Evaluation of China’s Rural Industrial Integration Development Level, Regional Differences, and Development Direction

Abstract

The report of the 19th National Congress of the Communist Party of China first proposed the strategy of rural revitalization. This proposal constitutes a major strategic deployment for work related to “agriculture, the countryside and farmers” based on China’s national and agricultural conditions and scientific analysis of the new problems faced by agricultural and rural reform in the new era. China’s agricultural development is facing multiple challenges, such as market competition, resource scarcity, environmental constraints, labor exodus, and technological innovation. Additionally, China’s agricultural production is inefficient, farmers’ income is low, and the hollowing out of the countryside has further intensified. To address these challenges, the Party Central Committee made the major decision to implement the rural revitalization strategy, and governments at all levels have introduced a series of policies to support agriculture. However, with the rapid advancement of industrialization and urbanization, the proportion of agricultural output to the total output of the national economy and the proportion of the agricultural labor force to the social labor force are not decreasing, and the idea of seizing the opportunities offered by agriculture is no longer able to solve the current dilemma faced by agricultural and rural development. Expanding the function of agriculture and actively docking with the industry and the service industry are a major strategic initiative for actively adapting to the new normal of economic development. They also represent major innovative thinking to accelerate the transformation of the agricultural development mode. Only when industries are prosperous can rural revitalization have a strong material foundation. To achieve industrial prosperity, industrial integration is the key. Therefore, the realization of rural industrial revitalization must take the path of industrial integration and development. At present, research on rural industrial integration is limited to the local regions, such as provinces and cities, and analysis at the level of national rural industrial integration is lacking. Accelerating the integrated development of rural industries is key to promoting rural revitalization. This paper scientifically establishes an evaluation index system for the level of rural industrial integration development based on five related aspects: society, the economy, resources, facilities, and the environment. Factor analysis is used to reduce the dimensionality of the evaluation index system, and cluster analysis is used to classify the rural industrial integration development level of each province in China into different tiers. The results show that there are obvious regional differences in the levels of rural industrial integration development in China. The provinces and cities with development levels that are higher than average (i.e., scores ranging from 0.291 to 0.915) are concentrated in the eastern coastal areas and inland riverine areas. In contrast, those with development levels that are lower than average (i.e., scores ranging from −0.504 to −0.750) are concentrated in Northern China, the northeastern noncoastal areas and northwestern areas. In addition, provinces can be divided into five development tiers of rural industrial integration. Based on this information, national, provincial, and municipal improvement strategies are proposed to address the differences in development among and the prospects for each province, and to effectively promote the integration of rural industries in China.

1. Introduction and Literature Review

Rural industrial integration is based on agriculture, with the multidirectional extension of the agricultural industry chain, the diversified expansion of the industrial scope, and the transformation of industrial functions, as well as the formation of new technologies and new business models through the integration and cross-reorganization between rural industries to achieve the cross-border flow of factors, the intensive allocation of resources, the cross-border integration of industries, and the optimization of the layout adjustment process. In recent years, with the continuous optimization of China’s rural development environment, a new rural industrial format has begun to emerge [1]. However, in the context of the new round of rapid changes in science, technology, and industry, the new development environment poses new challenges and new requirements for the unified development of China’s rural industries. Therefore, the Communist Party of China and the state attach great importance to the integration and the development of rural industries and have issued relevant policies. On 8 March 2021, the Regional Office of the Ministry of Agriculture and Rural Affairs and the Regional Office of the Ministry of Finance issued the Letter of Notification of the Overall Plan for the Implementation of the 2021 Agricultural Industry Integration Development Project. In accordance with the distribution of Central Committee Document No. 1 in 2020, the General Office of the Ministry of Agriculture and Rural Affairs coordinates tasks related to the national modern rural industrial area policy and optimizes the rural industrial development model to accelerate the development of integrated rural industries.

Therefore, the need to develop a rural three-industry integration system (RTIIS) is growing [2]. Promoting agricultural and nonagricultural integration has emerged as a critical policy focus for agricultural and rural economic development [3,4]. Rural industrialization has gradually evolved into a national development strategy.

The development of rural industries has been based on the comprehensive development of the primary, secondary, and tertiary industries, with a focus on the secondary and tertiary levels. In addition, developing these industries is the foundation for promoting rural revitalization and increasing farmers’ income, as well as the key to improving the agricultural quality, efficiency, and competitiveness. The rural industrial integration is an inevitable problem of agricultural development [5]. Theoretical and practical studies have demonstrated that relying on agriculture, optimizing capital allocation, and fully exploiting technological and resource advantages through the industrial linkages, technology penetration, and institutional and mechanism innovation can help the agricultural information industry develop in a coordinated manner and encourage agricultural and agricultural development within the rural economy, which will play an important role in reducing rural poverty [2]. In fact, the literature, particularly that on the comprehensive development of agricultural tourism or the primary, secondary, and tertiary industries in rural areas, has demonstrated the importance of developing an RTIIS in rural areas for improving the quality of life and income, overall economic development [4,5,6,7], the rate of utilization of production factors, resource-sharing to produce synergies [8], and agricultural competitiveness.

As an important tool for accelerating the modernization of the agriculture and rural areas and promoting the strategy of rural revitalization, the integration of rural industries is a topic of greater concern to scholars today. Scholars outside of China have studied the connotation of rural industrial integration based on the industrial development and the national conditions of their own countries. By extending the agricultural product chain and advocating for the use of multiple business methods, farmers’ income can be improved. Stieglitz [9] argued that the acceleration of technological change and the relaxation of the market have promoted the integration of telecommunications, computers, entertainment, and other markets. Shen Xiaozhong [10] summarized the development experience of “six industrializations” based on the current situation of industrial development, laying a foundation for the future development of rural areas. Industrial convergence is an economic phenomenon that appears with the development of industry. Greenstein [11] and others mainly described industrial convergence from the perspective of the sustainable development of industry.

There are few studies related to the development path of rural industrial integration outside of China, and they mainly focus on interdisciplinary knowledge promotion. Curran [12] proposed that the development path of industrial integration is formed through the intersection of multiple sciences based on national conditions and the social status. Namil Kima [13] and others argued that, through the integration of effective resources, the combination of production technology and the product market can be used as a driving force to promote the development of rural industrial integration, constituting a path for the realization of rural industrial integration. Chie Hoon Song [14] and others discussed the impact of population growth on rural economic development and the role of social development in promoting rural development. They argued that population growth and social development are important foundations for rural development and important development paths for promoting rural industrial integration.

There are few studies on RTIISs in international academia. Chinese scholars currently mainly conduct theoretical and qualitative studies on RTIISs [3,4,15,16,17,18,19,20,21,22,23]. In these studies, first, experts have summarized the future directions of RTIISs [4,7,15,16,24,25] and the drivers of RTIISs [21,26], based on their experiences in developing countries. Second, some experts [27,28] have empirically tested specific mechanisms to increase farmers’ income using an RTIIS. Third, some studies [2,17,29,30,31] have examined the development pathways and development models. At the same time, as research has intensified and the rural tri-sector has developed, the empirical studies on RTIISs, e.g., based on the system coupling theory [16], have begun to use interdisciplinary research tools, such as the propensity score matching-difference-in-differences (PSM-DID) method [18], panel threshold models [27], and integration evaluation systems [15]. Most researchers use the geometric mean of the coupled system [16] or [15] industrial integration and labor integration as a system of evaluation methods and specific accounting methods for RTIISs. Some studies have taken a systems perspective of RTIISs. However, previous studies have revealed only symbiotic relationships between the industries, indicating an organic relationship [4,32,33]. In addition, although theoretical studies on industrial integration have increased in recent years, industrial integration implies not a simple symbiosis of industries [34], but a deep integration of industries. In summary, there are few previous studies that provide a national evaluation system for rural tri-industries. Therefore, this paper proposes a national, provincial, and municipal evaluation system for the levels of rural tri-industry integration. On this basis, the corresponding improvement strategies are proposed to clarify the differences in development among, and the prospects for, each province and municipality, which is an approach conducive to promoting the development of rural industrial integration in China. Therefore, it is of policy significance, theoretical value, and practical value to study the integration of rural industries so that rural farmers can increase their incomes.

Novelty

In summary, the abovementioned studies have provided many useful discussions in the field of research related to rural industrial integration development, and they also provide references for this paper. However, there is still room for expansion in existing research, and this paper conducts further research in the following three ways. First, previous research on the level of industrial integration development has mainly focused on analyzing the coupling and coordination degree of the three rural industries and lacks overall classification, evaluation, and research on the level of the industrial integration development of each region in China. This paper addresses the shortage of research on the integration development of the three levels of industries and proposes targeted strategies based on the development status of the specific provinces and cities. Second, this paper scientifically constructs an index system for evaluating the development level of industrial integration in the country’s rural areas based on five factors: basic facilities, economic development, ecological construction, the resource endowment, and living standards. Additionally, as previously noted, this paper uses the latest reliable data, ensuring the timeliness of the empirical analysis and enabling realistic guidance on the direction of regional tri-industry integration at this stage.

The current level of rural industrial integration development is also systematically and quantitively analyzed. At the same time, this study uses the objective factor analysis method based on the rigor and comprehensiveness of historical statistics, compares the level of rural industrial integration development across the country through a scientific evaluation, and specifically analyzes regional differences. In addition, the clustering analysis method is used to subdivide the regions into different development stages. In terms of research data, the study involves examining the current level of rural industrial development integration based on that national, provincial, and municipal cross-sectional statistics from 2020; accurately analyzes the advantages and disadvantages of different parts of the country; and proposes specific improvement measures based on society-related scenarios that contain specific suggestions for rural industrial development integration at the country, province, and city scales.

2. Materials and Methods

2.1. Research Methods

Factor analysis was used [35]. Factor analysis is a multivariate statistical analysis method that extracts common factors from indicator clusters by considering the internal dependencies of the indicator correlation matrix. In this study, first, the data were z-score normalized, and data with different resolutions were transformed into a uniform measure of z-scores for comparison purposes to avoid the influence of the data resolutions. Then, the factor loading matrix was established, the indicators with larger loadings in each common factor were used as constituents to explain and name the common factors, and the scores of each common factor of the level of rural industrial integration development were calculated by combining the factor score coefficient matrix. Second, the relative importance of each common factor of the level of the rural industrial integration development was evaluated by using the weight of the variance contribution rate of each common factor, and after a rotation, the variance explanation rate (normalized) and the factor scores were multiplied and summed to obtain the comprehensive score for a more scientific and effective evaluation. Third, cluster analysis was used to classify the provinces into different tiers. The comprehensive scores of 1.081–0.915, 0.291–0.724, −0.283–0.007, −0.504–−0.406, and −0.916–−0.750 constitute the first, second, third, fourth, and fifth levels, respectively. Finally, corresponding suggestions for the different levels were provided.

2.2. Evaluation Index System Construction

The Strategic Plan for Rural Revitalization (2018–2022); the Guiding Opinions of the General Office of the State Council on Promoting the Integration and Development of the Rural Primary, Secondary and Tertiary Industries; the 13th Five-Year National Agricultural and Rural Informatization Development Plan; the National Agricultural Product Processing Industry and Rural Primary, Secondary and Tertiary Industry Integration Development Plan (2016–2020); the 14th Five-Year Plan for National Economic and Social Development of the People’s Republic of China and the Outline of the Vision Goals for 2035; and the Guiding Opinions of the Ministry of Agriculture and Rural Affairs on Expanding the Multiple Functions of Agriculture to Promote the High-Quality Development of Rural Industries were summarized. Nineteen indicators were extracted. The process for selecting the indicators was comprehensive and complete, and included all aspects of rural industrial integration, while taking into account the actual regional rural agricultural development based on an analysis of the current situation in each region. To objectively reflect the current situation of rural industrial integration and to ensure operability and systematization, this paper considers five factors for the evaluation index system: the basic facility level, economic development level, ecological construction level, resource endowment level, and living standards of villages. Additionally, it constructs an evaluation system for determining the rural industrial integration level by combining relevant articles on foreign industrialization and information on the specific domestic situation, based on practical experience.

2.3. Indicator System

Basic facilities refer to the infrastructure needed to develop the regional industries, and the five indicators selected for this factor are agricultural machinery power, rural hydroelectric power stations, the number of medical and health institutions, completed investment in rural household fixed assets, and the transportation load, as they reflect the material construction level of a region. Economic development refers to the economic efficiency of the regional industrial development, and the six indicators selected to describe the economic condition of a region’s industrial development from multiple dimensions are the gross domestic product of primary industry, the gross domestic product of secondary industry, the gross domestic product of tertiary industry, the producer price index of agricultural products, and the proportion of retail sales of consumer goods in townships and villages to the total retail sales of consumer goods. Ecological construction refers to the natural conditions of regional industrial development. Four indicators are selected: the construction of key ecological projects, the area of soil erosion control, the use of agricultural plastic film, and the use of renewable energy in rural areas (the number of household biogas digesters is used as the indicator). Notably, the use of agricultural plastic film is a negative indicator, as the greater the use of agricultural plastic film is, the greater the damage to the ecology. The remaining indicators are positive indicators, reflecting the degree of importance that a region attaches to ecological protection. The resource endowment refers to the amount of the resources in a region in terms of the industrial development, and the two indicators are irrigated arable land and the total output value of the agriculture, forestry, animal husbandry, and fishery industries. These indicators are selected as the material basis of the regional industrial integration development. Finally, the living standards mainly reflect the degree of social construction in a region. The two indicators are the number of cultural activities organized by large cultural institutions and the financial allocation of large cultural institutions. They are selected to measure the degree of participation and contribution of the government and society to regional industrial integration.

In summary, this paper scientifically selects indicators based on the data availability, and it systematically considers five factors, i.e., society, the economy, resources, facilities, and the environment, to construct an evaluation index system of the level of rural industrial integration development, as shown in

Table 1. Evaluation index system for the level of rural industrial integration development.

Target Level	System Level	Indicator Level	Indicator Units
Evaluation index system for rural industrial integration level A	$\mathrm{Level} \mathrm{of} \mathrm{basic} \mathrm{facilities} B_1$	$\mathrm{Total} \mathrm{power} \mathrm{of} \mathrm{agricultural} \mathrm{machinery} X_1$	Million kW
		$\mathrm{Township}-\mathrm{run} \mathrm{hydropower} \mathrm{stations} X_2$	Individuals
		$\mathrm{Number} \mathrm{of} \mathrm{medical} \mathrm{and} \mathrm{health} \mathrm{institutions} X_3$	Individuals
		$\mathrm{Completed} \mathrm{investment} \mathrm{in} \mathrm{fixed} \mathrm{assets} \mathrm{in} \mathrm{rural} \mathrm{households} X_4$	Billions
		$\mathrm{Transport} \mathrm{load} X_5$	Million tons
	$\mathrm{Economic} \mathrm{development} \mathrm{level} B_2$	$\mathrm{GDP} \mathrm{of} \mathrm{the} \mathrm{primary} \mathrm{industry} X_6$	Billions
		$\mathrm{Disposable} \mathrm{income} \mathrm{of} \mathrm{rural} \mathrm{residents} X_7$	Yuan
		$\mathrm{GDP} \mathrm{of} \mathrm{the} \sec \mathrm{ondary} \mathrm{industry} X_8$	Billions
		$\mathrm{GDP} \mathrm{of} \mathrm{the} \mathrm{tertiary} \mathrm{industry} X_9$	Billions
		$\mathrm{Producer} \mathrm{price} \mathrm{index} \mathrm{for} \mathrm{agricultural} \mathrm{products} X_{10}$	Percentage
		$\mathrm{Proportion} \mathrm{of} \mathrm{retail} \mathrm{sales} \mathrm{of} \mathrm{consumer} \mathrm{goods} \mathrm{in} \mathrm{towns} \mathrm{and} \mathrm{villages} \mathrm{to} \mathrm{the} \mathrm{total} \mathrm{retail} \mathrm{sales} \mathrm{of} \mathrm{consumer} \mathrm{goods} X_{11}$	Percentage
	$\mathrm{Ecological} \mathrm{construction} \mathrm{level} B_3$	$\mathrm{Construction} \mathrm{of} \mathrm{key} \mathrm{ecological} \mathrm{projects} X_{12}$	Hectares
		$\mathrm{Soil} \mathrm{erosion} \mathrm{control} \mathrm{area} X_{13}$	Thousands of hectares
		$\mathrm{Agricultural} \mathrm{plastic} \mathrm{film} \mathrm{use} X_{14}$	Tons
		$\mathrm{Rural} \mathrm{renewable} \mathrm{energy} \mathrm{use} (\mathrm{with} \mathrm{the} \mathrm{number} \mathrm{of} \mathrm{household} \mathrm{biogas} \mathrm{digesters} \mathrm{as} \mathrm{the} \mathrm{study} \mathrm{indicator}) X_{15}$	Households
	$\mathrm{Resource} \mathrm{endowment} \mathrm{level} B_4$	$\mathrm{Arable} \mathrm{irrigated} \mathrm{land} X_{16}$	Thousands of hectares
		$\mathrm{Total} \mathrm{output} \mathrm{value} \mathrm{of} \mathrm{the} \mathrm{agriculture}, \mathrm{forestry}, \mathrm{animal} \mathrm{husbandry}, \mathrm{and} \mathrm{fishery} \mathrm{industries} X_{17}$	Billions
	$\mathrm{Societal} \mathrm{living} \mathrm{standards} B_5$	$\mathrm{Number} \mathrm{of} \mathrm{cultural} \mathrm{activities} \mathrm{organized} \mathrm{by} \mathrm{large} \mathrm{cultural} \mathrm{institutions} X_{18}$	Number of times
		$\mathrm{Financial} \mathrm{allocation} \mathrm{for} \mathrm{large} \mathrm{cultural} \mathrm{institutions} X_{19}$	Million yuan

. Combined with the provincial cross-sectional data from 2020, the level of rural industrial integration development in 31 provinces of China (excluding Hong Kong, Macao, and Taiwan) is evaluated. Then, each province in China is divided into different development levels, and the improvement strategies are proposed. The data are mainly from the National Bureau of Statistics, the national economic and social development bulletins of various provinces, the statistical database of China’s agricultural and economic and social development, the China Agricultural Machinery Industry Yearbook, and the China Rural Statistics Yearbook. Some of the index data are obtained through calculation. The software used is IBM SPSS Statistics 24.

3. Results and Analysis

3.1. Factor Analysis

The data were first processed using z-score normalization.

Standard deviation formula:

$$\sigma =\sqrt{\frac{1}{n}{\sum }_{i=1}^n{\left(x_i-\mu \right)}^2}$$

(1)

z-score normalized conversion formula:

$$z=\frac{x-\mu }{\sigma }$$

(2)

Then, the Kaiser-Meyer-Olkin (KMO) test statistic and Bartlett’s test of sphericity were used to test the evaluation index data for the level of rural agricultural industry integration development. As shown in

Table 2. KMO and Bartlett’s test.

KMO		0.633
Bartlett’s test of sphericity	χ2	542.051
	df	171
	p	0

, the KMO value is greater than 0.6, and the significance of Bartlett’s test of sphericity is less than 0.01. Thus, the evaluation index system was suitable for factor analysis.

$${\chi }^2=\frac{1}{c}\left[\left(n-r\right)\ln MSe-{\displaystyle \sum }_{i=1}^r\left(n_i-1\right)\ln s_i^2\right]$$

Factor analysis was performed on the above processed data.

The factor analysis model for the p-dimensional variable x = [x1,…,xi,…,xp]T:

$$x=Af+\epsilon$$

(3)

Covariance matrix S:

$$s_{ij=\frac{1}{n-1}}{\sum }_{k=1}^n{x}_{ik}{x}_{jk}$$

(4)

Factor loading matrix:

$$\widehat{A}=\left[\sqrt{{\lambda }_1}{\gamma }_1,\sqrt{{\lambda }_2}{\gamma }_{2,\dots ,}\sqrt{{\lambda }_m}{\gamma }_m\right]$$

(5)

Cumulative variance contribution rate:

$$m=arg\underset{m}{\min }\left(\frac{{\sum }_{i=1}^m{\lambda }_i}{{\sum }_{i=1}^p{\lambda }_i}⩾r\right)$$

(6)

Common factor vector f:

$${\widehat{f}}_j={\widehat{A}}^T{S}^{-1}{x}_j$$

(7)

Special factor vectors of the original variables:

$${\widehat{\epsilon }}_j=x_j-\widehat{A}{\widehat{f}}_j$$

(8)

The eigenvalues and variance contribution rates were calculated by factor analysis, and the common factors were selected based on the principle that the initial eigenvalue was greater than 1, and the cumulative variance contribution rate was greater than 80%. The number of common factors was 4, and the cumulative variance contribution rate was 82.408%. Thus, the original 19 indicators could be replaced by 4 common factors, which were represented by F1, F2, F3, and F4 (

Table 3. Table of variance explained.

Factor Number	Characteristic Root			Explanation of Variance before Rotation			Explanation of Variance after Rotation
	Characteristic Root	Explanation of Variance %	Cumulative %	Characteristic Root	Explanation of Variance %	Cumulative %	Characteristics Root	Explanation of Variance %	Cumulative %
F1	8.163	42.962	42.962	8.163	42.962	42.962	6.809	35.836	35.836
F2	4.251	22.373	65.335	4.251	22.373	65.335	4.023	21.174	57.01
F3	1.876	9.874	75.209	1.876	9.874	75.209	2.598	13.672	70.682
F4	1.368	7.199	82.408	1.368	7.199	82.408	2.228	11.726	82.408

).

The rotated factor loading matrix (

Table 4. Rotated factor loading matrix.

Name	Factor Loading Coefficient				Degree of Commonality
	F1	F2	F3	F4
$\mathrm{Total} \mathrm{power} \mathrm{of} \mathrm{agricultural} \mathrm{machinery} X_1$	0.951	−0.11	−0.01	0.132	0.934
$\mathrm{Township}-\mathrm{run} \mathrm{hydropower} \mathrm{stations} X_2$	−0.16	0.833	0.017	0.264	0.79
$\mathrm{Number} \mathrm{of} \mathrm{medical} \mathrm{and} \mathrm{health} \mathrm{institutions} X_3$	0.784	0.313	0.113	0.2	0.764
$\mathrm{Completed} \mathrm{investment} \mathrm{in} \mathrm{fixed} \mathrm{assets} \mathrm{in} \mathrm{rural} \mathrm{households} X_4$	0.706	0.27	−0.01	0.479	0.801
$\mathrm{Transport} \mathrm{load} X_5$	0.68	0.489	0.044	−0.166	0.731
$\mathrm{GDP} \mathrm{of} \mathrm{the} \mathrm{primary} \mathrm{industry} X_6$	0.781	0.46	0.047	0.355	0.949
$\mathrm{Disposable} \mathrm{income} \mathrm{of} \mathrm{rural} \mathrm{residents} X_7$	−0.185	0.168	−0.68	−0.495	0.768
$\mathrm{GDP} \mathrm{of} \mathrm{the} \sec \mathrm{ondary} \mathrm{industry} X_8$	0.5	0.754	−0.24	−0.218	0.922
$\mathrm{GDP} \mathrm{of} \mathrm{the} \mathrm{tertiary} \mathrm{industry} X_9$	0.393	0.78	−0.35	−0.254	0.952
$\mathrm{Producer} \mathrm{price} \mathrm{index} \mathrm{for} \mathrm{agricultural} \mathrm{products} X_{10}$	−0.005	−0.39	0.153	0.689	0.651
$\mathrm{Proportion} \mathrm{of} \mathrm{retail} \mathrm{sales} \mathrm{of} \mathrm{consumer} \mathrm{goods} \mathrm{in} \mathrm{towns} \mathrm{and} \mathrm{villages} \mathrm{to} \mathrm{the} \mathrm{total} \mathrm{retail} \mathrm{sales} \mathrm{of} \mathrm{consumer} \mathrm{goods} X_{11}$	0.527	−0.27	0.406	0.28	0.591
$\mathrm{Construction} \mathrm{of} \mathrm{key} \mathrm{ecological} \mathrm{projects} X_{12}$	−0.113	−0.17	0.915	−0.036	0.88
$\mathrm{Soil} \mathrm{erosion} \mathrm{control} \mathrm{area} X_{13}$	0.132	0.007	0.906	0.105	0.849
$\mathrm{Agricultural} \mathrm{plastic} \mathrm{film} \mathrm{use} X_{14}$	0.905	0.011	0.115	−0.049	0.835
$\mathrm{Rural} \mathrm{renewable} \mathrm{energy} \mathrm{use} (\mathrm{with} \mathrm{the} \mathrm{number} \mathrm{of} \mathrm{household} \mathrm{biogas} \mathrm{digesters} \mathrm{as} \mathrm{the} \mathrm{study} \mathrm{indicator}) X_{15}$	0.23	0.174	0.109	0.828	0.78
$\mathrm{Arable} \mathrm{irrigated} \mathrm{land} X_{16}$	0.884	−0.11	0.065	0.004	0.797
$\mathrm{Total} \mathrm{output} \mathrm{value} \mathrm{of} \mathrm{the} \mathrm{agriculture}, \mathrm{forestry}, \mathrm{animal} \mathrm{husbandry}, \mathrm{and} \mathrm{fishery} \mathrm{industries} X_{17}$	0.829	0.4	0.017	0.311	0.945
$\mathrm{Number} \mathrm{of} \mathrm{cultural} \mathrm{activities} \mathrm{organized} \mathrm{by} \mathrm{large} \mathrm{cultural} \mathrm{institutions} X_{18}$	0.767	0.455	−0.23	−0.069	0.853
$\mathrm{Financial} \mathrm{allocation} \mathrm{for} \mathrm{large} \mathrm{cultural} \mathrm{institutions} X_{19}$	0.144	0.912	−0.11	−0.068	0.867

) and factor score coefficient matrix (

Table 5. Rotated factor score coefficient matrix.

Name	Factor
	F1	F2	F3	F4
$\mathrm{Total} \mathrm{power} \mathrm{of} \mathrm{agricultural} \mathrm{machinery} X_1$	0.194	−0.151	−0.072	−0.019
$\mathrm{Township}-\mathrm{run} \mathrm{hydropower} \mathrm{stations} X_2$	−0.16	0.318	0.075	0.188
$\mathrm{Number} \mathrm{of} \mathrm{medical} \mathrm{and} \mathrm{health} \mathrm{institutions} X_3$	0.098	0.034	0.038	0.026
$\mathrm{Completed} \mathrm{investment} \mathrm{in} \mathrm{fixed} \mathrm{assets} \mathrm{in} \mathrm{rural} \mathrm{households} X_4$	0.064	0.021	−0.077	0.215
$\mathrm{Transport} \mathrm{load} X_5$	0.094	0.088	0.095	−0.155
$\mathrm{GDP} \mathrm{of} \mathrm{the} \mathrm{primary} \mathrm{industry} X_6$	0.067	0.081	−0.003	0.131
$\mathrm{Disposable} \mathrm{income} \mathrm{of} \mathrm{rural} \mathrm{residents} X_7$	0.011	−0.022	−0.222	−0.139
$\mathrm{GDP} \mathrm{of} \mathrm{the} \sec \mathrm{ondary} \mathrm{industry} X_8$	0.039	0.164	0.004	−0.11
$\mathrm{GDP} \mathrm{of} \mathrm{the} \mathrm{tertiary} \mathrm{industry} X_9$	0.021	0.17	−0.039	−0.099
$\mathrm{Producer} \mathrm{price} \mathrm{index} \mathrm{for} \mathrm{agricultural} \mathrm{products} X_{10}$	−0.026	−0.095	−0.098	0.357
$\mathrm{Proportion} \mathrm{of} \mathrm{retail} \mathrm{sales} \mathrm{of} \mathrm{consumer} \mathrm{goods} \mathrm{in} \mathrm{towns} \mathrm{and} \mathrm{villages} \mathrm{to} \mathrm{the} \mathrm{total} \mathrm{retail} \mathrm{sales} \mathrm{of} \mathrm{consumer} \mathrm{goods} X_{11}$	0.099	−0.095	0.102	0.029
$\mathrm{Construction} \mathrm{of} \mathrm{key} \mathrm{ecological} \mathrm{projects} X_{12}$	−0.027	0.075	0.445	−0.178
$\mathrm{Soil} \mathrm{erosion} \mathrm{control} \mathrm{area} X_{13}$	−0.012	0.108	0.43	−0.115
$\mathrm{Agricultural} \mathrm{plastic} \mathrm{film} \mathrm{use} X_{14}$	0.184	−0.092	0.04	−0.137
$\mathrm{Rural} \mathrm{renewable} \mathrm{energy} \mathrm{use} (\mathrm{with} \mathrm{the} \mathrm{number} \mathrm{of} \mathrm{household} \mathrm{biogas} \mathrm{digesters} \mathrm{as} \mathrm{the} \mathrm{study} \mathrm{indicator}) X_{15}$	−0.059	0.07	−0.078	0.437
$\mathrm{Arable} \mathrm{irrigated} \mathrm{land} X_{16}$	0.19	−0.135	−0.009	−0.099
$\mathrm{Total} \mathrm{output} \mathrm{value} \mathrm{of} \mathrm{the} \mathrm{agriculture}, \mathrm{forestry}, \mathrm{animal} \mathrm{husbandry}, \mathrm{and} \mathrm{fishery} \mathrm{industries} X_{17}$	0.089	0.05	−0.019	0.105
$\mathrm{Number} \mathrm{of} \mathrm{cultural} \mathrm{activities} \mathrm{organized} \mathrm{by} \mathrm{large} \mathrm{cultural} \mathrm{institutions} X_{18}$	0.114	0.033	−0.066	−0.061
$\mathrm{Financial} \mathrm{allocation} \mathrm{for} \mathrm{large} \mathrm{cultural} \mathrm{institutions} X_{19}$	−0.071	0.283	0.075	−0.008

) were obtained through orthogonal rotation with Kaiser standardization. The main indicators with higher loadings on each common factor were used as the constituents, and the common factors were interpreted and named.

The first common factor, F1, had higher loadings on indicators such as the total power of agricultural machinery $X_1$, the number of medical and health institutions $X_3$, the completed investment in fixed assets in rural households $X_4$, the transport load $X_5$, the arable irrigated land $X_{16}$, and the total output value of the agriculture, forestry, animal husbandry, and fishery industries $X_{17}$. The above indicators mainly reflect the base level of resources and facilities. Thus, the common factor F1 was named the “resource and facility level factor”.

The second common factor, F2, had higher loadings on the GDP of the primary industry $X_6$, the GDP of the secondary industry $X_8$, the GDP of the tertiary industry $X_9$, and the financial allocation for large cultural institutions $X_{19}$. These indicators mainly reflect the economic and social development of a region. Thus, the common factor F2 was named the “economic development level factor”.

The third common, F3, had higher loadings on the construction of key ecological projects $X_{12}$ and the soil erosion control area $X_{13}$. These indicators mainly reflect the government’s attention to environmental protection and financial support, as well as the state of environmental control.

The fourth public factor, F4, had higher loadings on the disposable income of rural residents $X_7$, the rural renewable energy use (with the number of household biogas digesters as the study indicator) $X_{15}$, and the producer price index for agricultural products $X_{10}$. These indicators mainly reflect the living standards of a region. Thus, the common factor F4 was named the “societal living standard factor”.

Based on the rotated factor score coefficient matrix (Table 5), the following score equations for each common factor were obtained, and the scores and rankings of each common factor were calculated by combining the standardized data (

Table 6. Scores and rankings of China’s rural industrial integration development.

Region		F1		F2		F3		F4		F
		Score	Ranking	Score	Ranking	Score	Ranking	Score	Ranking	Score	Ranking
North China	Beijing	−1.25	30	0.851	5	−0.88	28	−0.83	27	−0.504	26
	Tianjin	−1.172	29	−0.057	10	−0.686	25	−1.228	30	−0.75	27
	Hebei	1.506	3	−0.46	21	0.021	13	0.174	11	0.438	7
	Shanxi	−0.343	19	−0.263	19	−0.356	18	0.958	6	−0.151	21
	Inner Mongolia	0.018	12	0.285	7	−0.89	29	2.983	1	0.291	10
Northeast China	Liaoning	0.072	11	−0.111	13	−0.344	17	0.029	13	−0.077	18
	Jilin	−0.19	16	−0.743	26	−0.31	16	−0.415	19	−0.406	25
	Heilongjiang	0.836	7	−0.659	25	−0.596	22	−0.129	15	−0.035	15
East China	Shanghai	−1.354	31	2.019	3	−1.304	30	−0.741	23	−0.283	24
	Jiangsu	1.045	6	1.649	4	−0.477	19	−0.828	26	0.644	4
	Zhejiang	−0.074	13	2.279	2	−0.183	15	−0.392	17	0.544	6
	Anhui	1.167	5	−0.132	14	-0.163	14	-0.769	25	0.246	12
	Fujian	−0.791	25	0.126	8	0.912	7	−0.459	20	−0.113	20
	Jiangxi	−0.382	20	−0.592	23	1.062	5	−0.023	14	−0.083	19
	Shandong	2.901	1	0.818	6	−0.497	20	−0.732	22	1.081	1
Central China	Henan	2.001	2	−0.465	22	0.748	8	−0.888	28	0.632	5
	Hubei	0.104	10	−0.031	9	0.943	6	0.198	10	0.258	11
	Hunan	0.388	9	−0.218	18	1.366	4	−0.383	16	0.322	8
South China	Guangdong	−0.794	26	2.803	1	1.66	3	0.218	9	0.915	2
	Guangxi	−0.195	17	−0.896	27	1.754	2	−0.753	24	−0.051	16
	Hainan	−0.779	24	−1.274	31	0.055	12	−1.362	31	−0.819	29
Southwest China	Chongqing	−0.772	22	−0.172	16	0.273	11	0.102	12	−0.256	23
	Sichuan	0.417	8	−0.14	15	2.442	1	0.652	7	0.724	3
	Guizhou	−0.61	21	−0.322	20	0.478	10	1.371	4	−0.029	14
	Yunnan	−0.144	15	−0.076	12	0.585	9	2.032	2	0.321	9
	Tibet	−0.875	28	−1.117	29	−0.677	24	−0.973	29	−0.916	31
Northwest China	Shaanxi	−0.286	18	−0.065	11	−0.55	21	1.399	3	−0.056	17
	Gansu	−0.118	14	−0.626	24	−0.798	27	1.189	5	−0.24	22
	Qinghai	−0.849	27	−1.195	30	−0.608	23	−0.405	18	−0.838	30
	Ningxia	−0.774	23	−1.029	28	−0.745	26	−0.576	21	−0.816	28
	Xinjiang	1.295	4	−0.188	17	−2.234	31	0.582	8	0.007	13

).

$$\begin{gathered} \mathrm{F}1=0.194 \times X_1-0.160 \times X_2+0.098 \times X_3+0.064 \times X_4+0.094 \times X_5+0.067 \times X_6+0.011 \times X_7+ \\ 0.039 \times X_8+0.021 \times X_9-0.026 \times X_{10}+0.099 \times X_{11}-0.027 \times X_{12}-0.012 \times X_{13}+0.184 \times X_{14}- \\ 0.059 \times X_{15}+0.190 \times X_{16}+0.089 \times X_{17}+0.114 \times X_{18}-0.071 \times X_{19} \end{gathered}$$

(9)

$$\begin{gathered} \mathrm{F}2=-0.151 \times X_1+0.318 \times X_2+0.034 \times X_3+0.021 \times \mathrm{S}{X}_4+0.088 \times X_5+0.081 \times X_6-0.022 \times X_7+ \\ 0.164 \times X_8+0.170 \times X_9-0.095 \times X_{10}-0.095 \times X_{11}+0.075 \times X_{12}+0.108 \times X_{13}-0.092 \times X_{14}+ \\ 0.070 \times X_{15}-0.135 \times X_{16}+0.050 \times X_{17}+0.033 \times X_{18}+0.283 \times X_{19} \end{gathered}$$

(10)

$$\begin{gathered} \mathrm{F}3=-0.072 \times X_1+0.075 \times X_2+0.038 \times X_3-0.077 \times X_4+0.095 \times X_5-0.003 \times X_6-0.222 \times X_7+ \\ 0.004 \times X_8-0.039 \times X_9-0.098 \times X_{10}+0.102 \times X_{11}+0.445 \times X_{12}+0.430 \times X_{13}+0.040 \times X_{14}- \\ 0.078 \times X_{15}-0.009 \times X_{16}-0.019 \times X_{17}-0.066 \times X_{18}+0.075 \times X_{19} \end{gathered}$$

(11)

$$\begin{gathered} \mathrm{F}4=-0.019 \times X_1+0.188 \times X_2+0.026 \times X_3+0.215 \times X_4-0.155 \times X_5+0.131 \times X_6-0.139 \times X_7- \\ 0.110 \times X_8-0.099 \times X_9+0.357 \times X_{10}+0.029 \times X_{11}-0.178 \times X_{12}-0.115 \times X_{13}-0.137 \times X_{14}+ \\ 0.437 \times X_{15}-0.099 \times X_{16}+0.105 \times X_{17}-0.061 \times X_{18}-0.008 \times X_{19} \end{gathered}$$

(12)

The composite score was calculated by multiplying the variance explained (normalized) by the rotated factor score. The formulas for the current data are as follows:

(35.836 × F1 + 21.174 × F2 + 13.672 × F3 + 11.726 × F4)/82.408

(13)

F = 0.435 × F1 + 0.257 × F2 + 0.166 × F3 + 0.142 × F4

(14)

Combining the above equations, the comprehensive score F of the level of rural industrial integration development was obtained, and the scores were ranked (Table 6). On the basis of the above evaluation and comparison scores, this study then focused on the geographical differences in and the general characteristics of the regional distribution of the levels of rural industrial integration development.

The F evaluation scores show that Hebei, Inner Mongolia, Jiangsu, Zhejiang, Anhui, Shandong, Henan, Hunan, Hubei, Guangdong, Sichuan, Yunnan, and Xinjiang have scores higher than 0. Additionally, their levels of rural industrial integration development are higher than the domestic average. The scores of the remaining 18 provinces are all below the average level. The geographical distribution of F shows that the provinces and cities with scores above the domestic average are concentrated in the eastern coastal and inland riverine regions. Those with scores below the domestic average are concentrated in Northern China, the northeastern noncoastal and the northwestern regions.

From the F1 evaluation scores, we see that Hebei, Inner Mongolia, Liaoning, Heilongjiang, Jiangsu, Anhui, Shandong, Henan, Hunan, Hubei, Sichuan, and Xinjiang all have scores higher than zero. This result indicates that these provinces have rich natural resources and industrial infrastructure facilities. The scores of the remaining 20 provinces are all lower than the domestic average, indicating that these provinces still lack natural resources and industrial infrastructure facilities. The geographical distribution of F1 shows that the provinces that exceed the domestic average are concentrated in the northeastern, inland, and southwestern regions, and they are rich in natural resources and have good industrial infrastructure conditions. The provinces that are below the domestic average are concentrated in Northern China and the eastern coastal regions, such as Beijing, Tianjin, Guangdong, and Shanghai, where natural resources are relatively scarce.

As shown by the F2 evaluation scores, the top five provinces and cities are Guangdong, Zhejiang, Shanghai, Jiangsu, and Beijing, all of which are China’s major economic provinces (municipalities directly under the central government). The scores for Inner Mongolia, Fujian, and Shandong also exceed the domestic average. These results indicate that the economic level of rural industrial integration development in these eight provinces is good and that government departments pay substantial attention to people’s livelihood. The scores of the remaining 23 provinces are lower than the domestic average, indicating that these provinces and cities still have room for improvement in terms of socioeconomic development and investment in people’s livelihood. The geographical distribution of F2 shows that the provinces (cities) that exceed the domestic average are concentrated along the more economically developed eastern coast. The provinces that have values lower than the domestic average are mainly located in the inland regions, among which the last three provinces are located in the relatively less developed southwestern and northwestern regions of China.

As shown by the F3 evaluation scores, Hebei, Fujian, Jiangxi, Henan, Hunan, Hubei, Guangdong, Guangxi, Hainan, Chongqing, Sichuan, Guizhou, and Yunnan all score above the domestic average. These 13 provinces have invested in and provided more support for environmental protection, and they have paid more attention to the environment. The remaining 18 provinces have to improve the level of environmental construction. From the geographical distribution of F3, it can be judged that the provinces and cities that exceed the domestic average are mainly concentrated in the inland and riverine regions with relatively large financial expenditures on environmental protection, as well as in Northwest and Northern China. The provinces that are below the domestic average are mainly located in the northeast and southeast coastal regions.

As shown by the F4 evaluation scores, Hebei, Inner Mongolia, Shanxi, Liaoning, Hubei, Guangdong, Chongqing, Sichuan, Guizhou, Yunnan, Shaanxi, Gansu, and Xinjiang have scores above 0, indicating that the livelihoods of the people in these provinces exceed the domestic average. The scores of the remaining 18 provinces are all lower than the domestic average, which indicates that there is still an opportunity to improve the living standards of the people in these provinces. The regional distribution of F4 shows that the provinces with scores higher than the domestic average are mainly located in the northwestern, southwestern, and inland regions, where the price level is not too high and people’s lives are more comfortable. The provinces with scores lower than the domestic average are mainly located in the eastern coastal regions, where the economy is developed and prices are higher.

3.2. Cluster Analysis

From the above factor analysis, we obtained the comprehensive scores of each province. To further understand the differences in the level of rural industrial integration development between the regions in China, we used cluster analysis to stratify the provinces and municipalities, which is helpful in proposing the targeted development strategies for each level.

From the analysis shown in

Table 7. China’s rural industrial integration development level by tier.

Level	Number	Region	Combined Score Range	Average Score
Tier 1 (higher level)	2	Shandong, Guangdong	1.081–0.915	0.998
Tier 2 (high level)	10	Hunan, Yunnan, Inner Mongolia, Anhui, Hubei, Jiangsu, Henan, Sichuan, Hebei, Zhejiang	0.291–0.724	0.442
Tier 3 (average level)	12	Chongqing, Gansu, Shanghai, Guangxi, Shaanxi, Heilongjiang, Guizhou, Xinjiang, Liaoning, Jiangxi, Fujian, Shanxi	−0.283–0.007	−0.114
Tier 4 (low level)	2	Beijing, Jilin	−0.504–−0.406	−0.455
Tier 5 (lower level)	5	Hainan, Ningxia, Qinghai, Tianjin, Tibet	−0.916–−0.750	−0.690

and Figure 1, we can draw the following conclusions. The first tier consists of Shandong and Guangdong, with an average score of 0.998. They are classified as the provinces with a higher development level. The second tier consists of Hunan, Yunnan, and ten other provinces, having scores that range from 0.291 to 0.724 and an average score of 0.442, which is higher than the domestic average. They are classified as provinces with a high development level. Twelve provinces, including Chongqing and Gansu, have scores ranging from −0.283 to 0.007 and an average score of −0.114, which is generally close to the domestic average. They are classified as provinces with an average level of development. Two provinces, Beijing and Jilin, have scores ranging from −0.504 to −0.406, which are slightly lower than the domestic average. They are classified as provinces with a low level of development. Five provinces, including Hainan and Ningxia, have scores in the fifth tier, and another five provinces have scores ranging from −0.916 to −0.750, with an average score of −0.690, which is much lower than the domestic average. Thus, they are classified as provinces with lower development levels.

4. Research Findings and Advancement Strategies

4.1. Research Conclusions

This paper scientifically establishes an evaluation index system for the level of rural industrial integration development based on five related factors: society, the economy, resources, facilities, and the environment. It uses factor analysis to evaluate the level of rural industrial integration development of each province in China, and it divides each province into different development tiers. The following are the key findings: (1) Due to the differences in socioeconomic levels, resource endowments, basic service facilities, and environmental health conditions, there are clear regional differences in the levels of rural industrial integration development among Chinese provinces. The provinces with development levels that are higher than average are primarily distributed in the eastern coastal areas and inland riverine areas. The provinces with development levels that are lower than average are primarily distributed in the western coastal areas. (2) Based on the level of rural industrial integration development, Chinese provinces are divided into five development tiers, with Shandong and Guangdong at the top, Hunan and Yunnan between the top and the middle, Chongqing and Gansu at the middle, Beijing and Jilin between the middle and the bottom, and Hainan and Ningxia at the bottom.

4.2. Development Strategy

4.2.1. National Level

Integrated rural industrial development should be encouraged, and the layout of rural industrial development should be systematically optimized. First, effective links between integrated rural industry development and strategic rural revitalization development planning should be established. Land use planning and rural planning should be conducted, and the spatial pattern of rural industrial agglomeration should be readjusted [36]. Second, specific efforts should be implemented to manage the layout of major functional areas, as well as advantageous agricultural and secondary products in an integrated manner. In addition, the state should build these functional areas for the rural grain and rice industries, as well as special agricultural and secondary product areas, to guide the transfer and convergence of rural industrial development elements to advantageous areas and gradually form specialized economic patterns of rural industrial geographic agglomeration. Third, the state should encourage the organic linkage of new urbanization and rural industrial integration development. It should guide the rural secondary and tertiary industries at the county, town, and industrial park levels, and eventually form a city production center with integrated development. Additionally, the linkages for rural industrial integration development need to be systematically enhanced. Given that the factors influencing the quality and efficiency of rural industrial integrated development change over time, the government must adhere to the guiding ideology of “the foundation is in agriculture, the benefits are in the farmers” and take measures to extend the rural industrial chain, improve the profits of the entire industrial chain of rural farmers, and systematically improve rural industrial integrated development [37]. Given the advantages of connecting these mechanisms, the country can also comprehensively respond to long-term changes in the development of integrated rural industries through this set of policies. Furthermore, a new policymaking and management department for rural industrial integration development in the central and western regions should be considered to improve the overall coordination of planning, regulations, and policies, as well as the supervision and management of various functional departments. The relevant departments should jointly visit and conduct research in the field, and actively and carefully listen to the suggestions from county, district, village, and industry representatives to fully determine the scope and specific needs related to rural industrial integration development and to optimize the coordination and linkage mechanisms [38]. Fourth, the government should improve the infrastructure, beautify the living environment, and carry out the comprehensive management of the rural living environment. The construction of an ecologically pleasant and beautiful countryside does not simply recreate the villages and alter the housing. Rather, it carries out the scientific overall planning for the village construction and development under the premise of respecting the regional cultural environment, inheriting buildings with regional historical and cultural characteristics, demolishing self-built houses that are indiscriminately occupied and built, planning and building residential areas with rural characteristics that can satisfy rural residents and capture tourists, and improving the appearance of villages. At the same time, we should deeply excavate and inherit the folk customs in the transformation, provide ideological and cultural education to villagers, and strengthen the construction of village customs. The rural construction should also consider the surrounding field environment and integrate with the field scenery to realize the harmony of rural life with the humanistic environment and natural environment. At the same time, to build and improve all kinds of infrastructure in the rural areas, to protect and beautify the natural environment for rural livability, to deal with “dirty” rural living areas and the difficulty in processing the “three wastes” of agricultural production, and to address the lagging operational management of rural environmental protection infrastructure and other issues, the government should increase the amount of capital investment. Doing so will solve the shortage of supporting funds for the rural environmental protection, strengthen the protection of public services, establish a place for waste classification and disposal, improve the environment of toilets and their treatment systems, improve the utilization rate of rural water and electricity, introduce advanced “three wastes” treatment technology, and take the road of green development and harmonious development. Therefore, to restore and improve the appearance and quality of the ecological environment of rural life and to strive to have a better living environment and quality of life for the residents of rural areas, we can build a beautiful countryside in the true sense of living. Finally, the government should accelerate the construction of a perfect guarantee system and credit system. A perfect guarantee system and abundant credit loan products of financial institutions can help solve the problems of difficult financing for the farmers and enterprises lacking collateral. Strengthening the cooperation between new agricultural business entities, banking institutions and guarantee institutions can effectively reduce the risk of bank loans and encourage the banks to provide credit loans when farmers have good credit and guarantee, so as to meet the financing needs of the practicing entities. In practice, we can explore the establishment of a government-supported guarantee system, increase the financial support and tax incentives for guarantee institutions, and guide guarantee institutions to lower the fees for financing the guarantee and re-guarantee business, in accordance with the principle of capital preservation and profit-making operation, so as to provide loan guarantee services for rural industrial integration subjects. We can establish a unified credit collection system for agricultural business subjects, carry out credit information collection and evaluation for agricultural business subjects, and establish the incentives for trustworthiness and penalties for the breach of trust. We should establish a long-term mechanism combining creditworthiness incentive and disciplinary action, and provide reference for financial institutions’ credit.

4.2.2. Provincial Level

Specifically, as provinces with higher development levels, Shandong and Guangdong have been national models for the integrated development of rural industries. Taking Shandong as an example, overall, the development of Shandong agriculture has been a relatively smooth process, maintaining an upward trend yearly. However, there are still factors, such as capital, talent, and technology, that are needed to address the lack of support and environmental problems. Shandong should extend its agricultural and secondary product industries externally to cultivate a new type of industrial integration development. It should also ensure the implementation of policies that lead to industrial integration, which would be conducive to considering the actual interests of farmers. Additionally, it should develop tourism-related agriculture and take the lead in sustainable development to better promote the integrated development of the rural industries. Considering Guangdong as an example, the development of agricultural industry integration in Guangdong is still in the growth stage and is being promoted yearly. However, some regions still have not expanded the agricultural industry chain, integrated the development of the agricultural service industry, promoted agricultural income and employment and urban–rural integration, etc. A more robust promotion of the development of new agricultural industries and agricultural product processing and service industries is needed. Guangdong should gradually innovate and increase interest in agricultural industry integration, increase the innovative investment in the socialized agricultural service system, and decrease the gap between the development of the agricultural service industry and the Pearl River Delta region.

The high-level provinces include Jiangsu and Zhejiang on the eastern coast; Hunan, Anhui, Hubei, Henan, and Hebei inland along the river; Yunnan and Sichuan in the southwest; and Inner Mongolia to the north. To further promote the integrated development of the rural industries, Jiangsu should strengthen the government investment in ecological and environmental protection and improve people’s well-being, while maintaining good existing socioeconomic development and infrastructure and resource advantages. Zhejiang should increase investment in the rural infrastructure, improve ecological and environmental construction, and enact relevant policies to decrease prices on the basis of maintaining relatively good economic development. Hunan, Hubei, Henan, and Hebei should consolidate their achievements in infrastructure construction and public service development, accelerate their economic growth rates to maintain their original natural ecological levels, and reduce the producer price index to lower production costs. Anhui should maintain its original public infrastructure in the rural areas to strengthen ecological environmental protection and encourage farmers to increase production and income. Yunnan should maintain the relatively high income of the residents and the natural ecological environment to strengthen infrastructure construction and add new momentum for sustainable economic development. Inner Mongolia should increase the investment in the ecological construction and build an excellent natural ecological environment. Sichuan is in Tianfu County and has a substantial amount of natural resources, and a healthy natural ecological environment. Thus, it should turn these advantageous resources into economic advantages, encourage farmers to increase the production and income, and prompt the integrated development of rural industries.

The provinces with average development levels include Shanghai and Fujian, which are located on the eastern coast, and 12 provinces, including Chongqing, Guangxi, Guizhou, Gansu, Shanxi, Shaanxi, Jiangxi, Heilongjiang, Liaoning, and Xinjiang in the northwest, located inland along the river. The above empirical results show that, in terms of resources and the infrastructure construction level, Xinjiang and Heilongjiang rank in the top 10 in China and Liaoning ranks 11th. These results indicate that these three provinces have a better level of infrastructure construction and rural industrial resources. The remaining provinces rank below the average level, with low development levels, and they need to transform and improve their industries. In terms of the economic development level, Shanghai ranks 3rd, Fujian ranks 8th, and the remaining 10 provinces are all below average and are relatively less developed economic regions. In terms of the level of ecological construction, Fujian, Guangxi, and Guizhou rank among the top 10 provinces in the country, and Chongqing ranks 11th, indicating that the financial expenditures and support for the environmental protection in these provinces are relatively large. In terms of living standards, Chongqing, Shaanxi, Guizhou, Gansu, Shanxi, Shaanxi, Liaoning, and Xinjiang have higher than average scores, indicating that their prices are appropriate, the cost of living for farmers is not too high, and the quality of life in rural areas is better. In general, the abovementioned provinces need to enhance the economic and social construction, focus on improving people’s living standards and basic support facilities, increase the number of leading enterprises and well-known brands in related fields, and promote the integrated development of rural industries.

Beijing and Jilin are at the low development level. For example, in Jilin City, the degree of industrial integration experienced a downward trend from 2017 to 2019. The problems included the slow start of rural industrial integration, the low quality of rural industrial integration products, the low level of digital agriculture development, the lack of support for rural industrial integration operational mechanisms, and the lack of cooperation in terms of rural industrial integration. The problems were related to the lack of high technological capability levels in the rural areas; the immature development of the technical level of the related industries; the lack of government guidance, supervision, and management; the lack of assistance for industrial integration development; and the lack of corresponding capital market financing. Jilin Province should highlight agriculture and accelerate the integration of rural industries, expand the agricultural industry chain, improve product quality, increase the relevant knowledge and skills of farmers, and reserve high-end labor supplies. It should also systematically improve industrial integration efforts, increase supervision, expand cooperation around the field of “agriculture+”, and, finally, form diversified industrial integration development. The model should promote the process of integrated development of the rural industries.

The provinces with a lower level of development include Hainan, Ningxia, Qinghai, Tianjin, and Tibet, and their scores in terms of the four common factors are lower than the domestic average. Thus, the development in these provinces needs to be comprehensively improved. It is necessary to improve the degree of economic development, strive to improve the living standards of residents, raise the level of consumption, and increase the profits of business managers involved in the integration of rural industries. Furthermore, it is necessary to increase the policy support for ecological and environmental management, and systematically improve the institutional mechanism for comprehensive environmental management. In addition, technical training and guidance on industrial integration development need to be provided, high-level talent needs to be cultivated, and tax incentives and financial subsidies need to be implemented to promote rural industrial integration development.