Analysis of Regional Differences and Influencing Factors of Rural Governance Modernisation in China

The realization of Chinese-style modernization is a big game, because it is a modernization with a huge population, a modernization of common prosperity for all people, a modernization that harmonizes material and spiritual civilization, a modernization of harmonious coexistence between human beings and nature, and a modernization that takes the road of peaceful development [1-3]. This determines that there is no template that we can follow and emulate, and we can only reach it by our own efforts and exploration [4]. In contrast, the modernization of rural governance will face more difficulties, for the realization of Chinese-style modernization of the fundamental problems and bottlenecks, only fully aware of this, in order to maintain a clear mind and strategic determination, the comprehensive construction of a modern socialist country, the most arduous and burdensome task is still in the countryside [5-7].

The modernization of rural grassroots governance is advancing rapidly, the most important manifestation of which is the transition from minimalist governance to sectional governance, where grassroots governance is mainly about completing top-down standardized national tasks [8-9]. China’s rural areas are vast and complex, different regions, different rural areas as well as different farmers have different needs [10], and it is difficult to dovetail the top-down rigid requirements with the bottom-up special circumstances, so that widespread formalism and high governance costs have emerged in grassroots governance, resulting in a certain degree of ineffective governance [11-13]. The problem is that the grassroots governance system is formal and complete but lacks flexibility, the real needs of villagers are not effectively expressed, and the subjectivity of villagers is lost [14].

The current problem in the theory and practice of grassroots governance modernization is that it is aimless, lacks the understanding of the environment, object and task of grassroots governance modernization [15], and simply copies the standard of modernization of the national governance system and governance capacity to the grassroots level in the countryside, which produces a serious maladaptation syndrome [16], the most typical is the ineffectiveness or inefficiency of grassroots governance, and a large amount of governance resources are invested in it, and the grassroots governance is Extreme involution [17], but mostly idle, governance resources failed to turn into governance performance, much less into the mobilization of farmers [18].

This paper investigates the current situation of regional differences in rural governance modernization, based on the theory of rural grassroots governance modernization. It analyzes the factors influencing the modernization of rural governance in China through the CRITIC-entropy weight method, TOPSIS comprehensive evaluation method and regression analysis method, and measures the efficiency of modernized rural governance in each province and region, and analyzes the characteristics of regional differences in modernized rural governance in each province and region. Then, an evaluation index system of rural governance modernization level is constructed, which includes five first-level indicators, namely, rural basic governance capacity, rural public affairs governance, rural public security governance, rural public service governance and rural public environment governance modernization level, to analyze the overall level of China’s modernized rural governance. Finally, it explores the influencing factors of China’s modernized rural governance.

2

Method

2.1

Modernization of rural grass-roots governance

Governance refers to the sum of many institutional ways in which various public or private individuals and institutions manage their common affairs, which is a kind of practical activity in which various actors participate in the process of social affairs and manage them through consultation, negotiation, consensus and cooperation, etc., with the aim of establishing and maintaining a stable, orderly and win-win cooperative order. The essence of governance is the plurality of governing subjects and the multidimensionality of governing objects (objects). The mode of governance is diverse, mainly through negotiation, consultation, and cooperation to reach consensus. The establishment and soundness of the system is the key and foundation of governance, including a series of explicit or implicit principles, guidelines, norms, standards, agreements, procedures, and so on.

Rural grassroots governance is the extension of the concept of governance in the countryside and the expansion of governance thinking in rural society, while the modernization of rural grassroots governance is the ultimate point of rural grassroots governance. Rural grassroots governance refers to the leadership of the party committee and the government at the grassroots level, with the rural grassroots party organization as the leading core, giving full play to the strength of various types of organizations in rural areas, villagers’ groups and other multi-body forces, to realize the process of rural society’s pluralistic main bodies to participate in the process of rural self-governance, and in the process of mutual cooperation, mutual promotion, and to jointly crack the modernization of the process of rural governance problems, and to achieve the effective allocation of public resources and maximization of public interests. Maximizing public resources and interests is the ultimate goal, with the ultimate goal of modernizing rural grassroots governance. Rural grassroots governance is the unity of subject and object, i.e., the subjects take actions to mediate various conflicts in the countryside to realize effective governance of rural grassroots society, and the ultimate direction is to realize the modernization of rural grassroots governance.

2.2

Regional differences in the modernization of rural governance

This paper selects statistical data from 30 provinces, autonomous regions and municipalities directly under the central government of China from 2010 to 2015, focusing on analyzing the characteristics of modernized rural governance in China from the end of the 11th Five-Year Plan to the end of the 12th Five-Year Plan. The data are mainly derived from the China Rural Statistics Yearbook 2011-2016, the China Urban and Rural Construction Statistics Yearbook 2010-2015, the China Statistics Yearbook 2011-2016, the China Education Statistics Yearbook 2010-2015, the China Environment Statistics Yearbook 2011-2016, and statistics from the Department of Comprehensive Planning of the Ministry of Transportation and Communications.

According to existing research, from an overall perspective, the level of modernized rural governance in China is not high, and from the perspective of regional differences, the areas with higher levels of modernized rural governance are mainly concentrated in Beijing, Tianjin and the southeast coastal region, and from 2010 to 2015, the areas with better levels of modernized rural governance shifted even more towards the southeast coastal provinces, and there are large differences in the level of modernized rural governance between regions, with the gap between the maximum and minimum values of the evaluation of the level of modernized rural governance in 2010 and 2015 being 0.4529 and 0.6021 respectively, indicating that regional differences have a tendency to further expand. The gap between the maximum and minimum values of the evaluation of the modernized rural governance level in 2015 was 0.4529 and 0.6021, respectively, which indicates that regional differences have a tendency to further expand. From the perspective of the three major regions of the east, middle and west, after five years of development from 2010 to 2015, the eastern region has the highest level of modernized rural governance, with a quality evaluation mean value of 0.3418, followed by the west (0.1846) and the central region (0.1731), which suggests that in terms of the state of the level of modernized rural governance in the middle and west, the level of modernized governance in general is not in line with the overall level of economic development, and there may be a discrepancy between the level of modernized governance and the overall level of economic development. Mismatch, and there may be a state of disharmony with economic development. In terms of development speed, the level of modernized rural governance in the eastern region has increased the fastest, with an average increase of 0.0937, followed by the central and western regions. Therefore, a comprehensive consideration of the level of modernized rural governance and the level of economic development is needed for a more rational division of regions, which is extremely crucial for the central government to develop differentiated governance strategies according to local conditions.

2.3

A model for measuring regional differences in the modernization of rural governance

2.3.1

CRITIC-entropy weighting method combined weight model assignment

When using the comprehensive evaluation index system to measure a phenomenon, commonly used assignment methods include hierarchical analysis, data envelopment analysis, and entropy weighting. The subjective assignment method is mainly based on the researchers’ experience in judging the evaluation indexes. Among them, the subjective assignment method is mainly based on the researchers’ empirical judgment of evaluation indexes, which is easily affected by human subjective factors. In this paper, when measuring the level of high-quality development of China’s productive service industry, the evaluation indexes selected are obtained through the statistical yearbook data, and the original data and indexes are relatively clear, so this paper adopts the objective assignment method to analyze.

The CRITIC method is a type of objective weighting method, which can comprehensively consider the volatility (comparative strength) and conflict (correlation) between the indicators, and use this to comprehensively determine the weights of the indicators. However, the single CRITIC method does not take into account the discrete nature of the indicators, and the entropy weight method is a method of assigning weights to the indicators according to the degree of dispersion of the data of the indicators, i.e., the entropy weight method can effectively make up for the shortcomings of the CRITIC method. Since the entropy weight method can not analyze the mutual influence between the evaluation indicators, and the CRITIC method can not analyze the impact of the dispersion of the indicators on their weights, scholars have found that the entropy weight method and the CRITIC assignment method can be used in combination with the comprehensive consideration of the degree of dispersion of the indicators, but also take into account the comparative strength of the indicators as well as the interactions between the indicators, so that it can make a more objective, scientific and reasonable assessment of the evaluation indicators. The combination of entropy weight method and CRITIC weighting method can comprehensively consider the discrete degree of each index, as well as the comparative strength of the indexes and the interaction between the indexes, so that the evaluation of each index can be assigned objectively, scientifically and reasonably. Therefore, this paper refers to the practice of Wu Zhong et al. and chooses CRITIC-entropy weighting method combined weighting model to calculate the weights of indicators. The specific calculation steps are as follows:

Set the evaluation object selected a total of m , a total of n evaluation indicators, the original data is set to X_ij , i = 1,2,...,m, j = 1,2,...,n. The indicators selected in this paper are positive indicators, do not need to negative indicators for the normalization process, now only need to evaluate the indicators of the original data for the dimensionless processing, that is, the normalization of the original data for the normalization process. Normalization: (1) $x_{i j} = \frac{X_{i j} - X_{\min}}{X_{\max} - X_{\min}}$

1) The entropy weight method is used to calculate the weight of each evaluation index: (2) $p_{i j} = \frac{x_{i j}}{\sum_{i = 1}^{m} x_{i j}}$ (3) $e_{j} = - \frac{1}{In (m)} \sum_{i = 1}^{m} p_{i j} In (p_{i j})$ (4) $ω_{1 j} = \frac{1 - e_{j}}{\sum_{j = 1}^{n} (1 - e_{j})}$

2) The weight of each evaluation indicator is calculated by CRITIC assignment method: (5) $c_{j} = \frac{σ_{j}}{{\bar{x}}_{j}} \sum_{i = 1}^{m} (1 - | r_{i j} |)$ (6) $ω_{2 j} = \frac{c_{j}}{\sum_{j = 1}^{n} c_{j}}$

3) Finally, the combined weight of the j th indicator is calculated: (7) $ω_{j} = α ω_{1 j} + (1 - α) ω_{2 j}$

It is assumed that the entropy weighting method and the CRITIC assignment method have the same importance in the calculation of the composite weights, which is taken as α = 0.5.

2.3.2

TOPSIS integrated evaluation methodology

TOPSIS comprehensive evaluation method is a widely used method for multi-objective decision-making, and its basic principle is: calculate the optimal solution and the worst solution of each evaluation object, and then calculate the distance between the optimal solution and the worst solution and rank them, and the best case is that the object to be evaluated is the closest to the optimal solution and at the same time is the farthest from the worst solution, and the attributes involved in the evaluation of each optimal and the worst solution are the optimal and the worst values within the evaluation indexes in the group, respectively. Each optimal solution and the worst solution participate in the evaluation of the attribute is the optimal value and the worst value within the group of evaluation indexes.

The combination of CRITIC-entropy weighting model and TOPSIS method can effectively solve the problem of importance and correlation between variables that cannot be reflected by the traditional TOPSIS method, and then carry out relatively scientific and rationalized evaluation of existing objects. Therefore, this paper chooses the TOPSIS comprehensive evaluation method of the CRITIC-medium weighting model to measure the level of high-quality development of China’s productive service industry. The specific operational steps are as follows:

1) Calculate the weighting matrix: (8) $V = [\begin{matrix} v_{11} & \dots & v_{1 n} \\ ⋮ & ⋱ & ⋮ \\ v_{m 1} & \dots & v_{m n} \end{matrix}]$

where ν_ij = x_ij × ω_j, ω_j is the combined weight of the j nd indicator.

2) Determine the positive and negative ideal solutions: (9) $V^{+} = (v_{1}^{+}, v_{2}^{+}, \dots, v_{n}^{+}) = {\max v_{i j} | j \in J_{1}, \min v_{i j} | j \in J_{2}}$ (10) $V^{-} = (v_{1}^{-}, v_{2}^{-}, \dots, v_{n}^{-}) = {\min v_{i j} | j \in J_{1}, \max v_{i j} | j \in J_{2}}$

3) Calculate the distance between each evaluation object and positive ideal solution V⁺ and negative ideal solution V⁻: (11) $D_{i}^{+} = \sqrt{\sum_{j = 1}^{n} {(v_{i j} - v_{j}^{+})}^{2}}$ (12) $D_{i}^{-} = \sqrt{\sum_{j = 1}^{n} {(v_{i j} - v_{j}^{-})}^{2}}$

Calculate the proximity of each evaluation object to the optimal solution C_i : (13) $C_{i} = \frac{D_{i}^{-}}{D_{i}^{+} + D_{i}^{-}}$

2.3.3

Regression analysis

1)

Parametric regression

Assume that there is a correlation between variable x₁,x₂,...,x_k and random variable y, if for any x₁,x₂,...,x_k of the variables there is always a corresponding value of y . Then the relationship between the explained variable y and the explanatory variable x₁,x₂,...,x_k is modeled as: (14) $y = f (x_{1}, x_{2}, \dots, x_{k}) + ε$

Parametric regression is mainly divided into linear regression and nonlinear regression model, linear regression model should be established for linear relationship between variables, in which linear regression is divided into univariate linear regression and multiple linear regression. The traditional power load forecasting model is more commonly used for multiple linear regression model: (15) $Y = β_{0} + β_{1} X_{1} + \dots + β_{P} X_{P} + ε$

Where: β₀,β₁,...,β_p is the p+1 unknown regression parameters, where Y is the explained variable of linear regression x₁,x₂,...,x_p is the p general variable that is precisely measured and can be controlled, usually referred to as in equation (15) as a multiple regression model, and ε is the random error.

Of the parametric regressions, linear regression models are the most common and the one with the widest range of applications and less difficult applications. Many nonlinear problems in the model calculation are sometimes simplified to a linear model for calculation. Linear regression includes univariate linear regression and multiple linear regression, and univariate linear regression is a special case in multiple linear regression, which has only one type of independent variable.

Multiple linear regression model is widely used in the power load forecasting model, the power system load receives many influencing factors, including: temperature, humidity, policy and economic and other factors, thus assuming that there are p influencing factors with the load: x₁,x₂,...,x_p, then the historical load value of Y and the random variable x₁,x₂,...,x_p there is a certain coupling relationship in the medium and long-term load forecasting, assuming that obeys the multiple linear coupling relationship. The same expectation value is taken for both ends of Eq. (15):

(16)

\begin{array}{l} E (Y) = β_{0} + β_{1} X_{1} + \dots + β_{P} X_{P} + ε \\ ε \sim N (0, σ^{2}) \end{array}

If there exist n sets of observations for the random variable X₁,X₂,...,X_p,Y , then the i sub-sample values are:

X_t1,X_i2,...,X_ip,Y_i then the multivariate linear equation between the load forecast value and its influence factor can be expressed as: (17) ${\begin{matrix} Y_{1} = β_{10} + β_{11} X_{11} + \dots + β_{1 P} X_{1 P} + ε_{1} \\ ⋮ \\ Y_{i} = β_{i 0} + β_{i 1} X_{i 1} + \dots + β_{i P} X_{i P} + ε_{i} \\ ⋮ \\ Y_{n} = β_{n 0} + β_{n 1} X_{i 1} + \dots + β_{n P} X_{n P} + ε_{n} \end{matrix}$

The variables are represented using a matrix as shown in equation (18) below: (18) $Y = [\begin{matrix} Y_{1} \\ Y_{2} \\ M \\ Y_{n} \end{matrix}] X = [\begin{matrix} 1 & X_{11} & X_{12} & \dots & X_{1 p} \\ 1 & X_{21} & X_{22} & \dots & X_{2 p} \\ ⋮ & ⋮ & ⋮ & ⋮ & ⋮ \\ 1 & X_{n 1} & X_{n 2} & \dots & X_{n p} \end{matrix}] B = [\begin{matrix} b_{1} \\ b_{2} \\ ⋮ \\ b_{n} \end{matrix}] ε = [\begin{matrix} ε_{1} \\ ε_{2} \\ ⋮ \\ ε_{n} \end{matrix}]$

The regression equation is then expressed using the matrix form: (19) $Y = X B + ε$

The parameter matrix was obtained using the least squares parameter estimation method of Eq. (20): (20) $\hat{B} = {(X X)}^{- 1} X^{'} Y$ (21) $\hat{B} = [\begin{matrix} {\hat{b}}_{0} \\ {\hat{b}}_{1} \\ ⋮ \\ {\hat{b}}_{p} \end{matrix}]$

where ${\hat{b}}_{0}, {\hat{b}}_{1}, \dots, {\hat{b}}_{p}$ parameter regression coefficient, then the results of parameter estimation can be expressed as: (22) $\hat{Y} = X \hat{B} + ε$

2)

Non-parametric regression

Usually in parametric regression, whether it is a linear regression method or a nonlinear regression method, the functional relationship between the variables in the model needs to be assumed in advance, and the prediction results are obtained through data fitting, and then according to the results of parameter estimation. If the assumed model is not reasonable enough, there will be a large error and the regression effect is not good, however, in many problems, there is no fixed functional form between the actual variables, and the optimal regression of the function cannot be realized by the parametric regression and semiparametric regression methods. As a result, nonparametric regression methods are introduced to solve such problems. There are two main types of nonparametric regression methods: the first type of methods, such as local polynomial regression, spline regression, etc., which do not need to transform the explanatory variables and the explained variables. The other, on the contrary, such as alternating conditional expectation transformation regression, requires first a nonlinear transformation of the explanatory variables and the explanatory variables, and then a combined weighted sum. The alternating conditional expectation regression method is a nonparametric estimation method that utilizes the optimal transformation of expectations to obtain the coupled equations between multiple explanatory variables and the explained variables through continuous iterative optimization.

There are various types of nonparametric regression, and the weight function method was first proposed, in which the regression function Y = g(x) is estimated: (23) $Y = g_{n} (x) + ε$ (24) $g_{n} (x) = \sum_{i = 1}^{n} W_{i} (X_{i}) Y_{i}$

3

Results and Discussion

3.1

Analysis of regional differences in modernized rural governance

This paper uses the CRITIC-entropy weight method to obtain data about the indicators involved in the evaluation of China’s rural modernization governance efficiency in 2018, based on the 2019 China Statistical Yearbook, China Social Statistical Yearbook, China Urban and Rural Construction Statistical Yearbook, and China Rural Statistical Yearbook. Deap2.1 software was used to carry out modernization governance efficiency measurement of rural modernization governance efficiency in 31 provinces in China, and the results of rural modernization governance efficiency in each province are shown in Table 1.

Table 1.

The efficiency of rural modernized management of China’s provinces

Region	Province	Integrated efficiency	Pure technical efficiency	Scale efficiency	Scale compensation	Result judgment
National efficiency mean		0.479	0.732	0.625
North China	Beijing	1.000	1.000	1.000	Unchanged	Effective governance
	Tianjin	1.000	1.000	1.000	Unchanged	Effective governance
	Hebei	0.256	0.312	0.775	Diminishing	Ineffective governance
	Shanxi	0.262	0.483	0.565	Diminishing	Ineffective governance
	Inner Mongolia	0.227	0.546	0.546	Diminishing	Ineffective governance
	Mean	0.549	0.668	0.777
Northeast China	Liaoning	0.534	0.569	0.676	Diminishing	Ineffective governance
	Jilin	0.563	0.627	0.742	Diminishing	Ineffective governance
	Heilongjiang	0.771	0.758	0.818	Increasing	Ineffective governance
	Mean	0.623	0.651	0.745
East China	Shanghai	1.000	1.000	1.000	Unchanged	Effective governance
	Jiangsu	0.804	1.000	0.804	Diminishing	Weak governance
	Zhejiang	0.768	0.684	0.738	Diminishing	Ineffective governance
	Anhui	0.545	0.693	0.525	Diminishing	Ineffective governance
	Fujian	0.498	1.000	0.498	Diminishing	Weak governance
	Jiangxi	0.667	0.923	0.602	Diminishing	Ineffective governance
	Shandong	0.346	1.000	0.346	Diminishing	Weak governance
	Mean	0.661	0.900	0.645
Central China	Henan	0.224	0.458	0.526	Diminishing	Ineffective governance
	Hubei	0.326	0.405	0.648	Diminishing	Ineffective governance
	Hunan	0.328	0.624	0.674	Diminishing	Ineffective governance
	Mean	0.293	0.496	0.616
South China	Guangdong	0.486	0.536	0.557	Diminishing	Ineffective governance
	Guangxi	0.340	1.000	0.340	Diminishing	Weak governance
	Hainan	1.000	1.000	1.000	Unchanged	Effective governance
	Mean	0.609	0.845	0.632
Southwest China	Chongqing	0.464	1.000	0.464	Diminishing	Weak governance
	Sichuan	0.168	0.286	0.554	Diminishing	Ineffective governance
	Guizhou	0.118	0.276	0.575	Diminishing	Ineffective governance
	Yunnan	0.163	0.267	0.694	Increasing	Ineffective governance
	Tibet	0.654	1.000	0.654	Diminishing	Weak governance
	Mean	0.313	0.566	0.588
Northwest China	Shaanxi	0.254	0.258	0.976	Diminishing	Ineffective governance
	Gansu	0.258	1.000	0.258	Diminishing	Weak governance
	Qinghai	0.262	1.000	0.262	Diminishing	Weak governance
	Ningxia	0.228	1.000	0.228	Diminishing	Weak governance
	Xinjiang	0.335	1.000	0.335	Diminishing	Weak governance
	Mean	0.267	0.852	0.412

The average value of China’s modernized rural governance efficiency in 2018 was 0.479, and there were 14 provinces with effective (comprehensive efficiency equal to 1) or weakly effective (pure technical efficiency equal to 1, scale efficiency equal to or less than 1) modernized governance, accounting for 45.2% of all provinces, indicating that only the abovementioned 14 provinces have relatively effective allocation of governance resource inputs, and the governance efficiency as a whole is on the low side, and it is mainly distributed in the Northwest (4 provinces), East China (4 provinces), North China (2 provinces), South China (2 provinces) and Southwest China (2 provinces). The total number of provinces with ineffective governance of rural modernization is 17, accounting for 51.8% of all provinces, distributed in North China (3 provinces), Northeast China (3 provinces), East China (3 provinces), Central China (3 provinces), Southwest China (3 provinces), South China (1 province), and Northwest China (1 province), which indicates that the governance resources invested in the 17 provinces have not been deployed in a relatively effective manner. The descending order of regional comprehensive efficiency is East China (0.661) >Northeast China (0.623) >South China (0.609) >North China (0.549) >Southwest China (0.313) >Central China (0.293) >Northwest China (0.267), in which the comprehensive efficiency of East China, Northeast China, and South China are all greater than 0.6, and North, Southwest, Central and Northwest China regions only exert less than 60% of the efficiency level, and there are obvious differences in governance efficiency.

In terms of pure technical efficiency, the average value of governance efficiency in China’s rural modernization in 2018 was 0.732, with a total of 14 technically efficient (pure technical efficiency equal to 1) provinces, accounting for 45.2% of the evaluated units, indicating that the governance resource inputs were fully utilized in only 14 of them at the current scale. The total number of nontechnically efficient provinces is 17, accounting for 54.8% of all provinces, i.e., the governance resource inputs of these 17 provinces are not fully utilized at the current scale, and their scale efficiencies are all less than 1. Among them, two provinces, Heilongjiang and Yunnan, have increasing returns to scale, and the remaining 15 provinces have decreasing returns to scale, which suggests that the former have relatively insufficient inputs of technology and scale, while the latter have insufficient technical inputs and over-investment of scale. This shows that the former has the problem of relative insufficiency of both technical input and scale input, while the latter shows the reality of insufficient technical input and excessive scale input.

In terms of scale efficiency, the average value of scale efficiency of China’s modernized rural governance in 2018 was 0.625, and the total number of provinces with scale efficiency (scale efficiency equal to 1) was 8, namely Beijing, Tianjin, Shanghai, Hainan, Tibet, Qinghai, Ningxia, and Xinjiang, which accounted for 25.8% of the evaluated units, and their scale payoffs were unchanged, which indicated that the governance resources invested in the above 8 provinces reached the optimal allocation of scale The scale of governance resources invested by the above eight provinces has reached the optimal allocation scale. There are 23 non-scale effective provinces, accounting for 74.2% of all provinces, among which Heilongjiang and Yunnan have increasing returns to scale, and the remaining provinces have decreasing returns to scale, indicating that the governance resources invested in the first two provinces have the problem of insufficient investment in scale, while the other provinces show obvious excess investment in scale and need to appropriately reduce the scale of investment.

3.2

Analysis of factors influencing the modernization of rural governance

This section uses the TOPSIS comprehensive evaluation and regression analysis method, combined with the Strategic Plan for Rural Revitalization (2018-2022) and the Guiding Opinions on Strengthening and Improving Rural Governance, and constructs an evaluation index system for the modernization level of rural governance from five dimensions: rural basic governance capacity, rural public affairs governance, rural public security governance, rural public service governance and rural public environmental governance modernization level, as shown in Table 2.

Table 2.

The modernization level evaluation index system of rural governance

System layer	Criterion layer	Index meaning
Quality of rural governance modernization (QRGM)	The modernization of rural basic governance (RBG)	Rural residents per capita basic disposable income
		Rural residents high school and higher degree of education labor ratio
		The rural director or the secretary “one shoulder” lead the team
		The rural committee member university specialty and the above cultural proportion
		Total income of rural collective operations
		The minimum living allowance for rural residents is a ratio
	The modernization of rural public affairs governance (RPAG)	The level of development of rural party members
		Public decision execution rate of important rural matters
		The participation rate of villagers in rural important decision-making
		The construction level of the rural public affairs management organization system
		Rural “three resources” modernization information management coverage
		Rural “three affairs” modernization online disclosure rate
		The penetration rate of rural regulations
		The rate of development of rural civilized governance activities
	The modernization of rural public security governance (RPSG1)	The full allocation rate of rural public security officers
		Rural public safety law assistance and judicial rescue coverage
		The success rate of dispute settlement in rural neighbors
		The rural public safety law promotes the rate of education activities
		The operation rate of public safety monitoring facilities in rural areas
		Public safety and prevention and control of rural public security diseases
	The modernization of rural public service governance (RPSG2)	Rural community “one-stop” integrated service facilities coverage
		Availability of rural public service governance
		Number of rural public medical personnel
		Rural basic public service network efficiency
		Availability rate of rural public labor employment training service
		The complete placement rate of rural public cultural and physical services facilities
		Rural public obligation education service availability rate
	The modernization of rural public environment governance (RPEG)	Rural road hardening rate
		The penetration rate of public health toilets in rural areas
		Public penetration rate of rural drinking water
		Rural domestic gas public penetration
		The concentration of public living sewage in rural areas
		Rural public forest coverage

The following research hypotheses are proposed based on the modernization level of rural basic governance capacity, rural public affairs governance, rural public security governance, rural public service governance and rural public environmental governance:

H1: The level of modernization of rural basic governance capacity has a positive impact on promoting the modernization of rural governance.

H2: The level of modernization of rural basic governance capacity has a positive effect on the improvement of the modernization level of rural public affairs governance.

H3: The level of modernization of rural public affairs governance has a positive effect on promoting the modernization of rural governance.

H4: The level of modernization of rural public affairs governance has a positive effect on the improvement of the level of modernization of rural public security governance.

H5: The level of modernization of rural public security governance has a positive effect on promoting the modernization of rural governance.

H6: The level of modernization of rural public security governance has a positive effect on the improvement of the level of modernization of rural public service governance.

H7: The level of modernization of rural public service governance has a positive effect on promoting the modernization of rural governance.

H8: The level of modernization of rural public service governance has a positive effect on the improvement of the level of modernization of rural public environmental governance.

H9: The level of modernization of rural public environmental governance has a positive effect on promoting the modernization of rural governance.

H10: The level of modernization of rural public environmental governance has a positive effect on the improvement of the level of modernization of basic rural governance.

3.2.1

Analysis of modernized rural governance

Considering the principle of availability of research data, the panel data of 30 provinces in China from 2011 to 2021 were selected as the sample of this research (Tibet, Hong Kong, Macao, and Taiwan were excluded). The development trend of the comprehensive index and sub-index of China’s rural governance modernization level is shown in Figure 1.

China’s rural governance modernization level has shown a slow upward trend during 2011-2021, but the overall development level is not high, and the rural governance modernization level is below 40%. From the perspective of the comprehensive index of the first-level indicators, the modernization level of rural public environment governance has increased most significantly, from 23% in 2011 to 58% in 2021. The modernization level of rural public security governance is rapidly increasing, with an increase of about 30%. The modernization level of rural public service governance and rural public affairs governance has been steadily increasing, reaching almost 40%. The level of modernization of basic rural governance capacity remains essentially unchanged. The basic rural governance capacity in China is not functioning properly or effectively at this stage of its development, which is why it urgently needs in-depth innovation and remediation.

3.2.2

Analysis of factors influencing the modernization of rural governance

The structural equation model was analyzed using Amos 26.0 and the regression coefficients of the latent variables of the structural equation model and their significance indicators are shown in Table 3. The third column of the table, Estimate column, is the unstandardized regression coefficient value, the fourth column, S.E., is the standard error of the estimate, and the fifth column, C.R., called critical ratio, is the ratio of the regression coefficient value to the standard error of the estimate, which is equivalent to the t-test value.

Table 3.

Regression path coefficient and its significance

Hypothesis	Path	Estimate	S.E.	C.R.	P	Results
H1	RBG-QRGM	0.157	0.078	2.056	0.050	Accept
H2	RBG-RPAG	0.224	0.068	3.209	0.001	Accept
H3	RPAG-QRGM	0.264	0.076	3.145	0.010	Accept
H4	RPAG-RPSG1	0.267	0.106	2.418	0.001	Accept
H5	RPSG1-QRGM	0.032	0.043	0.615	0.236	Refuse
H6	RPSG1-RPSG2	0.254	0.114	2.628	0.050	Accept
H7	RPSG2-QRGM	0.362	0.065	6.015	0.010	Accept
H8	RPSG2-RPEG	0.678	0.079	6.742	0.001	Accept
H9	RPEG-QRGM	0.102	0.058	1.521	0.148	Refuse
H10	RPEG-RBG	0.314	0.098	2.654	0.010	Accept

From the results of the model estimation, it can be seen that three paths have path coefficients at the 0.001 significant level, three path coefficients at the 0.01 significant level, two path coefficients at the 0.05 significant level, and two paths do not pass the significance level test. Therefore, it can be concluded that the factors directly affecting the effect of modernized rural governance include the level of modernization of basic rural governance capacity, the level of modernization of rural public affairs governance and the level of modernization of rural public service governance.

The impact of the modernization level of rural public security governance on the effect of modernized governance did not pass the significance level test (p=0.236), and the regression coefficient between it and the modernization level of rural public service governance is 0.254, which can’t directly affect the effect of modernized rural governance. The modernization level of rural public environmental governance does not pass the significance test (p=0.148), and the regression coefficient between it and the modernization level of rural basic governance capacity is 0.314, which cannot directly affect the effect of modernized rural governance.

From the structural equation path diagram and variable significance test results, it can be seen that the level of modernization of rural basic governance capacity has a direct impact effect on the modernization governance effect in addition to the modernization governance effect, but also indirectly through the level of modernization of the governance of rural public affairs modernization effect of modernization governance effect, rural modernization governance effect influence factors effect as shown in Table 4, the level of modernization of rural basic governance capacity has a direct impact on the modernization governance effect is 0.157, and the total effect is 0.226, which is specifically manifested in the fact that for every 1 unit increase in farmers’ environmental protection cognition, the overall environmental governance effect rises by 0.226 units, and the former has a positive promoting effect on the latter. The modernization level of rural public affairs governance only has a direct influence (0.264) on the modernization governance effect, with no indirect influence path. The direct influence path of the modernization level of rural public security governance and the modernization level of rural public environmental governance on the environmental governance effect does not pass the significance test, and each unit increase in the modernization level of rural public security governance and the modernization level of rural public environmental governance raises the rural modernization governance effect by 0.018 and 0.029 units, respectively. The modernization level of rural public service governance has both direct and indirect effects on environmental governance effects, with a direct effect of 0.362 and a total effect of 0.375, and also indirectly affects modernized governance effects through the modernization level of basic rural governance capacity.

Table 4.

Effects of factors on rural modernization governance effect factors

Action relation	Direct effect	Indirect effect	Total effect
RBG-QRGM	0.157	RBG-RPAG-QRGM	0.226
RPAG-QRGM	0.264	——	0.286
RPSG1-QRGM	Unsignificant test	RPSG1-RPSG2-QRGM	0.018
RPSG2-QRGM	0.362	RPSG2-RPEG-QRGM	0.375
RPEG-QRGM	Unsignificant test	RPEG-RBG-QRGM	0.029

4

Conclusion

This study constructs an evaluation index system of modernized rural governance through the overall status of rural governance modernization and regional differences, and explores the influencing factors of rural governance modernization through a regression analysis model.

1) There are significant regional differences in the level of China’s rural governance modernization, and the average value of modernized rural governance efficiency is 0.479. Among the 31 provinces, East China, Northeast China, and South China play more than 60% of the comprehensive efficiency level, and the comprehensive efficiency of Southwest China (0.313), Central China (0.293), and Northwest China (0.267) is lower than 0.35. There is a clear governance efficiency spatial differences.

2) Between 2011 and 2021, China’s rural governance modernization level generally shows an upward trend, but the level of rural governance modernization is below 40%. Among the five first-level indicators, the modernization level of rural public environmental governance has increased most significantly, by 35% over the 10-year period. The modernization level of basic rural governance capacity has not significantly changed, indicating that modernized rural governance still needs to focus on basic governance.

3) The factors directly affecting the effectiveness of modernized rural governance include the modernization level of basic rural governance capacity, the modernization level of rural public affairs governance and the modernization level of rural public services governance. The modernization level of rural public security governance (p=0.236) and the modernization level of rural public environment governance (p=0.148) do not pass the test of significance on the effect of modernized governance, and do not have a direct impact on the effect of modernized rural governance.

4) The modernization level of rural basic governance capacity and the modernization level of rural public service governance have both direct and indirect effects on the modernization governance effect. The modernization level of rural public affairs governance has only a direct influence effect (0.264) on the modernization governance effect, with no indirect influence path. The modernization level of rural public security governance and the modernization level of rural public environmental governance fail to meet the significance test for the direct influence path on the impact of environmental governance.

Through concrete empirical research, this paper reveals significant differences in the efficiency of governance, which is of great significance to understand the regional characteristics of rural governance in China.

5

Recommendation

There is still some room for further discussion in this article. For example, the social and cultural factors in the process of rural governance and how to improve the governance ability of the inefficient provinces are not fully discussed in the article. The social and cultural background has a profound effect on the adaptability and effect of the rural governance model, and the future study should consider this dimension more. In addition, the article can be explored in more areas by the specific influence of the policy and the fit of the local actual situation.

Funding:

2021 Heilongjiang Province Philosophy and Social Science Research Planning Project “Research on the Mechanism for Enhancing Urban Grassroots Governance Capacity from the Perspective of Interactive Governance” (Project Number: 21ZZC253).

The 2020 Heilongjiang Province Talent Introduction and Research Initiation Fund Project “Innovative Mechanism Research on Urban Community Interactive Governance in Improving Grassroots Governance Efficiency” (Project Number: 1305122022).

2021 Basic Research Business Expenses for Provincial Undergraduate Universities in Heilongjiang Province: Research on Innovative Mechanisms for Youth Participation in Urban Community Interactive Governance in the New Era (Project Number: 2021YDW-02).

Central support for local university reform and development funding talent training projects

Research on the Connection between the National “Dual Carbon” Strategy and the Moral Construction of Market Economy Entities (Project Number: 14011202101).

Langue:: Anglais

Périodicité:: 1 fois par an
Sujets de la revue:: Sciences de la vie, Sciences de la vie, autres, Mathématiques, Mathématiques appliquées, Mathématiques générales, Physique, Physique, autres

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Analysis of Regional Differences and Influencing Factors of Rural Governance Modernisation in China

Dan Jiang

Publié en ligne: 17 mars 2025

Reçu: 23 oct. 2024

Accepté: 15 févr. 2025

DOI: https://doi.org/10.2478/amns-2025-0256

Mots clésRegression analysis, Entropy weight method, TOPSIS, Rural governance

© 2025 Dan Jiang, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Mots clés
Regression analysis, Entropy weight method, TOPSIS, Rural governance