







































 Humanities and Social Science Research; Vol. 8, No. 4; 2025 

ISSN 2576-3024   E-ISSN 2576-3032 

https://doi.org/10.30560/hssr.v8n4p31 

 31 Published by IDEAS SPREAD 

 

Research on the Realization Mechanism of Promoting Provincial 

Common Prosperity in China through Urban-Rural Integration 

Development 

Yue Zhang1, Youquan Liu1 & Linfei Wei1 

1 School of Mathematics and Statistics, Pingdingshan University, China 

Correspondence: Yue Zhang, School of Mathematics and Statistics, Pingdingshan University, Henan, China. 

 

Received: July 4, 2025; Accepted: July 17, 2025; Published: July 18, 2025 

 

Fund Project: Henan Provincial Soft Science Research Program Project "Research on the Realization Mechanism 

and Path Countermeasures of Promoting Henan's Common Prosperity through Urban-Rural Integrated 

Development"(252400410319). 

 

Abstract 

Under the guidance of the goal of common prosperity in the new era, urban-rural integration development is not 

only an inherent requirement for solving the problems of unbalanced and insufficient development in China’s 

provinces, but also an important path for steadily promoting common prosperity in the new development stage. 

Based on the panel data of 31 provinces (autonomous regions, municipalities) in China from 2014 to 2023, this 

paper uses the entropy method to measure the level of urban-rural integration development and common prosperity, 

conducts a spatio-temporal evolution analysis of urban-rural integration development and common prosperity level, 

and uses the spatial lag model to analyze the impact of urban-rural integration on common prosperity and its spatial 

effect. The results show that: 1) The urban-rural integration development and common prosperity in China are in 

a state of steady annual increase, and there are certain differences in the current levels among provinces; 2) The 

level of common prosperity in China has a significant positive spatial correlation, and there is an obvious positive 

spatial spillover effect among the levels of common prosperity in provinces. Finally, relevant policy suggestions 

for promoting provincial common prosperity in China through urban-rural integration development are put forward 

from aspects such as coordinating resource allocation, giving play to the role of policy intervention, implementing 

differentiated opening-up policies, promoting urban-rural education equality, optimizing the industrial structure, 

and supporting scientific and technological innovation. 

Keywords: urban-rural integration, common prosperity, spatial spillover effects 

1. Introduction 

The report of the 20th National Congress of the Communist Party of China clearly states that "Chinese 

modernization is the modernization of common prosperity for all people" and that "we must adhere to giving 

priority to the development of agriculture and rural areas, adhere to urban-rural integration development, and 

smooth the flow of urban and rural elements [1] ." Promoting the urban-rural integration development strategy and 

constructing an institutional mechanism adapted to urban-rural integration are the only way for China to narrow 

the urban-rural development gap and help farmers and rural areas achieve common prosperity. In recent years, the 

widening of the urban-rural development gap and the gap between the rich and the poor in residents’ incomes are 

all contrary to common prosperity. Therefore, driving the realization of common prosperity through urban-rural 

integration development is of great significance for promoting the high-quality development of China’s urban and 

rural social economy and improving the livelihood well-being of urban and rural residents. 

On December 16, 1953, the concept of "common prosperity" was first put forward in the "Resolution on the 

Development of Agricultural Producers’ Cooperatives". After continuous exploration and practice, after the reform 

and opening up, Deng Xiaoping put forward two theories on common prosperity, advocating that "let some people 

and some regions get rich first, so as to promote the achievement of the goal of common prosperity [2]  ". Thus, 

the theoretical research on common prosperity has gradually become more rational. After the 19th National 

Congress of the Communist Party of China first put forward the strategic decision of "establishing and improving 

the institutional mechanism and policy system for integrated urban-rural development", the research on integrated 



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urban-rural development in the academic circles of our country has started to show a blowout development trend. 

Kong Xiangzhi and Xie Dongdong proposed that integrated urban-rural development is the core path to consolidate 

the stage achievements of common prosperity and solve the problems of governance of rural relative poverty on 

the basis of systematically analyzing the evolution law of urban-rural relations [3] . In addition, Li Ning built an 

analytical framework for integrated urban-rural development to drive common prosperity around the three 

dimensions of space, resources and system, and carried out an analysis at the theoretical level [4]  . Guo Xiaoming 

and Ding Yanwu deeply analyzed the key points and difficult problems in promoting the process of common 

prosperity by means of integrated urban-rural development, and then proposed that to achieve the goal of common 

prosperity in the new stage, it is necessary to focus on promoting from the five dimensions of space, industry, 

elements, governance and system [5] . 

2. Theoretical Analysis and Research Hypothesis 

First of all, integrated urban-rural development can enhance the vitality of rural development. The free flow of 

various urban-rural elements has enabled the transformation of the agricultural development mode from traditional 

agriculture to modern agriculture, continuously optimized the agricultural industrial structure, and expanded the 

large-scale operation of land. At the same time, relying on the unique natural and cultural resources in the 

countryside, tourism projects are developed to drive the increase of farmers’ income, encourage rural migrant 

workers to return to their hometowns to start businesses, and form a virtuous circle of rural talent and resource 

development. The key to promoting the common prosperity of farmers in the new era lies in increasing their 

income. Relying on the all-round revitalization of the countryside, stimulating the internal vitality of the 

countryside, increasing farmers’ income in all-round and multi-channel ways, continuously expanding the scale 

of the middle-income group in rural areas, narrowing the urban-rural income gap, and enabling the vast number 

of farmers to achieve common prosperity synchronously [6]. 

Second, the integrated urban-rural development can optimize the allocation of urban-rural public resources. The 

rational allocation of public resources is an important guarantee for promoting high-quality economic development 

and achieving common prosperity for all members. At present, there are still many problems in the overall 

allocation of urban-rural public resources in China. Unequal development opportunities are an important reason 

for the unequal allocation of urban-rural public resources. Co-construction and sharing is an effective way to 

achieve common prosperity. By strengthening the connection between cities and rural areas, it can promote the 

coordinated development of cities and rural areas, drive sustained economic growth, narrow the development gap 

between urban and rural areas, and substantially promote the progress of common prosperity. Therefore, the 

following research hypotheses are proposed in this paper. 

Hypothesis 1: The integrated urban-rural development can directly promote the realization of common 

prosperity. 

Common prosperity is not one-sided regional prosperity but an overall concept for the whole society. In the process 

of promoting common prosperity, there are double spatial spillover effects of policy learning and resource 

reciprocity. First, when the common prosperity policy brings new development impetus, the models, policies, and 

innovative practices adopted by advanced regions can stimulate the enthusiasm and willingness to imitate of 

surrounding and other regions, promoting the formation of a positive spatial spillover effect of policy learning. At 

the same time, the difference in economic efficiency between developed and underdeveloped regions will further 

enhance the imitation motivation of backward regions, strengthening the promotion effect of the policy 

demonstration effect and trickle-down effect on neighboring regions. Second, the spillover effect of urban-rural 

integration on the level of common prosperity in neighboring regions cannot be ignored. Urban-rural integration 

can promote the development of cities and rural areas into a territorial community of harmonious coexistence, 

interest coordination, complementary functions, and interlaced integration, promoting urban-rural connection and 

spatial integration, and thus eliminating the opposition and division between urban and rural regions. This can 

promote urban and rural residents to share the fruits of development and accelerate the promotion of common 

prosperity. At the same time, the integrated urban-rural development also helps to establish a harmonious social 

relationship, create a stable economic environment, and lay a solid foundation for the common prosperity of 

neighboring regions. In addition, there is frequent communication among some regions, enhancing the connection 

of economic activities among regions, promoting the coordinated development among regions, and advancing 

regional common prosperity. Generally speaking, the spillover effect of the integrated urban-rural development on 

the common prosperity of neighboring regions is a positive linkage effect, driving the whole region towards the 

direction of common prosperity[7]. Therefore, the following hypothesis is proposed: 

Hypothesis 2: Common prosperity has a spatial spillover effect. 



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Hypothesis 3: The integrated urban-rural development positively promotes common prosperity in the form 

of spatial spillover. 

2. Measurement of the Integrated Urban-Rural Development and the Level of Common Prosperity 

2.1 Measurement of the Level of Integrated Urban-Rural Development 

As a key component of economic and social development, the integrated urban-rural development can not only 

achieve Pareto optimality in resource allocation, but also serve as a powerful "accelerator" leading the rural 

revitalization. Based on the research of scholars such as Fang Chuanglin[8], this paper constructs an evaluation 

index system for the level of integrated urban-rural development from three dimensions: urban-rural economic 

integration, urban-rural factor integration, and urban-rural spatial environment integration, as shown in Table 1. 

 

Table1. Index System for Integrated Urban-Rural Development 

Primary indicator Secondary indicator 
Indicator 

nature 

Urban-rural 

economic 

integration 

Ratio of per capita wage income of urban and rural residents Negative 

Ratio of per capita consumption expenditure of urban and rural residents Negative 

Ratio of per capita cultural, educational and entertainment consumption 

expenditure between urban and rural areas 
Negative 

Ratio of Engel coefficients between urban and rural areas Positive 

Urban-rural factor 

integration 

Ratio of the number of urban and rural residents participating in 

unemployment insurance to the total population 
Positive 

Proportion of non-agricultural added value in GDP Positive 

Ratio of non-agricultural employed population to agricultural employed 

population 
Positive 

Ratio of private car ownership to total population Positive 

Ratio of total agricultural machinery labor force to agricultural sown area Positive 

Urban-rural 

spatial 

environment 

integration 

Per capita park green space area Positive 

Forest coverage rate Positive 

Ratio of environmental protection expenditure to general budget 

expenditure of finance 
Positive 

Rate of harmless treatment of domestic waste Positive 

 

After dimensionless processing of various indicators of urban-rural integration development, the entropy method 

is used to calculate the weights of evaluation indicators and the urban-rural integration development indices of 

each region in the country, and the weighted sum is used to solve the urban-rural integration development level 

indices of 31 provincial regions in China from 2014 to 2023 (see the appendix table). The measurement of the 

urban-rural integration development level of some provincial regions is shown in Figure 1. 

 
Figure 1. Urban-Rural Integration Development Level of Some Provincial Regions 

0

0.1

0.2

0.3

0.4

0.5

0.6

2014 2015 2016 2017 2018 2019 2020 2021 2022 2023

Beijing Liaoning Shanghai Henan Chongqing Shanxi



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From 2014 to 2023, the urban-rural integration development level of 31 provincial regions in China showed an 

upward trend. Among them, the growth was rapid from 2017 to 2020; affected by the COVID-19 pandemic in 

China, the development speed slowed down from 2020 to 2023. The urban-rural integration development levels 

of northern regions such as Tianjin and Shanxi in China had the largest increase, and the policy dividends boosted 

the acceleration of urban-rural integration development in the capital and its surrounding areas. The urban-rural 

integration development index in the central region was lower than that in the economically developed regions 

along the southeast coast, but the development prospect was considerable. Driven by the strategy of building a 

moderately prosperous society in all respects, the disposable income structure of rural residents in the central 

region was significantly optimized, the proportion of income from wage work in the total income continued to rise, 

and at the same time, the investment in the field of spiritual and cultural consumption increased significantly. The 

infrastructure construction in the western region was continuously improved, the non-agricultural employment 

channels were continuously expanded, the transformation and upgrading of the manufacturing industry helped the 

coordinated development of industries, and drove the local fiscal and tax revenues to maintain a steady growth 

trend. In the past decade, the urban-rural gap in income, consumption level, public services, infrastructure, and 

social welfare in all provincial regions of the country has been gradually narrowing. 

2.2 Measurement of the Level of Common Prosperity 

Common prosperity is not simply material civilization prosperity, but common prosperity that unifies material and 

spiritual life, is prosperity that benefits all people, and is even more prosperity that takes into account both fairness 

and efficiency. This paper draws on the construction of the common prosperity indicator system by Zou Weiyong, 

Xu Lingli, etc. [9] , and combines the availability of data to divide common prosperity into two dimensions: sharing 

degree and prosperity degree, and constructs the common prosperity indicator system as follows: 

 

Table 2. Common Prosperity Indicator System 

First-level indicator Second-level indicator Indicator nature 

Sharing degree 

Urbanization rate Positive 

Registered urban unemployment rate Negative 

Per capita social security and employment expenditure Positive 

Per capita urban road area Positive 

Number of public transport vehicles per 10,000 people Positive 

Number of hospital beds per 1,000 people Positive 

Number of health technicians per 1,000 people Positive 

Number of public toilets per 10,000 people Positive 

Affluence level 

Per capita GDP Positive 

Per capita disposable income Positive 

Ratio of fiscal education expenditure to total fiscal expenditure Positive 

Per capita holdings of public library collections Positive 

 

 
Figure 2. The Development Level of Common Prosperity in Some Provincial Regions 

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

2014 2015 2016 2017 2018 2019 2020 2021 2022 2023

Beijing Liaoning Shanghai Henan Chongqing Shanxi



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After dimensionless processing of various indicators of common prosperity, the entropy method is used to calculate 

the weights of evaluation indicators and the urban-rural integration development indices of various regions across 

the country. By accumulating the weights of indicators, the weights of the two first-level indicator systems of 

sharing degree and prosperity degree are calculated. Using the corresponding weights of the indicators, the 

weighted sum is used to solve the common prosperity level index of 31 provincial regions in China from 2014 to 

2023. The measurement of the common prosperity development level of some provincial regions is shown in 

Figure 2. 

From 2014 to 2023, the gap in the common prosperity indices of provincial regions across the country has gradually 

narrowed, thanks to the promotion and implementation of government policies and measures. The common 

prosperity index in the eastern region is greater than that in the central and western regions. The eastern coastal 

area is the pioneer area of China’s reform and opening up and has strong economic strength. The high-quality 

economic development in the central and western regions started late. The main development mode is agriculture, 

with few high-tech industries. There are large gaps in regional infrastructure conditions, public service levels, 

social security degrees, etc. compared with the eastern region, and the development speed is slow. The level of 

common prosperity in these regions lags behind that in the eastern region. 

3. Research Methods 

3.1 Data Sources 

The research geographical unit of this paper is 31 provinces, municipalities and autonomous regions across the 

country (excluding Hong Kong, Macao and Taiwan regions due to statistical caliber deviations). The research 

period is from 2014 to 2023. The research data comes from the China Statistical Yearbook. Some indicator data 

comes from the statistical bulletins on national economic and social development released by the websites of 

provincial governments, the statistical data released by provincial statistical bureaus and relevant departments. 

There are missing values for some individual data, which are filled by extrapolation of adjacent years or 

interpolation method. 

3.2 Variable Selection 

Main variables. The core explanatory variable of the urban-rural integration development level is selected, and the 

common prosperity level is selected as the explained variable, and it is measured by constructing a multi-

dimensional indicator system and using the entropy method. 

Control variables. To more accurately analyze the impact of the urban-rural integration development level on 

common prosperity, referring to the existing research results of scholars, this paper selects five control variables: 

government intervention, degree of opening to the outside world, human capital, industrial structure, and scientific 

and technological innovation level. 

3.3 Model Construction 

(1) Spatial autocorrelation analysis. It is a research method to explore the attribute value of a certain element in 

space and the attribute values of its adjacent spatial elements, including two analysis types: global and local [10]. 

The Moran index is the most commonly used correlation index to evaluate spatiality, and its value range is [-1,1] , 

and the Z test is commonly used to judge its significance, which is divided into the global Moran index and the 

local Moran index. 

① Global Spatial Autocorrelation 

The global Moran’s I index is used to evaluate the spatial autocorrelation of resources in the entire study area. 

When the index is positive, it indicates a positive correlation among regions, showing an aggregated distribution 

pattern. The calculation formula for the global Moran’s I index is: 

I=
n ∑ ∑ wij

n
j=1

n
i=1 (xi-x‾)

∑ ∑ wij
n
j=1

n
i=1 (xi-x‾)2

=
∑ ∑ wij

n
j≠1

n
i=1 (xi-x‾)(xj-x‾)

S2 ∑ ∑ wij
n
j=1

n
i=1

                                             (1) 

② Local Spatial Autocorrelation 

The local Moran’s I index can identify the distribution pattern and manifestation form of autocorrelation at specific 

spatial locations. Its calculation formula is: 

Ii=
(xi-x‾)

S2
∑ wij

j≠i

(xj-x‾)                                                            (2) 



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In equations (1) and (2), I represents the Moran’s I index, xi, xj represents the attribute value of spatial unit i、j , 

w represents the spatial weight, x represents the average value of the observed values. wij represents the element 

of the spatial weight matrix, with the spatial adjacent value being 1 and the non-adjacent value being 0. 

(2) Spatial econometric model.Since regions are not independent economic entities and there is a certain spatial 

correlation. Based on this, this paper introduces the spatial Durbin model (SDM) to discuss the impact of high-

quality development of social security on common prosperity. First, use the LM test to judge whether it is 

appropriate to use the spatial Durbin model; second, judge the robustness of the spatial Durbin model through the 

LR and Wald tests; finally, conduct the Hausman test to verify whether to choose fixed effects or random effects. 

4. Empirical Result Analysis 

4.1 Spatial Correlation Test 

By reading a large number of literatures, it is found that when calculating the global Moran’s I index, the commonly 

selected spatial weight matrices include the adjacency distance matrix, economic distance matrix, economic 

geography matrix, and geographical inverse distance square matrix. After comparing the measurement effects of 

different matrices through experiments, it is found that the spatial economic geography nested matrix has the best 

effect. Therefore, this paper uses this matrix to calculate the global Moran’s I index. The test results of the global 

Moran’s I index of the level of common prosperity in each year in China are shown in Table 3. 

 

Table 3. Global Moran’s I index of the level of common prosperity 

Year Moran’s Index Sd(I) Z value 

2014 0.709 *** 0.102 7.306 

2015 0.701 *** 0.101 7.267 

2016 0.682 *** 0.100 7. 123 

2017 0.691*** 0.099 7.283 

2018 0.715 *** 0.099 7. 466 

2019 0.634 *** 0.101 6.640 

2020 0.741 *** 0.098 7.913 

2021 0.587 *** 0.103 6.005 

2022 0.560 *** 0.104 5.700 

2023 0.722 *** 0.098 7.735 

 

As can be seen from Table 3, the global Moran’s Index p values of common prosperity from 2012 to 2021 are all 

less than 0.05 and pass the test at the 95% confidence interval, indicating that the level of common prosperity in 

China shows significant spatial dependence characteristics, that is, spatial lag terms or error terms must be included 

in the econometric model to avoid estimation errors; the global Moran’s Index of common prosperity is all positive, 

indicating that there are positive spatial correlation characteristics in the level of provincial common prosperity in 

China, that is, in regions with a high level of common prosperity, the level of common prosperity in adjacent 

regions also remains at a relatively high level; from 2014 to 2023, the absolute value of the global Moran’s Index 

of common prosperity gradually decreases, indicating that the gap in the level of common prosperity among 

provinces is gradually narrowing. 

To study the spatial autocorrelation and agglomeration status of the level of common prosperity in each province, 

based on the local Moran’s Index, this paper uses Stata18 to draw Moran’s Index maps for 2014, 2017, 2020, and 

2023, as shown in Figures3,4, 5, and 6. 



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Figure 3. Moran’s Index Map of Common Prosperity 

in 2014 

Figure 4. Moran’s Index Map of Common Prosperity 

in 2017 

  

Figure 5. Moran’s Index Map of Common Prosperity 

in 2020 

Figure 6. Moran’s Index Map of Common Prosperity 

in 2023 

 

As can be seen from the figure, most provinces are in the first and third quadrants, and as the years increase, the 

number of provinces in the first and third quadrants gradually increases, that is, the number of provinces showing 

positive spatial autocorrelation gradually increases. 

High-High (H-H) agglomeration type: Provinces belonging to this agglomeration type are located in the first 

quadrant, mainly including Beijing, Jiangsu, Zhejiang, Shandong, Tianjin, etc. These provinces are relatively 

economically developed and have been in the high-high agglomeration type for many years. From 2014 to 2023, 

the number of provinces in the first quadrant gradually increases, indicating that the development level of common 

prosperity is gradually increasing. 

Low-High (L-H) agglomeration type: Provinces belonging to this agglomeration type are located in the second 

quadrant, mainly including Fujian, Liaoning and other regions. From a national perspective, the comprehensive 

development level of these provinces is in the middle range, and regions with prominent development advantages 

can effectively drive the surrounding areas. 

Low-Low (L-L) agglomeration type: Provinces belonging to this agglomeration type are located in the third 

quadrant, mainly including western regions such as Tibet, Guizhou, Gansu, Qinghai, and Yunnan. Restricted by 

their remote geographical locations, the economic and social development levels of these provinces are relatively 

low. Since they are all in border regions, the economic radiation capacity of the surrounding areas is insufficient, 

and it is difficult to form a surrounding driving effect, resulting in a continuous expansion of the development gap 

with the southeast coastal areas. 

High-Low (H-L) agglomeration type: Provinces belonging to this agglomeration type are located in the fourth 

quadrant, mainly including Xinjiang, Heilongjiang and other regions. 

Generally speaking, there are significant spatial differences in the development levels of different regions. The 

disparities in the level of common prosperity between the western regions and the more developed regions in terms 



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of economic development are relatively large, indicating an imbalance in the development level of common 

prosperity in China. 

4.2 Model Identification and Verification 

To select the most appropriate spatial econometric model for empirical research, it is first necessary to use the LM 

test to conduct Lagrange multiplier tests on the spatial error model and the spatial lag model. 

 

Table 4. Results of the LM Test 

 Inspection Statistic P-value 

SEM 

Moran’s I 1.802 0.028 

LM 3. 958 0.047 

RLM 14.268 0.000 

SLM 
LM 20.665 0.000 

RLM 30.975 0.000 

 

As can be seen from Table 4, the LM tests of SEM and SLM are both significant at the 95% confidence interval. 

Therefore, this paper can choose the spatial Durbin model for further research. Subsequently, the Hausman test is 

used to identify the effect type of the model. The result of the Hausman test is -3.76, which is negative. Therefore, 

this paper should choose the spatial Durbin model with fixed effects. As can be seen from Table 5, the P values in 

the LR test results are all less than 0.05 and pass the test at the 99% significance level. Therefore, the best spatial 

econometric model should be the two-way fixed effects spatial Durbin model. From Table 6, the Wald tests and 

LR tests of SEM and SAR have all passed the 90% significance test. Therefore, it is considered that the spatial 

Durbin model cannot be simplified into the spatial lag model or the spatial error model. 

 

Table 5. Results of LR Test 

Inspection Items Statistic Value P Value Inspection Items Statistic Value P Value 

"Individual" fixation and 

"two-way" fixation 
44.58 0.000 

"Time"fixation and 

"two-way" fixation 
352.11 0.000 

 

Table 6. Results of  Wald Test 

Inspection Items Statistic Value P Value Inspection Items Statistic Value P Value 

Wald Test for SEM 13.36 0.037 Wald Test for SAR 14.8 0.022 

LR Test for SEM 14.39 0.025 LR Test for SAR 13. 15 0.041 

 

4.3 Results Analysis of the Two-Way Fixed Effects Spatial Durbin Model 

This paper selects the spatial Durbin model (SDM) with two-way fixed effects for empirical analysis, and the 

fitting results of the model are as follows: 

 

Table 7. Econometric Results of the Two-Way Fixed Effects Spatial Durbin Model 

Variable Coef 
Main 

Coef 
Wx 

Z value P value Z value P value 

Level of urban-rural integrated development 0.935 3.66 0.000 0.698 2. 16 0.031 

Government intervention 0.046 0.49 0.047 0.053 0.57 0.006 

Degree of opening to the outside world -0.201 -1.34 0.018 -0.041 -0.47 0.006 

Human capital 0.131 0.81 0.049 0.581 1.79 0.044 

Industrial structure 0.068 0.72 0.012 0.465 1.44 0.014 

Level of scientific and technological innovation 0.342 1.55 0.041 0.634 1.96 0.050 

Spatial rho Coef: 0.396 P value: 0.015 

 

From the measurement results of the double fixed-effects spatial Durbin model, it can be seen that the value of 

spatial rho is 0.396,P value is 0.015, which passes the significance test. It is considered that there is spatial 



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autocorrelation in the level of common prosperity among provinces in China. The positive coefficient of the direct 

effect indicates that the level of urban-rural integration development in neighboring provinces has a significant 

positive impact on the level of local common prosperity; the passing of the significance test of the indirect effect 

shows that the level of urban-rural integration development in this region can not only promote the development 

of local common prosperity, but also drive the development of common prosperity in surrounding areas through 

the spatial spillover effect. Among them, the improvement of the level of urban-rural integration development, the 

role of government intervention, the growth of human capital, the optimization of the industrial structure, and the 

improvement of the level of scientific and technological innovation all have a positive impact on the realization of 

common prosperity; while expanding opening up to the outside world has an inhibitory effect on the realization of 

common prosperity. Whether in the short-term impact or long-term effect, the improvement of the level of urban-

rural integration development will increase the level of common prosperity. 

5. Conclusions and Policy Recommendations 

5.1 Conclusions 

This paper closely focuses on the level of urban-rural integration development and the level of common prosperity 

in China’s provincial regions, and comprehensively uses a variety of model methods to deeply analyze the 

realization mechanism of urban-rural integration development promoting common prosperity in China’s provincial 

regions, and obtains the following conclusions: (1) From a national perspective, except for the decline in the 

comprehensive scores of some provincial regions affected by the epidemic from 2020 to 2023, the level of urban-

rural integration development in China is generally on a steady upward trend, and there are certain differences in 

the level of urban-rural integration development in different regions. (2) Generally speaking, the level of common 

prosperity in China has significant positive spatial autocorrelation, and the level of common prosperity in each 

province shows a positive spatial aggregation state. (3) The improvement of the level of urban-rural integration 

development, the role of government intervention, the growth of human capital, the optimization of the industrial 

structure, and the improvement of the level of scientific and technological innovation all have a positive impact 

on the realization of common prosperity; while expanding opening up to the outside world has an inhibitory effect 

on the realization of common prosperity. 

5.2 Policy Recommendations 

(1) Collaborate on resource allocation, give play to the role of policy intervention, and improve rural guarantees. 

As the core area and important driving force for the high-quality improvement of non-urban regions, rural areas 

are the foundation for the stable development of society. Under the guidance of the concept of regional coordinated 

development, a cross-regional industrial cooperation platform should be established to achieve the gradient transfer 

of development achievements through the mechanism of complementary advantages, so as to form a virtuous cycle 

pattern of two-way flow of factors. 

(2) Differentiated opening-up policies to strengthen two-way factor flows. In the process of opening up to the 

outside world, the government should formulate supporting policies for rural industry protection and training 

programs for the labor force to mitigate the impact of international competition on rural areas. At the same time, 

efforts should be made to promote the two-way flow mechanism of urban and rural factors, deepen the market-

oriented reform process of urban and rural factors, give full play to the feedback effect of urban resources on rural 

areas, and improve the policy support system for rural migrant workers to start businesses back in their hometowns. 

(3) Optimize the industrial structure to achieve coordinated urban and rural economic growth. Each region should 

be based on its actual conditions, focus on cultivating characteristic agriculture and rural tourism projects, and 

promote the coordinated development of multiple industries. Strengthen industrial support and employment 

security, guide enterprises to give priority to providing local employment opportunities, and effectively broaden 

the channels for farmers to increase their incomes. At the same time, build a national unified large market 

governance system, improve the cross-regional supervision coordination framework, use blockchain technology 

to establish a factor circulation traceability system, and realize the paradigm shift of resource optimal allocation 

from Pareto improvement to Kaldor improvement. 

(4) Support scientific and technological innovation to contribute to common prosperity. Industrial technological 

innovation can not only rapidly improve the development speed of rural economy and society, promote the gradual 

improvement of residents’ consumption levels and living qualities, but also drive rural employment through 

industrial technological innovation and promote the continuous improvement of the rural industrial system. 

Therefore, the government should, through different means such as policy support, special funds support, and 

improvement of service levels and capabilities, continuously improve the level of industrial technology innovation 

and entrepreneurship, continuously increase the innovation activity level of enterprises and the participation degree 



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of mass entrepreneurship through incentive measures, enhance the overall level of industrial technology innovation 

in society, stimulate development vitality, update the economic driving engine, and promote common prosperity. 

Acknowledgments 

This paper was supported by Henan Provincial Soft Science Research Program Project "Research on the Realization 

Mechanism and Path Countermeasures of Promoting Henan's Common Prosperity through Urban-Rural Integrated 

Development"(No:252400410319). 

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