Measurement of the Modernization of Rural Governance Capacity: A Systematic Perspective

Abstract

The modernization of rural governance capacity (MRGC) is an important factor in enhancing the effectiveness of governance and promoting sustainable rural development. By interpreting the connotation of the MRGC, this study analyzes the dual structure of the rural organization system and its internal correlation based on the principle of system theory. The evaluation framework of the MRGC is constructed from the two dimensions of subject governance capacity (SGC) and inter-subject interaction force (IIF). Furthermore, an empirical study utilizing survey data from 15 towns, 248 villages, and 2579 villagers in Kang County, Gansu Province, China is conducted. The results show that Kang County’s overall MRGC, along with sub-dimensional SGC and IIF, remains at a moderate level. SGC manifests as balanced high performance and differentiated low performance. IIF variation is primarily driven by capability volatility. Regional analysis indicates that resource-rich areas exhibit higher SGC but carry greater systemic imbalance risks. Combined weights effectively rectify single-method biases in measurement design.

1. Introduction

Effective governance is a critical factor in advancing rural sustainable development [1], and strong governance capabilities serve as the foundation for enhancing the effectiveness of rural governance [2]. Rural governance capabilities influence this effectiveness through institutional design, organizational coordination, and value guidance [3]. Countries have adopted different rural governance measures. South Korea launched the New Village Movement in the 1970s. This movement has led to improvements in rural living conditions, agricultural economic development, and the enhancement of farmers’ awareness [4]. The European Union has proposed neo-endogenous strategies to address the lack of intervention by exogenous institutions and the inefficiency of endogenous initiatives in marginalized rural areas [5]. Canada is exploring new models of cross-sector collaboration involving non-profit organizations, businesses, and municipal governments, applied to community building and economic development [6]. India’s rural development goals focus on security, prioritize sustainable development across multiple sectors, and seek to enhance democratic participation through the construction of smart villages [7]. Indonesia has enacted village laws to promote village decentralization with the aim of shortening the excessively long chain of top–down planning procedures [8].

As the world’s largest developing country, China has a large rural population. Data show that in 2024, the nationwide rural population was 464.78 million, accounting for 33% of the total national population (Statistical Communiqué of the People’s Republic of China on the 2024 National Economic and Social Development. Available online: https://www.stats.gov.cn/sj/zxfb/202502/t20250228_1958817.html (accessed on 7 May 2025)). China has long attached importance to rural construction and development [9]. However, the development of urbanization has concurrently led to a massive outflow of the rural population. In the process of rural social governance, challenges, such as insufficient coordination among multiple subjects, unbalanced talent structure [10], lax rural organizations, weak collective economy [9], low participation enthusiasm, and excessive administrative workloads for autonomous organizations [11], have constrained the sustainable social and economic development of rural areas to a certain extent. Therefore, China has proposed the Rural Revitalization Strategy, aiming to achieve more high-quality, efficient, fair, and sustainable development in rural areas [12]. It has set the development goal of “effective governance” and the requirement of “accelerating the modernization of the rural governance system and governance capabilities”, emphasizing the principle of “adhering to the systems concept” and attaching importance to building a “social governance community”.

Against the backdrop of rural hollowing-out, rural governance places greater emphasis on building a rural organizational system, making it particularly important for multiple rural subjects to have sufficient capacity and effectively collaborate in governance activities. Therefore, objectively and accurately evaluating the level of rural governance capabilities by constructing measurement indicators, weightings, and evaluation models for the modernization of rural governance capabilities is of great significance for guiding the development of modernization and monitoring the modernization process.

2. Literature Review

The current academic discourse is engaged in deliberations regarding the conceptual connotation and measurement research on MRGC.

In the domain of conceptual connotation, rural governance is essentially “a process in which the state power extends to the countryside and makes rules for the society.” Therefore, rural governance capacity should include the state capacity extended to the rural society and the cooperative governance capacity between the state and the rural society in the exercise of national capacity [13]. Since the essence of rural governance is practical activities and “governance capacity is the ability of political power and citizen rights subjects to govern and participate in governance through certain institutional systems,” governance capacity modernization is “the modernization of the ability composition elements and realization of interest effectiveness of governance subjects” [14]. The MRGC is increasingly emphasizing systematic comprehensive deployment, multistakeholder participation, and the application of digital information technology [15,16,17], as well as modern features such as leadership capacity elitism, community-based provision of services, and public management capacity professionalization [18]. This study contends that MRGC is manifested in the following respects: Firstly, the establishment of a comprehensive and well-structured rural organizational system under the leadership of the Party organization gives rise to a governance community featuring complete subjects, rational structure, and complementary functions. Secondly, the governance subjects possess the essential elements for participating in modern rural governance activities; namely, the professionalization, standardization, and intellectualization of rural governance subjects. Thirdly, it is capable of comprehensively coordinating governance work, with multiple governance subjects collaborating and promoting rural public affairs efficiently and orderly.

In the domain of measurement research, studies primarily concentrate on two aspects: evaluation dimensions and measurement approaches. In terms of evaluation dimensions, scholars have proposed evaluation standards for rural governance from a theoretical perspective. Zaitul et al. [19] identified six principles of rural administrative governance, including fairness and capability, inclusivity, legitimacy and direction, participation, performance and information, and transparency and accountability. Morrison [20] constructed a governance index for rural areas from four dimensions: subject participation, diversity of tool combinations, tool robustness, and environmental support. Based on the PDCA cycle theory, Li and Wang [21] established the Chinese-style modern rural governance framework of “state analysis–mechanism construction–mechanism evaluation–mechanism improvement,” and proposed to evaluate the quality of the framework results from three dimensions: goal, organization, and performance.

In terms of measurement methods, scholars have constructed indicator systems for measuring the quality or level of rural governance from perspectives such as rural public affairs [22] and governance content [23]. They utilize macro open data [24] and micro survey data [25], and adopt subjective evaluation [19], objective evaluation [26], or subjective–objective comprehensive methods [20]. The indicator system was formed through a weighted linear combination to yield a composite index. Subsequently, the composite index was utilized to investigate the correlation effects, influence mechanisms, etc., with related fields. The indicator weights are usually determined using an objective weighting method [19,26], subjective weighting method [22], or a combination of objective and subjective weighting methods [27,28].

Relevant studies have offered effective discussions on how to measure rural governance capacity; however, some improvements can still be made. These are specifically reflected in the following aspects: Firstly, from the theoretical connotation, the modernization of rural governance includes the modernization of the rural governance system and governance capacity. However, existing studies emphasize the governance level of rural public affairs in various fields, and few studies focus on the measurement of MRGC. Secondly, the measurement of the interaction between the evaluation dimensions of rural governance capacity is ignored. Existing studies have concluded that governance capacity is equal to the linear combination of the measurement results of each dimension, and the impact of the interaction between different dimensions on governance capacity is lacking in the measurement, making it insufficient to reflect the complex system attributes of rural governance activities. Thirdly, there exists a sole method for empowering the indicator system, and the characteristics of the index are inadequately reflected. Existing studies typically employ only one method of empowerment. Owing to the distinct principles underlying each method, the data characteristics reflected by the weights vary, leading to different measurement results of the same indicator and data under dissimilar weights.

This study is anchored in the rural organizational system, proceeding from the perspective of subjectivity. In accordance with the principles of systems theory, it decomposes the MRGC into SGC and IIF, designing separate measurement systems for each. Concurrently, combined weighting methodologies are employed to determine the weights of the index system, and empirical research is conducted using survey data from 15 townships in Kang County, Gansu.

3. Theoretical Basis

3.1. Systems Theory

3.1.1. Systems and Systems Theory

Systems theory emerged in the 1920s and 1930s. Biologist Ludwig von Bertalanffy, the founder of general systems theory, argued that the mechanistic analytical framework used to explain biological phenomena was flawed—specifically, it treated living organisms as machines, describing their properties through the decomposition and simple summation of their elements. He, therefore, proposed viewing living organisms as organic wholes (systems) formed by interacting elements [29]. Qian [30] defined a “system” as an extremely complex research object, describing it as “an organic whole with specific functions composed of interdependent and interacting elements,” while emphasizing that this system is also a component of a higher-level system to which it belongs. Qian sought to apply systems theory to multiple fields, including society, law, management, agriculture, and geography [29]. Since then, many scholars have used systems theory as an analytical framework to study real-world socioeconomic issues.

The main perspectives of systems theory include fundamental principles, such as holism, hierarchy, openness, stability, purposefulness, and self-organization, which are also the common characteristics of all systems [29].

(1)

Holism represents one of the core and most distinct characteristics of systems theory. It denotes the existence of intricate interrelations among a system’s elements, whereby the functions of these elements are subordinate to the system’s overarching function. Critically, the system’s aggregate function does not equate to the mere arithmetic sum of its elements’ individual functions [31]. Owing to the interactive dynamics within the system, all constituent elements coalesce to form an organized totality [32], thereby engendering emergent functions that no single element can independently manifest. This gives rise to the phenomenon wherein the system’s functional capacity surpasses the simple additive sum of its elements’ capabilities.

(2)

Hierarchy refers to the phenomenon whereby a system exhibits distinct layers and hierarchical orderliness owing to differences in the modes of interconnection among its elements. Elements and hierarchical levels serve as the primary tools for depicting a system’s structure. Notably, there is a relativity between the system and its constituent elements, as well as between higher-level and lower-level subsystems [33].

(3)

Openness denotes the property of a system to continuously engage in exchanges of matter, energy, and information with its external environment. Through the introduction of negative entropy from the environment, the system reduces its total entropy, maintains a state far from equilibrium, and enhances the likelihood of self-organized evolution [29].

(4)

Stability refers to the capacity of an open system to maintain a certain level of self-stability under external influences, enabling it to self-regulate within a specific range to preserve and restore its original ordered state, structure, and functions [29].

(5)

Purposefulness refers to the characteristic of a system exhibiting a tendency toward a pre-determined state [29]. The overarching goal of the system serves as a pivotal guide for coordinating its elements [34], through which synergistic interactions among suboptimal elemental objectives facilitate the optimization of the system’s holistic functionality [35].

(6)

Self-organization refers to the capacity of a system, during its evolutionary process, to enable coordinated symbiosis among its internal elements and spontaneously generate spatial, temporal, or functional joint actions that form ordered structural activities—all without the coercive drive of external forces [36].

3.1.2. Applications of Systems Theory

Rural governance constitutes a complex system comprising multiple elements characterized by diversified stakeholders, multifaceted content, intertwined pathways, and multiple objectives that are closely interconnected, mutually reinforcing, and mutually promoting, thereby exhibiting distinct systemic characteristics [37].

(1)

Characteristics of Holism. Compared with “fragmented” governance approaches, the overall coordination of subjects allows for a more rational allocation of governance resources, thereby enabling the rural governance system to function more efficiently and sustainably [38]. Owing to the differences among multiple subjects in development goals, value orientations, ideological concepts, and information resources, the problem of “fragmentation” caused by decentralized actions often arises in the rural governance process [39], which restricts the improvement of governance effectiveness and sustainable development. The complexity and interconnection of rural governance demand a holistic perspective, regarding rural governance as an organic, unified whole composed of multiple interrelated and interacting elements. It is necessary to comprehensively consider the development and optimization of all elements, achieve coordinated and balanced development of all parts, and thus attain the optimal goal that the overall function is greater than the sum of the functions of the parts [37].

(2)

Characteristics of Hierarchy. The subjects of rural governance constitute a pluralistic governance community composed of grassroots Party organizations, local governments, villager autonomous organizations, rural economic and social organizations, and rural members participating in governance activities [40]. The hierarchy of the rural governance community is embodied in the relational structure among subjects, which also serves as a key influencing factor for governance efficiency [41]. According to resource dependence theory, no governance subject can possess all resources to achieve self-sufficiency, necessitating regular resource exchanges with other subjects [42]. In the governance process, vulnerable subjects often exchange their control rights for resources, which over time evolves into unequal hierarchical relationships of subordination and domination among subjects [43]. At present, rural governance in China has formed governance models dominated by administration, autonomy, or the market, with differences in the status and relational structures of subjects within the governance community across these models [41].

(3)

Characteristics of Openness. The external environment of the rural governance system encompasses the aggregate of production factors and the policy milieu. An increase in the quantity and quality of external inputs can facilitate the growth of systemic benefits [44]. This primarily involves the continuous influx of technology, capital, data, knowledge, and management—material, energy, and information that influence rural development—from the external environment during the rural governance process. In China, rural governance has established a pyramidal hierarchical structure of “central–local–grassroots” at the vertical level [45]. Through measures, such as governance framework design, foundational institutional support, public policy guidance, and resource investment, superior departments ensure resource provision and guidance for subordinate entities [46].

(4)

Characteristics of Stability. The process by which rural governance subjects handle specific matters follows a cycle of “detection (sorting)—matching—response—feedback—optimization.” Firstly, subjects organize the material, energy, and information at their disposal, subjecting them to standardized processing to form information sets. Subsequently, these sets are matched against the subjects’ “stimulus–response” rules, generating context-specific solutions, measures, and actions. Finally, the effectiveness of each action is validated through feedback on its outcomes, driving the continuous optimization of action plans and enhancing the robustness and adaptability of subjects’ governance capabilities [47]. When a single subject manages multiple tasks concurrently, variances in processing progress across tasks can induce performance fluctuations. Among multiple subjects handling parallel tasks, the accumulation of such progress disparities further amplifies fluctuations in governance capacity.

(5)

Characteristics of Purposefulness and Self-organization. The emergence of a governance community is premised upon the existence of shared objectives [48]. To achieve the collective goal of rural “good governance,” multiple subjects adopt diverse approaches to resolve interest conflicts and promote resource integration, thereby forming new interest alliances (interest communities) [45]. These alliances enable efficient resource exchange and allocation; through the mechanisms of shared obligations and mutual benefit, they generate spillover effects that lead to win–win outcomes, ultimately attaining maximum benefits unattainable by individual subjects [44]. The structure and order within interest alliances originate from the interactive influences among internal subjects of the system [49]. For individual subjects, the fundamental objective is to ensure their own survival, development, and growth. Consequently, they engage in resource exchanges with other subjects. To acquire resources more effectively, subjects divide labor according to their capabilities and conditions, taking on specific responsibilities in rural governance, such as capital investment, external liaison, information feedback, and action implementation [47].

3.2. Rural Governance System

3.2.1. Dual Structure

Rural governance is a social system composed of governance subjects and interaction among subjects (Figure 1). According to system theory, the structure of the system “depends on the elements within the system, the relationships formed by these elements, and the comprehensive manifestation of these relationships.” The dual structure can be expressed as $S=\left(T,R\right)$, where $S$ represents the system, $T$ represents the elements of the system, and $R$ represents the interaction between the elements [32]. The rural governance system is a social system, and governance capacity is the functional value of this system. It consists of two parts: the first is a diversified group of governance actors represented by element $T$, and the second is the interaction relationship $R$ formed by the functional positioning and operational mechanisms of these actors. Therefore, measuring the MRGS requires evaluating both governance subjects and their interactions.

3.2.2. Component Connotation

The elements of the system are the rural organization systems, which are composed of multiple subjects, and the SGC is reflected in the ability level of each subject. The theory of collaborative governance emphasizes the concept of “multipartner governance,” which includes the government, private sector, civil society, social groups, and hybrid institutions [50]. “The Guiding Opinions on Strengthening and Improving Rural Governance” issued by the General Offices of the CPC Central Committee and The State Council clearly stipulate that “Establish rural organization system with grassroots Party organizations as the leadership, rural self-governance organizations and affairs supervision organizations as the foundation, collective economic organizations and rural autonomous organizations as the ties, and other economic and social organizations as the supplementation.” In addition, villagers participate in rural governance activities through various organizations, which is an important basis for the effective operation of the rural organization system. Therefore, this study defines the rural governance subject as the rural organizational system composed of grassroots Party organizations (GPOs), the government, autonomous organizations (AOs), social and economic organizations (SEOs), and villagers. The SGC refers to the ability level that each rural governance subject possesses in governance activities.

The interaction forces among the subjects within the rural governance subject are manifested in three features: competitive synergy, fluctuation cycle, and dissipative structure [51]. The forms and directions of the interaction forces, caused by these three features, are all different from each other. Firstly, competitive synergy means that the system elements compete and cooperate in a non-linear form, making the elements related and interdependent to form an organic whole, in which competition is manifested as the difference between the elements and cooperation is manifested as the coupling of the elements. There are obvious differences in the governance capabilities of multiple subjects in the rural governance subject; thus, there is an internal power of competing and cooperating to form coupling and coordination [51]. The stronger the coupling between subjects, the stronger the force formed under the co-governance of multiple subjects. Secondly, the fluctuation cycle refers to the internal fluctuation of the system caused by the mutual influence of various parts of the system, as well as the exchange of information and energy with the external environment, and the interaction of positive and negative disturbances, which is reflected in the imbalance of the functions of the elements and the fluctuation within a certain range. The greater the fluctuation within the rural governance subject, the more easily each governance subject will be affected by the external environment, and the weaker the stability of the system will be. At this time, the positive and negative effects among the subjects influence each other alternately, which is not conducive to the cooperation among the subjects to form a resultant force. Thirdly, the dissipative structure refers to the non-equilibrium phase transition that may occur in the system when the element fluctuation exceeds the threshold value, and the elements in the system spontaneously organize and self-repair and finally adjust to form a new balanced structure. The governance capacity of each subject in the rural governance subject fluctuates within a certain range; however, when large adjustments and changes occur, such as the election of village committees, major security accidents, etc., the original balance of the system may be perturbed. In the long run, the characteristics of the dissipative structure of the system have a positive effect on changing the time and space function mismatch and an inefficient or ineffective state under the original balance, but the internal adjustment cycle is often long, and the chaos caused by the imbalance of the system in the short term will lead to a reduction in governance capacity. Therefore, the possibility of structural mutation of the system increases if each part of the system deviates from the equilibrium state, which is not conducive to the stable and orderly operation of all parts of the system.

3.2.3. Organic Unity Relationship

The SGC and IIF differ from but are also related to each other, and together, constitute the functional value of the rural governance system; that is, there is an organic unity relationship between the two.

The operational relationship of sub-dimensions of SGC is the main source of IIF. Firstly, competitive synergy originates from the institutional system of the rural governance subject. Rural governance is embedded in the triple and complex institutional environment of “administration–community–market” [52]. The superposition of multiple institutional mechanisms prompts the establishment of necessary connections among multiple subjects. For example, in governance practices, mechanisms such as the “4 + 2” system (“4 + 2” system refers to the democratic policy-making process on village affairs under the leadership of village Party organizations. “4” means four steps: Proposals should be put forward by the Party branch, jointly discussed by the village committee and the Party branch, and deliberated by Party members, and resolutions should be adopted by villagers’ representatives; “2” means transparency on two levels: resolutions and implementation results should be made known to the public),” the county-level joint review mechanism for candidates in the general elections of village Party branches and village committees, the mechanism for mediating and resolving conflicts and disputes, the supervision, reward, and punishment mechanism for village regulations and folk conventions, and the government’s service procurement mechanism have built bridges for the interaction among multiple subjects. Secondly, the fluctuation cycle and dissipation structure are derived from the changes in the entropy and enthalpy of the rural governance subject. Entropy and enthalpy are two state concepts in engineering thermodynamics that represent the degree of chaos [53] and energy state [54] of the system, respectively. The flow of energy between the elements of the system or between the elements and the external environment causes changes in the structure and function of the elements inside the system, resulting in changes in the entropy and the enthalpy of the system. Placed in the rural governance subject, the exchange of information, personnel, and resources within the organizational system or between the external environment causes differences in the governance capabilities of various subjects, which are reflected in the volatility and mutability of the system due to the different relative degrees of differences.

The IIF functions as the cohesive bond that facilitates the actualization of the SGC. The rural organizational system is a governance community composed of multiple heterogeneous individuals, organizations, institutions, etc. Its governance capacity is not just a simple linear stacking of the governance capacity of each subject, but the system effectiveness generated by the interaction of multiple subjects. However, the complexity of the interaction of governance subjects may lead to the phenomenon of “fragmentation” of governance subjects, which is manifested in the disorder of the powers and resources of the subjects and the dispersion and overlap of functions. Due to the lack of integrated linkage and common interests among the subjects, there will be situations where the progress of rural governance work is restricted because of the tense relationships among the subjects [55]. On the contrary, in order to achieve common goals and build an effective cooperative mechanism for multiple subjects, multiple subjects give play to their respective advantages under a certain order, which will result in the effect “1 + 1 > 2” [51].

4. Research Methods

4.1. Measurement Framework

The measurement system is used to design the method system of the SGC and IIF from three aspects: indicator, weight, and model (Figure 2). The SGC is mainly measured from the perspective of multiple subjects in rural areas, while the IIF is mainly measured from the interaction characteristics of system elements. Based on the measurement of SGC and the interaction forces among subjects, and following the principles of purposefulness, completeness, operability, independence, significance, and dynamism [56], the following measurement framework is constructed based on the idea of system theory:

4.2. Dimension 1: The Subject Governance Capacity (SGC)

4.2.1. The Indicator System of SGC

Subject governance capacity (SGC) constitutes a fundamental factor supporting the MRGC. The MRGC is a systematic project involving the joint participation of multiple subjects, with each subject’s capacity for governance directly determining the operational efficiency of the governance system. Based on the five subjects included in the rural organization system, namely, grassroots Party organizations, the government, autonomous organizations, social and economic organizations, and villagers, and considering that the most basic administrative organs in China are township governments, the township is taken as the measurement unit to design the measurement indicators for the governance capacities of each subject.

(1)

The capacity to build a grassroots Party, as the core leadership in rural governance, serves as a fundamental guarantee for the MRGC. The governance capacity of grassroots Party organizations requires them to have a certain influence in various fields, groups, and levels. It is necessary to build a strict organizational structure that “extends vertically to the bottom and horizontally to the edges.” The key lies in the capacity for building the Party’s organizational system, the capacity for building the leading groups and the cadre teams, the capacity for building the talent teams, and the capacity for building the Party member teams: (a) The capacity to develop an organizational system serves as the foundation for incorporating the MRGC. A robust organizational network ensures the effective implementation of the Party’s lines, principles, and policies in rural areas, enabling the overall coordination and integration of various governance resources. This capacity is measured based on the proportion of administrative villages with Party organizations. (b) The capacity for building leading cadres and cadre teams determines the scientificity and executive effectiveness of governance decisions. A high-caliber cadre team can accurately grasp the needs of rural governance and promote the effective implementation of governance measures, which is measured by the proportion of village Party secretaries who concurrently serve as village directors. (c) The capacity for talent team development provides sustainable intellectual support for rural governance. By attracting and cultivating professional talents, technical bottlenecks and experience deficiencies in governance can be addressed, and this capacity is measured based on the proportion of young Party members. (d) The capacity for Party member team development is key to organizing joint efforts in governance. Giving full play to the role of Party members as leaders and representatives can strengthen the connection between Party organizations and the masses, allowing for the people that form the foundation for governance to come together. This capacity is measured based on the proportion of active Party members. The coverage of party organizations in administrative villages nationwide had exceeded 99.9 percent as of June 2022 (The Organization Department of The CPC Central Committee. Statistical Bulletin of the Communist Party of China. Available online: http://gs.people.com.cn/n2/2022/0630/c183342-40017329.html (accessed on 4 November 2024)), and the indicator “proportion of administrative villages with Party organizations” does not have a degree of differentiation, so it is removed.

(2)

As the government is a leading actor in rural public affairs, its capacity for governance directly affects the fairness, inclusiveness, and development direction of rural governance. The governance capacity of the government requires that it can organize, coordinate, and integrate the production factors of various subjects according to the overall national strategic policy, and supervise the key links in rural construction, maintain social order, and ensure public safety, which is reflected in the rural public service capacity, public management capacity, and public security guarantee capacity [57]: (a) The capacity to provide public services is key to meeting villagers’ basic public needs and achieving equal access to basic public services between urban and rural areas. These public services primarily cover key areas of livelihood such as elderly care, medical care, and education. This capacity is measured based on indicators such as the rate of participation in rural social endowment insurance and the rate of coverage by convenience service centers. (b) The capacity for public management is reflected in the design and coordination of policy implementation, planning formulation, and other aspects of rural governance, ensuring the systematicity and standardization of the governance process. This capacity is measured based on the rate of coverage by rural revitalization plans. (c) The capacity to guarantee public safety is a prerequisite for the stable development of rural areas. By improving public security prevention and control systems and responding to and handling public emergencies, the rural security defense line can be strengthened. This capacity is measured based on the social sense of security. Based on the results of the national government hall census, as early as 2017, 38,513 convenience service centers had been set up in townships (streets) across the country, with a coverage rate of 96.8% (Results of the First National General Survey of Government Affairs Halls Announced: The Coverage Rate of Government Affairs Halls at or above the County Level Exceeds 90%. Available online: https://www.gov.cn/zhengce/2017-11/23/content_5241850.htm (accessed on 8 January 2025)). The coverage rate of convenience service centers in all regions is generally high, and the indicators are not differentiated, so the index of “coverage rate of convenience service centers” is excluded.

(3)

The capacity of autonomous organizations, as bridges connecting the government to their villagers, serves as the core carrier for achieving villagers’ self-governance. The governance capacity of autonomous organizations requires that they can understand and implement major national policies, laws, and regulations associated with agriculture and rural areas, and can clearly and accurately publicize and explain to the public, and effectively organize and mobilize other subjects to participate in governance activities, and in the construction of rural rule of law; at the same time, they should be able to effectively mediate conflicts and disputes, and maintain rural social harmony and stability [58]. Regarding factors affecting self-governance, three aspects can be measured: policy interpretation capacity, organizational mobilization capacity, and dispute mediation capacity: (a) Policy interpretation capacity can bridge the “last mile” of policy implementation, help villagers accurately understand governance requirements, and enhance their recognition of governance work. This capacity is reflected by a village director’s average years of education. (b) Organizational mobilization capacity is the foundation for facilitating villagers’ participation in governance. By guiding villagers to participate in the decision-making of public affairs, their endogenous motivation for self-governance can be stimulated. This capacity is reflected by the voting rate in village committee elections. (c) Dispute mediation capacity is an important factor that guarantees internal harmony in rural areas. A high capacity for dispute mediation allows for neighborhood conflicts to be resolved based on village rules, conventions, and moral norms, and thus, reduces governance costs. This capacity is reflected by the level of harmony in rural areas.

(4)

As important participants in rural governance, social and economic organizations provide diversified support to governance. The governance capacity of social and economic organizations requires them to focus on the development of rural industries, establish a comprehensive organizational service system with complete content and structure, complete internal systems and procedures, and attract villagers to participate in rural organizations [59]. Regarding this support, three aspects can be measured: institutional building capacity, villager organizing capacity, and operational development capacity: (a) Institutional building capacity is a prerequisite for standardizing social and economic organizations’ own operations. A sound internal management system can ensure their orderly participation in governance and enhance service credibility, which is measured based on the degree of improvement in the collective economic system. (b) Villager organizing capacity is reflected in organizing production cooperation and skill training, among other aspects, which can enhance the degree of organization in villagers’ social life. This capacity is reflected by the rate of participation in the collective economy and cooperatives. (c) Operational development capacity is key in driving rural industrial revitalization. By activating rural resources and developing characteristic industries, economic support can be provided for governance. This capacity is measured based on the annual income of the collective economy.

(5)

As villagers are the primary actors in rural governance activities, their capabilities and qualities constitute the core driving force for the transformation of governance effectiveness. The governance capacity of villagers requires that they be able to obtain village and social conditions and information on upper development policies, have the ability to analyze and judge problems, make suggestions on the handling of public affairs through legal procedures or reflect their own reasonable demands, and be skilled in using modern information technology, which is embodied in the ability to make recommendations, to comply with procedures, and to apply digitalization [60,61]. (a) Discuss and advise capability is the foundation for villagers to participate in rural governance. Accurate information transmission, effective decision-making suggestions, and unobstructed expression channels can ensure that the direction of governance fully meets villagers’ needs. This capacity is measured based on the proportion of villagers with a college education or above. (b) Process compliance capability is a prerequisite for maintaining governance order. Villagers’ recognition and compliance with laws, regulations, and village rules and conventions ensure the orderly conduct of governance activities. This capacity is measured based on villagers’ degree of compliance. (c) Digital application capacity is an important quality for adapting to modern governance. By using digital tools to participate in the handling of government affairs and information acquisition, the intelligent transformation of governance methods can be promoted. This capacity is measured based on the proportion of rural households with computers.

4.2.2. Weight Design

Compared with equal weights, differential weights are more consistent with the structural characteristics of the measurement object, and the measurement results also have higher sensitivity and reliability [62]. In this regard, the academic community mainly conducts objective weighting based on three data characteristics of indicators: variability, correlation, and uncertainty. Firstly, for indicator variability, the greater the variability, the more information the indicator contains, and thus a larger weight should be assigned to it. The variability is usually measured by the coefficient of variation and the variance contribution rate, which correspond to the coefficient of variation method and the factor analysis method, respectively. Secondly, for indicator correlation, the stronger the correlation, the greater the possibility that the indicator can be replaced by the linear combination of other indicators, the less independent it is, and the less effective information it contains; therefore, a smaller weight should be assigned to it. For the measurement of correlation, the independent weight method uses the multiple correlation coefficient, and the grey relational analysis adopts the degree of correlation between the indicator and the mother sequence. Thirdly, for indicator uncertainty, that is, the magnitude of the information entropy of the indicator, a larger weight is assigned to the indicator with greater information entropy. The entropy value method is commonly used to determine the weight.

In research, only a single method is usually adopted to determine the weights of indicators. However, since each method is based on different principles and each method has different sensitivities to different data structures, there are often significant differences in the calculated weights. In this regard, this study draws on the design logic of the CRITIC method [63] and designs indicator weights based on three weighting principles: variability, correlation, and uncertainty. Weights under the same principle are combined using the addition rule, while weights under different principles are combined using the multiplication rule. The quantity of information about an indicator is obtained by multiplying the three weights, and this quantity is normalized to form a weight system. This form of combined weighting is adopted to offset the deviations in measurement results caused by using different weighting methods. The specific methods are as follows:

(1)

Determine the indicator weights under the principle of variability. Calculate the indicator weights using the coefficient of variation method and the factor analysis method, and then synthesize the variability weights using the addition principle:

$$w_{Vkj}=\left(w_{CVkj}+w_{Fkj}\right)/2$$

(1)

In Equation (1), $k=1,2,\cdots ,5$, represents the $k$ governance subject; $j=1,2,\cdots ,m$, $m$ represents the number of governance capacity measurement indicators for each subject; $w_{CVkj}$ represents the weight of the $j$ indicator of the $k$ subject obtained using the coefficient of variation method, $w_{CVkj}={\displaystyle \frac{C{V}_{kj}}{{\displaystyle \sum C{V}_{kj}}}}$; $w_{Fkj}$ represents the weight of the $j$ indicator of the $k$ subject obtained using the factor analysis method.

(2)

Determine the indicator weight under the principle of correlation. The independence weight method and grey correlation analysis method are used to calculate the indicator weight, and the correlation weight is determined using the addition principle:

$$w_{Ckj}=\left(w_{Rkj}+w_{Gkj}\right)/2$$

(2)

In Equation (2), $w_{Rkj}$ represents the weight of the $j$ indicator of the $k$ subject obtained using the independent weight method; $w_{Gkj}={\displaystyle \frac{1/g_{kj}}{{\displaystyle \sum 1/g_{kj}}}}$ represents the weight of the $j$ indicator of the $k$ subject obtained using the grey relational analysis method, where $g_{kj}$ is the degree of correlation of the $j$ indicator of the $k$ subject.

(3)

Calculate the indicator weight under the uncertainty principle; that is, the entropy method is used to calculate the indicator weight $w_{Ekj}$.

(4)

Combine the above three weights according to the multiplication principle to obtain the information content of each indicator.

(5)

Normalize the amount of information of the indicators to obtain the weight of the $j$ measurement indicator for the governance capacity of the $k$ subject:

$$w_{kj}={\displaystyle \frac{Q_{kj}}{{\displaystyle \sum Q_{kj}}}}$$

(3)

(6)

The governance capacity of each subject adopts the average weighting method: $w_k=0.2$, $k=1,2,\cdots 5$.

4.2.3. Model Construction

The governance capacity measurement model of each subject in rural areas is as follows:

$$G_{ik}={\displaystyle \sum _j{x}_{ikj}{w}_{kj}}, i=1,2,\cdots ,n$$

(4)

In Equation (4), $n$ represents the sample size (i.e., the number of villages and towns in the survey) and $x_{ikj}$ represents the value of the $j$ indicator of the $k$ subject in the $i$ township.

The comprehensive index of the governance capacity of each township subject is represented as follows:

$$C_i={\displaystyle \sum _k{G}_{ik}{w}_k}$$

(5)

4.3. Dimension 2: The Inter-Subject Interaction Force (IIF)

4.3.1. The Indicator System of IIF

The inter-subject interaction force (IIF) serves as the dynamic guarantee for the MRGC. The efficient operation of a rural governance system not only relies on the individual capabilities of each subject but also depends more on the interaction relationships and operational mechanisms between subjects. For quantitative measurement of the interaction among subjects, existing research often uses the coupling coordination degree model [64], the Haken model [65], and the Logistic growth model [66] to measure the synergetic relationships among system elements. Simulation analysis methods are used to depict the evolutionary state of the system to reveal its fluctuation laws [67]. Additionally, the changing laws of system entropy are analyzed over time to describe the dissipative trend of the system. Scholars mainly depict the changing trends of the characteristics of interactions from the time dimension, yet there is seldom discussion on the state description based on cross-sectional data. This study analyzes the statistical characteristics of three system features: competitive synergy, fluctuation cycle, and dissipative structure. Based on the measurement results of the governance capacity of subjects, it designs measurement indicators for the coupling, volatility, and mutability of the rural governance system to measure the interaction state among governance subjects.

Competition synergy constitutes the basic form of interaction among multiple subjects in a rural governance system and serves as an important mechanism for improving overall governance efficiency. The coupling nature of the system reflects the degree of interdependence and mutual promotion between subjects, forming a governance synergy through resource complementarity and functional collaboration. The coupling coefficient can quantify the strength of the synergistic effect between subjects, which is usually calculated using a coupling degree model [68] and reflects the overall coordination of the governance system:

$$O_i={\displaystyle \frac{{\left({\displaystyle \prod C_i}\right)}^{1/k}}{\left({\displaystyle \sum C_i}\right)/k}}$$

(6)

Fluctuation cycles represent the dynamic characteristic of rural governance systems in adapting to environmental changes, reflecting the resilience and adaptability of governance capacity. The volatility of the system manifests as dynamic differences in the governance capacity of various subjects caused by changes in the external environment (such as policy adjustments and emergencies). The volatility coefficient can be measured by calculating the coefficient of variation in the governance capacity of each subject within the system, reflecting the stability and adaptability of the governance system:

$$C{V}_i={\displaystyle \frac{{\sigma }_i}{\overline{C_i}}}$$

(7)

In Equation (7), ${\sigma }_i=\sqrt{{\displaystyle \frac{1}{k}}{\displaystyle \sum _k\left(C_{ik}-\overline{C_i}\right)}}$ is the standard deviation of the capacities of each subject in township $i$.

Dissipative structure refers to an evolutionary trend of rural governance systems moving from disorder to order, embodying the possibility of innovative upgrades to governance models and structural imbalance. The mutability of the system refers to the qualitative transformation of governance structures and functions under external intervention or the accumulation of internal contradictions. The mutability coefficient can be measured by calculating the average deviation of subject governance capacity from their respective general levels, reflecting the innovation vitality and imbalance risks of the governance system:

$$M{D}_i={\displaystyle \frac{{\displaystyle \sum _k\left|C_{ik}-\overline{C_k}\right|}}{k}}$$

(8)

In Equation (8), $\overline{C_k}$ represents the governance capacity level of the $k$ subject under the equilibrium state. In this study, the average value of the governance capacity of the $k$ subject in all townships in the county is used to approximate the capacity level under the equilibrium state.

4.3.2. Weights and Models

For the measurement indicators of the three characteristics of competitive synergy, fluctuation cycle, and dissipative structure in the social system, the average weighting method is adopted after standardization:

$$w_O=w_{CV}=w_{MD}=1/3$$

(9)

The comprehensive index of IIF in rural governance is as follows:

$$F_i=\left(O_i+C{V}_i+M{D}_i\right)/3$$

(10)

4.4. Comprehensive Measurement of the MRGC

4.4.1. Comprehensive Measurement Indicator System

The MRGC is a complex systematic project, and its level is difficult to directly quantify. A comprehensive measurement of inter-subject capabilities and interaction forces needs to be conducted through observable and measurable explicit indicators. Furthermore, it is essential to consider the availability of data, the representativeness and scientificity of indicators, and to construct an evaluation indicator system for the level of modernization of rural governance capacity (

Table 1. Indicator system for measuring the MRGC.

Dimension	Objects	Features	Indicator	Attribute
SGC	GPO $X_1$	$\mathrm{Organizational} \mathrm{system}\text{-}\mathrm{building} \mathrm{capacity} X_{11}$	Proportion of administrative villages with Party organizations *	+
		$\mathrm{Leadership} \mathrm{and} \mathrm{cadre} \mathrm{teams} \mathrm{to} \mathrm{build} \mathrm{capacity} X_{12}$	Proportion of village Party secretaries doubling as village directors	+
		$\mathrm{Talent} \mathrm{team} \mathrm{building} \mathrm{capacity} X_{13}$	Proportion of young Party members	+
		$\mathrm{Party} \mathrm{members} \mathrm{building} \mathrm{capacity} X_{14}$	Percentage of active Party members	+
	GOV $X_2$	$\mathrm{Public} \mathrm{service} \mathrm{capacity} X_{21}$	Participation rate of social endowment insurance	+
			Coverage rate of convenient service centers *	+
		$\mathrm{Public} \mathrm{management} \mathrm{capacity} X_{22}$	Coverage rate of the Rural Revitalization Plan	+
		$\mathrm{Public} \mathrm{safety} \mathrm{guarantee} \mathrm{capability} X_{23}$	Residents’ sense of security	+
	AO $X_3$	$\mathrm{Policy} \mathrm{interpretation} \mathrm{capacity} X_{31}$	Average years of education for village directors	+
		$\mathrm{Organizational} \mathrm{mobilization} \mathrm{capacity} X_{32}$	Turnout in village committee elections	+
		$\mathrm{Dispute} \mathrm{mediation} \mathrm{capacity} X_{33}$	Degree of village harmony	+
	SEO $X_4$	$\mathrm{Institutional} \mathrm{building} \mathrm{capacity} X_{41}$	Degree of improvement of the collective economic system	+
		$\mathrm{Villager} \mathrm{organizing} \mathrm{capacity} X_{42}$	Proportion of participation in the collective economy and cooperative	+
		$\mathrm{Operational} \mathrm{development} \mathrm{capacity} X_{43}$	Collective economic annual income	+
	Villagers $X_5$	$\mathrm{Discuss} \mathrm{and} \mathrm{advise} \mathrm{capability} X_{51}$	Percentage of villagers with a college degree or above	+
		$\mathrm{Process} \mathrm{compliance} \mathrm{capability} X_{52}$	Degree of villagers’ compliance with rules	+
		$\mathrm{Digital} \mathrm{application} \mathrm{capacity} X_{53}$	Percentage of households with computers	+
IIF	$\mathrm{Competitive} \mathrm{synergy} X_6$	$\mathrm{Coupling} \mathrm{of} \mathrm{the} \mathrm{system} X_6$	Coupling coefficient	+
	Fluctuation $\mathrm{cycles} X_7$	$\mathrm{Volatility} \mathrm{of} \mathrm{the} \mathrm{system} X_7$	Volatility coefficient	−
	$\mathrm{Dissipative} \mathrm{structure} X_8$	$\mathrm{Mutability} \mathrm{of} \mathrm{the} \mathrm{system} X_8$	Mutability coefficient	−

* Deleted due to lack of discriminability. “+” denotes positive indicators, “−” denotes negative indicators.

).

4.4.2. Weights and Models for Comprehensive Measurement

According to the principles of system theory, the comprehensive index of the MRGC consists of two parts: the index of the governance subject capacity and the index of the interaction forces among subjects. Therefore, the measurement of MRGC should be represented as a linear relationship of the measurement results of these two parts:

$$H_i=\alpha C_i+\left(1-\alpha \right)F_i$$

(11)

In Equation (11), $\alpha$ is the principal ability coefficient, reflecting the degree of influence of the principal governance capacity on the rural governance capacity. Due to the openness and complexity of the rural governance system, the governance subject exchanges information with other subjects or the outside world, and the structure of the system changes with the evolution of the system during this process [69]. The capacity of governance subjects and their interaction are in a dynamic state of change, and it is difficult to accurately determine the relative degree of their effects on the system. The arithmetic average weighting method is the most effective method for eliminating the weighting differences [70], and is widely used in situations where it is difficult to judge the importance of indicators [20]. This study takes $\alpha =1/2$, meaning that SGC and IIF are considered to have equal importance.

4.5. Identification of Obstacle Factors

The obstacle degree model is an analytical tool for identifying and evaluating key factors that affect the achievement of goals. The obstacle degree indicates the extent to which each factor influences the development goals [71,72]. This model is used to identify the main factors affecting the modernization of rural governance capacity, with the calculation formula as follows:

$$O_{ij}={\displaystyle \frac{I_{ij}\cdot {\omega }_j}{{\displaystyle \sum _j{I}_{ij}\cdot {\omega }_j}}}$$

(12)

In Equation (12), $I_{ij}=1-x_{ij}$ represents the deviation of the $j$ indicator for the $i$ evaluation object, and ${\omega }_j$ is the weight of the indicator. $O_{ij}$ denotes the obstacle degree of the $j$ indicator for the $i$ evaluation object.

4.6. Research Area and Data Sources

Kang County is located in the southeast of Gansu Province, China, at the junction of Gansu, Sichuan, and Shaanxi provinces. With a total area of 2967.95 square kilometers, Kang County has jurisdiction over 18 towns and three townships. The majority of the county’s population is of Han nationality, and ethnic minorities are mixed. By the end of 2022, the registered rural population was 152,400, accounting for 78% of the county’s total population. Kang County has long attached importance to rural development, and has been awarded the titles of “the most beautiful County in China” and “Top 100 Counties of Healthy China and Health Tourism in 2022.” In terms of rural governance, the county has established a sound mechanism for resolving conflicts and disputes, strictly implementing the civil work method and investigating, mastering, and resolving contradictions and disputes among the masses in a timely manner. It has established “the Moral Red-Black List” and the selection mechanism of “the most beautiful Family” and “the beautiful courtyard,” and actively carries out voluntary activities, such as theoretical preaching and opinion collection using existing resources, such as galleries and pavilions, to encourage the people to change their customs and develop a civilized and healthy lifestyle, establish service stations, such as the civilized practice square and “Children’s care station,” promote the effective and efficient implementation of voluntary services, and obtain the satisfaction and happiness index of the masses using the “five-heart service.” It has established a rural governance model featuring “village regulations and conventions” to manage, “civilized advocacy” to influence, and “customary reforms” to transform, providing valuable practical experience for rural governance in Western China.

The data come from a survey of 15 townships in Kang County, Gansu province. The research group conducted research in Kang County in February 2023 and collected a total of 2579 individual questionnaires, 248 village-level questionnaires, and 117 village reports involving 20 townships. The questionnaire data were preprocessed by eliminating questionnaires that contained missing values and outliers, and those whose data from the three sources could not be matched. Since the rural governance capacity is measured at the township level in the design of the indicator system, the individual questionnaire data and the village-level data were combined and aggregated to the township level for analysis. Townships with too few individual questionnaires were excluded, resulting in measurement indicator data for 15 townships. Considering the adverse impact of the differences in the measurement units of the indicators on the measurement results, the minimum–maximum value method was used to standardize each indicator [73]:

$${x^{\prime }}_{ikj}={\displaystyle \frac{x_{ikj}-\min \left(x_{ikj}\right)}{\max \left(x_{ikj}\right)-\min \left(x_{ikj}\right)}}$$

(13)

$${x^{\prime }}_{ikj}={\displaystyle \frac{\max \left(x_{ikj}\right)-x_{ikj}}{\max \left(x_{ikj}\right)-\min \left(x_{ikj}\right)}}$$

(14)

Equation (12) was used to standardize the positive indicators, and Equation (13) was used to standardize the negative indicators. ${x^{\prime }}_{ikj}$ is the value obtained after the standardized processing of the $j$ measurement indicator of the $k$ governance subject in township $i$.

5. Results

5.1. Analysis of the MRGC

Considering both rural subject governance capacity and inter-subject interaction force (IIF) comprehensively, a comprehensive index for the modernization of rural governance capacity was constructed to measure the governance capacity of each township. The results (Figure 3) show that except for Wangguan Town, Dananyu Town, Pingluo Town, and Yangba Town, the values for subject governance capacity and IIF generally diverge across all townships. The average value of subject governance capacity is 0.446, with a standard deviation of 0.099, while the average value of IIF is 0.511, with a standard deviation of 0.192. Both follow a normal distribution, as verified with the Shapiro–Wilk test. On this basis, the paired-samples t-test produced a value of −1.326 (p = 0.206 > 0.05), and the Bartlett test produced a value of 5.399 (p = 0.019 < 0.05), indicating no significant difference in their means but heterogeneous variance, with the data fluctuation of IIF being significantly higher than that of subject governance capacity. Therefore, compared with subject governance capacity, IIF has a larger fluctuation range, and the dominant factor determining the level of rural governance capacity is the level of IIF.

The average value for the comprehensive index of the MRGC is 0.48, with a total of 10 townships exceeding this average, including four in the North Kang region, three in the Central Kang region, and three in the South Kang region. From a regional perspective (

Table 2. Comprehensive MRGC index for each region.

Regions	Indicators	MRGC	SGC	IIF
The North	Average	0.46	0.43	0.50
	Range	0.25	0.31	0.37
The Central	Average	0.51	0.48	0.55
	Range	0.38	0.28	0.52
The South	Average	0.47	0.44	0.50
	Range	0.36	0.16	0.64
Kruskal–Wallis Test	—	0.848	0.285	0.522
Bartlett Test	—	0.331	1.336	0.974

), the county is divided into three areas: the North, Central, and South regions. The levels of MRGC, SGC, and IIF in these three areas are basically equivalent, which is supported by the results of the Kruskal–Wallis test (all showing no statistical significance). In terms of the internal differences within each region, the MRGC values of the townships in the North Kang region are relatively concentrated, while the differences in the MRGC of the townships in the Central Kang and South Kang regions are relatively large. However, Bartlett test results demonstrate homogeneity of variance across these regions.

5.2. Analysis of SGC

5.2.1. Combination Weight

Combination weight can effectively synthesize the characteristics of index variability, correlation, and uncertainty, and correct the result bias of a single weighting method. In the case of the same indicator system and data, there are differences in the indicator weights determined from the single perspective of variability, correlation, and uncertainty (

Table 3. Results of indicator system weight combination calculations.

Indicators	Combination Weights	Variability Weights	Correlation Weights	Uncertainty Weights	Three Weights Range
$X_{12}$	0.36	0.35	0.40	0.28	0.046
$X_{13}$	0.23	0.25	0.30	0.34	0.010
$X_{14}$	0.41	0.39	0.30	0.38	0.055
$X_{21}$	0.28	0.36	0.26	0.39	0.097
$X_{22}$	0.55	0.33	0.57	0.37	0.255
$X_{23}$	0.17	0.31	0.29	0.24	0.079
$X_{31}$	0.26	0.36	0.28	0.28	0.040
$X_{32}$	0.46	0.29	0.43	0.40	0.031
$X_{33}$	0.28	0.35	0.28	0.31	0.009
$X_{41}$	0.18	0.28	0.42	0.22	0.122
$X_{42}$	0.04	0.25	0.25	0.09	0.149
$X_{43}$	0.78	0.47	0.34	0.69	0.271
$X_{51}$	0.33	0.34	0.32	0.36	0.012
$X_{52}$	0.13	0.23	0.34	0.20	0.049
$X_{53}$	0.54	0.42	0.33	0.45	0.037

). The maximum range of weights calculated using the three principles is 0.255, and the average range is 0.084. The differences in weights lead to inconsistent results in the comprehensive scores of rural subjects’ governance capacities (

Table 4. Scores of SGC.

Township	Combination Weights		Variability Weights		Correlation Weights		Uncertainty Weights		The Range of SGC Under Three Weights
	Scores	Rank	Scores	Rank	Scores	Rank	Scores	Rank
Anmenkou	0.480	6	0.517	5	0.550	4	0.480	7	0.070
Baiyang	0.393	11	0.423	13	0.454	14	0.402	12	0.051
Chengguan	0.519	4	0.510	7	0.506	8	0.524	3	0.018
Dannanyu	0.499	5	0.559	2	0.591	3	0.531	2	0.060
Douba	0.634	1	0.621	1	0.654	1	0.645	1	0.033
Lianghe	0.528	3	0.534	3	0.603	2	0.510	4	0.093
Miba	0.477	7	0.514	6	0.541	6	0.486	6	0.055
Nianba	0.358	13	0.387	14	0.459	11	0.355	14	0.104
Pingluo	0.469	8	0.484	9	0.532	7	0.465	9	0.068
Tongqian	0.363	12	0.465	10	0.457	12	0.415	11	0.049
Wangba	0.395	10	0.461	11	0.467	10	0.420	10	0.047
Wangguan	0.540	2	0.523	4	0.547	5	0.501	5	0.046
Yangba	0.447	9	0.494	8	0.494	9	0.472	8	0.023
Changba	0.230	15	0.235	15	0.224	15	0.238	15	0.014
Zhoujiaba	0.355	14	0.448	12	0.456	13	0.394	13	0.062
Standard Deviation	0.099		0.088		0.098		0.093
Shapiro–Wilk Test	0.970		0.892		0.867 *		0.954
Wilcoxon Signed Rank Test	—		2.670 **		3.181 **		1.822
Kendall’s Coefficient of Concordance						0.949 ***

* p < 0.05 ** p < 0.01 *** p < 0.001.

). The average range of the scores of subjects’ governance capacities based on the three weights is 0.053, and the average relative range (The average relative range is equal to the arithmetic mean of the ratios of the range of the three weights to their average) is 11.1%. This indicates that if a single method is used to measure the governance capacities of subjects, there will be a deviation of more than 10% in the capacity levels under different weights. The relative compromise of the combination weight proposed in this study can effectively balance the advantages and disadvantages of the three weight determination methods, synthesize the subject ability level of each region and the intra-regional ranking results, and correct the deviation in the results caused by a single weight. Regarding the consistency of evaluation results (Table 4), Kendall’s coefficient of concordance for the ranking of scores under the four weighting methods is 0.949 (p < 0.001), which is greater than 0.8, indicating that the ranking results obtained with the combined weighting method and the single weighting methods are highly consistent. From the perspective of the discrimination of evaluation results, the Shapiro–Wilk statistic was used to test the normal distribution characteristics of the subject governance capacity scores. However, the subject governance capacity scores calculated under the correlation weighting method showed significance (p < 0.05), indicating that they do not exhibit the characteristics of a normal distribution. Therefore, a Wilcoxon signed rank test, which is used to study differences between paired samples in non-normal data, was conducted [74]. The results show statistically significant differences (p < 0.01) between the subject governance capacity scores under the combined weighting method and those under the variability and correlation weighting methods. Moreover, the scores under the combined weighting method have a higher standard deviation than that for the other methods, indicating that this method can improve the discrimination of evaluation results.

5.2.2. Results of SGC

Several characteristics were observed based on the results of the SGC measurements (Figure 4):

Firstly, based on the absolute level, the SGC is ranked from high to low in the order of GPO, the government, AO, villagers, and SEO. The SGC of the first three subjects is above the medium, while that of the latter two subjects is relatively weak. The Kruskal–Wallis test statistic produced a value of 39.828 (p < 0.05), indicating that there are significant differences in the governance capabilities of the subjects. Secondly, there are differences in the governance capacities of various subjects among townships. Since the governance capabilities of the SEO and VLG do not follow a normal distribution (the p-values of the Shapiro–Wilk test are all less than 0.05), the Levene test was used to verify homogeneity of variance. The Levene test statistic produced a value of 4.801 (p < 0.05), indicating that there are statistically significant differences in the fluctuation patterns within different governance subjects. After excluding outliers, the interquartile ranges of the governance capacities of social organizations and villagers are relatively small. However, the relatively low median value indicates that the governance capacities of these two subjects in each township are in a balanced state at a low level, while the governance capacities of GPO, the government, and AO in each township are in a differentiated state at a relatively high level.

To comprehensively analyze the SGC across diverse regions, empirical SGC measurements of subjects within the rural organizational system and individual subjects in three distinct regions were computed (

Table 5. Scores of SGC in each region.

Regions	Indicators	SGC	GPO	GOV	AO	SEO	VLG
The North	Average	0.43	0.58	0.52	0.52	0.16	0.35
	Range	0.31	0.48	0.70	0.46	0.33	0.58
The Central	Average	0.48	0.45	0.52	0.54	0.45	0.42
	Range	0.28	0.31	0.42	0.44	0.81	0.39
The South	Average	0.44	0.71	0.64	0.40	0.19	0.27
	Range	0.16	0.33	0.54	0.30	0.12	0.45
Kruskal–Wallis Test	—	0.285	5.76	1.027	2.943	2.361	3.018
Levene Test	—	1.336	0.771	0.258	0.585	23.002 ***	0.036

*** p < 0.001.

). Based on the overall level, the SGC shows the following pattern: Central Kang > South Kang > North Kang. Based on the governance capacities of various subjects in each region, the AO, the SEO, and the villagers have relatively strong SGC in the Central Kang region, and the capacities of the five subjects are relatively balanced. In the South Kang region, the GPO and the government have obvious advantages in governance capacities, but the SEO capacity needs to be improved. There is no obvious advantage in the North Kang region, and due to the relatively weak governance capacities of SEO and villagers, the comprehensive capacity is relatively low. The Kruskal–Wallis test results show that there are no significant differences in the governance capabilities of various subjects across different regions (all p-values are greater than 0.05). Based on the internal differences within each region, the ranges of the SGC in all regions are lower than the ranges of individual subjects’ capacities. This indicates that the comprehensive index of rural SGC can effectively balance the differences among the capacities of various subjects. When broken down by region, the results show that there are relatively large differences in the capacities of various subjects in the North Kang region. The regional differences in the capacities of the GPO, the government, the AO, and the villagers are all relatively large. In the Central Kang region, the range of SEO capacities is relatively large. In the South Kang region, the internal differences in the capacities of various subjects are smaller compared with those of the other two regions. Comparison of the various subjects showed that the internal differences in the governance capacities of the government and the villagers are relatively higher than those of other subjects. The Levene test results show that the test statistic for the governance capabilities of the SEO is 23.002 (p < 0.05), indicating that there is no homogeneity of variance across the three regions, with data fluctuations in the Central Kang region being significantly higher than those in the other two regions. There are no significant differences in the fluctuation patterns of governance capabilities among the other four subjects across the three regions.

The measurement results of the SGC of each township (Figure 5) show that the average value is 0.45. There are a total of nine townships whose SGC values are higher than the average, accounting for 60% of the sample townships: four are in the North Kang region, two are in the Central Kang region, and three are in the South Kang region, accounting for 67%, 50%, and 60% of the regional sample numbers, respectively. The main factors contributing to the relatively strong SGC of these nine townships are different. In Douba Town, Wangguan Town, Chengguan Town, and Dananyu Town, the capacities of the GPO, the government, and the AO are relatively balanced and at a relatively high level. Additionally, the villagers in Wangguan Town have an obvious advantage in governance capacity, and the SEO in Douba Town and Chengguan Town in the Central Kang region are more prominent. Lianghe Town owes its strength to the relatively high levels of the capacities of GPO and the government. Anmenkou Town and Miba Township are influenced by the GPO capacity. The government governance capacities in Pingluo Town and Yangba Town are relatively prominent. In addition, the SEO capacity in Pingluo Town and the villagers’ governance capacity in Yangba Town are at a relatively high level compared with the other townships. There are six townships with values lower than the average: two in the North Kang region, two in the Central Kang region, and two in the South Kang region. The reason for this is that the advantage of a certain subject is not prominent, and the differences among various subjects are too large.

5.3. Analysis of IIF

The measurement scores of the IIF (Figure 6) show that the average IIF value in the whole county is 0.51, which represents a moderate level. The Kruskal–Wallis test statistic produced a value of 38.991 (p < 0.05), and the Bartlett test statistic produced a value of 12.854 (p < 0.05), indicating that there are significant differences in the levels and variances of the three types of forces. The coupling degrees of the governance capacities of various subjects are relatively high. The minimum value of the coupling degree is 0.72, and the coupling degrees of the subject capacities in three-fourths of the townships are higher than 0.8, indicating a high-level coupling state. The coupling levels among townships are relatively concentrated with small differences, which illustrates that the governance subjects in the various townships are in an orderly development state. In terms of volatility, the average value of the coefficient of variation within the rural organizational system is 0.53, which is a medium intensity level of variation. The differences in volatility among various townships are relatively larger than the differences in coupling and mutability. In terms of mutability, the mean difference in the levels of the SGC of various townships is relatively low. This indicates that the degree of deviation of the levels of the SGC of various townships from the system’s equilibrium state is very small, and the possibility of system imbalance and structural adjustment is extremely low.

The distribution of the IIF scores in different regions (

Table 6. IIF scores in each region.

Regions	Indicators	IIF	Coupling	Volatility	Mutability
The North	Average	0.50	0.57	0.48	0.44
	Range	0.37	0.55	0.40	0.67
The Central	Average	0.55	0.75	0.66	0.24
	Range	0.52	0.72	0.66	0.62
The South	Average	0.50	0.52	0.38	0.60
	Range	0.64	0.78	0.56	0.60
Kruskal–Wallis Test	—	0.522	1.710	1.815	3.188
Bartlett Test	—	1.590	1.066	1.632	0.058

) shows that, in terms of the overall trend, the IIF in the North Kang, Central Kang, and South Kang regions are similar, with the measurement indices concentrated between 0.5 and 0.6. However, from a statistical perspective, the p-values of both the Kruskal–Wallis test and the Bartlett test are greater than 0.05, meaning that there are no significant differences in the means and variances of inter-subject forces across the three regions. Additionally, the levels of coupling, volatility, and mutability of the subject capacities in the North Kang region are all intermediate among the three districts. However, there are relatively large differences in mutability within the region. The coupling of the subject capacities in the Central Kang region is relatively high; however, the internal differences are also relatively large, and the mutability level is relatively low, indicating that there is a certain risk of subject structure imbalance. The mutability level of the subjects in the South Kang region is relatively high, and the volatility level is relatively low, suggesting that the subject structure is relatively rational and stable; however, the differences in the capacities of various subjects are relatively large.

The measurement results of the IIF in various townships (Figure 7) show that as the comprehensive index level of the IIF decreases from high to low, there is a slow downward trend in coupling and a slow upward trend in mutability, while volatility shows a significant upward trend. For the organizational system of rural governance, the coupling of subjects plays a positive role, while volatility and mutability play negative roles. The measurement indicators were standardized and transformed into scores for various interaction forces. The results are shown in Figure 8. The average IIF value among the various townships is 0.51. A total of 10 townships had scores higher than the average value: four townships in the North Kang region, three in the Central Kang region, and three in the South Kang region, accounting for 67%, 75%, and 60% of the regional sample numbers, respectively. Analysis of the reasons for the results showed that Anjiakou Town, Tongqian Town, and Nianba Town owe their relatively high scores to the coupling and mutability of governance subjects; the scores for Douba Town and Chengguan Town are affected by the combination of coupling and volatility; the scores for Zhoujiaba Town, Yangba Town, and Pingluo Town are attributed to the relatively high coupling scores among the subjects; and Wangguan Town and Dananyu Town are influenced by volatility and mutability scores, respectively.

5.4. Obstacle Factor Analysis

5.4.1. Obstacle Factors at the Regional Level

The obstacle degree of specific indicators in each region was calculated using Equation (12), and a bar chart was drawn based on the magnitude of these obstacle degrees (Figure 9). The obstacle degrees of the inter-subject interaction forces (X6, X7, and X8) are relatively high in all three regions, so inter-subject interaction force is the main obstacle factor hindering the MRGC in each region. However, differences were found in the obstacle degree for specific interaction force among the three regions. For the North Kang region, the main obstacle factors are the coupling (X6), volatility (X7), and mutability (X8) of the MRGC system, with the obstacle degree decreasing gradually. For the South Kang region, the main obstacle factors are the system’s coupling nature (X6) and volatility (X7), but the difference in their obstacle degrees is negligible. For the Central Kang region, the primary obstacle factor is the system’s mutability (X8), which is significantly higher than other factors, consistent with the findings in Table 5. The obstacle degree regarding governance capacity is significantly lower than that of the inter-subject interaction force, which is mainly reflected in the operational development capacity (X43), public management capacity (X22), organizational system-building capacity (X11), and organizational mobilization capacity (X32). In addition, the Talent team building capacity (X13) and the public service capacity (X21) in the Central Kang region, the discuss and advise capability (X51) in the North Kang region, and the digital application capacity (X53) in the South Kang region also hinder the development of regional MRGC to a certain extent.

5.4.2. Obstacle Factors at the Township Level

The obstacle degrees of specific MRGC indicators for each township were calculated using Equation (12). The top five indicators with the highest obstacle degrees in each township were selected as the main obstacle factors (

Table 7. Main obstacle factors and obstacle degrees of township MRGC (%).

Regions	Township	Ranking of Obstacle Degrees
		1	2	3	4	5
The North	Dannanyu	X6 (20.11)	X7 (18.07)	X43 (15.30)	X8 (10.55)	X53 (9.92)
	Miba	X7 (20.80)	X6 (20.32)	X8 (14.95)	X43 (11.95)	X53 (6.42)
	Pingluo	X8 (22.10)	X7 (17.12)	X43 (9.93)	X53 (8.74)	X6 (8.55)
	Wangguan	X8 (18.65)	X6 (17.26)	X43 (16.41)	X7 (13.71)	X12 (5.05)
	Changba	X8 (23.01)	X7 (14.01)	X43 (10.32)	X6 (8.60)	X22 (7.13)
	Zhoujiaba	X43 (15.52)	X8 (13.23)	X7 (12.07)	X22 (10.68)	X53 (10.42)
The Central	Chengguan	X8 (33.54)	X7 (8.95)	X53 (8.73)	X22 (7.47)	X32 (6.53)
	Douba	X8 (42.80)	X53 (14.96)	X22 (6.29)	X13 (6.07)	X51 (6.00)
	Nianba	X7 (15.91)	X43 (14.98)	X8 (12.36)	X6 (8.28)	X53 (8.07)
	Wangba	X8 (23.84)	X6 (17.09)	X7 (15.73)	X43 (10.96)	X22 (6.18)
The South	Anmenkou	X7 (19.98)	X43 (16.79)	X53 (12.48)	X6 (9.81)	X32 (8.26)
	Baiyang	X6 (22.71)	X7 (22.71)	X8 (13.19)	X43 (10.18)	X53 (7.36)
	Lianghe	X7 (20.71)	X8 (18.29)	X6 (17.62)	X43 (14.34)	X53 (7.61)
	Tongqian	X7 (16.40)	X43 (14.65)	X22 (11.47)	X53 (10.84)	X8 (9.49)
	Yangba	X8 (18.26)	X7 (16.39)	X43 (12.69)	X6 (11.85)	X32 (8.89)

). Furthermore, indicators with an obstacle degree of 5% or higher were screened out, and the frequency of obstacle factors under the two criteria was counted. Based on these results, a frequency distribution chart of MRGC obstacle factors at the township level was drawn (Figure 10). Among these factors, the frequency of the main obstacle factors reflects the intensity of their impact on the MRGC, while the frequency of obstacle factors with a degree exceeding 5% reflects the scope (breadth) of their impact.

The results show that obstacle factors at the township level can be roughly divided into three categories. The first category includes obstacle factors with high impact intensity and wide scope, namely, the operational development capacity (X43), volatility (X7), mutability (X8), the digital application capacity (X53), and coupling (X6). These five obstacle factors all have a frequency exceeding 10 in both the main obstacle factors and those with an obstacle degree over 5%, indicating that the interaction of the rural governance system, the operational development of social organizations, and villagers’ information application capabilities poses overall constraints on the sustainable development of the MRGC. In particular, in Chengguan Town and Douba Town of the Central Kang region, the system’s mutability (X8) serves as the most significant obstacle factor, with its obstacle degree being significantly higher than that of the second-highest ranked obstacle factor.

The second category consists of obstacle factors with a certain impact scope but weak influence, including the organizational mobilization capacity (X32), the discuss and advise capability (X51), the talent team building capacity (X13), the organizational system-building capacity (X11), and the public service capacity (X21). These factors mainly involve institutional buildings, human capital, and social security. Currently, their impact on the MRGC is limited, but they may pose significant obstacles in long-term development, especially regarding the quality of rural human capital against the backdrop of population outflow.

The third category includes strong obstacle factors with a narrow scope, such as the public management capacity (X22) and the leadership and cadre teams to build capacity (X12). These indicators only act as obstacles in a small number of townships, such as Douba Town, Tongqian Town, and Zhoujiaba Town, but their obstacle degree is high. This indicates that these townships need to pay attention to issues related to government planning and design, as well as Party members’ age.

6. Discussion and Conclusions

6.1. Research Contributions

The innovative aspects of this study are principally represented in the following three areas:

(1)

Differing from previous studies that measured the MRGC from the perspective of rural public affairs, this research focuses on the pluralistic subjects of rural governance, highlights its modernization characteristics, and constructs a measurement index system for the MRGC. This approach provides a new perspective for measurement research focused on rural issues, offering an innovative framework that shifts the focus from transactional affairs to subject-oriented governance capacity assessment.

(2)

Prior studies have often overlooked the interrelations among sub-dimensions within the index system. By contrast, this research proceeds from the principles of systems theory, examining rural governance activities within complex economic–social contexts to conduct a statistical interpretation of their social system characteristics. It innovatively designs measurement indicators to quantify the intra-systemic interactions, enabling a more detailed analysis of system elements’ states and thus enhancing the accuracy of MRGC assessment.

(3)

In terms of index weighting, this study integrates three weighting principles—variability, correlation, and uncertainty—to design a combined weighting method. On the premise of ensuring that the original measurement ranking remains unchanged, this method effectively corrects the differences caused by a single weighting method while making the measurement scores more discriminative.

6.2. Conclusions

The following conclusions can be drawn from this study:

(1)

The IIF emerges as a critical determinant of the MRGC. Heterogeneity is prevalent in both SGC and IIF across townships, with relatively small variations in SGC but pronounced disparities in IIF. There is no significant difference in their means, but the variance of the IIF is significantly larger than that of the SGC. Thus, the primary driver of higher MRGC levels is the pulling effect of robust IIF, where subjects form mutually reinforcing, equitably balanced, and structurally rational cooperative relationships. Conversely, the lack of linkages among pluralistic subjects tends to lead to missed rural development opportunities, rendering them incapable of addressing external economic shocks and environmental crises [75].

(2)

The SGC of rural subjects in Kang County remains at a medium level, but significant heterogeneity exists among subjects, manifesting as two distinct states: heterogeneous high-level and homogeneous low-level. Notable disparities are observed in the SGC of various subjects: grassroots party organizations, governments, and self-governing organizations exhibit medium-level capabilities, whereas SEOs and villagers demonstrate relatively lower capacities. This indicates that GPOs can play a leading role in rural governance, and the primary-level Party members can effectively play their vanguard and exemplary roles. The government plays an important role in the service, management, and guarantee of public undertakings. AOs can achieve rural self-management, self-education, and self-service, to a certain extent. The villagers have the potential to participate in rural governance, while the capacities of SEOs need to be improved. In terms of the differences in subject capabilities, the governance capability disparities among high-level subjects are significantly greater than those among low-level subjects. This is attributed to villagers’ low educational attainment, insufficient democratic literacy, and ingrained habits, which slow their absorption of modern rural governance knowledge. As a result, passive participation in governance is common. Moreover, population out-migration has exacerbated the loss of highly educated groups, weakening governance participation vitality [76]. As for SEOs, rural financial institutions impose high thresholds and barriers on financial services; policy support features limited coverage and unstable safeguard mechanisms; and development approaches lack effective planning strategies, leading to low economic efficiency. These collectively result in a lack of endogenous motivation for SEOs in rural economic governance [77].

(3)

The Inter-Subjective Interaction Force (IIF) in Kang County remains at an intermediate level, with volatility identified as the primary factor influencing IIF levels. Significant disparities exist in the effects of various IIF components, presenting an order of coupling > volatility > abruptness. As the comprehensive IIF index declines from high to low (Figure 6), the degree of variation in volatility is notably higher than that in coupling and abruptness, indicating that the expansion of capability disparities among subjects is the dominant factor driving IIF reduction. Alejandro’s research [78] reaches a similar conclusion: disparities in actors’ capabilities, cognition, and technology are key barriers to cooperation, while effective information exchange can facilitate actors in forming shared goals and enhancing technological proximity.

(4)

Rural subjects in resource-endowed areas exhibit higher SGC but face a greater possibility of systemic structural imbalance. In the regional comparison, the SGC of rural subjects in the central region was found to be higher than that in the southern and northern regions, which can be attributed to more balanced governance capabilities among subjects in the central region—or, more specifically, the governance capabilities of SEOs and villagers in this region are significantly higher. The Levene test revealed that there is no homogeneity of variance in the governance capabilities of SEOs across the three regions. The variance of governance capabilities of social and economic organizations in the Central Kang region is significantly higher than that in the other two regions, indicating greater internal differences in levels within the Central Kang region. Under the policy orientation of centering on county seats, promoting new-type urbanization, and coordinating urban–rural development, county seats undertake multiple functions such as population aggregation, resource integration, information circulation, and public service provision [79]. As the location of the county seat, the Central Kang region is more prone to benefiting from the spillover effects of characteristic industry development, human resource aggregation, and basic public services in county construction. However, the scope of its radiating and driving effect is limited, and the towns in the Central Kang region that are not located in county seats have developed relatively slowly. In terms of the IIF, the central Kang region has a lower abruptness score than the other two regions, indicating the possibility of subject structural imbalance. This may give rise to new governance models or cause order chaos, necessitating the establishment of a dynamic monitoring and early warning mechanism and timely adjustment of development strategies.

(5)

The obstacle factors affecting the development of MRGC at both the regional and township levels include the coupling nature, volatility, and mutability of inter-subject interactions. From the regional perspective, the North Kang region faces constraints in all three aforementioned interactions; the South Kang region is only constrained in terms of coupling nature and volatility; while the Central Kang region is prominently constrained in mutability, which is consistent with the findings in Table 5. From the township perspective, the operational performance of rural SEOs and village’ information application capabilities also restrict the development of MRGC. The restrictive effects of institutional building in rural governance, human capital, and social security are to a certain extent universal but not strong. Government planning and design, as well as the age structure of Party organization members, have a strong impact within a narrow scope.

(6)

This study designed combined weights by integrating three data characteristics: variability, correlation, and uncertainty. The statistical test results show that the ranking of measurement results obtained via the combined weighting method is consistent with that of single weighting methods. Significant differences are found between its measurement scores and those from variability and correlation weighting methods. Furthermore, its standard deviation is larger than that of the three single-characteristic weighting methods. Therefore, besides ensuring that the original measurement ranking remains unchanged, the combined weighting method can effectively correct deviations caused by single weighting methods, as well as enhance the discrimination of measurement scores. Currently, combined weighting methods are increasingly applied to multicriteria decision-making problems in fields such as business economics, environment, and engineering [80].

6.3. Suggestions

Based on the research, this study argues that enhancing the MRGC should be approached from two aspects: activating subject capabilities and promoting inter-subjective interaction.

(1)

In terms of activating subject capabilities, efforts should be made to enhance the information acquisition, analytical decision making, and action implementation capacities of governance subjects [47]. First, led by government departments, data from multiple subjects and departments should be collected and integrated to establish a comprehensive rural governance information and monitoring early warning platform that is publicly disclosed to the public in accordance with the law to break information barriers. Second, all subjects should actively recruit returned college students and migrant workers with rich professional knowledge and strong working capabilities to participate in decision making, combining the advanced technologies possessed by returned personnel with the past experience of local personnel to make scientific decisions. Finally, administrative and market means should be comprehensively used, and measures, such as financial subsidies, financial loans, and project demonstrations, should be adopted to ensure the implementation of subject actions.

(2)

In promoting inter-subjective interaction, approaches like goal consensus, communication consultation, and rational allocation should be employed to foster mutual trust and collective action among pluralistic subjects. First, common goals must focus on addressing livelihood issues in rural development and meeting villagers’ aspirations for a better life [45]. Therefore, the priorities of rural governance should lie in infrastructure construction, public service improvement, and resident welfare enhancement [47]. Second, a combination of online and offline approaches should be adopted, utilizing platforms, such as WeChat, Tencent Meeting, and village councils, to facilitate information sharing, collaborative learning, trust-building among pluralistic subjects, and long-term stability of cooperative relationships. Finally, a sound mechanism for shared responsibility and interest allocation should be established: laws and regulations should be formulated to clarify the scope and intensity of subject powers based on their characteristics, responsibilities should be shared according to the magnitude of subject powers, and interests should be distributed in proportion to the shareholdings of subjects in local industries, ensuring the alignment of rights, responsibilities, and interests to stimulate the enthusiasm of subjects for governance participation.

6.4. Limitations and Future Research Directions

This study has several limitations:

(1)

In terms of the measurement index system, rural governance encompasses a multitude of complex issues. The measurement content of the MRGC should evolve as our understanding of its connotations increases, embodying characteristics of the times. Future research could thoroughly explore the scientific connotations of MRGC under new circumstances and objectives—such as informatization, intelligentization, common prosperity, and urban–rural integration—before designing the measurement index system accordingly. In addition, the object of research on the measurement of rural governance capacity is the level and status of factor input in the process of rural governance. However, good input does not necessarily mean good results [81]. Therefore, future research can shift to a result-oriented perspective and study the effectiveness of rural governance from fields such as rural culture, ecology, and people’s livelihood.

(2)

In terms of research scope, this study selected only cross-sectional data from 15 towns in Kang County for analysis, making it difficult to characterize the dynamic changes in MRGC in the temporal dimension. Future research can proceed from two aspects: first, selecting several sample counties for periodic follow-up surveys to analyze the dynamic evolution of MRGC over time; second, attempting to expand the research scope to regional or national panel data to explore the evolutionary paths and differentiation characteristics of provincial-level MRGC in temporal and spatial dimensions.

(3)

In terms of research methods, the weight assignment approach prioritized objectivity, mainly adopting combined weights of objective weighting methods. Future studies could incorporate subjective weighting methods into the combined framework and attempt new weighting techniques such as CILOS, IDOCRIW, and FUCOM. In terms of model design, this study constructs a composite index through the linear synthesis of factors based on the assumption that there is a linear relationship between the elements of the governance system. However, the governance system is a complex system, and there may be non-linear relationships between its elements. Future research can conduct in-depth analysis of the mechanisms and influences between elements to further improve the model design.