, X. Studying Social Networks in the Age of Computational Social Science. EPJ Data Sci. 2023, 12, 61. [Google Scholar] [CrossRef]
  53. Liu, G.; Tian, T.; Huang, C.; Mou, B. Complexity of Networks: A Structural Heterogeneity Approach. EPL 2023, 143, 31001. [Google Scholar] [CrossRef]
  54. Gul, H.; Al-Obeidat, F.; Amin, A.; Moreira, F. Analyzing Complex Networks: Extracting Key Characteristics and Measuring Structural Similarities. Expert Syst. 2024, 41, e13470. [Google Scholar] [CrossRef]
  55. Zhou, B.; Holme, P.; Gong, Z.; Zhan, C.; Huang, Y.; Lu, X.; Meng, X. The Nature and Nurture of Network Evolution. Nat. Commun. 2023, 14, 7031. [Google Scholar] [CrossRef] [PubMed]
  56. Li, S.; Guo, H.; Sun, Z.; Liu, Z.; Jiang, H.; Zhang, H. Patterns of Typical Chinese Urban Agglomerations Based on Complex Spatial Network Analysis. Remote Sens. 2023, 15, 920. [Google Scholar] [CrossRef]
  57. Xu, H.; Zhao, J.; Yu, X.; Mei, X.; Zhang, X.; Yan, C. A Model Assembly Approach of Planning Urban-Rural Transportation Network: A Case Study of Jiangxia District, Wuhan, China. Sustainability 2023, 15, 11876. [Google Scholar] [CrossRef]
  58. Li, J.; Ma, H.; Cao, Y.; Chu, J.; Ming, M. Assessment of Village Resilience in Flood Storage Areas Based on Complex Networks: From the Perspective of Safety and Low-Carbon Development. Indoor Built Environ. 2023, 32, 1727–1743. [Google Scholar] [CrossRef]
  59. Wang, Y.; Zhao, P.; Xie, S.; Zhang, W. Mesoscale Structure in Urban-Rural Mobility Networks in the Pearl River Delta Area: A Weighted Stochastic Block Modeling Analysis. ISPRS Int. J. Geo-Inf. 2023, 12, 183. [Google Scholar] [CrossRef]
  60. Wang, R.; Su, H.; Xu, T.; Jiang, W.; Liu, H.; Wang, W.; Chen, C.; Ma, X.; Chen, Y.; Wang, W. The Association between Urbanization and Depression in the Elderly: A Network Analysis from the Complexity Science Perspective. J. Affect. Disord. 2024, 356, 72–79. [Google Scholar] [CrossRef]
  61. Eshghali, M.; Krokhmal, P.A. Risk-Averse Optimization and Resilient Network Flows. Networks 2023, 82, 129–152. [Google Scholar] [CrossRef]
  62. Cheng, Y.; Liu, H.; Yi, Y. Constructing Ecological Corridors in Response to Extensive Rural Sprawl in a Highly Developed Urban Agglomeration. Urban Ecosyst. 2024, 27, 335–347. [Google Scholar] [CrossRef]
  63. Li, M.; Chen, L.; Zhao, F.; Tang, J.; Bu, Q.; Wang, X.; Yang, L. Effects of Urban-Rural Environmental Gradient on Soil Microbial Community in Rapidly Urbanizing Area. Ecosyst. Health Sustain. 2023, 9, 0118. [Google Scholar] [CrossRef]
  64. Mitchell, M.; Hendricks, J.; Schatt, D. Road Network Analyses Elucidate Hidden Road Flooding Impacts under Accelerating Sea Level Rise. Front. Environ. Sci. 2023, 11, 1083282. [Google Scholar] [CrossRef]
  65. Liu, W.; Chen, R. Migration Networks Pattern of China’s Floating Population from the Perspective of Complex Network. Chin. Geogr. Sci. 2024, 34, 327–341. [Google Scholar] [CrossRef]
  66. Tao, J.; Bai, W.; Peng, R.; Wu, Z. Sustainable Regional Straw Utilization: Collaborative Approaches and Network Optimization. Sustainability 2024, 16, 1557. [Google Scholar] [CrossRef]
  67. Wang, Y.; Cheng, S.; Cheng, Z.; Cheng, Y. Exploring the Construction and Analysis Method of Landscape Spatial Structure Based on Complex Networks. Environ. Plan. B Urban Anal. City Sci. 2024, 51, 745–762. [Google Scholar] [CrossRef]
  68. De Domenico, M.; Solé-Ribalta, A.; Cozzo, E.; Kivelä, M.; Moreno, Y.; Porter, M.A.; Gómez, S.; Arenas, A. Mathematical Formulation of Multilayer Networks. Phys. Rev. X 2013, 3, 041022. [Google Scholar] [CrossRef]
  69. Zhou, J.; Hou, Q. Complex Network-Based Research on the Resilience of Rural Settlements in Sanshui Watershed. Land 2021, 10, 1068. [Google Scholar] [CrossRef]
  70. Zhou, J.; Hou, Q. Resilience Assessment and Planning of Suburban Rural Settlements Based on Complex Network. Sustain. Prod. Consump. 2021, 28, 1645–1662. [Google Scholar] [CrossRef]
  71. Aquilué, N.; Filotas, É.; Craven, D.; Fortin, M.; Brotons, L.; Messier, C. Evaluating Forest Resilience to Global Threats Using Functional Response Traits and Network Properties. Ecol. Appl. 2020, 30, e02095. [Google Scholar] [CrossRef]
  72. Bazzana, D.; Foltz, J.; Zhang, Y. Impact of Climate Smart Agriculture on Food Security: An Agent-Based Analysis. Food Policy 2022, 111, 102304. [Google Scholar] [CrossRef]
  73. Chen, C.; Meurk, C.D.; Jia, Z.; Lv, M.; Wu, S.; Jia, J. Incorporating Landscape Connectivity into Household Pond Configuration in a Hilly Agricultural Landscape. Landsc. Ecol. Eng. 2017, 13, 189–204. [Google Scholar] [CrossRef]
  74. Schouten, M.; Opdam, P.; Polman, N.; Westerhof, E. Resilience-Based Governance in Rural Landscapes: Experiments with Agri-Environment Schemes Using a Spatially Explicit Agent-Based Model. Land Use Pol. 2013, 30, 934–943. [Google Scholar] [CrossRef]
  75. Aydin, N.Y.; Duzgun, H.S.; Heinimann, H.R.; Wenzel, F.; Gnyawali, K.R. Framework for Improving the Resilience and Recovery of Transportation Networks under Geohazard Risks. Int. J. Disaster Risk Reduct. 2018, 31, 832–843. [Google Scholar] [CrossRef]
  76. Huang, H. Learning from Exploratory Rural Practices of the Yangtze River Delta in China: New Initiatives, Networks and Empowerment Shifts, and Sustainability. J. Rural Stud. 2020, 77, 63–74. [Google Scholar] [CrossRef]
  77. Peth, S.A.; Sakdapolrak, P. Resilient Family Meshwork. Thai-German Migrations, Translocal Ties, and Their Impact on Social Resilience. Geoforum 2020, 114, 19–29. [Google Scholar] [CrossRef]
  78. Wei, L.; Lin, X.; Lu, Y.; Sun, J. Rural Territorial Types in Urban and Rural Integrated Areas Taking Jiangsu Province in China as an Example. Environ. Dev. Sustain. 2024, 26, 18903–18928. [Google Scholar] [CrossRef]
  79. Yu, C.; Han, Z.; Gao, J.; Zheng, Q.; Zhang, X.; Gao, H. Mechanisms of Rural Sustainable Development Driven by Land Use Restructuring: A Perspective of “Scale-Space” Interactions. Sustainability 2023, 15, 12600. [Google Scholar] [CrossRef]
  80. Yu, C.; Sun, J.; Gao, J. Organic Connection between Poverty Alleviation and Rural Revitalization from the Perspective of Government Public Information—A Big Data Analysis based on 53 Poor Counties in Henan Province. J. Xinyang Norm. Univ. (Philos. Soc. Sci. Edit.) 2021, 41, 38–44. (In Chinese) [Google Scholar]
  81. Zhang, X.; Sun, L.; Shao, Z.; Zhou, X. Accessibility Evaluation of Public Service Facilities in Villages and Towns Based on POI Data: A Case Study of Suining County, Xuzhou, China. J. Urban Plan. Dev. 2023, 149, 05023019. [Google Scholar] [CrossRef]
  82. Yu, C.; Gao, J.; Han, Y.; Wang, Y.; Sun, J. Eliminating Deprivation and Breaking through Dependence: A Mechanism to Help Poor Households Achieve Sustainable Livelihoods by Targeted Poverty Alleviation Strategy. Growth Chang. 2022, 53, 1436–1456. [Google Scholar] [CrossRef]
  83. Yu, H.; Du, S.; Zhang, J.; Chen, J. Spatial Evolution and Multi-Scenario Simulation of Rural “Production-Ecological-Living” Space: A Case Study for Beijing, China. Sustainability 2023, 15, 1844. [Google Scholar] [CrossRef]
Sustainability 16 05850 g001
Figure 1. Schematic diagram of the evolutionary process of RNR.
Figure 1. Schematic diagram of the evolutionary process of RNR.
Sustainability 16 05850 g001
Sustainability 16 05850 g002
Figure 2. Schematic diagram of rural network construction.
Figure 2. Schematic diagram of rural network construction.
Sustainability 16 05850 g002
Sustainability 16 05850 g003
Figure 3. An analytical framework for RNR at the county level.
Figure 3. An analytical framework for RNR at the county level.
Sustainability 16 05850 g003
Table 1. Relationship between network resilience and regional resilience.
Table 1. Relationship between network resilience and regional resilience.
ConnotationStructure CharacteristicsExplanationReferences
ResilienceDegree DistributionHighly interconnected core nodes can enhance network functionality and resilience against external shocks.Crespo et al., 2014 [28]
Small WorldThe small world network structure combines close local connections with short global paths and demonstrating elastic properties.Alain N’Ghauran et al., 2022 [29]
AdaptabilityModular StructureA modular structure allows the network to function properly despite disturbances or malfunctions in specific modules.Wrobel, 2015 [30];
Duenas et al., 2024 [31]
ConnectivityThe network’s overall connectivity ensures uninterrupted connectivity during local failures, enabling the network to remain globally connected and adaptable despite potential node or edge failures.Li et al., 2024 [27];
Auerbach et al., 2022 [32]
TransformationKey Node IdentificationNodes with high centrality may play a critical role in shaping network transformations.Parsons, 2019 [33];
Morse, 2018 [34]
Core–Periphery StructureCore nodes are essential for the network’s critical tasks and functions, while edge nodes are crucial for adapting to network changes.Wyss et al., 2015 [35];
Faiyetole et al., 2024 [36]
Table 2. Interrelationships between different networks in a synergistic development perspective.
Table 2. Interrelationships between different networks in a synergistic development perspective.
Network InteractionInterrelationshipsComparisonsReferences
Social Networks
vs.
Economic Networks
  • Social Networks Promote Economic Network Generation
  • Economic Networks Strengthen Social Network Structure
  • Structural Similarity
  • Evolutionary Synchronization
  • Spatial Convergence
Woodhouse, 2006 [44];
Hendrickson et al., 2020 [45];
Chen et al., 2024 [46]
Social Networks
vs.
Ecological Networks
  • Social Networks Accommodate Ecological Network Changes
  • Ecological Networks Improve Social Network Structure

  • Structural Complementarity
  • Evolutionary Asynchrony
  • Spatial Interlacing
Wang et al., 2021 [47];
Xiao, 2023 [48];
Xu et al., 2023 [49]
Economic Networks
vs.
Ecological Networks
  • Ecological Networks Constrain Economic Networks Extension
  • Economic Network Follows Ecological Network Structure
  • Structural Coupling
  • Evolutionary Concession
  • Spatial Exclusivity
Yang et al., 2024 [50];
Vigano et al., 2023 [51]
Table 3. Comparison of network prediction simulation methods.
Table 3. Comparison of network prediction simulation methods.
Predictive OrientationTarget AudienceScenario SettingAdjusting RulesReferences
Network ChangesNode ReductionRandom ModeRandomly delete nodes.Wang et al., 2022 [14];
Li et al., 2023 [58];
Zhou et al., 2021a [69]
Extreme ModeSort by node attributes and delete them one by one.
Specify ModeDelete specified nodes by specific impact.
Node IncreaseLeak FillingAdd nodes at specific locations.Aquilué et al., 2020 [71]
Node ModificationFunctional DevelopmentEnrich specific node functions.
Connection ProbabilityParticipation ProbabilityConstruct different networks based on node participation probability.Bazzana et al., 2022 [72]
Spread ProbabilityEstablish spread probability based on node distance.Chen et al., 2017 [73]
Impact EffectsEnvironmental DeteriorationEnvironmental CompensationComparison of schemes based on fixed or differentiated compensation.Schouten et al., 2013 [74]
Natural CalamitiesRoad RestorationComparison of schemes considering proximity, hierarchy, and timeliness.Aydin et al., 2018 [75]
Table 4. Node and edge setup for rural networks.
Table 4. Node and edge setup for rural networks.
CompositionSettingType
SNIRNLCN
NodeNode SelectionWith the cross-scale and cross-level structure of “County-Town-Village” at different geographic units, points of village-level entities are the basic nodes of the rural network. In this context, center villages substitute the township nodes, while the county nodes are represented by the aggregate of the county center nodes in a cross-scale examination.
Node AttributesPopulation StructureIndustrial StructureLand Use Structure
EdgeConnection JudgmentInterpersonal CommunicationIndustrial CooperationLandscape Connectivity
Connection WeightsFrequency of Human InteractionScale of Industrial InvestmentArea of Connected Landscape
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Yu, C.; Zhou, Z.; Gao, J. Rural Network Resilience: A New Tool for Exploring the Mechanisms and Pathways of Rural Sustainable Development. Sustainability 2024, 16, 5850. https://doi.org/10.3390/su16145850

AMA Style

Yu C, Zhou Z, Gao J. Rural Network Resilience: A New Tool for Exploring the Mechanisms and Pathways of Rural Sustainable Development. Sustainability. 2024; 16(14):5850. https://doi.org/10.3390/su16145850

Chicago/Turabian Style

Yu, Chao, Zhiyuan Zhou, and Junbo Gao. 2024. "Rural Network Resilience: A New Tool for Exploring the Mechanisms and Pathways of Rural Sustainable Development" Sustainability 16, no. 14: 5850. https://doi.org/10.3390/su16145850

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop