2.1 Examine the mechanism of interaction between rural revitalization and the digital economy

The digital economy and rural revitalization aim to serve disadvantaged groups in rural areas, lift them out of poverty, and increase their incomes, so the two have the same intrinsic goal and must have a coupling interaction. At present, a new generation of digital technology is accelerating its penetration into agriculture and rural areas, providing an excellent opportunity for digital empowerment to promote rural revitalization. The deep integration of digital technology and agricultural and rural development will realize scientific production, visualization of governance, intelligent life, and convenient services in the countryside, promoting prosperous industry, ecological livability, civilized countryside, effective governance, and affluent life in the countryside. At the same time, rural revitalization provides space for the reshaping development of digital economy infrastructure, digital governance level, industry digitization, digital industrialization, etc. The specific path and inner mechanism of the interactive coupling and synergistic development of the digital economy and rural revitalization are shown in Fig 1.

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Fig 1. Mechanism of interaction between the digital economy and rural revitalization.

https://doi.org/10.1371/journal.pone.0277910.g001

1. The interaction mechanism between the digital economy and the prosperity of rural revitalization industries. The digital economy improves the ability to capture information in agricultural production and obtains accurate agricultural production data to achieve precise control of agriculture. With the support of digital technology, the implementation of e-commerce transformation has solved the information blockage existing in the traditional agricultural market. With the openness of the internet platform, barriers to the application and service of agricultural technology are broken, and the technical needs of farmers are quickly delivered. Digital finance achieves the allocation of supply and demand of rural funds at a lower cost, solves the problems of financial geographical discrimination and supply-based financial inhibition in rural areas, improves the possibility for small farmers to access financial services, and provides strong support for the integrated development of the three rural industries. The need for digitalization in rural revitalization drives the development of digital economy infrastructure and industry digitization, which in turn drives the development of digital economy infrastructure.
2. The interaction mechanism between the digital economy and ecological livability in rural revitalization. The development based on the Internet of Things realizes the transparency of the agricultural production process and the delicate operation of production factors such as fertilizers, chemical fertilizers, and pesticides to protect the rural ecological environment. With the new generation of IoT and mobile internet platforms, we can strengthen the resource utilization and pollution prevention of livestock and poultry breeding, improve the control of agricultural surface source pollution, and improve the information level of rural ecological and environmental improvement. China’s "beautiful countryside" project will rise in level as a result of improving the living environment in rural areas. This, in turn, will help the country’s digital economy industry and digital economy infrastructure get better and better.
3. The interaction mechanism in rural revitalization is between the digital economy and rural civilization. Urbanization has caused an exodus of people from the countryside. "Hollow villages" are emerging, and intangible cultural heritage is gradually disappearing. The digital economy can more economically and efficiently record local cultural characteristics and resources with high humanistic value, such as multimedia. Digital technology is embedded in rural public spaces and facilities, forming new industries such as intelligent rural tourism, field complexes, and characteristic towns, using digital technology to disseminate and display the characteristics of rural culture, enhance the expressiveness and attractiveness of rural culture, and realize rural cultural revitalization. The popularity of the Internet has improved farmers’ access to information and provided a new model and channel for farmers’ training. Cultural digitization furthers the development of digital industrialization in the digital economy.
4. Digital inclusion and good governance. Big data and rural governance, rural governance grid, "rural + government services", digital rural and public services, and intelligent rural have become the primary forms of rural governance. Digital technology enables data sharing and sharing to break down information. Institutional barriers broaden the channels for villagers to participate directly or indirectly in governance, transform rural governance’s primary mode from a one-dimensional vertical management mode to a pluralistic shared governance mode, and improve rural governance performance. People in rural areas are getting more and more of their government work done online, which helps the digital economy develop.
5. The interaction mechanism between the digital economy and the improvement of people’s livelihoods in rural revitalization. Digital technologies break the physical distance between teachers and students in urban and rural areas, share quality education resources co-efficiently, and promote balanced and fair education in urban and rural areas. The Internet platform is used to share quality medical resources downward, promote balanced urban and rural medical care, and provide convenient, public welfare, consulting, and e-commerce services. E-commerce poverty alleviation encourages people to buy things online and start businesses, which in turn encourages the transformation and upgrading of digital industries in poor areas. It also has a big impact on raising the incomes of poor families and improving their living standards.

To sum up, the digital economy empowers rural revitalization in such aspects as industrial prosperity, ecological dependence on livability, rural civilization, rural governance, and people’s livelihoods, respectively. Rural revitalization, on the other hand, makes room for the development of the digital economy in areas like digital economic infrastructure, industrial digitization, governance digitization, and digital industrialization, and the two work together to help each other grow.

2.2 Analysis of the existence of optimal coupling and coordination

From the perspective of development theories, the digital economy can promote capital accumulation through its low cost and comprehensive coverage and change the level of technology to promote rural revitalization. In a complex economic system, the connection between the digital economy and the countryside is complicated, and in some regions, the development even shows deviation. Therefore, it is of solid theoretical value to prove the existence of optimal coupling and coordination from a theoretical perspective, which is conducive to sorting out the coupling mechanism. In this paper, we look at two investment factors: the digital economy and rural revitalization. We assume that labor and technology are not factors in the analysis: (1)

Where: economic output Y is determined by the digital economy D, rural revitalization R, technology T, and labor force L. α and β are the elasticities of the digital economy and rural revitalization respectively, both taking values greater than 0 and less than 1. For analysis, the growth rate of the labor force is assumed to be n,the depreciation rate of the digital economy δ_D and the depreciation rate of rural revitalization δ_R are equal, i.e., δ_D = δ_R = δ. The propensity to save in the digital economy and rural revitalization are S_D and S_R respectively. The accumulation equation for the digital economy is D = s_DY−δ_DD, the accumulation equation for rural revitalization is R = s_RY−δ_R R, and the accumulation equation for labor is L = nL. Assuming that economic output can only be distributed between consumption C and investment I, then Y = C+I, which can be further deduced as C = (1−s_D−s_R)Y.

Suppose that the total level of social welfare U is determined by social consumption C. When the economy reaches equilibrium, is maximized, and ε is the discount rate. The constraint is then transformed as: (2)

This creates the following Hamiltonian function: (3)

Taking the first-order partial derivatives of the above equations for the digital economy D and rural revitalization R, respectively, yields:

Let the first-order partial derivative equal to zero, and further find the conditions for the optimal coupling relationship between the digital economy and rural revitalization at the static optimum: (4)

The ratio of optimal coupling and coordination between the digital economy and rural revitalization is equal to the ratio of the elasticity coefficients of both. When the digital economy and rural revitalization reach equilibrium in factor flow, the two systems are in a state of coupling and coordination; when either system matches too many factors, the coupling is out of balance. In order to achieve optimal coupling, the factors will be transferred to the other system until the coupling is coordinated. Therefore, there is an optimal coupling and coordination relationship between the digital economy and rural revitalization.

2.3 The degree of coupling and the coupling coordination model

The previous section looked at how the digital economy and rural revitalization work together in a complex economic system. This section build a model of how the two work together.

(5)

C_v indicates the average deviation of the digital economy and rural revitalization; the smaller the value, the smaller the deviation and the stronger the coupling. When C_v = 0, i.e., the coupling degree C = 1, the two systems are less deviated from each other, and the coupling is better. The closer the coupling degree C is to 0, the worse the coupling level between the two systems. Although the coupling degree can determine the degree of interaction between the digital economy and rural revitalization, the development of the digital economy and the promotion of rural revitalization cannot be entirely consistent. When the values of μ₁ and μ₂ are relatively low, and the scores are similar in a specific region, the use of a coupling degree may lead to the phenomenon of "pseudo-evaluation" and make the evaluation results inaccurate. Following this formula, this paper builds a model that shows how well different parts of the country work together.

(6)

D is the coupling coordination degree, C is the coupling degree of the digital economy and rural revitalization, T is the comprehensive reconciliation index of the digital economy and rural revitalization, which can reflect the level of coordination between the digital economy and rural revitalization. a and b are coefficients to be determined, a+b = 1, a and b are both assigned a value of 0.5. The coupling coordination of the digital economy and rural revitalization is divided into five stages: moderate dissonance, on the verge of dissonance, barely coordinated, moderately coordinated, and highly coordinated. The coordination between the digital economy and rural revitalization was divided into five stages: moderate dissonance, near dissonance, barely coordinated, intermediate coordination, and extreme coordination. When 0<D≤0.4, it indicates that the digital economy and rural revitalization are at the stage of moderate dissonance, i.e., the digital economy fails to promote the development of rural revitalization effectively, and rural revitalization fails to transform into the driving force of the digital economy, and the two are at the stage of mutual constraint. When 0.4<D≤0.5, it indicates that the digital economy and rural revitalization are at the stage of mutual consent, which means that the digital economy and rural revitalization are at the stage of imminent dysfunction, i.e. the level of coupling and coordination between the two is low, but the power of the digital economy and rural revitalization is decreasing. When 0.5<D≤0.6, it means that the digital economy and rural revitalization are at the stage of barely coordinated, and there is more room for improvement in the matching of the two systems in terms of structure and scale. When 0.6<D≤0.8, it indicates that the digital economy and rural revitalization are at an intermediate level of coordination, and the digital economy and rural revitalization are at a high level, with a good relationship between the two systems, which is mutually influencing and promoting each other. When 0.8<d≤1, it indicates that the digital economy and rural revitalization are at the best and most ideal stage of coupling and coordination. At this point, as the digital economy grows, the level of rural revitalization grows, which in turn helps the digital economy grow even more, and the spiral between the two is getting better.

3.1. Details of the composition of the rural revitalization indicator system

This paper constructs the indicator system for China’s rural revitalization level according to the three-step selection process of indicators. Firstly, based on the reports of the 18th and 19th National Congresses of the Party, the Strategic Plan for Rural Revitalization (2018~2022), the Opinions of the State Council of the Central Committee of the Communist Party of China on the Implementation of the Rural Revitalization Strategy, and the Law on the Promotion of Rural Revitalization, five first-level indicators were selected, namely, prosperous industry, pleasant ecological living, civilized countryside, effective governance, and affluent living. Secondly, 25 secondary indicators were selected, considering the actual situation of measuring rural revitalization. Based on the specific connotation and data availability of the secondary indicators and the comparability of the indicators, the specific measurement formulae for the 25 secondary indicators were formulated. Specifically, rural revitalization in China is the target level, with five primary indicators (subsystems) of prosperous industry, ecological livability, civilized countryside, effective governance, and prosperous living, and five secondary indicators under each primary indicator, covering a total of 25 secondary indicators. The entropy weighting method is used to figure out the weight of each secondary indicator based on the sample data. The weight of the primary indicators is then based on the secondary indicators.

3.2 Construction of the evaluation index system for China’s digital economic development

The core concept of this paper in constructing the digital economy development evaluation index system is to, based on the environment of digital governance, with digital economy infrastructure investment, vigorously promote digital industrialization and industrial digital integration development. At present, the following types of indicators are used to measure the level of China’s digital economy’s development: First, the Digital City Development Index is provided by Tencent and the Global Digital Economy Index by Ali Research Institute; second, the indicator system is reconstructed based on the research framework of CITIC Tong Research Institute; and third, the digital economy efficiency coefficient is used as a measurement variable. The indicators of different dimensions of digital economic development all contain useful information on digital economic development, and considering only one or a certain dimension of indicators will lead to a one-sided understanding of digital economic development. Therefore, this paper takes into account the new trends and characteristics of digital economic development and builds a regional digital economic development indicator system in China based on the availability, continuity, reliability, and comparability of existing indicators. As shown in Table 1, the indicator system has four main indicators and 21 other indicators.

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Table 1. Index system of China’s rural revitalization and digital economy development.

https://doi.org/10.1371/journal.pone.0277910.t001

1. Indicators of digital economic infrastructure. A digital economy is an economic form in which new digital technologies are widely used, and the prerequisite for the application of digital technologies is a sound digital economy infrastructure. For example, there are indicators that show how long optical fiber cables are and how many mobile phone base stations there are. There are also indicators that show how many Internet broadband access ports there are and how many Internet users there are.
2. Industrial digitalization indicators. The digitalization of industries brings about increased output and efficiency through the convergence and penetration of ICT products and services in other areas, especially in the three industries. This paper uses the digital financial inclusion index, the level of enterprise informationization, the number of express businesses, e-commerce sales, etc. to measure this.
3. Digital governance indicators. Digital governance is an important guarantee for the healthy and orderly development of the digital economy, covering government, policy, industry, innovation, property rights, corporate governance, and other levels. This paper selects indicators such as the number of years of education per capita, the intensity of investment in R & D expenditure, the number of enterprises in the digital economy, the total amount of technology contract transactions, the number of patent applications, the number of patent applications granted, and the number of government websites (the level of digital government) to measure the level of digital governance.
4. Digital industrialization indicators. Digital industrialization refers to the added value of an information industry characterized by digital technology, including digital technology innovation and digital industrial production, mainly including electronic information manufacturing, information and communication industries, software service industries, and Internet-related industries. This paper uses digital industry employees, the total industrial output value of the digital industry, telecommunications business volume, and software industry revenue to measure digital industrialization indicators.

3.3 Data sources and description

This paper uses panel data from 30 provinces in China from 2011 to 2020 as the sample for examination, and in view of the availability and comparability of data, the Tibetan region, as well as Hong Kong, Macao, and Taiwan regions, are not considered for the time being. The data comes mostly from the China Statistical Yearbook, China Rural Statistical Yearbook, China Urban and Rural Construction Statistical Yearbooks, China Population and Employment Statistical Yearbook, and China Population and Employment Statistical Yearbook, among other sources. The data were downloaded from the EPS data platform. Some missing values were filled in by interpolation, and some of the most recent year’s data was manually processed. In order to facilitate the subsequent spatial and temporal variation analysis, the paper divides the 31 provinces of China into four economic regions, namely the eastern region, the central region, the northeastern region, and the western region, according to the 2011 classification method of the National Bureau of Statistics.

4.1 An examination of the spatial and temporal characteristics of China’s digital economy and rural revitalization

(1) Overall spatial and temporal characteristics of the digital economy and rural revitalization

According to the digital economy and rural revitalization index system constructed in Table 1, the digital economy and rural revitalization indices of 30 provinces in China from 2011 to 2020 were first measured by the entropy weight method. Then the coupling coordination degree of China’s rural revitalization and digital economy was calculated according to the coupling degree model and the coupling coordination degree model constructed in the previous section. Fig 2 shows the development trend of the digital economy and rural revitalization across the country and the four major regions, showing the following three characteristics: Firstly, both the digital economy and rural revitalization show a relatively stable upward trend with a noticeable consistent trend, which is the basis for a coupling and coordination relationship between the two. Secondly, the digital economy and rural revitalization have had the same detailed changes. For example, nationwide, there is a similar inflection point in 2019. In the eastern region, the central region, the western region, and the northeastern region, the digital economy and the development of rural revitalization also showed similar fluctuations in 2016 and 2019, indicating an inevitable coupling and coordination relationship between the two on a local scale. Thirdly, the development of rural revitalization is ahead of the digital economy, which reveals that China’s rural revitalization strategy of "prosperous industry, pleasant ecology, civilized countryside, effective governance, and prosperous life" has accelerated the shortcomings in rural development and rapidly improved the quality of rural revitalization development. The digital economy provides more possibilities for developing the rural economy and serving rural revitalization, while rural revitalization provides fertile ground for the digital economy to improve quality and efficiency, innovate and develop. The quality of digital economy development in rural revitalization has been low, which shows that the integration and development of the digital economy is one of the main things that is holding back rural revitalization right now.

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Fig 2. Trends of the digital economy and rural revitalization.

https://doi.org/10.1371/journal.pone.0277910.g002

(2) Spatial and temporal distribution pattern of the digital economy and rural revitalization

The spatial pattern of the digital economy and rural revitalization in China is shown in Fig 3. The main reasons for the rapid development of the digital economy and rural revitalization in all regions of China during the period under review are: firstly, the digital economy development strategy has led to the rapid application and popularization of Internet information technology and e-commerce in China; secondly, the foundation of the digital economy and rural revitalization in most regions of China was relatively weak in 2011, which made it easy to form a high growth trend; and thirdly, with the policy support and practice of the state in fully supporting rural development, all sectors of the country have made every effort to solve the problem of rural revitalization. Furthermore, in practice, especially since the 19th National Congress, which explicitly proposed rural revitalization, various industries have made every effort to address the weak links in rural development, providing a virtual environment for the rapid development of the digital economy and rural revitalization.

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Fig 3. Spatial and temporal distribution of the development level of China’s digital economy and rural revitalization.

https://doi.org/10.1371/journal.pone.0277910.g003

As for the digital economy, the average development level of China’s digital economy from 2011 to 2020 was 0.148, growing from 0.088 in 2011 to 0.218 in 2020, achieving leapfrog development. The growth rate is slowing down, indicating that China’s digital economy is becoming more mature as the digital economy strategy steadily advances. There is a clear imbalance in China’s digital economy between regions at the regional level. It is the eastern region that has the most progress in digital economics, followed by the central and northeastern regions. The western region is the least developed in this area.

In terms of rural revitalization, the average level of rural revitalization development in China from 2011 to 2020 was 0.304, which has developed from 0.251 in 2011 to 0.352 in 2020, with an average annual growth rate of 4.53%, indicating that the strategy of poverty eradication and rural revitalization is effective. In terms of spatial distribution, areas with better development prospects for rural revitalization are concentrated on the eastern coast and spreading to the central part of China year by year. Heilongjiang Province and the Xinjiang Uygur Autonomous Region have seen a faster increase in rural revitalization, far higher than their neighboring provinces.

(3) Characteristics of the spatial distribution of the coupling coordination degree between the digital economy and rural revitalization

The spatial pattern of the coupling coordination degree of China’s digital economy and rural revitalization system is shown in Fig 4. The coupling coordination relationship between China’s digital economy and rural revitalization has gone through three development stages: moderate dissonance, near dissonance, and barely coordinated. The average coupling coordination degree in 2020 was 0.605, which is close to the state of moderate coordination. The analysis of the degree of coupling coordination between the two subsystems of the digital economy and rural revitalization shows that the comprehensive development trend of the two subsystems is consistent. With the rapid improvement of the comprehensive level of the digital economy subsystem, the coupling coordination of the digital economy and rural revitalization subsystem is on an increasing trend, indicating that the digital economy and rural revitalization in China have initially formed the endogenous momentum of synergistic development. However, in terms of development, the overall coupling and coordination between China’s digital economy and rural revitalization are still at a low level. China is at a stage where the rural digital economy lags behind the demand for rural revitalization.

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Fig 4. Spatial distribution characteristics of the coupling and coordination degree of digital economy and rural revitalization in China.

https://doi.org/10.1371/journal.pone.0277910.g004

From a regional perspective, the coupling and coordination degree of China’s digital economy and rural revitalization show an apparent characteristic of "strong in the east and weak in the west," and the coupling and coordination degree of the digital economy and rural revitalization from 2011 to 2020 has experienced a gradual diffusion and improvement from the east to the center to the west. In recent years, the coupling and coordination degree between the central and western regions and the northeast region has increased rapidly, but the overall trend of "strong in the east and weak in the west" remains unchanged. The degree of coupling and coordination between China’s digital economy and rural revitalization shows an obvious "polarization and balance" phenomenon but tends to develop in a balanced manner in the same echelon. At present, Beijing, Shanghai, Zhejiang, Tianjin, Jiangsu, and Guangdong, as polarized regions, are the regions with the best development of coupling and coordination between inclusive digital finance and rural revitalization. Especially Beijing, Shanghai, and Zhejiang, whose coupling and coordination level has reached a high coordination stage, forming a gap with other regions in China and showing a particular polarized spatial pattern. In contrast, the difference in coupling and coordination levels among other regions is insignificant. In recent years, all regions have developed from varying degrees of dissonance to intermediate levels of coordination, from moderate dissonance in 2011 to reaching intermediate levels of coordination by 2020, and have made great strides. Overall, there is a balance between the non-polarised regions.

(4) Examine the coupling and coordination constraints between the digital economy and rural revitalization

The "Zhou’s Constraints Identification Index" is proposed and constructed for the first time to identify and visualize the bottlenecks and critical areas that restrict the coupling and coordination between China’s digital economy and rural revitalization. The specific index model is as follows: (7)

Where, U_1it is the level of the digital economy subsystem in the region i and year t. U_2it and is the level of the rural revitalization subsystem in the region i and year t. Cons denotes the Zhou’s constraint identification index of China’s digital economy and rural revitalization, the value of which is in the range of (0,+∞), Cons = 1 indicating that the two systems have a slight deviation and the coupling is good. If it is less than 1, it means that the development level of China’s digital economy is lagging behind the demand for rural revitalization, and the farther the deviation from 1, the more serious the lag is, then the digital economy becomes a constraint to the coupling of the two. If the value Cons_it is greater than 1, it means that the development level of China’s digital economy is over-saturated relative to the demand for rural revitalization, and the farther the deviation from 1, the more serious the over-saturation is, then rural revitalization becomes a constraint to the coupled and coordinated development of the two.

The spatial pattern is shown in Fig 5: From 2011 to 2015, China generally showed that the development level of the digital economy lagged behind the revitalization of the countryside. However, the growth rate of the digital economy was faster than that of the revitalization of the countryside. The digital economy relative to countryside revitalization has become a bottleneck, except for Guangdong and Shanghai, oversaturated, and Jiangsu and Hunan, close to coordination in 2011. Guangdong and Shanghai are oversaturated, while Jiangsu and Hunan are close to coordination. All other regions have different degrees of digital economy lagging, which has become a bottleneck for rural revitalization and urgently needs to develop digital economy assistant rural revitalization vigorously. Digital economy development is faster than rural revitalization, and the constraints of China’s digital economy on rural revitalization have generally eased from 2011 to 2015. The outcome reflects the addition of Beijing, Liaoning, Shandong, Jiangsu, Zhejiang, and Sichuan to the over-saturated areas of the digital economy in Guangdong and Shanghai in 2015 near-coordinated areas of Hunan, Fujian, Hubei, Anhui, Henan, Shaanxi, and Chongqing.

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Fig 5. Spatial distribution of Zhou’s constraint identification index in China.

https://doi.org/10.1371/journal.pone.0277910.g005

From 2017 to 2020, the development of the digital economy was accompanied by a more rapid enhancement of rural revitalization, especially after the 19th National Congress put forward the rural revitalization strategy, and a series of policies and measures were introduced in various regions, which rapidly promoted the enhancement of rural revitalization. The level of development of the digital economy in that period is increasingly lagging behind the needs of rural revitalization development and has become an essential constraint to rural revitalization. The result reflects the severe degree of digital economy constraints in the development of rural revitalization in all regions of China in 2020. How to break the situation that the development of the digital economy lags behind the needs of rural revitalization development is an urgent task.

4.2 Spatial differences and decomposition of the coupling and coordination degree between the digital economy and rural revitalization

According to the Dagum Gini coefficient and decomposition method, the overall differences in the coupling and coordination degree of the digital economy and rural revitalization in China are decomposed into three parts: intra-group differences refer to the differences within the East, Central, West, and Northeast regions; inter-group differences refer to the differences between East-Central, East-West, East-Northeast, Central-West, Central-Northeast and West-Northeast regions. Super-variance density reflects the contribution of cross-over between different regions to the overall differences.

(1)Overall Differences

Fig 6 shows the overall Gini coefficient of the coupling coordination between China’s digital economy and rural revitalization and the intra-regional Gini coefficient trend of the four major regions. It can be seen that the overall Gini coefficient of China’s coupling and coordination of digital economy and rural revitalization is on a fluctuating upward trend, from 0.079 in 2011 to 0.088 in 2020, with an average annual growth rate of 1.37%, which indicates that the spatial differences of China’s coupling and coordination of digital economy and rural revitalization are generally on a widening trend. Specifically, from 2011 to 2015, the overall Gini coefficient decreased continuously from 0.079 to 0.063, with an average annual growth rate of -5.646%, indicating the spatial variation in the degree of coordination between China’s digital economy and rural revitalization has been expanding. However, the Gini coefficient rose sharply from 0.060 to 0.076 in 2016, indicating the spatial difference of coupling and coordination between the two was changing in that period.

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Fig 6. Overall Gini coefficient.

https://doi.org/10.1371/journal.pone.0277910.g006

(2)Intra-regional differences

Fig 6 shows that there is a lot of variation in the level of coordination between the digital economy and rural revitalization in the four major regions of China.

Firstly, the intra-regional Gini coefficient is highest in the eastern region, which shows a trend of "slowly decreasing-rapidly increasing-slowly decreasing." The trend means that the intra-regional differences in how the digital economy and rural revitalization work together in the eastern region are more significant than in the other three regions. The intra-regional differences in the degree of coordination between the digital economy and rural revitalization in the eastern region show a significant spatial convergence. The result is related to the large differences in the level of green innovation among cities in the eastern region. Specifically, Beijing, Shanghai, Zhejiang, and Guangdong in the eastern region have the highest levels of digital economic activity and rural revitalization in China. In contrast, Hebei Province and Hainan Province, both in the eastern region, have a lower digital economy and rural revitalization level, causing a big difference in the eastern region and a more significant Gini coefficient.

Secondly, although the intra-regional Gini coefficient of the western region is lower than that of the eastern region, it has the fastest growth rate, from 0.149 in 2011 to 0.191 in 2020, with an average annual growth rate of 1.538%. The Gini coefficient of the coupling of the digital economy and rural revitalization in the western region catch up with the eastern region at a breakneck speed and finally reach and surpass the Gini coefficient of the eastern region in 2019. Thirdly, the Central and Northeastern regions are the most critical.

Third, the intra-regional Gini coefficients of the central and northeastern regions have long been the lowest among the four regions, indicating that they have minor intra-regional differences but with apparent phase characteristics. After China’s 19th Party Congress, the Gini coefficient rose because of rural revitalization and the digital economy policies, which has led to a widening gap between the two regions.

(3)Regional Differences

As shown in Fig 7, the inter-regional differences between the digital economy and rural revitalization indicate the magnitude and trend of the differences.

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Fig 7. Inter-regional Gini coefficient.

https://doi.org/10.1371/journal.pone.0277910.g007

Firstly, from the perspective of the magnitude of the difference, the Gini coefficient between the eastern and western regions is the largest, with an intra-group age value of 0.622, followed by the eastern and central regions, with an intra-group mean value of 0.182. The intra-group mean value in the central and western regions is 0.155, and 0.128 in the eastern and northeastern regions. The differences between the eastern and western regions, the eastern and central regions, the central and western regions, and the eastern and northeastern regions are insignificant. The degree of coordination between the digital economy and rural revitalization differs most between the eastern and western regions, the eastern and central regions, the central and western regions, and the eastern and northeastern regions. In addition, the Gini coefficients between the central and northeastern regions and the western and northeastern regions are the smallest, with mean values of 0.108 and 0.110, respectively. The inter-regional differences are driven by the four inter-regional differences between the eastern and western regions; the eastern and central regions, the central and western regions, and the eastern and northeastern regions.

Secondly, analyzing the changing trend of the gap and the average annual decline rate, we can see that the gap between the eastern and central regions, the eastern and western regions, the eastern and northeastern regions, and the western and northeastern regions has been decreasing, with an average annual growth rate of -8.69%, -4.36%, -4.73%, and -3.35%, respectively, showing the inter-regional convergence trend. The gap between the eastern and central regions has the fastest decline rate. The fastest rate is the average annual growth rates of the Gini coefficients between the central-western, and central-northeastern regions are 0.43% and 5.38%, respectively, after 2019, which indicates that the regional differences in the coordination of the digital economy and rural revitalization in China will be driven by the combination of the central-western and central-northeastern regions for a certain period in the future.

(4)Variation sources

As shown in Fig 8, the contribution of inter-regional variation, intra-regional variation, and hyper-variance density to the coupling and coordination degree of China’s digital economy and rural revitalization is significant. During the examination period, the contribution of inter-regional differences to the overall variation in the coupling coordination degree has been dominant, followed by intra-regional differences, and the contribution of hypervariable density is the smallest, which indicates that the primary source of the overall variation in the coupling coordination degree of China’s digital economy and rural revitalization is inter-regional differences. The contribution rate trend shows that the inter-regional contribution rate trend is relatively stable and shows a convergence trend. The intra-regional and super-variable density contribution rate trends are slowly increasing, but they are still much smaller than inter-regional differences. The trend suggests that inter-regional variation will still play a big role in the overall change in the next few years.

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Fig 8. Breakdown of sources of variation.

https://doi.org/10.1371/journal.pone.0277910.g008

5.1. Spatial correlation analysis

In order to examine the spatial dependence and spillover of the coupling and coordination degree between the digital economy and rural revitalization in different regions, the spatial proximity weight matrix, geographic distance weight matrix, economic distance weight matrix, and economic geographic weight matrix were constructed in this paper. The spatial Moran’s I index of the coupling and coordination degree of the digital economy and rural revitalization in 1999–2019 under these four spatial weight matrices was calculated, and the results are shown in Fig 9. The results show that the global Moran’s I index is significantly positive during the period under consideration, indicating that the level of coupling and coordination between the digital economy and rural revitalization is influenced by the level of coupling and coordination of neighboring areas and that the spatial distribution pattern of coupling and coordination between the digital economy and rural revitalization is high-high and low-low. From the values of Moran’s I index, except for the spatial proximity weight matrix and the economic distance weight matrix in 2000, which are significant at the 5% level, all the others are positive at the 1% level, indicating that the positive spatial correlation between the digital economy and rural revitalization coupling and coordination is relatively stable. The Moran’s I index of the spatial proximity weight matrix is stable between 0.17 and 0.47, and the Moran’s I index of the geographic distance weight matrix and economic distance weight matrix is stable between 0.15 and 0.32. So, geographic distance and economic distance are essential elements in the coupling and coordination of the digital economy and rural revitalization, and it is necessary to conduct differentiated research at economic and geographic scales. In this paper, the economic-geographic spatial weight matrix is selected for further study because it integrates the stable influence of economic and geographical factors on the spatial pattern, and the Moran’s I index is stable between 0.05 and 0.15, which is lower than the other three spatial weight matrices. The economic-geographical spatial weight matrix makes the empirical results robust.

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Fig 9. Moran test for different spatial weight matrices.

https://doi.org/10.1371/journal.pone.0277910.g009

5.2 Absolute β-convergence

The models were screened by the LM test, Hausman test, and fixed effects test to analyze the absolute β-convergence of the coupled development of the digital economy and rural revitalization in the whole country and the eastern, central, western, and northeastern regions. As shown in Table 2, the Robust LM-error test and the LR-SDM-SEM test are not significant under the economic geographical weight matrix, indicating that the spatial Durbin model can be degraded to a SAR model. The Hausman test rejects the original hypothesis at the 1% significance level, indicating that a fixed-effects model should be used. The individual and time effects were tested by LR-both-ind and LR-both-time, respectively, both of which rejected the original hypothesis at the 1% significant level, so the model uses a time-area double fixed effect. The general model was checked out by diagnostic tests. In this paper, the SAR model with two-way fixed effects was used to test the beta convergence process of the coupling degree of China’s digital economy and rural revitalization, as well as the beta convergence process of the beta convergence process.

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Table 2. Model selection test structure.

https://doi.org/10.1371/journal.pone.0277910.t002

Table 3 shows that the absolute β convergence coefficients of the national, eastern, central, western, and northeastern regions are all negative, except for the central region, which is significantly non-zero at the 1% level, indicating that there is significant spatial convergence in the degree of coupling and coordination between the digital economy and rural revitalization in the national and the four regions. The absolute value of the β convergence coefficient is West > National > East > Northeast > Central, implying that the western region converges faster than the east-central and northeast regions because of the digital economy’s development level and rural revitalization. The western region generally has a low convergence rate. With the continuous promotion of the construction of ecological civilization in the Yellow River Basin and the introduction of policies to support rural areas in the West, the development level of the digital economy and rural revitalization in the western region has rapidly improved, and the spatial convergence has shown a higher marginal effect. At the same time, there are still huge differences between the digital economy and rural revitalization in the east and the west. Under the siphoning effect, resources in the central and western regions continue to flow to the east, which may lead to a rapid convergence of the spatial coordination between the digital economy and rural revitalization in the west to a low level of equilibrium.

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Table 3. Regression result.

https://doi.org/10.1371/journal.pone.0277910.t003

5.3 Relative convergence

Conditional beta convergence refers to the inclusion of a set of control variables that have an impact on the degree of coordination of the coupling of the digital economy and rural revitalisation and are generally more realistic. Drawing on Elhorst (2014) [40], the Hausman test confirms the applicability of a two-way fixed effects model for time and area, and both the spatial LR test and the Wald test confirm the spatial autoregressive model (SAR). In view of this, the spatial conditional β convergence was tested according to the spatial autoregressive model of Lesage (2008) [41], as in Eq 8: (8)

Where λ is the spatially lagged regression coefficient, W_ij is the spatial weight matrix, X_i,t is the set of control variables, u_i and v_t are personal and temporal fixed effects, respectively, which can effectively control for unobservable regional and temporal trend factors that do not vary over time, and ε_it is the error term. After adding the control variables, if β is significantly less than 0, then there is conditional spatial convergence, and vice versa, there is spatial divergence.

Combining the development of the digital economy and the actual situation of rural revitalization in China, the level of financial support to agriculture, industrial structure, level of inclusive financial development, urban-rural income gap, and economic development level was identified as the influencing factors. Details are shown in Table 4

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Table 4. Variables and descriptions.

https://doi.org/10.1371/journal.pone.0277910.t004

Based on the β absolute convergence analysis model, five control variables, including the level of financial support to agriculture, industrial structure, the level of inclusive financial development, the urban-rural income gap, and the level of economic development, were added to obtain the conditional β convergence estimation results of the coupling coordination degree between the digital economy and rural revitalization for the whole country and the four regions. As shown in Table 5, the conditional β convergence coefficients of the national, eastern, and western regions are all significantly negative, which proves that there is a significant conditional convergence process in the above regions, and the degree of coordination between the digital economy and rural revitalization will converge to their respective steady-state levels under the influence of different influencing factors. Spatial differences tend to decrease over time, and there is a "catch-up effect" for the lagging regions. In terms of the convergence trend, the western region and the whole country have the fastest convergence rate, followed by the eastern region, while the central and northeastern regions have weak convergence. Therefore, there is both absolute and conditional convergence across the country as a whole and the four regions, which also indicates that, in the long run, the spatial differences in the degree of coupling and coordination between the digital economy and rural revitalization in China are gradually decreasing, and this trend is relatively stable.

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Table 5. Model estimation results.

https://doi.org/10.1371/journal.pone.0277910.t005

The effects of control variables on the convergence of the coupling coordination, specifically, First, the level of financial support for agriculture generally affects the convergence of the coupling coordination degree between China’s digital economy and rural revitalization, especially significantly contributing to the convergence of the coupling coordination degree in the eastern, central, and northeastern regions. Financial support to agriculture is the basis for improving the coupling and coordination between China’s digital economy and rural revitalization. That each region should get more money from the government to keep looking for new ways to grow the rural digital economy and make sure that there are enough materials and technology to help revitalize the countryside.

Secondly, the impact of urbanization on the degree of coupling and coordination between the digital economy and rural revitalization varies from region to region, with urbanization hindering the convergence of the degree of coupling and coordination in the national and eastern regions and positively affecting the central, western, and northeastern regions. This phenomenon may be because the current urbanization process in China is a "Great Leap Forward" type of low-quality development, which is obsessively pursuing the growth of the urban population share. At the same time, farmers are encouraged to move to the cities without being properly resettled, which slows down the revitalization of the country.

Thirdly, the level of financial inclusion development significantly contributes to the convergence of the digital economy and rural revitalization coupling in the national and western regions, while the impact on the central and northeastern regions is positive but not significant. In order to improve the quality of rural revitalization, there must be a smooth flow of money and a rational allocation of funds. This should be done to solve the problem of wasted money in the eastern region and to ease the contradiction of total money in the less developed western region.

Fourth, GDP per capita significantly contributes to the convergence of the coupling coordination between the digital economy and rural revitalization to high values in both national and western regions. With the increase in per capita income, residents will have more opportunities to borrow the Internet, stimulating platform consumption and driving the digital economy’s development. At the same time, rural social welfare and the rural ecological environment will also improve, which will promote rural revitalization.

Fifthly, the coefficient of foreign investment is significant at the 5% level. The introduction of foreign investment currently brings advanced digital technology and digital management experience, generating a positive spillover effect and promoting the improvement of the coupling and coordination between domestic digital economic development and rural revitalization.

5.4 Sensitivity testing

(1) The effects of various weight settings

By setting the weights of the digital economy and rural revitalization to a total of nine weight combinations from 0.1 to 0.9, respectively, the calculated dynamics of the coupling and coordination degree of the digital economy and rural revitalization are shown in Fig 10. The influence of different weights on the degree of coordination between the digital economy and rural revitalization in China is undeniable, with the weighting of rural revitalization-digital economy 0.9–0.1 gradually changing to rural revitalization-digital economy 0.1–0.9, the level of coordination between the digital economy and rural revitalization gradually increasing and then gradually decreasing. When the weight of the digital economy reaches 0.4, the level of coupling and coordination reaches its maximum. The reasonable weight of the digital economy in the process of coupling and coordinating the development of China’s digital economy and rural revitalization is between 0.4 and 0.5, which also proves that the weight of 0.5 for the digital economy and 0.5 for rural revitalization set in this paper is reasonable.

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Fig 10. Effect of various weight settings.

https://doi.org/10.1371/journal.pone.0277910.g010

(2) Robustness of β-convergence

Therefore, the combination of 0.4 digital economy and 0.6 rural revitalization weights was selected to calculate the degree of coordination between China’s digital economy and rural revitalization, and the β convergence model was regressed. The results show that when the weight of the digital economy is set at 0.4, there is still a significant absolute β convergence and conditional β convergence process, and the influence of the factors in the conditional β convergence does not change significantly, which also indicates that the study results are robust.