Empowerment and Pathways of Digital Economy in Rural Revitalization: A Case Study of Low Urbanization Areas in China

Abstract

Synergistic development is a pivotal force in advancing regional modernization. China has effectively promoted rural revitalization through the collaborative integration of various digital factors, thereby narrowing the urban–rural development gap and laying a solid foundation for achieving sustainable development. This study examines factors driving rural revitalization through the analysis of 193 cases in less urbanized Chinese regions from 2018 to 2022 using panel-data qualitative comparative analysis. This study identifies digital infrastructure as the key to rural development, while the importance of industrial digitalization is increasingly recognized. We found two combinations of the digital economy that can promote rural revitalization: one that links digital infrastructure with digital industry, and the other that combines digital infrastructure with data elementization. Moreover, we also analyzed how regions with different innovation capabilities and economic foundations should develop differentiated strategies to achieve rural revitalization. This research provides a scientific basis for various regions to formulate rural revitalization strategies that are in line with their own characteristics, which will help achieve the goal of regional sustainable development.

1. Introduction

Globally, rural revitalization has become a focal point for countries around the world. Rural revitalization is a key link in achieving global sustainable development and common prosperity. Implementing the rural revitalization strategy is not only related to the well-being of rural residents and the long-term development of the country, but also to global sustainable development and common prosperity [1]. However, with the development of the world process, significant achievements have been made in the development of rural areas. However, compared with cities, there are still problems of unbalanced and insufficient development, especially in areas with low urbanization rates, where the gap is even more pronounced. The flow of urban and rural resource elements shows obvious one-way characteristics, with serious talent loss in rural areas, insufficient capital investment, and backward technological level [2], rural revitalization is facing unprecedented opportunities and challenges.

Fortunately, the rapid development of the digital economy has provided new avenues and methodologies to address these issues [3,4]. The digital economy has high penetration, high usability, and high value-added characteristics, which can break down resource barriers, integrate production factors, and effectively alleviate the problem of imbalanced rural development. All regions of the world attach great importance to the implementation of the digital economy empowering rural revitalization strategy. The European Union has launched the “EU Smart Village Initiative”, involving key frontier technologies such as big data, the Internet of Things, logistics and transportation, and data sharing applications [5], to help improve the efficiency and competitiveness of rural traditional industries. The ePathshala online educational resource platform provided by the Indian government for rural schools empowers rural students to access the same educational content as urban students, narrowing the urban-rural education gap [6]. The Chinese government has promulgated the “Digital Rural Development Strategy Outline” [7], and the Japanese government has proposed the “Rural Vitality Plan” and the “Agricultural Information Technology Innovation Plan”, all emphasizing the application of digital technology in rural developments, injecting new vitality into rural revitalization [8].

It is evident that the digital economy has progressively become a new driving force for rural revitalization [9]. However, most of the existing studies explore the relationship between digital economy and rural revitalization from a single perspective, and few studies explore the multiple solutions of the digital economy empowering rural revitalization. Studies have shown that coordinated development is the key force in promoting regional modernization [10]. Therefore, this paper will use systematic thinking to subdivide the digital economy into five dimensions: informatization foundation, digital industrialization, industrial digitization, digital governance, and data elementalization. It will take the complex system theory as the framework and embed technical-economic paradigm theory and innovation diffusion theory to explore how the combination of different elements of the digital economy affects rural revitalization.

The innovations and contributions of this study are as follows:

First, the panel-data qualitative comparative analysis (QCA) is innovatively applied to the research of digital economy empowering rural revitalization. This paper explores the change trend and dynamic law of digital economy empowering rural revitalization in the time dimension, and it reveals the interaction and synergy between different antecedents, and it provides a comprehensive perspective for understanding how digital economy empowers rural revitalization.

Second, build a new theoretical framework. Based on the in-depth review of the relevant theories of digital economy and rural revitalization, this paper constructs a theoretical framework of technical-economic paradigm theory, innovation diffusion theory and complex system theory, and expounds in detail how the elements of digital economy affect rural revitalization through different combinations. It provides a solid theoretical basis for the study of digital economy empowering rural revitalization and fills the deficiency of existing research in theoretical integration and mechanism analysis.

Third, reveal the configuration path of digital economy empowering rural revitalization. Through panel-data qualitative comparative analysis, this paper captures the multidimensional characteristics of digital economy and identifies what kind of digital economy condition configuration can promote rural revitalization from five dimensions: digital infrastructure, digital industrialization, industrial digitization, digital governance, and data elements. It not only reveals the synergy between different elements of the digital economy, but it also summarizes the specific path of digital economy empowering rural revitalization, providing scientific basis and practical guidance for formulating rural revitalization strategies.

2. Literature Review

2.1. Empowerment Mechanisms of the Digital Economy

Existing research on the empowerment mechanisms of the digital economy mainly focuses on three aspects: data-element empowerment, digital-technology empowerment, and platform-economy empowerment. Firstly, data, as a core production factor of the digital economy, have characteristics such as infinite replicability, low marginal cost, and non-exclusivity. It is the foundation of digital technology and the “cell” of the digital economy and even the national economy. Stylos et al. [11] believe that big data can better predict customer behavior patterns in dynamic industries (such as tourism, travel, and hospitality), thereby enhancing organizational decision-making and business performance in these industries. Secondly, digital technology is the support of the digital economy. Its application can break the traditional temporal and spatial constraints, improve the speed and accuracy of information transmission, and promote the efficient allocation and collaborative use of resources. Saxena et al. [12] put forward that digital technology plays a key role in formulating accurate ESG reports, helping organizations develop more sustainable strategies and policies. Zhang and Liu [13] proposed that digital financial technology significantly reduces regional carbon emissions by supporting the industrialization of digital technology and the digitization of traditional industries. Thirdly, the platform economy is an organizational form of the digital economy. By building open and shared platforms, it connects supply and demand sides to achieve optimal resource allocation and maximize value. Kozinets et al. [14] believe that consumer feedback platforms provide consumers with feedback on their market and consumption experiences, helping them make more informed choices. Wang et al. [15] proposed that the development level of cross-border e-commerce platforms effectively promotes the global expansion capability of trading enterprises.

2.2. Influencing Factors of Rural Revitalization

Academic discussions on the influencing factors of rural revitalization cover multiple dimensions such as prosperous industries, livable ecology, rural civilization, effective governance, and affluent lives. Prosperous industries are the foundation and guarantee of rural revitalization. Sabet and Khaksar [16] believe that the participation of social capital in the construction of rural industries is a central principle for improving various aspects of rural sustainable development. In terms of livable ecology, a good ecological environment is an important support for rural revitalization. Du and Jiao [17] found that the construction of rural infrastructure improves the rural living environment by optimizing sanitary conditions, strengthening water supply, and enhancing informatization levels, forming a good rural landscape and promoting rural economic development. Rural civilization is an important part of rural revitalization. Chen et al. [18] argued that rural cultural heritage has various power entanglements and interactions, and has become a development resource for many rural areas in China. Effective governance is an important guarantee for rural revitalization. Xi [19] indicated that grassroots cadres play a crucial role in rural construction. They are the implementers of policies, the promoters of rural development, and the spokespeople for the interests of the masses. Affluent lives are one of the goals of rural revitalization. Wang [20] indicated that digital inclusive finance can promote rural entrepreneurship and stimulate rural consumption by reducing the service costs of financial institutions and increasing residents’ income.

2.3. Relationship Between the Digital Economy and Rural Revitalization

Scholars mostly study the relationship between the digital economy and rural revitalization from aspects such as improving agricultural production efficiency, promoting the integration of rural industries, improving the rural ecological environment, enhancing rural governance levels, and promoting farmers’ employment. First, the digital economy improves agricultural production efficiency and product quality through precision agriculture, smart agriculture, and other technical means. Qin et al. [21] pointed out that the digital economy emphasizes innovation-driven development, promoting the sustainable transformation and upgrading of traditional agriculture through technological and business model innovations. Zhang et al.’s research [22] shows that the digital economy can significantly improve the agricultural green total factor productivity. Secondly, the digital economy can promote the integration and development of rural industries, forming an “agriculture+” industrial form. Zhang et al. [23] believe that the digital economy has strong permeability and integration, promoting the integration of urban and rural areas and industries. Wu and Chen [24] found a significant positive relationship between the digital economy, market potential, and rural revitalization. The digital economy promotes rural revitalization by stimulating local market potential and expanding the rural consumer market. Third, the digital economy improves the rural ecological environment through environmental monitoring, resource recycling, and other technical means. Cheng et al. [25] pointed out that the digital economy promotes the greening and livability of rural life, the green and low-carbon production in rural areas, and the quality of rural ecological baseline to different extents. Fourth, the digital economy can enhance the rural governance level. Lv and Shi [26] pointed out that the digital governance of rural grassroots lets rural governance modes achieve a qualitative leap, making information more intuitive and clearer to convey to everyone, and reducing the pressure on grassroots government staff to collect information and materials house by house. Fifth, the digital economy can promote farmers’ entrepreneurship. Khan et al. [27] indicated that the use of mobile internet can significantly increase farmers’ entrepreneurial probability. Zhou et al. [28] also pointed out that improving farmers’ digital literacy can improve rural residents’ entrepreneurial activities and alleviate rural multidimensional relative poverty.

In summary, existing research provides a valuable theoretical basis for understanding the specific application modes and implementation paths of the digital economy in rural revitalization. However, there are still the following three deficiencies:

Firstly, most existing research studies the impact of the digital economy on rural revitalization from a single perspective, lacking in-depth discussions on the all-round integration of the digital economy and rural revitalization. Some research suggests that a multi-party collaborative governance model should be used to promote rural revitalization [29].

Secondly, regarding the specific path of the digital economy empowering rural revitalization, existing research mostly stays at the macro-level policy interpretation and case analysis, lacking research on differentiated paths for different regions and rural types.

Thirdly, research on the digital economy and rural revitalization requires a large amount of high-quality data support, but there are currently significant difficulties with data acquisition and organization. Some studies rely on limited case data or macro-statistical data, making it difficult to comprehensively reflect the actual effectiveness of the digital economy empowering rural revitalization. This study believes that selecting more specific prefecture-level city data can improve the pertinence and effectiveness of policies.

Based on this, this paper adopts the panel-data QCA method, thinks from the perspective of complex systems theory, combines the technical-economic paradigm theory and innovation diffusion theory, identifies various antecedent condition configurations of the digital economy empowering rural revitalization and their changes over time, and explores differentiated rural revitalization paths according to differences in economic conditions, innovation capabilities, etc., in different regions. It provides more refined and operational guidance for policymakers and practitioners, and strong support for drawing new blueprints for rural development in various regions.

3. Theoretical Foundations and Model Construction

3.1. Theoretical Foundations

3.1.1. Technical-Economic Paradigm Theory

Originating in the 1980s and proposed by Christopher Freeman and Carlota Perez, technical-economic paradigm theory emphasizes the fundamental impact of technological revolutions on economic structures and industrial development. The transformation of technological paradigms triggers changes in economic paradigms, driving industrial restructuring and economic model transformation [30]. In the digital economy era, emerging technologies are digital technologies, and new production factors are data resources. The technical-economic paradigm of the digital economy era is characterized by the efficient use of data resources and digital technologies, driving technological innovation, economic model changes, and social institutional structural changes. This provides theoretical support for analyzing how various elements of the digital economy drive the transformation of rural economic models and social structures.

3.1.2. Innovation Diffusion Theory

Proposed by Rogers in the 1960s, innovation diffusion theory defines the process of spreading innovations through specific channels within a specific community over a specific period. It reveals how new ideas, technologies, or products spread and are widely adopted in society through media and social networks. Explicit diffusion is reflected in the formation and release of normative documents, while implicit diffusion is reflected in the embedding and practical application of innovations within organizations. In the context of the digital economy empowering rural revitalization, this theory explains the dissemination and application of digital technologies and other innovations in rural areas [31], and it reveals the dual mechanisms of explicit and implicit diffusion.

3.1.3. Complex Systems Theory

Complex systems theory emphasizes the dynamic interactions among elements within a system. In complex systems, outcomes arise from the interplay of multiple factors, characterized by causal complexity and multiplicity. Complex systems have multiple equilibrium features, meaning there is no single optimal equilibrium state; they can switch or coexist in different stable states. This highlights the non-linearity and uncertainty of complex systems [32]. Additionally, complex systems exhibit asymmetry, where different factors may have varying impacts on outcomes, with some being decisive and others less so. In the context of the digital economy empowering rural revitalization, this theory suits the complexity and dynamism of rural systems. Rural revitalization is a multi-dimensional goal-oriented systems engineering project requiring multi-party collaboration, aligning with the characteristics of complex systems. The interactions among various parties drive the evolution of rural development paths. The digital economy, as a complex technological system, forms multiple equilibriums in rural areas, accelerating the rural revitalization process. From this theoretical perspective, we can better understand the collective behavior and systemic support generated by the digital economy in rural revitalization, assisting in achieving comprehensive and sustainable rural development.

3.2. Model Construction

3.2.1. Deconstruction of the Digital Economy

The digital economy consists of interdependent elements. Our research breaks it down reasonably. Digital infrastructure, the foundation of the digital economy, refers to the infrastructure built with advanced information and communication technologies to support digital transformation, including network infrastructure, cloud computing platforms, big data centers, etc. We consider informatization infrastructure as one element of the digital economy. Referring to the “China Digital Economy Development Research Report (2023)” [33] by the China Academy of Information and Communications Technology, this study divides the digital economy into four parts: digital industrialization, industrial digitization, digital governance, and data elementization. These four aspects can effectively empower the digital economy to support rural revitalization. Digital industrialization refers to new industries and business models formed under the current digital economy, involving digital technology research, development, application, and related product and service provision. Industrial digitization is the digital transformation of traditional industries driven by digital technology. Digital governance optimizes government governance, social management, public services, and policymaking through digital technology, data analysis, and information systems. Data elementization is the process of managing, configuring, utilizing, and circulating data as an independent production factor.

3.2.2. The Digital Economy’s Empowerment of Rural Revitalization

Firstly, digital infrastructure provides necessary guarantees for the comprehensive development of the digital economy [34]. From the perspective of the technical-economic paradigm theory, digital infrastructure serves as a technical support, corresponding to the technological innovation foundation, fueling the development of the digital economy. Within the innovation diffusion theory, it is also an innovation source, driving the popularization and application of new technologies and applications across different fields. In rural areas, Liu and Li [35] indicated that the internet penetration rate, smart agricultural facilities, and other digital infrastructure directly impact the speed of rural digital transformation, playing a key role in enhancing economic efficiency and promoting rural industrial upgrading. The spread of rural internet access has already transformed the agricultural sectors of developed countries, and many regions in developing countries are also undergoing information and communication technology revolutions [27].

Secondly, digital industrialization brings about highly flexible and innovative new industrial models. It can create entirely new products or services through cross-border integration and innovative applications. According to the technical-economic paradigm theory, digital industrialization drives profound changes in the economic model: on the one hand, it promotes the optimization and upgrading of the industrial structure, making the digital economy a new engine for economic growth; on the other hand, it changes traditional business models and transaction methods, giving birth to new forms of platform economies and sharing economies. Based on the innovation diffusion theory, the internet industry diffuses from the innovation source to rural areas, expanding new rural business forms, driving the development of rural e-commerce [36], directly increasing the income and consumption levels of rural residents [34]; creating shared platforms, breaking down urban–rural information barriers, and promoting the optimal allocation of urban–rural resources and integrated development [37], thereby enhancing the quality of rural life.

Thirdly, industrial digitization is the inevitable path for the transformation and upgrading of traditional industries. Through digital transformation, traditional industries can break through original development limitations and achieve intelligent production processes, personalized product services, and refined corporate operations. This is closely related to the technological innovation and economic model transformation in the technical-economic paradigm theory and reflects the innovative application in the innovation diffusion theory. Abiri et al. [38] showed that intelligent and informatized production methods can significantly boost agricultural productivity. The deep integration of digitization and rural real economy not only occurs in agricultural production but also extends to processing, logistics, marketing, and other links, achieving full-industry-chain digital transformation.

Fourthly, digital governance can improve the social governance approach, enhancing governance efficiency. According to the technical-economic paradigm theory, technological innovation is a key driver of social institutional structural changes. Under digital governance, the social governance approach evolves from traditional experiential governance to precise and intelligent governance. Digital governance also represents the diffusion of innovation at the social level in the innovation diffusion theory. To meet modern information society management needs, digital governance by the government not only enables real-time supervision and targeted poverty alleviation of the rural economy [39] but also promotes the coordinated allocation of rural resources [40]. Online–offline collaborative governance makes rural governance more resilient [41], with an increasingly diversified governance structure and varied governance models.

Fifthly, data elementization, as a key part of technological innovation, uses advanced data processing and analysis to deeply mine and efficiently utilize massive data. The digital economy significantly changes agricultural development by leveraging data, a crucial production factor. Through data analysis, it can more accurately understand agricultural production, market demands, consumer preferences, etc., driving industrial structure optimization [42]. Blockchain technology ensures transparent and tamper-proof transaction data. Its application in agricultural product traceability guarantees data authenticity [43], meeting consumers’ demand for healthy food and enhancing agricultural product market competitiveness.

In summary, rural areas use the concepts, technologies, models, and means brought by the digital economy to promote industrial transformation and upgrading, sustainable development, and achieve the strategic goal of comprehensive rural revitalization.

3.2.3. The Complex Mechanism of the Digital Economy Empowering Rural Revitalization from a Complex Systems Perspective

The digital economy’s five core elements—digital foundation, digital industrialization, industrial digitization, digital governance, and data elementization—form a new engine for rural revitalization. Based on complex system theory, these elements are interdependent and jointly constitute the digital economy’s ecosystem, powerfully enabling rural revitalization.

Firstly, the improvement of digital infrastructure promotes the development of digital industrialization, which in turn drives industrial digitization by providing technical support and solutions. The outcomes of industrial digitization are optimized and enhanced through digital governance, with data elementization running through all of them, offering decision-making support and forming a closed loop. In rural areas, as the internet, telecommunications, and other infrastructure improve, digital technology permeates various rural fields, giving rise to new industrial forms like digital rural tourism and e-commerce. The application of digital technology in agriculture enables precision and smart agriculture [44]. Through agricultural IoT technology, agricultural production processes are precisely controlled and managed, improving resource efficiency and crop yields.

Secondly, digital infrastructure provides essential technical support for digital and industrial digitization. The development of these two also promotes the iterative upgrades and technological innovations of digital infrastructure. On one hand, network infrastructure construction ensures smooth internet access for rural residents and enterprises, driving rural digital industrial development, transforming digital technology into productivity, forming new rural industries and business models, and achieving digital connections and information sharing in the agricultural industrial chain [45,46]. On the other hand, the application of digital technology improves agricultural production efficiency and product quality, providing practical scenarios and data support for digital infrastructure upgrades. The higher requirements of rural industrial digitization transformation for digital infrastructure also drive its continuous technological innovation and optimization.

Thirdly, digital and industrial digitization are the main bodies of data development and utilization, jointly promoting data products into market transactions to realize data value. The conversion of data element value reshapes digital and traditional industries, unlocking new potential. On one hand, during the extension of the digital industry to industrial digitization, traditional e-commerce platforms accumulate data resources, providing decision-making support for retail, achieving data-driven retail digitization. For example, e-commerce platforms analyze consumer purchase behavior and preferences, transcending geographical boundaries to offer rural retailers precise market positioning and product recommendations [47], helping them optimize product ranges and inventory management, and improve sales efficiency and customer satisfaction [36]. On the other hand, during the expansion of industrial digitization to digital industrialization, data products from smart agriculture and other new business forms drive the precision of agricultural production methods. Analyzing agricultural production data, such as soil and climate prediction data, enables precise fertilization, irrigation, and pest control, improving traditional agricultural production efficiency and product quality while reducing costs and resource waste.

Fourthly, digital governance optimizes rural governance systems using digital technology, improving governance efficiency and transparency. It also provides key application scenarios for data elementization. The comprehensive coverage of “digital village” aids in building digital platforms and digitally preserving cultural resources, promoting rural cultural and economic development [48]. The construction of digital governance platforms improves rural governance efficiency and transparency, increasing farmers’ satisfaction with government services [49,50]. Data collected by these platforms can analyze market demands and optimize industrial layouts, driving sustainable rural industrial development. For example, analyzing rural tourism data helps understand tourist preferences and behaviors, enabling targeted tourism product and service development [51].

There are significant differences in the development of the digital economy across regions, leading to different paths for rural revitalization. Due to differences in resource endowments, economic foundations, talent reserves, etc., rural areas have different starting points, speeds, and directions for digital economy development. Consequently, the effectiveness of the digital economy’s elements in empowering rural revitalization may vary in time and space. These differences not only manifest in the varying degrees of contribution of the digital economy’s elements to rural revitalization but also in the complexity of interactions among the elements. Over time and across spaces, the impact of the digital economy’s elements on rural revitalization may change. The imbalance in rural development in time and space further indicates that rural revitalization is a complex and dynamic process.

In summary, the complex mechanism of the digital economy empowering rural revitalization from a complex systems perspective involves the interaction and dynamic changes in multiple elements. Therefore, this paper constructs a theoretical model from five dimensions: digital foundation, digital industrialization, industrial digitization, digital governance, and data elementization, as shown in Figure 1.

4. Research Design

4.1. Research Method

The panel-data qualitative comparative analysis method is to introduce panel-data into set theory and explain the causal relationship over time. Especially when analyzing complex social phenomena, it can reveal how different factors interact over time and affect the results [52]. The traditional QCA method is based on the perspective of configuration to analyze the heterogeneity, asymmetric relationship and equivalence path between cases, but it ignores the influence of time on conditional configuration, which makes the research static and unsaturated in the theoretical construction and empirical test stages [53]. In order to solve the above problem of “time blind zone”, foreign scholars Garcia Castro and Ariño [52] creatively developed a panel-data QCA method for analyzing panel-data. To explore the condition configuration of specific results, follow the following steps:

➀

Variable calibration. Similar to the traditional fuzzy set qualitative comparative analysis method (fsQCA), it is necessary to calibrate the membership of the cause variable and the result variable to between 0 and 1, so as to clearly represent the different states of each condition variable (such as high and low). Usually, after setting the three anchor points of “full membership”, “intersection”, and “full non membership”, the calibration function in fsQCA3.0 software developed by Ragin is directly used for operation. Since the software will default not to analyze the situation that the membership score is exactly 0.5 in subsequent experiments, it will be adjusted by adding 0.001 to retain the case sample.

➁

Construct a truth table. The truth table is used to show all possible combinations of conditions and their corresponding results. In panel-data QCA, it is usually generated by using the “truthTable” function in R studio after setting the threshold. Threshold includes consistency threshold, PRI threshold, and frequency threshold: Consistency is used to explain the interpretation strength of a certain condition combination on the result, and a generally acceptable consistency should be greater than or equal to 0.8; PRI is used to avoid the situation that the existence of a condition combination will lead to results, and the generally acceptable PRI should be greater than or equal to 0.75; frequency is used to avoid contingency or instability caused by too few cases. It can be set as 1 or 2 in small sample studies, but it should be appropriately increased in large sample studies [54].

➂

Simplify the combination of conditions. Analyze the condition combination and results in the truth table, and use the “minimize” function in R studio to Boolean minimize the condition combination to obtain the complex solution, reduced solution and intermediate solution, and obtain all key indicators of each configuration, i.e., consistency, PRI, coverage, and summary consistency. By analyzing the similarities and differences in the conditions in the intermediate solution and the simple solution, the core conditions are determined, and finally the causal path of the core is extracted.

➃

Calculate group consistency and coverage. Further explore the changes of panel-date configurations in time dimension and individual dimension. The consistency and coverage of cases between and within groups are calculated based on the “cluster” function in R studio, and the robustness of the solution is judged by calculating the consistency adjustment distance between and within groups. It is generally believed that if the adjustment distance is less than 0.2, there is no significant difference between the time dimension and the individual dimension. If it is greater than 0.2, the dynamic changes in the configuration in different dimensions can be further studied.

In the field of social science research, qualitative comparative analysis (QCA), as a method integrating qualitative and quantitative advantages, provides a new perspective for the study of complex social phenomena. Its application scope is expanding and has become a trend to explore new scientific paradigms [55]. In the research of digital economy empowering rural revitalization, the qualitative comparative analysis method of panel-data has unique advantages as follows:

On the one hand, the panel-data qualitative comparative analysis method can conduct in-depth analysis of the multidimensional elements of the digital economy. By analyzing the complex relationship between multiple elements of the digital economy [56], we can comprehensively understand how the digital economy has a non-linear and complex causal effect on rural revitalization through different element combinations, and can reveal the “same destination” effect of digital economy configuration on rural revitalization.

On the other hand, panel-data qualitative comparative analysis method can analyze time series data [57]. By capturing the impact of digital economy elements and element configuration on rural revitalization at different time points, it is helpful to reveal the change trend and dynamic law of digital economy’s empowerment on rural revitalization in time dimension. In the long-term practice of rural revitalization, through the analysis of the dynamic changes in digital economy elements in different periods of time, it can be found that some elements have a promoting effect on Rural revitalization in a certain period, but their effect may be weakened or enhanced in another period, so as to provide guidance for timely adjustment and the development of digital economy to empower rural revitalization.

4.2. Sample Selection

Taking high-income countries divided by the World Bank (usually regarded as developed countries) as an example, the urbanization rate of most countries exceeds 80% [58]. Therefore, this study selects prefecture level city development data with an urbanization rate of less than 80% in China from 2018 to 2022 as case samples. As the largest developing country in the world, China’s rural development experience has significant reference value for other developing countries [59]. Cities with an urbanization rate of less than 80% retain more rural characteristics, providing a sample closer to the actual development situation of many developing countries for this study to ensure that the research results can more accurately reflect the actual needs and challenges of rural areas in developing countries in the development of the digital economy.

4.3. Variable Measurement

4.3.1. Result Variable

Our study selects Chinese cities as case, hence the measurement of rural revitalization adheres to the standards of official Chinese documents, which means assessing the development level of rural revitalization across five dimensions: prosperous industries, livable ecology, civilized rural customs, effective governance, and affluent living [60,61], encompassing a total of 21 specific indicators (as shown in

Table 1. Variable names and definitions.

Dimension	Sub-Indicator	Specific Indicator	Data Source
Industrial Prosperity	Production Level	Grain production capacity (ten thousand tons)	Local Statistical Yearbook
		Agricultural per capita mechanical power (kilowatts)	Local Statistical Yearbook
	Production Efficiency	Labor productivity (CNY/person)	China City Statistical Yearbook Yearbook
	Industrial Chain Extension	Main business income of large-scale agricultural product processing enterprises (CNY billion)	Local Statistical Yearbook
Ecological Livability	Ecological Environment	Greening coverage rate (%)	China City Statistical Yearbook
		Comprehensive utilization rate of livestock and poultry manure (%)	Local Statistical Yearbook
	Living Environment	Sanitation toilet popularization rate (%)	Local Statistical Yearbook
		Village life garbage centralized treatment village proportion (%)	Local Statistical Yearbook
Civilized Customs	Cultural Construction	Number of cultural stations (pieces)	Local Statistical Yearbook
		Cable TV comprehensive coverage rate (%)	Local Statistical Yearbook
	Educational Construction	Average years of education for rural residents (year)	Local Statistical Yearbook
		The proportion of full-time teachers at compulsory education schools with a bachelor’s degree or above (%)	Local Statistical Yearbook
Effective Governance	Governance Capability	The proportion of village party secretaries and directors “one shoulder pick” (%)	Local Statistical Yearbook
	Governance Measures	The proportion of administrative villages with planned village construction (%)	Local Statistical Yearbook
		The proportion of administrative villages carrying out village rectification (%)	Local Statistical Yearbook
Prosperous Life	Income Level	Per capita disposable income of farmers (CNY/person)	Local Statistical Yearbook
		Urban-rural resident income ratio (%)	China City Statistical Yearbook
		Rural resident Engel coefficient (%)	Local Statistical Yearbook
	Life Management	Village road hardening rate (%)	Local Statistical Yearbook
		The number of cars per hundred households (vehicles)	Local Statistical Yearbook
		The number of health personnel per thousand rural residents (people)	Local Statistical Yearbook

).

4.3.2. Conditional Variables

First, digital infrastructure. The construction of digital infrastructure is the first step to achieving the empowerment of the digital economy in rural revitalization and provides the basic conditions for rural areas to access the digital economy. Our research uses the proportion of administrative villages with broadband internet services to measure the rural internet penetration rate, with data sourced from the official websites of city governments across China.

Second, digital industrialization. Referring to relevant studies [62], digital industrialization is seen as an important driving force for the economy. Our study uses the scale of the digital industry to measure the state of digital industrialization, specifically the number of digital enterprises entering the market in that prefecture-level city. Data comes from the Qiyan China Big Data Platform for Social-Science (CBDPS).

Third, industrial digitalization. Referring to relevant studies [63], industrial digitalization refers to the integration of digital technology and the real economy, and digital inclusive finance relies on the application of digital technology to achieve the optimization and upgrading of the industrial chain, thereby promoting the digital process of the physical industry. Therefore, our study uses the digital inclusive finance index to measure industrial digitalization. Data comes from the “Peking University Digital Inclusive Finance Index”.

Fourth, digital governance. The government’s attention to a certain issue is usually measured by text analysis, that is, the proportion of the frequency of a series of words highly related to the digital economy can reflect the government’s attention to the digital economy. In view of the authority and continuity of the government work report of the local government at the prefecture level, it is used as a measurement sample, and referring to the measurement strategy of Liu et al. [64], a vocabulary library highly related to digital governance is established, which mainly includes two aspects: “digital emerging technology” and “digital application”; a total of 121 words highly related to digital governance, and the proportion of the vocabulary library frequency is used to measure government attention. Samples come from the websites of various local governments at the prefecture level.

Fifth, data elementization. The scale of regional data are relatively vague, and data transactions are a key way to achieve the marketization of data factors. Therefore, our study measures the level of data elementization activity in a region based on whether there is a data trading business, specifically the number of companies in the region that are currently involved in the “data transaction” business. Data comes from the official website of Aiqicha.

4.4. Variable Calibration

Based on existing theories and research, our study conducts a unified calibration of all cases, employing the direct calibration method to set three anchor points for full membership, midpoint, and non-membership at the 95th percentile, 50th percentile, and 5th percentile, respectively [65]. The specific calibration anchors for the variables are shown in

Table 2. Variable calibration results.

Variable Name		Calibration
		Complete Membership	Crossover Point	Non-Membership
Result variable	Level of Rural Revitalization	0.326	0.275	0.059
Conditional variable	Digital infrastructure	22.493	18.017	6.736
	Digital industrialization	31,444.400	3911.000	997.800
	Industrial digitalization	315.175	262.606	217.669
	Digital governance	4.355	2.022	0.666
	Data elementization	169.000	33.000	3.000

.

5. Results and Analysis

5.1. Necessary Condition Analysis

Similar to the principle of the traditional static QCA test for necessary conditions, a single condition consistency greater than 0.9 is considered a necessary condition for the result, that is, the condition variable should appear in the configuration that produces high or non-high levels of rural revitalization. However, in panel-data QCA, Garcia-Castro and Ariño [52] suggest that it is necessary to further calculate the consistency-adjusted distance to determine the robustness of the necessary condition. When the adjusted distance is less than 0.2, the summary consistency is high, otherwise, it indicates that the experiment is affected by time effects and individual effects. In view of the fact that the antecedent conditions are all “factors” of the digital economy, and “a single non-high condition leading to a high result” may be affected by other confounding factors, this study will focus on exploring the correlation between “high-high” and “non-high-non-high” combinations.

Table 3. Necessary condition analysis results.

Conditional Variable	High-Level Rural Revitalization				Non-High-Level Rural Revitalization
	Summary Consistency	Summary Coverage	Between-Group Consistency Adjusted Distance	Within-Group Consistency Adjusted Distance	Summary Consistency	Summary Coverage	Between-Group Consistency Adjusted Distance	Within-Group Consistency Adjusted Distance
High Digital infrastructure	0.901	0.899	0.099	0.056	0.458	0.443	0.013	0.574
Non-High Digital infrastructure	0.441	0.457	0.214	0.518	0.895	0.897	0.099	0.196
High Digital industrialization	0.603	0.726	0.199	0.448	0.537	0.627	0.226	0.518
Non-High Digital industrialization	0.690	0.607	0.113	0.378	0.765	0.651	0.119	0.322
High Industrial digitalization	0.643	0.676	0.481	0.308	0.602	0.612	0.583	0.350
Non-High Industrial digitalization	0.631	0.621	0.470	0.350	0.681	0.649	0.496	0.280
High Digital governance	0.633	0.671	0.107	0.350	0.628	0.643	0.125	0.350
Non-High Digital governance	0.663	0.648	0.084	0.336	0.679	0.641	0.099	0.322
High Data elementization	0.603	0.670	0.072	0.504	0.593	0.637	0.038	0.560
Non-High Data elementization	0.673	0.631	0.026	0.420	0.693	0.628	0.020	0.420

shows the necessary condition analysis results for high and non-high levels of rural revitalization. It is found that among the five antecedent conditions, only the high digital infrastructure condition has a summary consistency greater than 0.9, and the between-group consistency-adjusted distance is less than 0.2, which can be judged as a necessary condition for the improvement of rural revitalization levels. Taking Tianshui City as an example, this city has actively promoted the construction of 5G communication infrastructure and accelerated the construction of digital villages, improving the quality and efficiency of rural governance, and achieving an increase in the quality of life for rural residents. In addition, the within-group consistency-adjusted distance of the other four antecedent conditions is all greater than 0.2, indicating that there is a significant regional effect between these four antecedent conditions and the result. In the between-group consistency-adjusted distance, there are two cases with values greater than 0.2, which need further analysis for possible time effects.

By analyzing the between-group consistency and coverage of the two scenarios (as shown in

Table 4. Cases where the between-group consistency-adjusted distance is greater than 0.2.

Causal Combination Scenario			Year
			2018	2019	2020	2021	2022
Scenario 1	High industrial digitalization and high level of rural revitalization	Between-group Consistency	0.291	0.507	0.620	0.897	0.936
		Between-group Coverage	0.942	0.831	0.759	0.634	0.557
Scenario 2	Non-high industrial digitalization and non-high level of rural revitalization	Between-group Consistency	0.977	0.909	0.798	0.401	0.345
		Between-group Coverage	0.520	0.675	0.671	0.772	0.859

), we find that, first, the consistency of high-level industrial digitalization (necessary condition) has shown a rapid upward trend from 2018 to 2022 (as shown in Figure 2). This indicates that with the passage of time, rural revitalization development is increasingly dependent on the promotion of industrial digitalization, and digital development has given vitality to rural industries. Second, the dependency (necessary condition) of rural revitalization that has not yet reached a high level on the promotion of industrial digitalization gradually decreased from 2018 to 2022, indicating that there are other restrictive factors in these areas that limit rural development, which is worth further study.

5.2. Configuration Analysis

From an overall perspective, QCA focuses on the analysis of “configuration effects”, which can better answer the question of the asymmetry of causal relationships, that is, the factors leading to high levels and non-high levels may differ [66]. During the construction of the truth table, it is necessary to set three key parameters: the consistency threshold, the PRI consistency threshold, and the frequency threshold. The parameters for the high-level rural revitalization configuration are 0.90, 0.90, and 3, respectively, and the parameters for the non-high-level rural revitalization configuration are 0.90, 0.70, and 3, respectively. The configuration analysis also needs to calculate the consistency-adjusted distance to judge robustness. With the help of R4.3.2 to construct the truth table, the following configuration analysis results are formed according to the standards (as shown in

Table 5. Analysis results of conditional configuration.

Conditional Variable	High-Level Rural Revitalization		Non-High-Level Rural Revitalization
	1	2	1a	1b	2a	2b
Digital infrastructure	⚫	⚫	×	×	×	×
Digital industrialization	⚫				×	×
Industrial digitalization			⚫	⚫	⚫
Digital governance			⚫			⚫
Data elementization		⚫		⚫
Consistency	0.951	0.951	0.918	0.916	0.926	0.917
PRI	0.891	0.893	0.795	0.782	0.808	0.813
Coverage	0.576	0.572	0.412	0.360	0.405	0.461
Unique Coverage	0.100	0.034	0.031	0.019	0.033	0.128
Between-Group Consistency Adjusted Distance	0.043	0.046	0.084	0.075	0.093	0.078
Within-Group Consistency Adjusted Distance	0.042	0.042	0.168	0.168	0.168	0.182
Overall Consistency	0.934		0.907
Overall PRI	0.875		0.820
Overall Coverage	0.828		0.632

Note: “⚫” represents the presence of the condition; “×” represents the absence of the condition.

).

Table 5 shows the configuration analysis results for high and non-high levels of rural revitalization. Each configuration consistency is greater than 0.90, and the summary consistency for high and non-high levels of rural revitalization is 0.934 and 0.907, respectively, indicating that the configuration has a strong explanatory power for the result. The between-group consistency-adjusted distance and within-group consistency-adjusted distance for each configuration are all less than 0.2, indicating that the condition configuration d is not influenced by temporal or case-specific effects, and indirectly indicates that the configuration is robust. Next, each configuration and its related cases will be discussed in depth.

5.2.1. High-Level Rural Revitalization Configuration

Configuration 1 (Digital infrastructure + Digital industrialization) indicates that under the condition of relatively complete digital infrastructure, the development of digital industry in rural areas can significantly improve the level of rural revitalization. The construction of digital infrastructure provides a solid foundation for the development of the digital industry in rural areas, and the vigorous development of the digital industry, in turn, promotes the further upgrading of digital infrastructure. The representative areas are Shijiazhuang and Huzhou. Shijiazhuang City has made solid efforts to build high-standard and high-quality communication infrastructure in rural areas, achieving a comprehensive upgrade of the true gigabit optical network in all townships, providing strong technical support for narrowing the urban–rural life gap and eliminating the “digital divide” between urban and rural areas. By doing so, villagers are able to learn to use network resources and platforms to improve their production skills and quality of life. Gaoyi County in Shijiazhuang has actively created an e-commerce live broadcast base, equipped with high-speed networks and professional equipment, providing a professional venue for farmers to carry out e-commerce live broadcast activities and training. A mobile phone connects urban and rural areas, opening up production and sales, and live broadcast has become a “new agricultural activity”. More and more agricultural products are sold through e-commerce live broadcast platforms, leaving the village and entering this city and households, finding a “good home”, and the income of villagers is also increasing steadily. Huzhou City has vigorously developed the construction of new infrastructure hardware in rural areas, built a digital agriculture industrial system with “industrial brain + future farm” as the core, and promoted the upgrading of traditional agriculture to smart agriculture. For example, Deqing County has built a digital agriculture demonstration park and IOT application demonstration site to improve the employment efficiency and production efficiency of agricultural enterprises. At the same time, it explores the integrated development mode of “agriculture, culture and tourism”, widens the energy efficiency space of rural ecological resources, and stimulates the driving force of rural green development. For example, Baofu Town of Anji County carries out the activity of “three beauties and one innovation”, which integrates the living environment with mountains and rivers, and creates beautiful vegetable gardens, beautiful courtyards, and beautiful fields.

Configuration 2 (Digital infrastructure + Data elementization) indicates that under the condition of having a good digital infrastructure, data elementization has a significant positive impact on rural revitalization. The digital infrastructure provides a technical platform for the circulation and utilization of data, while data elementization activates the potential of data resources, helping farmers optimize production methods, increase the added value of agricultural products, and inject new momentum into rural development. The representative areas are Zhangjiakou and Ankang. In Zhangjiakou, the broadband penetration rate in all administrative villages of this city has reached 100%, and the cable TV network and rural live broadcast satellites have achieved “village to village” coverage, providing a solid foundation for rural digitalization. Under the dual promotion of digital infrastructure and data elementization, Huailai County in Zhangjiakou, with its unique geographical and resource advantages, has vigorously developed the big data industry, gathered high-quality data resources, and built a cluster of regional data centers, becoming one of the fastest-growing areas in the country’s big data industry. Relying on the big data industry, Huailai County has not only promoted effective management of agricultural production, increased the yield and quality of crops, and thus increased the income of villagers; it has also improved the efficiency and quality of rural public services, providing more convenient services for villagers. Hanyin County of Ankang City took the first batch of digital village pilot projects in the province as an opportunity to digitally upgrade the infrastructure construction of honeysuckle industrial park in Sanliu Village, and build a series of facilities such as soil detection, meteorological monitoring, pest monitoring and water and fertilizer integration. Through these digital facilities, the park can obtain soil moisture, temperature, weather and other data in real time, and it can use data analysis to guide agricultural activities such as precision irrigation and fertilization, so as to improve the yield and quality of honeysuckle. At the same time, according to market demand and consumer preferences, the planting and processing methods of honeysuckle are optimized to improve the market competitiveness of products.

5.2.2. Non-High-Level Rural Revitalization Configuration

Configuration 1 (~Digital infrastructure + Industrial digitalization + Digital governance/~Digital infrastructure + Industrial digitalization + Data elementization), indicates that under the condition of low-level digital infrastructure, even if simultaneous introduction of industrial digitalization and digital governance or concurrent implementation of industrial digitalization and data elementization, the level of rural revitalization may still not be satisfactory. Insufficient digital infrastructure may lead to incomplete rural network coverage, slow network speeds, and thus limit the implementation and effectiveness of digital applications. Digital agriculture requires efficient data transmission and processing capabilities to support intelligent production of crops; digital governance requires a secure information technology environment to support real-time supervision and response in rural areas; data elementization relies on data circulation platforms to activate the potential of data resources and achieve precise management of the agricultural production process. Taking Chengdu as an example, although this city has made efforts to promote industrial digitalization and digital governance, such as implementing the “Chengdu Digital Illumination of Thousands of Villages Plan”, providing comprehensive services such as industrial digitalization and governance digitalization through the independently developed digital rural comprehensive information service platform, and the new model of data resource development and utilization has been included in the “National Digital Economy Innovation Development Pilot Zone Construction Case Collection”, the digital infrastructure is still relatively weak, which limits the level of rural revitalization.

Configuration 2 (~Digital infrastructure + ~Digital industrialization + Industrial digitalization/~Digital infrastructure + ~Digital industrialization + Digital governance), indicates that under the condition of low-level digital infrastructure and digital industrialization, even if industrial digitalization or digital governance is strengthened, the results of rural revitalization may still be unsatisfactory. Taking Jingdezhen as an example, this city has actively promoted industrial digitalization in the rural revitalization strategy, such as using digital means to enhance the added value of the ceramic industry, using digital technology to transform traditional crafts, and promoting the transformation and upgrading of rural industries. However, due to the relatively low level of informatization and digital industrialization, these efforts may not have fully realized their intended effects, and the overall level of rural revitalization still needs to be improved. On the other hand, Yingtan City has made certain progress in digital governance, such as implementing the “Smart Village Pass” digital rural platform, which has improved the efficiency and effectiveness of rural governance through digital means. However, the low level of digital industrialization and the inability to achieve broadband access in every village will limit the in-depth development of digital governance and the comprehensive revitalization of the rural industry.

5.3. Robustness Test

To ensure the robustness of the analysis results, it is necessary to adjust the calibration threshold, consistency threshold and case frequency threshold for sensitivity analysis [67] to enhance the reference of the result path. On the basis of ensuring the existence of the results, this paper increases the consistency threshold in the high-level rural revitalization configuration from 0.90 to 0.98, the PRI threshold in the low-level rural revitalization configuration from 0.70 to 0.75, and the case frequency threshold from 5 to 10. The configuration results are shown in

Table 6. Results of robustness analysis.

Classification	High-Level Rural Revitalization		Non-High-Level Rural Revitalization
Conditional Variable	1	2	1
Digital infrastructure	⚫	⚫	×
Digital industrialization	⚫		×
Industrial digitalization	×	×	⚫
Digital governance		⚫	⚫
Data elementization		⚫	⚫
Consistency	0.982	0.984	0.939
PRI	0.939	0.940	0.761
Coverage	0.361	0.321	0.253
Unique Coverage	0.115	0.075	0.253
Between-Group Consistency Adjusted Distance	0.014	0.012	0.061
Within-Group Consistency Adjusted Distance	0.042	0.028	0.14
Overall Consistency	0.981		0.939
Overall PRI	0.942		0.761
Overall Coverage	0.436		0.253

Note: “⚫” represents the presence of the condition; “×” represents the absence of the condition.

. Through the analysis, it is found that the position of digital infrastructure as a necessary condition is still stable; the new configuration results are all subsets of the original configuration, which verifies the reliability of the original results.

6. Further Analysis and Discussion

6.1. Analysis of Digital Economy Empowerment Pathways for Rural Revitalization in Cities with Different Innovation Capabilities

Innovation is an important support for the comprehensive revitalization of rural areas, and cities with different innovation capabilities have significant differences in the selection of pathways for rural revitalization [68]. The digital economy provides new momentum and platforms for rural revitalization, empowering cities with different innovation capabilities to adopt different development strategies according to their own characteristics. Based on the number of digital economy-related invention patents authorized in the cities in the current year, they are divided into innovation-leading cities and innovation-lagging cities.

Table 7. Digital economy empowerment pathways for rural revitalization in cities with different innovation capabilities.

Classification	Innovation-Leading Cities’ Rural Revitalization Configuration			Innovation-Lagging Cities’ Rural Revitalization Configuration
Conditional Variable	1	2	3	1	2a	2b
Digital infrastructure	⚫	⚫	⚫	⚫	⚫	⚫
Digital industrialization		⚫	⚫	×	×
Industrial digitalization	×		×	×		×
Digital governance	×	×	⚫
Data elementization	×	×	⚫		⚫	⚫
Consistency	0.974	0.977	0.975	0.960	0.976	0.976
PRI	0.925	0.927	0.909	0.907	0.940	0.938
Coverage	0.349	0.357	0.228	0.522	0.438	0.422
Unique Coverage	0.087	0.095	0.060	0.154	0.069	0.053
Between-Group Consistency Adjusted Distance	0.041	0.026	0.029	0.032	0.014	0.026
Within-Group Consistency Adjusted Distance	0.090	0.080	0.070	0.050	0.040	0.040
Overall Consistency	0.973			0.953
Overall PRI	0.933			0.902
Overall Coverage	0.504			0.645

Note: “⚫” represents the presence of the condition; “×” represents the absence of the condition.

shows the different pathways for digital economy empowerment of rural revitalization in the two types of cities.

6.1.1. Innovation-Leading Cities’ Digital Economy Empowerment Configuration for Rural Revitalization

A higher level of scientific and technological innovation capability can provide a continuous endogenous driving force for the digital economy to empower rural revitalization. As shown in Table 7, Configuration 1 (Digital infrastructure + ~Industrial digitalization + ~Digital governance + ~Data elementization) indicates that such cases only have a strong level of digital infrastructure, suggesting that the improvement of digital infrastructure is crucial for rural development. For example, Lishui City has vigorously promoted the construction of 5g base stations in rural areas, and the city has basically established a trinity promotion system of “government + operator + service provider”. There are a variety of digital application scenarios in the fields of smart agriculture, rural governance, public services and so on, such as AI monitoring system in Chuanliao Town, Qingtian County, and intelligent inspection system for three-dimensional waters of Xianxia Lake in Hushan Village, Hushan Township, Suichang County. Configuration 2 (Digital infrastructure + Digital industrialization + ~Digital governance + ~Data elementization) indicates that such cases have relatively perfect digital infrastructure and a high level of digital industrialization, suggesting that for innovation-leading cities, their innovation capabilities can drive the emergence of more new business models in rural areas, such as e-commerce, which can effectively promote rural development. For example, Yantai’s home access level of optical fiber is leading in China, which enables digital technology to penetrate into all fields in rural areas and provides a solid foundation for the development of digital industry. The development of digital industrialization not only promotes the further improvement and upgrading of the digital foundation but also brings new industrial forms and economic growth points to the countryside, such as “Internet+order agriculture”, “digital rural tourism”, and other new formats and models. Configuration 3 (Digital infrastructure + Digital industrialization + ~Industrial digitalization + Digital governance + Data elementization) indicates that the conditional variables are basically in an existing state, suggesting that innovation-leading cities can also choose a more balanced mode of digital economic development to promote the progress of rural revitalization. For example, Chongqing City has carried out digital rural pilot work, using platforms such as the “Cun Cun Wang” rural e-commerce comprehensive service platform to broaden the sales channels of agricultural products, establishing multiple agricultural big data platforms such as the National Chongqing (Rongchang) Hog Big Data Center to integrate industrial chain data, and also using digital means to improve the efficiency of rural governance.

6.1.2. Innovation-Lagging Cities’ Digital Economy Empowerment Configuration for Rural Revitalization

In comparison, innovation-lagging cities have certain disadvantages in the development of the digital economy. As shown in Table 7, Configuration 1 (Digital infrastructure + ~Digital industrialization + ~Industrial digitalization) indicates that such cases only have a strong level of digital infrastructure, suggesting that for rural areas with a higher level of development in innovation-lagging cities, digital infrastructure is still a necessary condition, and there is probably the existence of other non-digital economy factors that significantly promote rural development. Configuration 2 (Digital infrastructure + ~Digital industrialization + Data elementization/Digital infrastructure + ~Industrial digitalization + Data elementization) indicates that such cases have relatively perfect digital infrastructure and a high level of data elementization, suggesting that cities with poorer innovation capabilities can strengthen the rational use of data resources while improving digital infrastructure. They can choose to develop a coordinated development mode of digital infrastructure and data elementization to empower rural revitalization. For example, Dandong City, even though its innovation capacity needs to be enhanced, has achieved 5G network coverage and established an agricultural big data platform. This provides farmers with scientific planting suggestions and market forecasts, optimizes the allocation of resources, and improves the precision and efficiency of agricultural production, thereby laying a solid technological foundation and data support for rural revitalization.

6.2. Analysis of Digital Economy Empowerment Pathways for Rural Revitalization in Cities with Different Economic Foundations

The economic foundation of different prefecture-level cities will affect their different roles and path choices in rural revitalization. Based on the size of the per capita GDP of each city, they are divided into high-economic-foundation cities and low-economic-foundation cities.

Table 8. Digital economy empowerment pathways for rural revitalization in cities with different economic foundations.

Classification	High Economic Foundation Cities’ Rural Revitalization Configuration				Low Economic Foundation Cities’ Rural Revitalization Configuration
Conditional Variable	1	2	3	4	1	2	3
Digital infrastructure	⚫	⚫	⚫	⚫	⚫	⚫	⚫
Digital industrialization		⚫				×	×
Industrial digitalization	×		×	⚫	×
Digital governance	×		⚫	×		⚫
Data elementization	×		⚫	⚫			⚫
Consistency	0.971	0.950	0.983	0.965	0.961	0.967	0.978
PRI	0.909	0.897	0.943	0.883	0.913	0.909	0.939
Coverage	0.361	0.598	0.301	0.313	0.577	0.467	0.473
Unique Coverage	0.043	0.187	0.032	0.013	0.121	0.037	0.037
Between-Group Consistency Adjusted Distance	0.032	0.046	0.017	0.041	0.035	0.023	0.017
Within-Group Consistency Adjusted Distance	0.070	0.070	0.050	0.060	0.050	0.050	0.040
Overall Consistency	0.947				0.955
Overall PRI	0.895				0.904
Overall Coverage	0.723				0.710

Note: “⚫” represents the presence of the condition; “×” represents the absence of the condition.

shows the different path choices for digital economy empowerment of rural revitalization in the two types of cities.

6.2.1. Configurations for Rural Revitalization in High-Economic-Foundation Cities

Leveraging the advantage of a strong economic foundation, rural areas can more easily build a high-quality modern rural economic system. As shown in Table 8, Configuration 1 (Industrial digitalization + ~Digital governance + ~Data elementization) indicates that such cases only have a strong level of digital infrastructure, which again highlights the crucial role of improving digital infrastructure in rural development. Configuration 2 (Digital infrastructure + Digital industrialization) indicates that such cases have relatively perfect digital infrastructure and a high level of digital industrialization, suggesting that cities with a higher economic level can attract more enterprises and create more job opportunities by establishing business incubators, industrial parks, and industrial chains, thereby enhancing the vitality of rural areas. For example, Changzhou has a strong economic foundation, which provides solid capital and industrial support for its digital infrastructure construction. The coverage rate of agricultural informatization in Changzhou reached 65%, and an e-commerce Industrial Park for agricultural products was built. The scale of the e-commerce cluster for aquatic products from Changdang lake, shrimp from Liyang, and seedlings from huaxiangyuan was expanded year by year, and the transaction volume of agricultural e-commerce was about CNY 4.2 billion. Configuration 3 (Digital infrastructure + ~Industrial digitalization + Digital governance + Data elementization) indicates that under the condition of relatively perfect digital infrastructure, the coordinated development of digital governance and data elementization can promote rural revitalization, suggesting that cities with a stronger economic foundation can choose to reasonably utilize rural data to build digital governance platforms and promote rural development through macroeconomic regulation by the local government. Shantou has been approved to build a “double gigabit” city, and the broadband construction of administrative villages has also achieved all-optical network coverage. On this basis, Shantou actively promotes digital governance, builds an open and shared grass-roots service management platform through the “Internet + rural governance” mode, realizes real-time monitoring and dynamic management of rural data, and turns rural governance from “post disposal” to “source prevention” to provide strong support for early warning and emergency treatment of rural affairs. Configuration 4 (Digital infrastructure + Industrial digitalization + ~Digital governance + Data elementization) indicates that under the condition of relatively perfect digital infrastructure, the coordinated development of industrial digitalization and data elementization can promote rural revitalization, suggesting that cities with a stronger economic foundation can also choose to reasonably utilize rural data to mobilize market enthusiasm, assist rural enterprises in market forecasting and industrial optimization, and promote rural development. Yan’an city cooperates with Huawei to build Yan’an cloud 2.0 platform to provide technical support for rural digitization. The platform integrates and analyzes the data of rural population flow and resource utilization and optimizes the construction of rural infrastructure. In terms of smart agriculture, Yan’an has built a number of smart orchards and digital orchards to accurately monitor and manage agricultural production with the help of Internet of Things technology. For example, the 30,000-ton cold storage of Luochuan Shaanxi fruit group realized digital integrated management, which significantly improved the storage and preservation capacity of agricultural products.

6.2.2. Configurations for Rural Revitalization in Low-Economic-Foundation Cities

In contrast, cities with a weaker economic foundation have a lower starting point for digital empowerment. As shown in Table 8, Configuration 1 (Digital infrastructure + ~Industrial digitalization) indicates that such cases only have a strong level of digital infrastructure, suggesting that in cities with a weaker economic foundation, infrastructure remains a bottleneck for rural development. Configuration 2 (Digital infrastructure + ~Digital industrialization + Digital governance) indicates that such cases have relatively perfect digital infrastructure and a high level of digital governance, suggesting that cities with a poorer economic foundation can enhance their digital governance capabilities while improving their digital infrastructure, thereby achieving efficient resource management and optimizing social services. Configuration 3 (Digital infrastructure + ~Digital industrialization + Data elementization) indicates that such cases have relatively perfect digital infrastructure and a high level of data elementization, suggesting that cities with a poorer economic foundation should strengthen the utilization of existing data resources to promote data-driven economic growth and industrial development.

7. Conclusions

7.1. Research Conclusions

This study, using the complex system theory as the analytical framework and based on the panel-data qualitative comparative analysis method, takes 193 cases from 2018 to 2022 in low urbanization areas of China as samples to explore the necessary conditions and promotion paths of rural revitalization from the perspective of the digital economy. The main research conclusions are as follows:

First, informatization foundation is the only necessary condition for rural revitalization. In both configurations of high-level rural revitalization, informatization foundation exists, while informatization foundation is missing in both configurations of non-high-level rural revitalization. This shows that the construction of digital infrastructure plays a more universal role in the process of promoting the comprehensive development of rural areas, which is enough to prove that the construction of digital infrastructure is the premise of the development of a digital economy and provides continuous power for rural revitalization. In addition, the necessity of industrial digitalization is also increasing year by year, which means that the digital transformation of rural industry plays an increasingly important role in promoting rural revitalization.

Secondly, two different configurations of digital economy conditions can both lead to high-level rural revitalization, and four different configurations of digital economy conditions can lead to non-high-level rural revitalization. This indicates that multiple configurations of digital economy conditions have the same effect, and there is a substitutive or complementary relationship between the conditions.

Thirdly, in the cases of achieving high-level rural revitalization, rural areas prefer to develop the digital industry or data elements on the basis of building digital infrastructure; in the cases of low-level rural revitalization, rural areas not only neglect the construction of digital infrastructure but also the development of the digital industry. Even if there is a simultaneous introduction of industrial digitalization and digital governance or concurrent implementation of industrial digitalization and data elementization, without the support of digital infrastructure, their effectiveness will be limited.

Fourthly, innovation-leading cities can choose to focus on developing digital infrastructure to support the construction of rural revitalization in the digital economy, or they can opt for a more balanced approach to digital economy development to promote rural revitalization. For innovation-lagging cities, they can choose to develop digital infrastructure in conjunction with data elementization to aid rural revitalization.

Fifthly, high-economic-foundation cities, in addition to promoting the construction of digital infrastructure, can choose to focus on the development of the digital industry, the synergy of digital governance and data elementization, and the synergy of industrial digitalization and data elementization. For low-economic-foundation cities, they can choose to focus on the development of digital governance or data elementization.

7.2. Policy Suggestions

The above research conclusions provide prospective strategic insights and paradigms for localities to achieve rural revitalization.

First, rural areas should pay attention to strengthening the construction of digital infrastructure. The government should increase financial investment in the digital infrastructure construction of rural areas, especially in the fields of broadband networks, 5G networks, Internet of Things (IoT) facilities, and data centers. Strengthen the coverage of remote areas to ensure that rural areas have equal digital connections with urban areas. In areas with poor innovation capabilities and economic foundations, the government should take the initiative to assume responsibility, apply for special funds, and improve the operational efficiency and service capacity of rural infrastructure. Adjacent areas can take measures to jointly build and share infrastructure to reduce construction costs and improve the efficiency of resource allocation.

Second, rural areas should focus on the joint development of multiple factors. In the process of rural revitalization, digital infrastructure, digital industrialization, industrial digitalization, digital governance, and data elementization should be integrated and coordinated to promote synergy and improve overall benefits. For low urbanization rate areas, while improving digital infrastructure, they can selectively strengthen the level of digital industrialization or data elementization. For rural areas with their own characteristics, they can develop digital industries, such as e-commerce, live broadcasts for sales, and digital tourism, to broaden the market for rural products, increase added value, and improve the economic level of rural areas; for areas with rich data resources, data elementization can be a focus, by establishing data platforms to explore and utilize local data resources, and improve the overall development level of industries such as agriculture, education, and tourism, promoting rural revitalization.

Third, rural areas can choose diversified digital economic development strategies. According to the resource endowment, industrial characteristics, development needs, and market environment of different rural areas, multiple digital economic development paths and strategies can be adopted to achieve the diversity, sustainability, and flexibility of economic growth. For example, rural areas with weaker economic foundations can choose to strengthen digital governance or to make fuller use of data elements, on the premise of improving digital infrastructure. Both options can help rural areas achieve effective development.

7.3. Research Limitations

This study uses the panel-data qualitative comparative analysis method to study the precursor factors of rural revitalization from the perspective of the digital economy, making certain theoretical contributions to the empowerment of rural revitalization by the digital economy, and providing some policy suggestions for rural development. However, this study still has some limitations that need to be improved in future research.

First, there are limitations in the definition of variables. In this study, the influencing factors of the digital economy are defined as several key variables, but the definition and measurement methods of these variables may have certain limitations. For example, the definitions of digital governance and data elementization may be too simplified and may fail to cover the complexity and diversity of these concepts in practice. Therefore, future research can further refine and expand the definitions of these variables and explore more multi-dimensional measurement methods to more accurately capture the actual impact of the digital economy on rural revitalization.

Second, this study focuses only on the digital economy factors and does not consider other possible influencing factors. The actual situation is that the revitalization of some rural areas may be jointly promoted by policy support, natural resource advantages, local traditional industries, and other factors, which this experimental method cannot exclude. Therefore, future research should take into account more variables and analyze the multi-dimensional driving factors of rural revitalization in a broader framework, so that the conclusions are more universal and accurate.