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Analysis and study of digital economy level measurement index

Haipeng He (School of Economics, Guangdong University of Technology – Longdong Campus, Guangzhou, China)
Zirui He (School of Economics, Guangdong University of Technology – Longdong Campus, Guangzhou, China)
Xiaodong Nie (School of Economics, Guangdong University of Technology – Longdong Campus, Guangzhou, China) (Key Laboratory of Digital Economy and Data Governance, Guangdong University of Technology, Guangzhou, China)

Journal of Internet and Digital Economics

ISSN: 2752-6356

Article publication date: 18 October 2024

Issue publication date: 6 November 2024

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Abstract

Purpose

This study aims to assess the level of development of the digital economy by constructing a comprehensive measurement system. It explores regional differences within China’s digital economy, highlighting the varying degrees of digital infrastructure, industrialization, governance and innovation capabilities across provinces.

Design/methodology/approach

A multidimensional analytical framework including digital infrastructure, industrialization, digitization, governance and innovation was developed. Entropy methods were used to calculate the weights of each dimension. The coupled coordination degree model and the Tobit model with random effects panel are applied to analyze the current situation, discrepancies and influencing factors.

Findings

This study reveals significant regional differences in the development of China’s digital economy, characterized by a pattern of “strong in the east, weak in the west; high in the south, low in the north.” This geographical imbalance exacerbates the “polarization effect” and the “siphon effect,” where resources and growth tend to concentrate in already developed areas, further intensifying regional inequalities. The development of the digital economy is driven by principles of innovation, coordination and sharing, which facilitate the creation and dissemination of new technologies and collaboration across different sectors. However, this progress is also constrained by considerations of environmental sustainability (green) and economic openness.

Originality/value

This paper contributes to the body of knowledge by providing a novel multidimensional measurement system for the level of digital economy development. The unique application of the coupled coordination degree model and Tobit model to analyze regional differences and influencing factors provides insights into the dynamics of China’s digital economy.

Keywords

Citation

He, H., He, Z. and Nie, X. (2024), "Analysis and study of digital economy level measurement index", Journal of Internet and Digital Economics, Vol. 4 No. 3, pp. 187-217. https://doi.org/10.1108/JIDE-05-2024-0020

Publisher

:

Emerald Publishing Limited

Copyright © 2024, Haipeng He, Zirui He and Xiaodong Nie

License

Published in Journal of Internet and Digital Economics. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode

1. Foreword

1.1 Background

The rapid development of the digital economy signals that the global economy is stepping into a brand new era of growth, which is particularly important in the current context of numerous uncertainties and challenges facing the world economy. With the rapid advancement of digital technologies such as 5G, artificial intelligence, big data and cloud computing, we are witnessing an unprecedented era of change, which has not only revolutionized business models and industrial structures, but also reshaped the map of global competition. 5G technology has accelerated the pace of development of industries such as smart manufacturing, telemedicine, and online education, and opened up new avenues for the digital transformation of traditional industries. At the same time, the increasing improvement of global digital infrastructure has made the digital economy a key force in promoting cross-border cooperation and reforming the global governance system.

Numerous governments have realized the importance of the digital economy, elevated it to the level of national strategy, and launched policies to support the research, development and application of digital technologies, in an attempt to construct a more open and well-connected world economy. At the G20 Summit in 2023, digital economy cooperation is one of the focal points of the agenda, and member states have unanimously decided to strengthen international collaboration in areas such as digital trade and data governance, and to jointly face the challenges and opportunities encountered in the course of the development of the digital economy, demonstrating that the digital economy is gradually transforming itself into a new arena for global economic cooperation, and is injecting new momentum into the realization of the global goals of inclusive and sustainable development.

Digital governance and innovation play an increasingly critical role in promoting innovation in social management and services. Building a digital government not only enhances the efficiency and transparency of government services, but also strengthens people’s participation in and monitoring of government decisions. A number of countries have begun to use big data and artificial intelligence technologies to optimize urban management and enhance public services, which not only improves the quality of life of residents but also promotes the modernization of the social governance system.

Overall, the digital economy has become an indispensable part of the modern economic architecture, and at the same time is one of the core drivers of global economic growth. It has injected new vitality and direction into the development of the global economy and society by promoting the construction of digital infrastructure, digital industrialization and the digital transformation of traditional industries. Closely aligned with the new development concepts of innovation, coordination, green, openness and sharing, the rapid evolution of the digital economy not only helps to promote high-quality economic growth, but also opens up new paths for global economic cooperation, while providing a solid pillar for the realization of the sustainable development goals.

1.2 Literature review

The measurement system of the level of digital economy at home and abroad is still imperfect, and different experts and scholars have their own different insights on the measurement of the level of digital economy, but they all focus on several central aspects–infrastructure construction, governance level, innovation and development, and digital industrialization of the digital economy–in order to carry out measurement research. Chen and Tian (2024) proposed Internet penetration rate and the number of Internet domain names as key indicators in the digital infrastructure dimension, while Liu and Guo (2023) considered the number of Internet broadband access ports, emphasizing the role of basic Internet conditions as a fundamental support for the entire digital economy. The digital industrialization dimension was noted to be closely related to the total industrial output value of digital industry, reflecting the economic contribution and market influence of digital technology in the whole industry. In making specific assessments, Wang (2023) considered the number of employees in the digital industry, while Xue et al. (2023) and Xu and Li (2022) paid attention to indicators such as software business revenue, suggesting that digital industrialization is not only a manifestation of technological development, but is also related to the creation of talent resources and output value. With respect to the dimension of industrial digitization, Zhang and Shi (2023) and Zhao and Zhao (2023) agree that the Digital Financial Inclusion Index is an important indicator of the level of digitization of financial services, and that it reflects the role of fintech in improving the efficiency and penetration of financial services. For digital governance, although this area is not as well researched as the other dimensions, Bai et al. (2024) and Zhang et al. (2023) propose indicators of the level of digital governance, and Zhu and Cao (2023) focus on the level of government support, which reflect the government’s ability in guiding and regulating the development of the digital economy. In terms of digital innovation capability, different scholars measure it with different indicators, for example, (Su et al., 2022) focuses on the level of education, (Huang et al., 2022) highlights the R&D investment in research and development, whereas Zhou et al. (2023) and Wu (2022) look at the number of R&D personnel, and Xia and Jia (2023) emphasizes the number of patents applications granted, and these indicators reflect the potential for technological innovation on the one hand, and on the other hand, are important factors driving digitalization.

Through the reference analysis of several scholars' papers, it is found that the system researched by most scholars usually contains only three or four of the five dimensions of digital infrastructure, digital industrialization, industrial digitization, and digital innovation capability, and rarely considers all five dimensions at the same time. In addition, it is also found that most scholars use entropy value method or principal component analysis method and other dimensionality reduction methods to measure the score, and then calculate the coupling coordination degree of the two subsystem scores to carry out spatial correlation analysis, and very few scholars calculate the coupling coordination degree of the scores of more than two subsystems, and the measurement of the level of the digital economy contains a number of subsystems, and only two aspects may not be sufficient to account for the existence of the coupling between the other subsystems. relationship, Li (2022) analyzed the four subsystems of digital economy development, namely digital infrastructure, digital industry development, digital network application, and digital scientific research support, by calculating the degree of coupling and coordination, which provides us with a new reference. Finally, most scholars only stay in the spatial correlation analysis of the coupling degree of coordination, but lack of further exploration of the coupling degree of coordination, (Liu et al., 2023) will be the level of economic development, technological innovation and other indicators and coupling degree of coordination of the Tobit regression test is worthy of reference, which further analyzes the specific regulation of certain indicators to promote the development of the regional degree of coupling coordination.

1.3 Research innovation

This paper constructs a comprehensive measurement system to assess the development level of the digital economy and proposes a novel multidimensional analytical framework, covering five key dimensions: digital infrastructure, industrialization, digitization, governance, and innovation. The five-dimensional digital economy measurement framework introduced in this paper expands upon existing research by adding the dimensions of “governance” and “innovation,” which are relatively rare in traditional digital economy analyses. This significantly broadens the understanding and assessment of digital economy development. The introduction of the “governance” dimension aims to evaluate the role of factors such as policy environments, legal regulations, and institutional guarantees in supporting the development of the digital economy. The effectiveness of digital governance is crucial for ensuring the efficient operation of digital infrastructure and the orderly development of digital industrialization. Furthermore, the inclusion of the innovation dimension emphasizes the critical role of technological innovation and digital technology iterations in driving economic transformation. In the context of rapidly evolving information technologies, innovation is an essential indicator for measuring the long-term sustainability of the digital economy. Compared to traditional analyses that focus primarily on digital infrastructure, industrialization, and digitization, the addition of these two dimensions strengthens the comprehensive assessment of the driving forces and safeguard mechanisms of digital economy development. This ensures a more holistic understanding of the synergistic effects and potential issues among key elements during the development of the digital economy. Therefore, these added dimensions are indispensable. They not only broaden the macro-level understanding of digital economy development, ensuring a more comprehensive and in-depth evaluation, but also provide strong support for formulating more precise policies. By adopting the entropy method to calculate the weight of each dimension, the contribution and importance of each dimension are objectively quantified. Additionally, this paper uniquely applies the coupling coordination degree model and the random-effects panel Tobit model to analyze regional differences and influencing factors. This innovative analytical approach not only reveals significant regional disparities in digital economy development but also provides profound insights into the integration and coordination among various dimensions, offering strong support for further optimizing strategies for digital economy development.

Another important innovation of this paper is the in-depth analysis of the coupling coordination degree of inter-regional digital economic development, and the key factors affecting the coupling coordination degree are explored through the Tobit model. It is found that innovation, coordination and sharing have significant positive effects on coupling coordination, while green development and openness show a negative correlation. Through this multi-dimensional analysis of the coupling coordination degree, this paper not only elucidates the strengths and shortcomings in the development of the digital economy in each region, but also puts forward targeted policy recommendations, emphasizing the importance of technological innovation, social security and green development. At the same time, the paper also points out the structural challenges posed by openness, and suggests a strategy combining gradual liberalization and strengthening of internal adjustment capacity to alleviate the adjustment pressure in the short term. These conclusions provide new ideas and references for policymakers in promoting the coordinated development of the regional digital economy, demonstrating the paper’s unique contribution in theory and practice.

2. Research design

2.1 Construction of digital economy development level measurement index system

  • (1)

    Principles for the selection of indicators

Scientific principles: Indicators need to be able to adapt to the development trend of digitization and accurately reflect the current status and trends in digital infrastructure construction, digital industrialization process, digital transformation of industries, digital governance effectiveness and digital innovation capacity. The selected indicators should be able to accurately measure the characteristics of the target object and reduce ambiguity and error.

Systemic principles: The indicator system should cover all key dimensions of digital transformation, including infrastructure, industrial development, governance mechanisms, etc. It should be divided according to different levels, such as the macro, meso and micro levels, to help analyze and assess it in greater detail. In addition, there should be intrinsic logical links and mutually supportive relationships between indicators to form an interconnected evaluation network.

Principle of operability: Indicators should be selected to ensure that relevant data can be more easily obtained in practice, and their measurement methods should be simple and easy to use so as to facilitate their wide application. In the process of selecting and applying indicators, it is necessary to consider the balance between costs and benefits to ensure the economy of the evaluation system.

The principle of comparability: The definition and measurement of indicators need to be standardized to ensure comparability across time, regions or objects. On the premise of ensuring basic comparability, the indicator system should have a certain degree of flexibility to adapt to the need for comparison in different contexts, and consideration should also be given to whether it can be formed into a time series, which will help to analyze trends and track them over time.

  • (2)

    Statistics on the number of indicators

The construction principles of primary indicators are based on two main reasons. First, they provide a comprehensive reflection of the overall digital economy operation system. The five dimensions of the digital economy measurement framework—digital infrastructure, digital industrialization, industrial digitization, digital governance, and digital innovation capability—are based on core elements of digital economy development and are designed with a systematic approach. Digital infrastructure assesses the coverage and stability of information and communication networks, serving as the foundation that supports the functioning of the digital economy. Digital industrialization evaluates the development of industries centered on digital technologies, reflecting their direct contribution to the economy. Industrial digitization focuses on the breadth and depth of traditional industries adopting digital technologies for transformation and upgrading, revealing the penetration of digital technologies into the economic structure. Digital governance emphasizes the role of governments in guiding the digital economy and their level of digital management. Digital innovation capability measures innovation inputs and outputs, reflecting the research and development capabilities and innovation achievements in digital technologies. These five dimensions cover the key aspects of digital economy development, ensuring that the measurement system comprehensively and objectively reflects the overall level of the digital economy. Second, these dimensions have appeared frequently in several reference studies, indicating their prominence and reference value in the research of digital economy measurement.

The selection principles of secondary indicators are as follows: based on the established primary indicators, this study selected 29 relevant references with a high number of citations that constructed indicator systems by using the keyword “digital economy measurement.” The primary and secondary indicators from these systems were statistically analyzed (primary indicators with similar meanings were grouped into the same category). “Frequency 2” refers to the occurrence of secondary indicators in the 29 references, while “Frequency 1” represents the number of times the primary indicators, under which the secondary indicators fall, matched those already established. If the primary indicator matched, Frequency 1 was increased; otherwise, other indicators were further analyzed until all 29 references were fully evaluated. Given that multiple indicators appeared infrequently, further analysis and consolidation of such indicators would add complexity. Therefore, this study only presents the top-ranked and most frequently used secondary indicators under each primary indicator (as shown in Table 1).

  • (3)

    Construction of the indicator measurement system

Combining several references and the statistical results of the number of indicators mentioned above, and considering the basic principles of indicator screening, the indicators with more than 5 statistical counts of both first-level and second-level indicators are retained to construct the measurement system of digital economy level in this paper, and the entropy method is utilized to calculate the weights of the indicators (e.g. Table 2). The four secondary indicators of Internet penetration rate, long-distance fiber optic cable line length, Internet access ports and number of Internet domain names are categorized as digital infrastructure; the four secondary indicators of total industrial output value of digital industry, employees in digital industry, software business income and total telecommunications business are categorized as digital industrialization; the four indicators of e-commerce volume, enterprise informatization level, enterprise website coverage rate and digital financial inclusion index are categorized as industrial digitization; the four indicators of digital political economy level are categorized as industrial digitalization; the four indicators of digital economy level are categorized as industrial digitalization. The four indicators of e-commerce volume, enterprise informatization level, enterprise website coverage and digital inclusive finance index are categorized as digitalization of industries; the level of digital government, the number of enterprises in the digital economy and the strength of government support are categorized as digital governance; and the four second-level indicators of per capita years of education, intensity of R&D investment, full-time equivalents of R&D personnel and the number of patents filed and granted are categorized as digital innovation capacity.

  • (4)

    Explanatory note on indicators

  • Internet penetration rate (X1): number of Internet users as a share of resident population.

  • Long-distance fiber optic cable line length (X2): Long-distance fiber optic cable line length.

  • Internet broadband access port (X3): Internet broadband access port.

  • Number of Internet domain names (X4): Number of Internet domain names.

  • Gross industrial output value of digital industries (X5): Gross industrial output value of the manufacturing industry of communications equipment, computers and other electronic equipment.

  • Employees in the digital industry (X6): Average number of employees at the end of the year in the information transmission, software and information technology services industry sector.

  • Software business revenue (X7): Revenue from software-related products and services.

  • Total telecommunication services (X8): total use of telecommunication services.

  • E-commerce sales (X9): total online business transactions.

  • Level of enterprise informatization (X10): Percentage of enterprises with e-commerce trading activities.

  • Enterprise website coverage (X11): number of websites per 100 enterprises.

  • Digital Inclusive Finance Index (X12): Peking University Digital Inclusive Finance Index.

  • Level of digital government (X13): Number of government websites.

  • Number of enterprises in the digital economy (X14): number of legal entities in the information transmission, computer services and software industry.

  • Strength of government support (X15): frequency of words in government work reports.

  • Years of education per capita (X16): (number of illiterates × 1 + number of people with elementary school education × 6 + number of people with lower secondary school education × 9 + number of people with upper secondary school and middle school education × 12 + number of people with college and bachelor’s degree or higher education × 16)/total number of people over 6 years of age.

  • R&D investment intensity (X17): ratio of internal R&D expenditure to GDP.

  • Full-time equivalent of R&D personnel (X18): full-time equivalent of R&D personnel in industrial enterprises above large scale.

  • Number of patent applications granted (X19): No. of patents granted for 3 types of inventions, utility models and appearances.

2.2 Research technique

  • (1)

    Entropy Approach Model

Referring to Ding (2020), He (2021), Li et al. (2022b) and many other scholars who use entropy method model to downsize the digital economy measurement index system, this paper adopts this method to calculate and analyze. The entropy value method is a model for calculating the weight of indicators, which can objectively show the degree of influence of each indicator in the whole system. In this paper, the entropy value method is used to determine the weight of each secondary indicator and measure the comprehensive index of the development of digital economy. Before calculating the weight of each indicator, this paper applies the method of polar deviation to standardize the data, so as to eliminate the influence of the index outline.

  • •

    Normalization

    (1)Positive indicators:Xij′=Xij−min⁡Xijmax⁡Xij−min⁡Xij
    (2)Negative indicators:Xij′=max⁡Xij−Xijmax⁡Xij−min⁡Xij

Where Xij denotes the standardized data of the jth evaluation indicator of the ith city, and Xij′ denotes the raw actual value of the jth evaluation indicator in the ith city, max Xij , min Xij denote the maximum and minimum values of the jth evaluation index, respectively.

  • •

    Calculation of indicator weights

  • Step 1: Calculate the weights of the indicators Pij

    (3)Pij=Xij′∑i=1nXij′
    In the second step, the entropy value of the indicator is calculated Ej
    (4)Ej=−∑i=1nPijln⁡Pijlnn

    • Step 3: Calculate the weights of the indicators Wj

    (5)Wj=1−Ej∑j=1m(1−Ej)

Where Wj denotes the weight of the jth indicator and m is the number of evaluation indicators.

  • •

    Calculation of the Comprehensive Evaluation Index Si

    (6)Si=∑j=1mWj×Xij′

  • (2)

    Coupled Coordination Degree Model

The coupled coordination degree model is a quantitative analysis method used to study the interrelationships and coordination between elements within a system. Drawing on the research basis of this model by scholars Li (2022) and Li et al. (2021), this paper adopts the coupled coordination degree model to measure the interrelationships and coordination relationships among the five dimensions of development at the level of the digital economy.

The coupling coordination degree model involves the calculation of a total of three index values, namely, the coupling degree C, the coordination index T, and the coupling coordination degree D. The specific formulas are as follows:

(7)C=[U1×U2×U3×U4×U5(U1+U2+U3+U4+U55)5]15
(8)T=aU1+bU2+cU3+dU4+eU5
(9)D=C×T

Among them, D is the coupling degree of coordination, taking the value of the range of [0,1], the larger the D indicates that the development of the five dimensions between the more coordinated, and vice versa indicates that the five synergistic degrees are low; C is the degree of coupling, taking the value of the range of [0,1], the larger the C indicates that the better the coupling state of the five dimensions is, and otherwise, the worse the coupling state of the five dimensions is, and it will tend to develop in disorder; T is the comprehensive coordination index of five dimensions; U1 denotes the digital infrastructure score, U2 denotes the digital industrialization score, U3 denotes the industrial digitization score, U4 denotes the digital governance score, U5 denotes digital innovation capability score; a, b, c, d, e are all weights, each taking the value of 0.2.

On this basis, quantitative analysis of the coupling coordination degree between the five dimensions, with reference to Ni (2022) scholars for the division of the coupling coordination degree, the coupling coordination degree of the development of the five dimensions of the digital economy level measurement is divided into 10 grades, and the specific classification and the corresponding values are shown in Table 3.

  • (3)

    Moran Index Model

Spatial correlation is the study of interdependence and interaction within a certain spatial unit, i.e. the degree of dispersion and agglomeration of a phenomenon. Considering the possible spatial correlation of the coupling coordination degree of the five dimensional indicators of digital infrastructure, digital industrialization, industrial digitization, digital governance and digital innovation capacity, and referring to the spatial effect research on the coupling coordination degree of the digital economy and the development of green transformation of the manufacturing industry by Yang et al. (2023), this paper therefore adopts the Moran index (Moran) to measure it. The specific formula is as follows:

  • •

    Global spatial autocorrelation modeling.

    (10)Moran′s I=n∑i=1n∑j=1nWij×n∑i=1n∑j=1nWij(Di−D¯)(Dj−D¯)∑i=1n(Di−D¯2)

  • •

    Local spatial autocorrelation models.

    (11)Locla Moran′s I=Di−D¯[(∑j=1,j≠inWij)/n−1]−D¯2∑j=1,j≠inWij(Di−D¯)

Where n denotes the 30 provinces, and Wij denotes the economic distance weight matrix, and Di is the coupling coordination degree of the five dimension scores of province i. The value range of Moran index is [−1,1]. When it is greater than 0, it means positive correlation, and the larger value means the more obvious spatial correlation; Moran index is less than 0, it means negative correlation, and the smaller value means the larger spatial difference; Moran index is equal to 0, it means there is no spatial correlation or random distribution.
  • (4)

    Random Effects Panel Tobit Model

This paper refers to the research method of Liu et al. (2023) to screen the new five dimensional indicators representing innovation, coordination, green, openness and sharing in the five development concepts respectively. Considering that the coupling coordination degree has the characteristic of being segmented, it satisfies the setting conditions of the Tobit model with restricted explanatory variables. In this paper, the random effects panel Tobit model is used to test the equations as follows:

Dit=α0+α1Innovit+α2Coordit+α3Greenit+α4Openit
(12)+α5Shareit+α6Trendit+λit+δit
Where D stands for coupling coordination, Innov stands for technology market turnover (billion yuan) taken in logarithm, Coord stands for national fiscal expenditure on social security and employment (billion yuan) taken in logarithm, Green stands for completed investment in projects to treat wastewater (million yuan) taken in logarithm, Open stands for total imports and exports as a share of GDP (%), and Share stands for the number of health technicians per 10,000 people (persons), Trend is the potential time trend; α0 Constant terms, the α1 ,α2 , α3 , α4 , α5 , and α6 is the influence coefficient; λit is a provincial control variable,  δit is the random interference term.

2.3 Data sources

In this paper, in order to more accurately study the trend of the digital economy measurement indicators, the data of various indicators from 1998 to 2022 were collected. Among them, the digital inclusive finance index comes from the fourth issue of Peking University’s digital inclusive finance index, the level of digital government comes from the China Internet Network Information Center (CNNIC), the data of 16 indicators, such as Internet penetration, telephone penetration, and the length of long-distance fiber optic cable lines, are all from the National Bureau of Statistics, and the strength of governmental support comes from the analysis of the provincial government’s 2015–2022 work report and digital economic policies using Python. The government support comes from Python’s statistics on the word frequency of the 2015–2022 government work reports of each province related to the digital economy policy.

Because of the large amount of missing data in the period of 1998–2014 for a number of indicators, such as the level of digital government and software business revenue, this paper only studies the development of the digital economy measurement indicators in the period of 2015–2022. In addition, the Internet penetration rate of each province in 2022 is missing, and the ARIMA model was used to fill in the prediction; the digital government level of each province in 2016 is missing, and considering that it is in the middle of the year, it is inconvenient to use the ARIMA model for prediction, so the interpolation method was used to fill in.

3. Empirical findings

3.1 Digital economy development comprehensive score calculation

  • (1)

    The five dimensions of the digital economy are scored

In order to further analyze the development of digital economy-related aspects in each province, this paper uses the entropy method to calculate scores for the five dimensions of digital infrastructure, digital industrialization, industrial digitization, digital governance, and digital innovation capacity, respectively, and obtains the following results:

From the multi-dimensional analysis of the digital economy development of provinces across the country through the entropy method (e.g. Table 4), the results show significant regional differences and development imbalance. Guangdong excels in digital infrastructure U1 (0.625), digital industrialization U2 (0.456), industrial digitization U3 (0.552) and digital governance U4 (0.483), and digital innovation capacity U5 (0.667), demonstrating overall leading digital economy strength. Beijing excels in digital industrialization (0.541) and industrial digitization (0.574), while Jiangsu scores higher in digital governance (0.408) and digital innovation capability (0.554). In contrast, western and remote regions such as Ningxia, Qinghai and Xinjiang generally scored lower on all dimensions, reflecting the regional imbalance in digital economy development. Such differences not only reflect the disparities in economic fundamentals and innovation capabilities across regions, but also highlight the challenges faced in the process of digital transformation.

Analyzing the impact among neighboring provinces, the spillover effect and regional synergy of digital economy development can be observed. Taking the Tze River Delta (YRD) region as an example, Jiangsu, Zhejiang and Shanghai have similar and generally high scores in all dimensions, reflecting the positive impact of integrated regional development. As a strong digital economy province, Guangdong’s high score has had an obvious demonstration and driving effect on the digital infrastructure of neighboring provinces such as Fujian (0.465), contributing to the overall level of the region. However, this influence may also bring negative effects, such as the huge gap between Beijing and Tianjin in digital industrialization (0.541 vs. 0.050) that may lead to an over-concentration of resources and talents towards the dominant region, exacerbating regional development imbalance. Similarly, the difference in industrial digitization between Sichuan and Chongqing (0.227 vs 0.235) reflects the possible competitive relationship between neighboring provinces, which may affect the effectiveness of regional synergistic development. Therefore, in promoting the development of the digital economy, it is necessary to fully consider the interactions between regions and formulate strategies that balance synergistic development and differentiated competition.

  • (2)

    Composite Score for Digital Economy Level Measurement

This paper calculates the comprehensive scores for the 19 secondary indicators corresponding to the five primary indicators under the digital economy level measurement to study the overall development status of the digital economy level in each region. In order to verify the reasonableness of the calculation results, with reference to Li and Wang (2022), who calculated the mean of the comprehensive score of the digital economy development level from 2008–2019 using the comprehensive weighted TOPSIS for ranking, and He et al. (2023), who calculated the mean of the evaluation score of the digital economy development from 2013–2020 using the entropy method for ranking, the digital economic level of this paper from 2015 to 2022 was Measurement of the average value of the comprehensive score for ranking to match the test, considering the impact of its factors by time change, the provincial ranking change within two is considered to be a match through, the calculation found that the matching pass rate of 0.667 and 0.767, respectively, that the results of this paper’s calculations are better, and can be further analyzed. The calculation results are as follows:

Through an in-depth analysis of the composite scores of digital economy level measurements of Chinese provinces in 2015–2022 (combined with Table 5 and Figure 1), we can clearly observe the overall trend and regional differences in the development of the digital economy. The data show that the overall digital economy development level of all provinces in China shows a year-on-year increase, but at the same time, it also highlights significant regional imbalances. Guangdong, Beijing, Jiangsu and Zhejiang were firmly in the top four in the country during the observation period, constituting the first echelon of digital economy development. The leading position of these provinces mainly stems from their continuous investment and accumulation in technology infrastructure, talent resources and innovation ecosystems. For example, Guangdong Province’s score grows from 0.330 in 2015 to 0.746 in 2022, an increase of 126%, reflecting its overall strengths in digital industrialization, industrial digitization and digital governance. Shandong and Shanghai fluctuate slightly between the fifth and sixth positions, further consolidating the eastern region’s leading position in digital economy development. These leading provinces not only provide development experience for other regions, but also drive the synergistic development of neighboring regions to a certain extent, such as the integrated development of the Yangtze River Delta region, which reflects this positive effect.

The data also reveal a trend of “Few provinces have strong development, while more provinces have weak development” in the development of the digital economy. Only about one-third of the provinces have a stable level of development above the national average, while about two-thirds of the provinces are below the average. This uneven development trend shows a clear gradient from the eastern region to the western region. Provinces in the western and northeastern regions, such as Xinjiang, Gansu, Qinghai, and Ningxia, ranked low and made limited improvements throughout the observation period. This disparity reflects the challenges faced by these regions in terms of digital infrastructure, talent pool, innovation capacity and industrial structure. It is worth noting that provinces in the middle of the rankings, such as Sichuan, Fujian and Hubei, show some volatility in their level of development. In particular, Hubei scored lower than the national average in 2015, 2016, 2018 and 2021, which may be related to the particular difficulties the province encountered in those years, such as the COVID-19 epidemic that began in late 2019, which had a significant impact on Hubei’s economy. This volatility also highlights the complexity and vulnerability of the development of the digital economy, which is affected by multiple factors, such as the speed of technological upgrading, macroeconomic fluctuations and unexpected events.

Overall, the development of China’s digital economy shows a pattern of “strong in the east and weak in the west, high in the south and low in the north”. Guangdong, with an average score of 0.543, is far ahead of other provinces as the first, and Beijing, Jiangsu, Zhejiang, Shandong and Shanghai, with average scores higher than 0.27, are in the first echelon of the development, widening the gap with other regions. The formation of this gap is related to a number of factors: first, provinces with higher levels of development have invested more in technological infrastructure, such as Internet broadband access, data center construction, etc., which provides a solid physical foundation for the development of the digital economy; second, these regions are usually areas where educational resources and high-tech talents are concentrated, for example, Beijing, as the national center of politics, culture, education, and international exchanges, has a large number of institutions of higher education and scientific research. For example, Beijing, as the national political, cultural, educational and international exchange center, has a large number of higher education institutions and research institutes; again, these regions have a large number of high-tech enterprises with strong innovation capabilities in artificial intelligence, big data, cloud computing and other fields, which have driven the rapid development of the regional digital economy. This uneven development reflects the current situation of China’s digital economy development, and also provides an important basis for future policymaking and regional coordinated development.

3.2 Coupled harmonization of the five dimensions

The coupling coordination degree is calculated for the mean values of the 2015–2022 scores of the five dimensions of digital infrastructure, digital industrialization, industrial digitization, digital governance, and digital innovation capacity in the digital economy level measurement, and the coordination of development within the province and the differences between regions are analyzed based on the results (Table 6).

As can be seen in Figure 2, the overall coupling and coordination among the five dimensions in each region shows a year-on-year increasing trend, indicating that the degree of development imbalance within the provinces in each region has decreased. However, from the overall data, there is an imbalance in China’s regional development, with the eastern coastal provinces generally leading in economic and technological development, while some central and western regions face more challenges. This imbalance is also reflected in the five dimensions of digital infrastructure, digital industrialization, industrial digitization, digital governance and digital innovation capacity, which require coordination and support at the national level to promote balanced regional development. The regions are analyzed separately according to the coordination level:

Neutral and Primary Coordination: Guangdong and Jiangsu demonstrate high economic and technological indicators, and despite a relatively low Coordination Index (T), they generally achieve a better balance of development, suggesting that these regions are pursuing economic growth while paying more attention to the coordination between the five dimensions of development at the level of the digital economy.

Barely coordinated: Beijing, Zhejiang and Shandong have relatively high coupling (C) and coordination index (T) indicators, but relatively low coupling coordination (D) indicators, indicating that while the level of the digital economy is developing faster in these regions, there are certain imbalance problems in the five development dimensions, and further optimization of resource allocation is needed.

On the verge of dislocation and mild dislocation: For example, in Fujian, Anhui, Sichuan and other places, the digital economy level of these provinces develops well, but the coupling degree of the five development dimensions is not high. It shows that in the process of pursuing the development of digital economy, there are problems such as unbalanced, uneven distribution of resources and insufficient synergistic effect.

Moderate dislocation: Inner Mongolia, Heilongjiang, Jilin and other provinces face more serious dislocation problems, with a low level of digital economy, as well as relative weakness in the development of the five dimensions, reflecting a more prominent development imbalance in these regions, and an urgent need for comprehensive measures to promote the coordinated development of the regional digital infrastructure, digital industrialization, industrial digitization, digital governance, and the ability to innovate digitally.

3.3 Spatial correlation measure of the coupled coordination degree level

  • (1)

    Overall Spatial Relevance

The data and analyses in Table 7 provide an in-depth view of how the spatial autocorrelation of specific variables has changed between 2015 and 2022. The global Moran index, a key measure of spatial autocorrelation, reveals the tendency for these variables to cluster in their spatial distribution. Positive Moran’s Index values indicate a tendency to cluster similar values within the study area, and this tendency shows some fluctuation over the period analyzed.

As can be observed from Table 7, the Moran Index (I) remains positive throughout the study period and is significant at the 0.01 or 0.05 significance level. This result strongly suggests that there is a significant positive spatial correlation between the digital economy development levels of Chinese provinces, i.e. geographically similar provinces tend to exhibit similar digital economy development levels. Specifically, the Moran index gradually decreases from 0.308 in 2015 to 0.193 in 2020, and then slightly rebounds to 0.258 in 2022. This trend of change suggests that, although the spatial positive correlation is always present throughout the observation period, its strength goes through a process of weakening and then slightly strengthening.

Further analysis of the changes in the Moran Index reveals some deep-seated phenomena. The continuous decline of the Moran Index from 2015 to 2020 reflects that the spatial convergence of China’s digital economy development has increased, and the influence of geographic location on the level of development of the digital economy has weakened during this period. This may be due to the fact that the popularization of digital technology and policy support have enabled some originally backward regions to achieve rapid development during this period, thus narrowing the gap with developed regions. However, from 2020 onwards, the Moran Index shows a slight rebound, which may imply that regional differences in the development of the digital economy have begun to have a tendency to widen again. This phenomenon may be related to a number of factors, such as the differential impact of the new crown epidemic, the differences in the resilience of different regions in the post-epidemic era, as well as the higher demand for high-end talent and technology as the development of the digital economy enters a new phase. Nevertheless, the Moran Index in 2022 (0.258) is still significantly lower than the level in 2015 (0.308), indicating that the overall spatial pattern of China’s digital economy development has substantially changed compared with that of eight years ago, with closer inter-regional connections, but still with significant spatial agglomeration effects.

  • (2)

    Local Spatial Correlations

Table 8 lists the local Moran indices and corresponding p-values for the developmental coupling and coordination of the five dimensions of the digital economy level measure. Among them, a positive Moran index corresponds to the first and third quadrants of the image, while a negative one corresponds to the second and fourth quadrants (e.g. Figure 3), and a p-value of less than 0.1 indicates that the spatial correlation passes the significance test. For the eight provinces of Shanghai (0.001), Jiangsu (0.000), Zhejiang (0.000), Fujian (0.056), Guangdong (0.056), Hainan (0.050), Gansu (0.010), and Xinjiang (0.009), their p-values are less than 0.1, which indicates that they passed the significance test at the 10% level, which means that they have passed the test of significance with their neighboring regions in terms of the five indicators of the digital economy level measurement. There is a significant spatial correlation between them and the neighboring regions in terms of the coupling coordination of the five indicators of the economic level measurement. In addition, it is also found that the Moran index levels of the coastal regions of Shanghai, Zhejiang, Jiangsu, Fujian, and Guangdong are 0.941, 1.182, 1.194, 1.270, and 1.292, respectively, and their localized Moran indices are significantly positive and large, indicating that these regions have formed a high level of agglomeration of digital economy development.

One observation that can be drawn from these data is that even though Chinese provinces are currently making progress in the development of the five dimensions of the Digital Economy Level Measure, differences in local specificities lead to varying degrees of coordinated development between them and their neighboring provinces. These differences may be related to factors such as the provinces' economic structure, industrial policies, resource endowments, technological inputs, and innovation capabilities. For policymakers, focusing on these salient differences can help target the design and implementation of policies to further promote coordinated interregional development.

4. Analysis of the factors influencing the coupling coordination degree

4.1 Interpretation of influencing factors

  • (1)

    Innovation

Innovation is an important driving force for regional economic growth and structural optimization. (Wang et al., 2022) considered the technology market turnover in exploring the innovation development of the subsystem of high-quality economic development, suggesting that it has a facilitating role in advancing the coordinated development of the region. Through technological innovation and management innovation, it can improve the efficiency of resource utilization, promote industrial upgrading and transformation, and enhance the core competitiveness of the region. At the same time, innovation can promote the dissemination and sharing of interregional knowledge, strengthen interregional linkage development and improve overall coupling coordination.

  • (2)

    Harmonization

Coordination is the basic requirement for realizing harmonious regional development. The comparative measurement system of high-quality development constructed by Liu and Lun (2023) takes into account social security stations and employment expenditures in coordinated development, which is used to study and solve the problem of unbalanced development. It requires attention to the balance between economic and social benefits, short-term and long-term interests, and internal regional development and external interaction in regional development. By strengthening policy coordination, planning alignment and mechanism innovation, the problem of unbalanced regional development can be effectively resolved, the optimization of resource allocation promoted, and interregional complementarity and synergy achieved, thereby increasing the degree of coupled coordination.

  • (3)

    Green

Green development is an important way to cope with environmental changes and promote sustainable development. Referring to Zhou et al. (2020) and Zhang (2019)'s summary of the screening of green development indicators, and considering the principles of indicator screening, this paper considers the completed investment of the treatment of wastewater project as a measure of the green level. Through the promotion of green technology, the development of green industry, the implementation of green consumption and other measures, it can promote the win-win situation of economic growth and ecological environmental protection. Green development helps to improve the regional environmental quality, guarantee the sustainable use of natural resources, and promote the ecological balance between regions, thus improving the coupling coordination.

  • (4)

    Openness

Openness is an effective way to improve the vitality and efficiency of regional development. Yang and Qin (2024) considered the ratio of total import and export trade to GDP as an indicator of open development, suggesting that trade openness is one of the influencing factors in promoting high-quality economic development. By strengthening opening up, external resources, capital and technology can be introduced, market space can be broadened, and international exchanges and cooperation can be promoted. At the same time, opening up helps to promote interregional information communication, strengthen the interconnection of the industrial chain and supply chain, improve the efficiency of interregional collaboration, and thus enhance the degree of coupling coordination.

  • (5)

    Sharing

Sharing economy is a new model of modern economic development. Wang (2020) considers the number of health technicians at the level of shared development, reflecting the level of public service provision, which helps to study the promotion of high-quality economic development. By optimizing resource allocation and improving resource use efficiency, the sharing economy can promote industrial integration and innovation, expand service areas, and provide personalized and diversified services. The implementation of the sharing economy can promote interregional resource sharing and capacity complementarity, strengthen social participation and benefit sharing, thus enhancing interregional links and collaboration and improving regional coupling and coordination.

4.2 Tobit test calculation

In the results of the model test (e.g. Table 9), the indicator of innovativeness shows a strong positive correlation (coefficient of 0.0738, p < 0.01) with the degree of coupling coordination, indicating that the enhancement of interregional innovation capacity, especially the activeness of the technology market, has a significant driving effect on promoting the coordination of regional development. The coordination indicator also shows a positive correlation (coefficient of 0.0876, p < 0.01), revealing that the state’s investment in social security and employment plays a key role in narrowing the regional development gap and balancing social development. The green development indicator shows a negative correlation with the degree of coupling coordination (coefficient of −0.013, P providing 0.01), which indicates that interregional efforts in environmental protection have been effective, and the improvement of the environmental situation is becoming an important part of coordinated regional development. The sharing indicator shows a positive correlation (coefficient of 0.0019, p < 0.01), emphasizing the important role of balanced allocation of social infrastructure, especially public health resources, in improving regional coupling coordination. The time trend indicator shows a positive correlation (coefficient of 0.0037, p < 0.01), as time passes, the positive influence of factors such as the turnover of the technology market, the national financial expenditure on social security and employment, and the investment in the project of treating wastewater on the degree of coupling coordination gradually appears, and the overall level of coupling coordination is improving year by year.

The openness indicator shows a negative correlation with the degree of coupling coordination (coefficient of −0.0028, p < 0.01), although statistically the openness indicator shows a statistically significant negative relationship, this does not necessarily indicate that opening up has had a negative impact on economic development, and that a high degree of openness may have brought about structural challenges that have put pressure on certain regions to adjust in the short term, and that, after applying the lagged effects The coefficient declined after a one-year lag, but it is difficult to explore the long-term effect due to the limitation of data years.

5. Conclusions and policy recommendations

5.1 Conclusions

  • (1)

    The development of digital economy presents a pattern of “East strong and west weak, south high and north low”.

China shows significant regional differences in the process of digital economy development, which is not only reflected in the overall level, but also in the uneven development of various dimensions. Eastern coastal provinces such as Guangdong, Zhejiang and Jiangsu have formed a strong economic foundation and a favorable digital economy ecosystem thanks to the policy dividends of early reform and opening-up, superior geographic location and advanced industrial structure. These regions are leading in digital infrastructure, digital industrialization, industrial digitization, digital governance and digital innovation capability, forming a virtuous cycle of digital economy development. In contrast, central and western provinces are generally lagging behind in the level of digital economy development due to their remote geographical location, weak economic foundation and relatively lagging industrial structure. The construction of digital infrastructure in these regions still needs to be strengthened, the degree of digital industrialization is relatively low, the digital transformation of industries faces greater challenges, and the capacity of digital governance and digital innovation needs to be improved. In addition, in promoting the development of the digital economy, the central and western regions tend to focus more on short-term economic growth and neglect long-term sustainable development, further aggravating the imbalance in the development of the digital economy.

This development imbalance not only affects the overall competitiveness of regional economies, but also exacerbates the development gap between regions. Today, with the rapid development of the digital economy, the application and popularization of digital technology has become an important indicator of a region’s level of economic and social development. The unbalanced development of provinces in key dimensions of the digital economy directly affects their position and role in the national and even global digital economy competition. What is more noteworthy is that this unbalanced development is exacerbating social differentiation. Those groups that are able to quickly integrate into the digital economy and master digital technologies will have more development opportunities and economic gains, while marginalized groups face greater employment pressure and life challenges. This situation is particularly evident in the central and western regions, which not only exacerbates the urban-rural gap, but also affects social stability and harmony. Therefore, how to narrow the regional gap in the development of the digital economy and promote the balanced development of all regions has become an important issue that needs to be urgently resolved in the process of China’s march towards becoming a digital economy powerhouse.

  • (2)

    There exists the phenomenon of “Polarization effect” and “Siphon effect” in the development of digital economy.

Through the analysis of the spatial autocorrelation of the level of digital economy development in each province, we find that there is the phenomenon of “polarization effect” and “siphon effect” coexisting. The polarization effect is reflected by the fact that the global Moran index is positive (0.193–0.311) and statistically significant during the period of 2015–2022, indicating that there is a significant positive spatial correlation in the level of digital economy development. This reflects the driving effect of developed regions on neighboring regions, forming a spatial pattern of high–high agglomeration. In particular, the eastern coastal regions such as Guangdong, Jiangsu, Zhejiang, etc., whose comprehensive scores and rankings have always been leading and showing a rising trend year by year, have driven the common development of the neighboring regions. The siphoning effect is reflected by the fact that although the global Moran index is positive, its value decreases from 0.308 in 2015 to 0.193 in 2020, reflecting a weakening of spatial correlation. This may imply that developed regions are attracting neighboring resources while driving the development of the surrounding areas, exacerbating the regional disparity. The analysis of coupled coordination shows that even in economically developed provinces such as Beijing and Zhejiang, there is still an imbalance in the development of their five dimensions, reflecting the phenomenon of over-concentration of resources to certain dimensions. The dynamic balance of the two effects, with the Moran index picking up after 2020 (0.258 in 2022), suggests that the polarization effect and the siphon effect may be reaching some kind of dynamic balance. This equilibrium reflects the effects of the national policy of coordinated regional development, but also indicates that regional disparities still exist.

On the one hand, the rapid development of the digital economy in the eastern region has widened the gap with the central and western regions, and if effective measures are not taken, this gap may widen further, affecting the overall balanced development of the national economy. On the other hand, while pursuing the rapid development of the digital economy, the eastern region itself is also facing resource constraints, environmental pressure and other problems, and needs to realize more sustainable development through transformation and upgrading and innovation drive. Based on this, to promote the positive spatial correlation of inter-regional digital economy development, it is not only necessary for the eastern region to continue to play a leading role, but also for the central and western regions to actively participate in and follow up, and to accelerate the pace of their own digital economy development by learning from the experience of advanced regions. At the same time, the national level should further optimize the regional development strategy, strengthen inter-regional exchanges and cooperation through policy guidance and resource allocation, and form a digital economy development network covering the whole country, so as to jointly promote the balanced development of China’s digital economy.

  • (3)

    The development of digital economy is promoted by innovation, coordination and sharing, and restrained by green and opening.

In this paper, the random effects panel Tobit model is used to analyze the effects of several factors on the regional coupling coordination degree. The results show that innovation ability, coordination and sharing have significant positive effects on promoting the degree of regional coupling coordination. This shows that the promotion of regional technological innovation capacity and the level of coordinated social development is an important way to promote the coordinated development of digital economy. Technological innovation can not only enhance the industrial competitiveness, but also drive the development of the relevant industrial chain, forming the industrial cluster effect. In addition, social coordinated development, including education, health care, social security and other aspects, can provide a stable social environment for the development of the digital economy and high-quality human resources. Therefore, it is recommended that all regions further increase their investment in R & D, encourage enterprises to carry out technological innovation, and at the same time strengthen the construction of social infrastructure and enhance the level of social services, create a social environment conducive to the development of digital economy.

Green development and openness are negatively correlated with the level of coupling coordination, indicating that the structural challenges of green development and openness need to be taken seriously in promoting the digital economy. While a high degree of openness can lead to additional resources and opportunities, it can also lead to greater adjustment pressures in some regions in the short term. In addition, although green development is an inevitable requirement of sustainable development, it may bring some constraints to economic growth in the short term. Therefore, it is recommended that the formulation of digital economy development policies should focus on the balance between green development and openness. It is possible to raise the environmental awareness of the public and enterprises by strengthening the publicity and education on environmental protection and sustainable development, and at the same time formulate reasonable policies to gradually promote the optimization and upgrading of industrial structure, reduce the negative impact on the environment. In terms of openness, it is suggested that the strategy of gradual opening up and strengthening internal adjustment capacity should be adopted to ensure the steady development of regional economy and reduce the negative impact of adjustment.

5.2 Policy recommendations

  • (1)

    Optimizing the policy environment for the digital economy

The policy environment is an external condition for the development of the digital economy, directly affecting the degree of acceptance and scope of application of digital technologies by enterprises and individuals. For the central and western regions and other regions lagging behind in the development of the digital economy, national and local governments need to formulate and implement more targeted and incentivized policy measures. This includes, but is not limited to, the provision of tax breaks, financial support, and preferential land-use policies, especially for enterprises and organizations that are committed to the research and development of digital technologies, the construction of digital infrastructures, and the cultivation of talents for the digital economy. At the same time, policy publicity and implementation should be strengthened to ensure that the policy dividends actually reach the target groups.

In addition, the Government should also actively build a multi-level and wide-ranging policy communication and feedback mechanism, promptly collect opinions and suggestions on digital economy policies from enterprises, consumers, scholars and other parties, and continuously optimize and adjust the content of the policies so as to make them closer to the market and social needs. Through these measures, the external barriers to the development of the digital economy can be effectively reduced, creating a more favorable environment for the promotion and application of digital technology.

  • (2)

    Strengthening digital infrastructure

Digital infrastructure is the material foundation for the development of the digital economy, and its coverage and service quality are directly related to the level of development of the digital economy. For central and western regions and other regions lagging behind in the development of the digital economy, it is necessary for the Government to increase investment, prioritize the construction and upgrading of network infrastructure, improve network coverage and data transmission speeds, and ensure that all regions can enjoy high-quality network services. In addition, attention should be paid to the construction of new digital infrastructure, such as cloud computing and big data centers, to provide strong support for data storage, processing and analysis.

Strengthening digital infrastructure not only promotes the digital transformation of local economies, but also attracts external investment, promotes employment and improves the quality of life of residents. Therefore, governments should take a variety of measures to accelerate the construction of digital infrastructure, such as attracting the participation of social capital through public-private partnership (PPP) models or setting up special funds to support the construction of key projects. There is also a need to strengthen cross-regional infrastructure connectivity, promote resource sharing and enhance overall service efficiency and standards.

  • (3)

    Promoting industrial upgrading and digital technology applications

The widespread application of digital technology is the key to promoting the development of the digital economy. For all regions, especially in the central and western regions, it is necessary to encourage, through policy guidance and market mechanisms, the transformation and upgrading of traditional industries using digital technologies to improve the level of intelligence and informatization of industries. This includes, but is not limited to, supporting enterprises to introduce advanced digital technology equipment, conduct online business, and build smart factories. At the same time, enterprises should also be encouraged to increase their investment in research and development and explore the application of digital technology in new product development, production process optimization, marketing and other aspects, so as to enhance their core competitiveness.

  • (4)

    Promoting regional cooperation as a development community

The development of the digital economy should not be limited to a single region or country, but should be a regional and even global cooperation and win-win situation. For China, the eastern region, which is leading the development of the digital economy, should give full play to its advantages in technology, capital and talents, and cooperate more closely with the central and western regions to share the fruits of development. Such cooperation can take place through the establishment of cross-regional project cooperation, technology exchange platforms, talent training and exchange programs.

At the same time, the Government should actively promote the joint construction and sharing of major projects such as big data centers and cloud computing infrastructure across provincial boundaries, and strengthen the connectivity of digital infrastructure between different regions through policy coordination and financial support. In addition, the establishment of a regional digital economy development fund can be explored to support regional digital economy innovation projects and startups, and promote the overall upgrading and optimization of the digital economy ecosystem in the region.

  • (5)

    Multi-dimensional enhancement of regional coupling and harmonization development

Policymakers and relevant departments should continue to strengthen technological innovation and coordinated social development, especially through activating the technology market and increasing spending on social security and employment, in order to promote balanced interregional development. At the same time, they should increase investment in environmental protection and promote green development as an important strategy for promoting coordinated regional development.

In addition, a balanced allocation of public health resources is crucial to enhancing regional coupling and coordination, and resource allocation mechanisms should be optimized to ensure equitable access to these resources across regions. With regard to the structural challenges posed by openness, it is recommended that a strategy combining gradual liberalization and strengthening of internal adjustment capacity be adopted to alleviate the pressure of adjustment in the short term, and at the same time, long-term studies should be conducted to gain a deeper understanding of the long-term effects of openness on regional development, so as to formulate a more comprehensive liberalization strategy. Through the implementation of these comprehensive strategies, coupled and coordinated interregional development can be further promoted, and overall social harmony and progress can be realized.

Figures

Average value and growth rate of digital economy development level score by province

Figure 1

Average value and growth rate of digital economy development level score by province

Changes in the level of coupling harmonization by province

Figure 2

Changes in the level of coupling harmonization by province

Localized distribution of Moran’s index

Figure 3

Localized distribution of Moran’s index

Summary of statistical indicators for the 29 references

Level 1 indicatorsNumber of statisticsSecondary indicatorsNumber of statisticsBibliography
Digital infrastructure7Internet penetration12Xu and Zhang (2020), Yang and Jiang (2021), Cai (2018), He et al. (2023), Wu and Wang (2022), Wang and She (2021), Ding (2020), Lei (2020), Jia (2020), Li et al. (2023a), Chen et al. (2023), Jin et al. (2022), Lian et al. (2022), Wang et al. (2023), Cheng and Zou (2022), Shen and Zhou (2023), Wang et al. (2021), Wan and Luo (2022), Li et al. (2023c), Pan et al. (2021), Chen et al. (2023), Li et al. (2023b), Wang (2023), Liu et al. (2022), Wu and Shi (2023), Li et al. (2022a), Meng et al. (2023), Liu (2023), Wang and Li (2023)
12Length of long-distance fiber optic cable lines14
17Internet broadband access port17
11Number of Internet domain names11
5Number of IPV4 addresses5
2Websites per 1,000 population2
Digital industrialization11Gross industrial output value of the digital industry14
7Digital industry practitioners14
12Revenue from software operations16
12Total telecommunication services18
4Number of electronic information manufacturing industries4
1Number of Internet Top 100 Companies1
Industrial digitization9E-commerce sales14
9Enterprise informatization level14
8Enterprise website coverage12
8Digital Inclusive Finance Index13
2Internet share of industrial enterprises2
3Dualization Integration Index3
Digital governance6Level of digital government8
5Number of digital economy enterprises9
6Government support8
1Government Administration Application Index1
1Science and technology investment intensity1
Digital innovation capabilities6Years of schooling per capita8
12R&D investment intensity16
12Full-time equivalent of R&D personnel16
14Number of patent applications granted18
2Total technology contract turnover2
2Number of new product openings2

Source(s): Table created by authors

Indicator system for measuring the level of digital economy

Level 1 indicatorsSecondary indicatorsInformation entropyUtility valueWeightsIndicator properties
Digital infrastructure (0.150)Internet penetration0.9780.0220.015+
Length of long-distance fiber optic cable lines0.9630.0370.026+
Internet broadband access port0.9530.0470.033+
Number of Internet domain names0.8910.1090.076+
Digital industrialization (0.402)Gross industrial output value of the digital industry0.8150.1850.130+
Digital industry practitioners0.8870.1130.079+
Revenue from software operations0.8300.1700.119+
Total telecommunication services0.8940.1060.074+
Digitization of industry (0.140)E-commerce sales0.8780.1220.086+
Enterprise informatization level0.9660.0340.024+
Enterprise website coverage0.9890.0110.008+
Digital Inclusive Finance Index0.9670.0330.023+
Digital governance (0.091)Level of digital government0.9940.0060.004–
Number of digital economy enterprises0.9090.0910.064+
Government support0.9670.0330.023+
Digital innovation capacity (0.216)Years of schooling per capita0.9740.0260.018+
R&D investment intensity0.9740.0260.018+
Full-time equivalent of R&D personnel0.8710.1290.090+
Number of patent applications granted0.8730.1270.089+

Source(s): Table created by authors

Criteria for determining the coupling coordination relationship

Coupling coordination degreeCoupling coordination levelCoupling coordination degreeCoupling coordination level
0∼0.1Extreme disorder(A)0.5–0.6Forced coordination(F)
0.1–0.2Major maladjustment(B)0.6–0.7Primary coordination(G)
0.2–0.3Moderate dysregulation(C)0.7–0.8Intermediate coordination(H)
0.3–0.4Mild dysregulation(D)0.8–0.9Good coordination(I)
0.4–0.5On the verge of dysregulation(E)0.9–1.0Quality coordination(J)

Source(s): Table created by authors

Calculation results of the mean values of the five dimensions scores

ProvinceU1U2U3U4U5ProvinceU1U2U3U4U5
Beijing0.4370.5410.5740.1900.166Henan0.2830.0790.1720.2190.173
Tianjin0.0980.0500.1860.1820.146Hubei0.2350.0850.2260.2590.167
Hebei0.2380.0570.1550.1550.131Hunan0.2260.0540.1890.2960.156
Shanxi0.1570.0260.1280.3370.073Guangdong0.6250.4560.5520.4830.667
Inner Mongolia0.1790.0230.1330.1080.060Guangxi0.1850.0370.1320.1550.057
Liaoning0.1890.0800.1540.2130.113Hainan0.0650.0120.2040.2560.036
Jilin0.1290.0290.0890.1730.055Chongqing0.1220.0500.2350.3030.121
Heilongjiang0.1740.0290.0930.1250.058Sichuan0.3510.1420.2270.1550.126
Shanghai0.2240.2600.5020.2430.194Guizhou0.1700.0340.1460.2780.049
Jiangsu0.3440.2790.3110.4080.554Yunnan0.1560.0370.1620.1510.049
Zhejiang0.3220.2140.3400.2900.460Shaanxi0.1730.0880.1820.1860.096
Anhui0.2120.0530.2470.5420.181Gansu0.1150.0180.1200.1620.045
Fujian0.4650.0820.2290.2140.185Qinghai0.0980.0030.1310.1440.025
Jiangxi0.1720.0320.1770.1720.118Ningxia0.0540.0040.1290.1150.053
Shandong0.3290.1640.3470.4590.314Xinjiang0.1460.0200.0870.1260.035

Source(s): Table created by authors

Calculation of the composite score of the digital economy level measurement

Province20152016201720182019202020212022
Guangdong0.330(1)0.376(1)0.426(1)0.515(1)0.598(1)0.673(1)0.679(1)0.746(1)
Beijing0.276(2)0.307(2)0.340(2)0.372(2)0.425(2)0.465(2)0.542(2)0.609(2)
Jiangsu0.257(3)0.276(3)0.298(3)0.341(3)0.383(3)0.437(3)0.443(3)0.480(3)
Zhejiang0.208(4)0.233(4)0.245(4)0.281(4)0.333(4)0.378(4)0.369(4)0.420(4)
Shandong0.178(6)0.203(6)0.226(6)0.256(5)0.275(5)0.322(5)0.350(6)0.385(5)
Shanghai0.189(5)0.209(5)0.226(5)0.245(6)0.270(6)0.310(6)0.351(5)0.382(6)
Sichuan0.108(8)0.125(8)0.143(8)0.167(9)0.210(9)0.241(7)0.223(8)0.249(7)
Fujian0.118(7)0.156(7)0.198(7)0.212(7)0.219(7)0.202(10)0.212(9)0.236(8)
Hubei0.101(10)0.115(10)0.131(9)0.150(12)0.183(10)0.199(11)0.194(11)0.214(9)
The national average0.103(9)0.117(9)0.130(10)0.151(10)0.176(12)0.196(12)0.197(10)0.209(10)
Henan0.094(13)0.110(12)0.121(12)0.151(11)0.182(11)0.207(9)0.184(12)0.197(11)
Anhui0.098(11)0.113(11)0.129(11)0.187(8)0.219(8)0.238(8)0.232(7)0.197(12)
Hunan0.080(15)0.100(13)0.111(13)0.143(13)0.170(13)0.189(13)0.179(13)0.172(13)
Chongqing0.071(18)0.089(18)0.097(17)0.117(15)0.138(16)0.160(14)0.159(14)0.171(14)
Hebei0.078(16)0.092(16)0.100(16)0.112(17)0.139(15)0.157(16)0.141(17)0.164(15)
Shaanxi0.078(17)0.094(15)0.102(15)0.114(16)0.143(14)0.159(15)0.150(15)0.155(16)
Liaoning0.097(12)0.099(14)0.106(14)0.121(14)0.137(17)0.152(17)0.148(16)0.149(17)
Jiangxi0.061(20)0.064(21)0.078(19)0.095(20)0.124(18)0.139(18)0.133(20)0.141(18)
Tianjin0.086(14)0.089(17)0.087(18)0.099(19)0.113(19)0.129(20)0.136(18)0.132(19)
Guangxi0.047(26)0.055(27)0.065(26)0.077(23)0.110(22)0.121(23)0.103(22)0.124(20)
Guizhou0.045(27)0.061(25)0.074(21)0.089(21)0.110(21)0.134(19)0.135(19)0.117(21)
Shanxi0.065(19)0.068(19)0.078(20)0.109(18)0.112(20)0.126(21)0.116(21)0.113(22)
Inner Mongolia0.054(23)0.064(22)0.067(23)0.073(24)0.083(24)0.089(24)0.088(25)0.101(23)
Yunnan0.049(25)0.065(20)0.066(25)0.079(22)0.109(23)0.122(22)0.095(23)0.097(24)
Heilongjiang0.055(21)0.063(23)0.067(24)0.072(26)0.080(26)0.087(25)0.081(28)0.093(25)
Jilin0.051(24)0.056(26)0.060(27)0.069(27)0.081(25)0.085(27)0.085(27)0.082(26)
Hainan0.055(22)0.061(24)0.069(22)0.073(25)0.078(27)0.086(26)0.091(24)0.082(27)
Xinjiang0.040(29)0.042(29)0.052(29)0.056(29)0.066(29)0.078(29)0.075(29)0.080(28)
Gansu0.042(28)0.049(28)0.055(28)0.063(28)0.073(28)0.084(28)0.088(26)0.073(29)
Qinghai0.036(31)0.040(30)0.042(31)0.053(30)0.056(30)0.062(30)0.068(30)0.066(30)
Ningxia0.036(30)0.038(31)0.044(30)0.046(31)0.054(31)0.061(31)0.062(31)0.058(31)

Source(s): Table created by authors

Calculation results of the coupling coordination degree of each region

ProvinceCTDCoupling development levelsProvinceCTDCoupling development levels
Guangdong0.9900.5560.742HHebei0.9090.1470.366D
Jiangsu0.9700.3790.606GTianjin0.9020.1320.345D
Beijing0.8800.3810.579FJiangxi0.8520.1340.338D
Zhejiang0.9700.3250.562FShanxi0.7280.1440.324D
Shandong0.9500.3230.554FGuizhou0.7570.1350.320D
Shanghai0.9410.2850.517FGuangxi0.8440.1130.309D
Fujian0.8640.2350.450EYunnan0.8380.1110.305D
Anhui0.7850.2470.441EInner Monggolia0.8080.1000.285C
Sichuan0.9270.2000.431EHeilongjiang0.8420.0960.284C
Hubei0.9320.1950.426EJilin0.8360.0950.282C
Henan0.9230.1850.413EGansu0.7690.0920.266C
Hunan0.8710.1840.401EHainan0.5910.1150.260C
Liaoning0.9420.1500.376DXinjiang0.7790.0830.254C
Chongqing0.8400.1660.374DNingxia0.6270.0710.211C
Shaanxi0.9490.1450.371DQinghai0.5420.0800.208C

Source(s): Table created by authors

Global Moran’s index calculation results

VariablesIE(I)sd(I)zp-value*
d20150.308−0.0340.1232.7760.003***
d20160.311−0.0340.1242.7930.003***
d20170.299−0.0340.1232.7110.003***
d20180.280−0.0340.1232.5510.005***
d20190.232−0.0340.1232.1720.015**
d20200.193−0.0340.1231.8550.032**
d20210.237−0.0340.1232.2070.014**
d20220.258−0.0340.1232.3710.009***

Note(s): ***, ** and * represent level of significance at 1%, 5% and 10%

Source(s): Table created by authors

Localized Moran’s index calculation results

ProvinceIiE(Ii)sd(Ii)zp-value*ProvinceIiE(Ii)sd(Ii)zp-value*
Beijing0.145−0.0340.4420.4070.342Henan−0.084−0.0340.231−0.2170.414
Tianjin−0.295−0.0340.401−0.6490.258Hubei−0.031−0.0340.910.0040.498
Hebei0.098−0.0340.3640.3620.359Hunan−0.141−0.0340.805−0.1320.448
Shanxi0.227−0.0340.340.7690.221Guangdong1.292−0.0340.8361.5870.056*
Inner Monggolia−0.551−0.0340.477−1.0820.140Guangxi0.236−0.0340.241.130.129
Liaoning0.006−0.0340.2120.1890.425Hainan0.311−0.0340.2091.6490.050*
Jilin0.208−0.0340.3990.6080.272Chongqing−0.03−0.0340.9180.0050.498
Heilongjiang0.339−0.0340.2491.5010.067*Sichuan−0.112−0.0340.331−0.2350.407
Shanghai0.941−0.0340.3113.1380.001***Guizhou0.243−0.0340.3050.9110.181
Jiangsu1.182−0.0340.3183.8280.000***Yunnan0.275−0.0340.3270.9460.172
Zhejiang1.194−0.0340.3123.9410.000***Shaanxi0.055−0.0340.2970.3010.382
Anhui−0.107−0.0340.345−0.210.417Gansu0.539−0.0340.2472.3170.010**
Fujian1.270−0.0340.821.590.056*Qinghai0.472−0.0340.4011.2620.103
Jiangxi−0.026−0.0340.3760.0230.491Ningxia−0.052−0.0340.788−0.0230.491
Shandong−0.394−0.0340.421−0.8530.197Xinjiang0.575−0.0340.2592.3560.009***

Note(s): ***, ** and * represent level of significance at 1%, 5% and 10%

Source(s): Table created by authors

Random effects panel tobit test results

(1)
VariablesDegree of coupling coordination D
lninnov0.0738***
(0.001)
lncoord0.0876***
(0.001)
lngreen−0.0013***
(0.000)
open−0.0019***
(0.000)
share0.0028***
(0.000)
trend0.0037***
(0.000)

Note(s): ***, ** and * represent level of significance at 1%, 5% and 10%

Source(s): Table created by authors

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Acknowledgements

This paper was supported by Collaborative Education Project of the Ministry of Education (Big Data Analysis and Tools [Grant No. ZNJZ20231537], Artificial intelligence [Grant No. ZNJZ20231533] and Algorithm Design and Analysis [Grant No. ZNJZ20231527] and the Undergraduate Teaching Engineering Project of Guangdong University of Technology (Exploration and Practice of Talent Cultivation in the Integration of Digital Economy Industry and Education in Local Engineering Colleges [Grant No. 244]). We would like to thank Huawei Technologies Co., Ltd for technical tools and resources. We would also like to thank friends, teammates and family for steadfast support.

Corresponding author

Xiaodong Nie can be contacted at: niexiaodong_DEGDUT@126.com

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