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Open Access

Peer-reviewed

Research Article


A STUDY OF THE COUPLING BETWEEN THE DIGITAL ECONOMY AND REGIONAL ECONOMIC
RESILIENCE: EVIDENCE FROM CHINA

 * Jingshan Gu ,
   
   Contributed equally to this work with: Jingshan Gu, Zongting Liu
   
   Roles Conceptualization, Formal analysis, Funding acquisition, Methodology,
   Writing – original draft, Writing – review & editing
   
   Affiliation School of Economics and Management, Weifang Institute of Science
   and Technology, Weifang, China
   
   ⨯
 * Zongting Liu
   
   Contributed equally to this work with: Jingshan Gu, Zongting Liu
   
   Roles Funding acquisition, Writing – original draft, Writing – review &
   editing
   
   * E-mail: lzt370123@163.com
   
   Affiliation School of Management, Shandong University of Technology, Zibo,
   China
   
   https://orcid.org/0009-0006-7327-0855
   
   ⨯


A STUDY OF THE COUPLING BETWEEN THE DIGITAL ECONOMY AND REGIONAL ECONOMIC
RESILIENCE: EVIDENCE FROM CHINA

 * Jingshan Gu, 
 * Zongting Liu

x
 * Published: January 19, 2024
 * https://doi.org/10.1371/journal.pone.0296890
 * 


 * Article
 * Authors
 * Metrics
 * Comments
 * Media Coverage

 * Abstract
 * 1 Introduction
 * 2 Literature review
 * 3 Research methods
 * 4 Evaluation indicators and data sources
 * 5 Comprehensive development level analysis
 * 6 Coupling coordination performance analysis
 * 7. Discussion
 * 8 Conclusion and recommendations
 * Supporting information
 * References

 * Reader Comments
 * Figures





ABSTRACT

The contemporary economic landscape has placed significant emphasis on the
digital economy and economic resilience, progressively emerging as pivotal focal
points for examining the high-quality development of economic systems. However,
there remains to be more research on several critical topics. This includes the
characteristics of coordinated development between the digital economy and
economic resilience systems and their interdependence. In response, this study
formulates a comprehensive evaluative framework for digital economy development
and regional economic resilience, grounded in the intrinsic mechanisms of both
domains. It conducts a thorough evaluation employing entropy weight-TOPSIS
methodology. Additionally, leveraging coupling theory, a coordination model’s
coupling degree serves as the foundational framework for scrutinizing the
symbiotic advancement of the digital economy and economic resilience, along with
their interdependent nature. The research sample comprises data from 31
provinces and municipalities in China (excluding Hong Kong, Macao, and Taiwan)
from 2011 to 2020. Spatial autocorrelation and Geodetector methodologies probe
the evolutionary traits and driving factors underlying the coordinated
developmental relationship between these two systems. The findings indicate an
upward trajectory in China’s annual comprehensive development index for digital
economy development (from 0.233 to 0.458) and regional economic resilience (from
0.393 to 0.497). The coupling and coordination between the two systems, measured
from 0.504 in 2011 to 0.658 in 2020, demonstrate a consistent growth pattern
with an average annual increase of 3.01%. These levels exhibit continuous
improvement, with comprehensive economic zones manifesting hierarchical results
within the coupling range of [0.5, 0.8]. Notably, agglomeration development
evinces a pronounced spatial positive correlation, while local Moran scattering
points are primarily concentrated in localized migration leaps. Factors such as
foreign-funded enterprises’ total import and export volume, online payment
capability, and fiber-optic cable length greatly influence the coupling
relationship. In contrast, other variables exhibit a lower and more fluctuating
degree of weighted impact. This study establishes a foundation for the
synergistic and effective development of the digital economy and economic
resilience within the Chinese region. Simultaneously, it offers valuable
insights for research of related subjects in global contexts.


FIGURES

    

Citation: Gu J, Liu Z (2024) A study of the coupling between the digital economy
and regional economic resilience: Evidence from China. PLoS ONE 19(1): e0296890.
https://doi.org/10.1371/journal.pone.0296890

Editor: Fuyou Guo, Qufu Normal University, CHINA

Received: August 31, 2023; Accepted: December 18, 2023; Published: January 19,
2024

Copyright: © 2024 Gu, Liu. This is an open access article distributed under the
terms of the Creative Commons Attribution License, which permits unrestricted
use, distribution, and reproduction in any medium, provided the original author
and source are credited.

Data Availability: All relevant data are within the manuscript and its
Supporting Information files.

Funding: The author(s) received no specific funding for this work.

Competing interests: The authors have declared that no competing interests
exist.


1 INTRODUCTION

With the continuous evolution of the Internet, there has been a sustained
progression in the capabilities of information technology pertaining to human
processing, storage, analysis, and transmission. Digital processing techniques
have emerged as they transcend geographical constraints, enabling information
components’ swift and efficient dissemination. This surge in capability has
generated significant market demand for digital technology. In response, the
digital technology domain swiftly expanded into the commercial sphere, heralding
the commencement of the digital economy era [1]. Given the influence of the
multifaceted pandemic and the intricate international scenario, all nations are
now confronted with a new trial in economic development. Economic resilience has
emerged as a pivotal determinant, functioning as the linchpin for ensuring
robust economic advancement, particularly in adverse environments. The
burgeoning digital economy, as an innovative economic paradigm, has infused new
vigor into the economic landscape. It has not only facilitated the evolution of
the economic framework across various dimensions but also established itself as
a vital impetus, empowering economic resilience to confront risks and adapt to
shifts and allocations in resources.

From the start of the 21st century, the global economy has observed heightened
dynamism, and among the innovative economic forms, the digital economy has
exhibited an exponential growth trajectory. As stated in the Digital China
Development Report (2022) released by the China Academy of Information and
Communication Technology (AICT), China’s digital economy attained a scale of
45.5 trillion yuan in 2021, constituting 39.8% of the nation’s GDP. The
establishment of "Digital China" has emerged as a crucial pillar for ensuring
the stability of China’s national economy. The rapid growth of China’s digital
economy is closely linked to its robust material and technological
infrastructure and its vast market resources, which provide the necessary
objective conditions. Additionally, China’s economic solid resilience plays a
pivotal role by fostering an inclusive and open development model supported by
policies that drive digital transformation [2]. Over the past few years, China’s
economic system has showcased outstanding resilience when confronted with
internal and external shocks. It possesses a stable economic foundation and the
capacity to withstand and adapt to risks, ensuring the achievement of
high-quality economic development objectives. With ample room for buffering on
both the supply and demand sides, China’s economic resilience continues to
improve, presenting significant potential for sustainable development. The
country boasts advantages across various industrial chains, particularly
emerging high-end industries. Furthermore, a conducive environment exists for
developing new types of businesses, including information and communication
technology, e-commerce, and the Internet economy [3]. As the world’s most
promising market, China possesses diverse and abundant market demand and
resilience, making it highly attractive for resource factors. Innovative
technologies are critical in developing the digital economy, driven by market
demand, which fosters innovation, facilitates technological advancements, and
stimulates industry growth. Nevertheless, despite the rapid growth of the
digital economy and the systematic improvement of economic resilience, the
coordination and promotion relationship between these two domains still needs to
be clarified. Exploring the development path under a synergistic mode,
identifying the evolving characteristics of the stage of synergistic development
between China’s digital economy and economic resilience, and examining the
geopolitical disparities are all crucial aspects to consider. Addressing these
aspects is crucial for keeping China’s economic development on track within the
new typical economic environment.

Similarly, on a global scale, the digital economy, as a new economic form,
aligns more closely with the characteristics of current economic and social
development stages. Moreover, the core connotation of economic resilience
provides the necessary support for developing the digital economy. The fluidity
of the digital economy, capable of transcending geographical limitations,
directly impacts the adaptability and adjustment capabilities within regional
economic resilience. Conversely, this influence will also offer support in the
process of collision and integration between the digital economy and traditional
economy, accelerating the assimilation of different economic forms [4].
Additionally, the innovative value brought about by the digital economy is bound
to exhibit marginal increments, exerting a potent permeation effect on the
emphasis placed by regional economic resilience on innovation and transformative
capacity, thereby providing an entirely new impetus. As a result, the driven
economic resilience will feed into a more favorable environment for innovation,
exerting a counterforce on the digital economy.

In summary, the two systems interact and exhibit interdependence in their
development paths. The coordinated development of both the digital economy and
regional economic resilience constitutes a crucial subject for global
governance. However, current research regarding the digital economy and regional
economic resilience predominantly focuses on the changes in individual system
development and their respective driving forces. There needs to be more
perspectives that elucidate the performance characteristics of coordinated
development under the mutual influence of the digital economy and regional
economic resilience. Additionally, a comprehensive evaluation system to assess
the changing paths of the coordination relationship between the two systems
remains to be clarified. Methods for calculating the performance of interactions
between the two systems are still being explored, posing challenges to this
study. Nevertheless, given the geopolitical development disparities, identifying
the phased characteristics and evolutionary process of the coordinated
development between the digital economy and regional economic resilience and
discovering practical principles conducive to the joint governance of both
systems holds significant importance for maintaining economic order and
stability in different economic entities.

In conclusion, as global economic challenges continue to escalate, the discourse
surrounding the digital economy and economic resilience is gradually becoming a
focal point of scholarly attention. Seizing the new opportunities in digital
economic development, contemplating the coordinated development of economic
resilience, and the healthy, high-quality development of the digital economy
constitute a crucial aspect of economic advancement. However, there is a need
for further refinement in understanding how to study the inclusive and mutually
beneficial system direction between the two, subsequently enhancing methods for
improving economic order. To address this, this paper focuses on the
collaborative development shifts between China’s digital economy and economic
resilience, observes the coupling performance and path evolution mechanisms of
the two systems under geopolitical disparities, and provides guidance for the
enhancement and upgrading of regional economic systems. It also holds reference
value for innovative economic forms and enhanced competitiveness in various
countries.

This paper’s main contributions lie in several key aspects. Firstly, the current
research on the relationship between digital economic development and regional
economic resilience predominantly focuses on the unilateral impact of digital
economic development on regional economic resilience. There is limited study on
the synergistic effects and development characteristics of both. This paper
innovatively shifts the focus to the collaborative development paths of the two
systems and identifies their distinctive features. Secondly, a comprehensive
evaluation index system for both the digital economy and regional economic
resilience is constructed to reflect the developmental capabilities of both
systems. Thirdly, guided by coupling theory, we employ a coupling coordination
model to measure the interaction between the two systems, categorizing them
based on different coupling coordination levels. This, combined with
temporal-spatial distribution discussions, provides a reference for selecting
research methodologies. Lastly, using Chinese regions as samples, we conduct
spatial autocorrelation analysis and Geodetector to explore the characteristics
of coordinated development from multiple perspectives. This enhances the
research’s purpose and provides a basis for the efficient collaborative
development of the digital economy and economic resilience in Chinese regions.
Furthermore, it is an enlightening reference for research on similar themes in
other regions worldwide.


2 LITERATURE REVIEW

Tapscott (1996) is credited with introducing the concept of the digital economy,
emphasizing the description of the digital technology industry and the
productivity of the digital industry [5]. As research progressed, the digital
economy has become a new economic form, succeeding the agricultural and
industrial economies. The widely accepted definition in economics acknowledges
the digital economy as a diverse and open economic structure centered around
data as its fundamental element. The digital economy primarily depends on modern
information networks as its main conduit and digital technological innovation as
its driving force. This is achieved through the implementation of various new
models and technologies within the realm of digitization [6]. Many academics
have researched the digital transformation of the global economy and the quest
for high-quality economic development within the context of the rapidly
expanding digital economy worldwide [7–9]. These studies explore the impact of
the digital economy on established economic sectors and investigate its
potential to foster high-quality businesses. Both the "Fourteenth Five-Year
Plan" and Vision 2035 of China prioritize fostering the overall growth of the
digital economy. The objectives include accelerating the balanced expansion of
the digital economy, facilitating its deep integration with the real economy,
and nurturing it to become a strategic pillar for China’s high-quality economic
development. The study of the digital economy goes beyond conventional logic,
standards, and qualitative and quantitative research techniques in contrast to
the traditional economy [10]. In order to address the shortcomings of
conventional research methods in economic studies, it uses tools including
extensive data analysis, digital modeling, and high-dimensional data [11]. The
advent of numerous high-dimensional means and tools has also aided the
improvement and advancement of conventional econometric techniques. These new
tools make it possible to concentrate on data analysis and make it simpler to
build accurate prediction models with great adaptable covariates [12]. The
significant influence of the digital economy system’s performance has prompted
scholars to investigate the radiation relationship between the digital economy
and the traditional economic theory system. Researchers are examining the impact
of the digital economy on the real economy, which encompasses the study of the
"dandelion effect" [13]. Furthermore, researchers are examining ways to enhance
regional economic resilience, increase resilience, and enhance the quality of
development paths. Considering whether the economic environment can stimulate
new economic growth momentum [14]. The growth of the digital economy has also
produced a "long tail effect" and other consequential impacts on the development
of local economies [15]. These issues are crucial for studying the theoretical
framework of the digital economy and contribute to constructing a structural
framework. Nevertheless, despite scholars’ extensive research, there has been
relatively limited analysis from the perspective of the interaction between the
digital economy and regional economic resilience. The research structure in this
area needs further expansion, and the relevant system requires improvement to
address these gaps.

The concept of regional economic resilience finds its roots in the study of
ecological resilience, engineering resilience, and evolutionary resilience [16].
In the field of regional economics, early scholars, including Reggiani (2002),
introduced this concept [17]. The early conceptualizations were derived from the
emulation of natural ecosystems, applying the notion of "resilience" to
illustrate the system’s ability to undergo self-repair and bounce back from
external shocks [18]. Scholars have made further advancements in their research
on regional economic resilience as the understanding of the concept has
deepened. In particular, the ideas of disequilibrium and evolutionary theory in
Evolutionary Economic Geography (EEG) have significantly shaped this research
[19]. The theoretical framework and empirical studies on regional economic
resilience have resulted in various viewpoints. Martin et al. (2016), known for
their work on adaptability theory, propose that regional economic resilience
lies in its ability to interact with complex systems such as the market,
competition, and the environment [20]. Enabled by this resilience, the regional
economic system can recover from shocks and readjust its development path,
ultimately generating a new trajectory better adapted to its growth. Through
research generalization, Martin et al. (2016) emphasize the continuity of
regional economic resilience from four dimensions. Drawing on the adaptive cycle
model, Simmie et al. (2010) categorize the evolutionary perspective of the
regional economy into four stages: restructuring, development, maintenance, and
release [21]. Emphasizing the dynamic nature of the regional economy, this
framework highlights its capacity to adapt and transform as time progresses.
Boschma et al. (2013) and other scholars have examined enhancing regional
economic resilience by exploring new paths [22]. Utilizing spatial measurements,
they analyze regional economic resilience from various perspectives, focusing on
the intrinsic mechanisms that drive the update and development of regional
industries within the context of the new trajectory. The recovery of economic
production within China’s regions has become a crucial issue in the
post-epidemic era. Consequently, there has been a significant surge in the study
of regional economic resilience in China. Chinese scholars have proposed
research frameworks and summaries based on their exploration of international
studies on regional economic resilience [23,24]. Additionally, they have
conducted empirical analyses and engaged in discussions on regional economic
resilience from various perspectives. In their study, Song et al. (2022)
analyzed China’s economic resilience characteristics, traced the evolution of
this resilience, and explored the influencing factors behind its effects [25].
Researchers have also concentrated on understanding regional economic resilience
mechanisms, a term with different spatial and temporal characteristics. The
"region-economy-system" structure has been studied by several academics [26,27].
Evolutionary economic geography has extensively investigated the spatial
spillover impact of regional economic resilience and the variables that
influence it [28]. The recent development of research on regional economic
resilience in China has led to differing views concerning establishing an
indicator system. Most studies still rely on conclusions drawn from developed
countries, often overlooking the heterogeneity among different regions within
China, which is influenced by the external environment.

In conclusion, the current research on developing the digital economy and
regional economic resilience has yielded some preliminary conclusions. However,
there is still a need to strengthen the investigation of the cross-directional
relationship between these two themes. The existing literature predominantly
focuses on the one-sided impact of the digital economy on regional economic
resilience [29,30], with limited research investigating how digital economic
development influences changes in economic resilience and the coordinated role
of these two factors. In the post-epidemic era, delving into the synergistic
differences in regional economic resilience amid rapid digital economy
development and assessing the alignment between economic resilience and the
digital economic system in various regions can offer valuable guidance for
countries’ economic recovery endeavors.

Therefore, building upon existing research, this study integrates the digital
economy and economic resilience systems. It selects the 31 provinces in China
(excluding Hong Kong, Macao, and Taiwan) as research samples for 2011–2020. The
study analyzes the coupling relationships by constructing a comprehensive
evaluation index system for both systems. Additionally, it employs spatial
autocorrelation models and Geodetector to address the following questions: (1)
What is the comprehensive development trend of both digital economic development
and regional economic resilience in China? (2) How does the two systems’
temporal variation in coupling coordination manifest? What distinctive
characteristics exist among different regions? (3) Does China’s level of coupled
development exhibit spatial correlations, and what are its evolutionary
features? (4) What significant differences exist in the impact of different
driving factors on the changes in coupling coordination between the two systems?
Investigating these questions will draw relevant conclusions, and corresponding
discussion suggestions will be put forward.


3 RESEARCH METHODS


3.1 ENTROPY WEIGHT-TOPSIS METHOD

Research on indicator empowerment methods for evaluating the digital economy and
economic resilience can be categorized into subjective and objective approaches.
Subjective empowerment commonly employs the Analytic Hierarchy Process (AHP)
method. However, the final results of subjective empowerment may need more
practical considerations and tend to be subjective. This paper employs the
entropy value method for objective indicator empowerment to ensure accuracy and
scientific rigor in the evaluation results and reduce the influence of solid
subjectivity in the empowerment process. This approach minimizes interference
from human factors and enhances the reliability of the empowerment process.

After assigning weights to each indicator of the digital economic development
and regional economic resilience systems, the TOPSIS calculates the
comprehensive development index for both systems. This method involves computing
the positive and negative ideal solutions for the evaluation index system. A
relative fitness degree is determined by comparing the relative distances
between alternative solutions and the positive and negative ideal solutions.
This fitness degree is then used to rank and evaluate the excellence of the
comprehensive development index [31,32]. The steps for calculating the
comprehensive development index for both systems are as follows:

 1. Standardize the data of each indicator of digital economic development and
    regional economic resilience system.
 2. The information entropy and weight of each indicator are calculated using
    the entropy method.

(1)

Where Ej denotes the entropy value of the jth indicator, n is the number of
evaluation objects, and Xij denotes the value of the jth indicator of the ith
object after standardization.

(2)

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

(3) Build a weighted matrix and calculate the positive and negative deviation
square method as well as the positive and negative ideal solutions using the
Euclidean distance [33].

(3)(4)(5)

In this context, Zij represents the weighted matrix value for the jth indicator
of the ith object, while Z+ and Z− denote the positive and negative ideal
solutions for the jth indicator, respectively.

(4) Calculate the distance and proximity of each evaluation object to the
positive and negative ideal solutions [34].

(6)(7)(8)

and represent the distance of the ith evaluation object to the positive and
negative ideal solutions, respectively. Ri signifies the relative proximity of
the ith solution to the ideal solution. These values are used as the
corresponding values for both systems’ comprehensive evaluation development
index.


3.2 COUPLING COORDINATION MODEL

The concept of coupling originates from physics and is employed to quantify the
interactions and influences between two or more systems. Introducing the
coupling coordination model makes it possible to reflect the degree of coupling
coordination between digital economic development and regional economic
resilience. This model enables the assessment of the collaborative development
situation between the two systems. The formula is as follows [35]: (9) (10) (11)

The degree of coupling is denoted by the variable C, whereas the comprehensive
evaluation of digital economic development and regional economic resilience is
represented by X and Y, respectively. T denotes the comprehensive coordination
index between digital economic development and regional economic resilience
systems. In this context, the weights assigned to the digital economic
development and regional economic resilience systems are typically considered
equally important. Therefore, the weight value is set as α = β = 0.5. The
variable D represents the degree of coupling coordination, which reflects the
coupling level between the two systems. Scholars have classified the coupling
coordination degree [36–38] into eight categories, further divided into three
levels, as illustrated in Table 1.

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Table 1. Criteria for classifying the degree of coupling coordination.



https://doi.org/10.1371/journal.pone.0296890.t001


3.3 SPATIAL AUTOCORRELATION MODEL

Spatial autocorrelation examines regional variations and correlations by
measuring spatial location, aggregation, and heterogeneity. The spatial
autocorrelation model encompasses global spatial autocorrelation and local
spatial autocorrelation.

3.3.1 GLOBAL SPATIAL AUTOCORRELATION.

The global Moran’s I statistic is employed for measurement to analyze the
overall spatial distribution of a specific attribute in the research area. The
expression is as follows [39,40]: (12)

In this context, Ⅰ represents the global Moran’s index, where xi and xj denote
the coupling coordination degree between digital economic development and
regional economic resilience in regions i and j, respectively. represents the
mean value of coupling coordination for all research areas. Additionally, n
indicates the total number of study regions, and wij represents the spatial
weight matrix.

3.3.2 LOCAL SPATIAL AUTOCORRELATION.

The local Moran’s I is employed to measure the coupling coordination degree
value in each local space within the study area, considering the spatial
correlation between this region and neighboring regions [39,40]: (13)

The meaning of each index in the formula is the same as the global Moran’s I
index.


3.4 GEO-DETECTORS

Geodetector is a statistical method of driver detection analysis of drivers
based on spatial dissimilarity, yielding a q-value reflecting the consistency of
the spatial distribution pattern of the independent and dependent variables with
the following formula: (14)

Where h = 1,…,i denotes the stratification of variable Y or factor X, Nh and N
are the number of cells in stratum h and the whole, respectively, and σ2 are the
variance of stratum h and the overall Y value. SSW and SST are the intra-layer
variance and the overall total variance. q is taken as [0,1]; the closer q is to
1, the stronger the influence of factor Y and variable X, and the weaker the
opposite is.


4 EVALUATION INDICATORS AND DATA SOURCES


4.1 EVALUATION INDICATOR SYSTEM

Scholars have yet to reach a consensus on the evaluation index system for the
digital economy, and an internationalized evaluation system for the digital
economy has yet to be established. However, research on the evaluation system
for the digital economy should prioritize practicality. Therefore, this paper,
after considering the embedded relationship between the digital economy and
various aspects of the economy, society, and daily life, as well as the current
state of innovation and development in the digital economy, incorporates
findings from the research on the evaluation system of digital economy
development by Zhao et al. (2020) [41], Shen et al. (2022) [42], and Bai et al.
(2021) [43]. It selects evaluation criteria from three dimensions:
innovation-driven, level of development, and industry enhancement, encompassing
nine influential elements. Regarding the current economic resilience evaluation
index system, research can be broadly categorized into two approaches. The first
approach, proposed by Martin et al. (2019), introduces a regional economic
resilience evaluation system [44–46]. This system calculates the regional
economic resilience index by selecting a single indicator that demonstrates
sensitivity to economic resilience, such as GDP, GDP growth rate, data on the
unemployed population, etc. This method is relatively mature and has been widely
adopted by many scholars. The second approach takes a comprehensive perspective
by considering various representative factors influencing economic resilience in
the region. This involves establishing a complete indicator evaluation system
and researching regional economic resilience using a combination of multiple
indicators. This paper argues that while Martin et al.’s proposed evaluation
system for the economic resilience of regional economies is highly practical and
has been widely utilized in empirical research, a single indicator cannot fully
capture the complexity of China’s economic resilience. Moreover, a significant
disparity in economic openness exists among different regions, making reliance
on a single indicator overly simplistic. Therefore, considering China’s economic
development characteristics, we opt for multiple indicators to establish a
comprehensive indicator system for evaluating economic resilience within regions
from various dimensions. We refer to the research on regional economic
resilience systems by Li et al. (2022) [47], Zhao et al. (2022) [48], and Zhu et
al. (2020) [49] to establish a comprehensive evaluation system comprising
fifteen elements under three quasi-measurement layers: defense and recovery
Adaptation and conditioning, and innovation and transformation.

Building upon the insights above, a comprehensive indicator evaluation system is
formulated to assess the interaction mechanism between economic resilience and
digital economy development systems. The rationale for selecting specific
indicators is elucidated based on the referenced literature, and detailed
meanings and explanations are presented in Table 2.

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Table 2. Comprehensive evaluation index system of digital economy development
and regional economic resilience.



https://doi.org/10.1371/journal.pone.0296890.t002


4.2 DATA PROCESSING

The raw indicators were standardized to eliminate scale differences, considering
variations in the impact of different hands on the two systems. These indicators
were then categorized into positive and negative groups based on their
respective contributions to the system. The following processing method was
employed: (15) (16)

Where μij is the normalized value; xij is the initial value of the jth indicator
in year i; max xj is the maximum value of the jth indicator; min xj is the
minimum value of the jth indicator.

The industrial structure rationalization index is represented by the modified
Thiel index, which is an index originally proposed by Theil to measure regional
income disparity or inequality. This index is widely used in studies [50] to
assess the level of rationalization in the industrial structure. The formula for
calculating the modified Thiel index is as follows: (17)

Where Yi represents the total output value of the ith industry, Li denotes the
total employment of the ith industry, Y denotes the total output value, and L
means the total employment. When TL equals zero, the economy is in equilibrium,
and the structure is considered more irrational as the TL value increases.


4.3 DATA SOURCES

Given the concepts of digital economy and regional economic resilience have
emerged relatively recently, it is crucial to acknowledge that early statistics
and data might need more completeness. This study focuses on the 31 provinces,
autonomous regions, and municipalities directly under the central government of
China. The selected data for analysis encompasses the years 2011 to 2020 and
includes pertinent indicators of digital economic development and regional
economic resiliency.

The data used in this study are derived from several official publications,
which include the China Statistical Yearbook, China Urban Statistical Yearbook,
China Electronic Information Industry Statistical Yearbook, China Science and
Technology Statistical Yearbook, and provincial statistical yearbooks from 2011
to 2020. Additionally, the digital financial inclusion index data are obtained
from the Digital Finance Research Center of Peking University and the open
research platform on digital finance provided by the Ant Financial Services
Group. The interpolation method is employed to address any missing data,
allowing for the estimation of values for the missing data points.


5 COMPREHENSIVE DEVELOPMENT LEVEL ANALYSIS

In this study, the data processing of multiple indicators related to digital
economic development and regional economic resilience in China’s comprehensive
development relies on the entropy weight-TOPSIS method. This analysis enables us
to examine the composite development index’s trend evolution between 2011 and
2020. Fig 1 depicts the results.

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Fig 1. Comprehensive development index trend.



https://doi.org/10.1371/journal.pone.0296890.g001

From the data illustrated in Fig 1, it is clear that China’s digital economy
witnessed substantial growth in its overall level of advancement from 2011 to
2020. The general development index has exhibited significant growth, increasing
from 0.2329 in 2011 to 0.4580 in 2020 at a rate of 49.15%. The development index
has consistently demonstrated sustained positive growth throughout the research
period, indicating a promising trend in China’s digital economy’s development
process. This positive trend highlights the strong driving force of innovation,
continuous advancements in the degree of development, and a significant increase
in the digital industry’s production capacity. Concerning regional economic
resilience, the 2011–2020 progress trend curve exhibits a steady ascent. In
contrast to the "leapfrog" increase in the digital economic development index,
regional economic resiliency has grown 12.1% more slowly. Regional economic
resilience reflects how the regional economy evolves under various external and
internal factors. The continuous increase in economic resilience indicates the
growth of the economic system. As the second-largest economy in the world, China
maintains a substantial economic volume and reliable economic support, which
creates favorable conditions for further economic growth. It is worth noting
that the Economic Resilience Index experienced a slight decline in 2020.
Considering the backdrop of the COVID-19 outbreak in China during this period,
the economic system demonstrated resilience by making continuous adjustments to
ensure a successful recovery from the crisis and maintain self-stability [51].
However, various elements that provide resilient operation were somewhat
hindered by the crisis’s impact, thus limiting the development of economic
resilience and causing a downturn.


6 COUPLING COORDINATION PERFORMANCE ANALYSIS


6.1 TIME-SERIES CHARACTERISTICS

A coupled coordination model is utilized to calculate the comprehensive
development index of digital economic development and regional economic
resilience, enabling the analysis of interaction between the two research
targets. This calculation aims to identify the coupled and coordinated
time-series characteristics of China’s 31 provinces and municipalities
concerning digital economic development and regional economic resilience between
2011 and 2020.

Throughout the research period, the coupling degree between China’s digital
economic development and regional economic resilience has consistently remained
above 0.8, as determined using Formula (9). When the coupling degree approaches
1, it indicates a convergence to an ordered state, demonstrating a positive and
significant interaction between the two systems. Formula (11) is also applied to
determine the degree of coupling coordination between the two systems. The
changes in the national coupling coordination degree across the study period are
matched to the corresponding intervals based on the predefined division
standards established in this study. Due to space limitations, the 2011, 2015,
and 2020 calculations are selected for visualization using ArcGIS10.2 software,
as depicted in Fig 2.

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Fig 2. Coupled coordinated horizontal distribution evolution (the administrative
boundaries of Provincial-level are republished from [52] under a CC BY license,
with permission from the resource and environment data centre, original
copyright 2022).



https://doi.org/10.1371/journal.pone.0296890.g002

The coupled coordination degree between China’s digital economy development and
regional economic resilience has consistently improved throughout the study
period. The least favorable performance observed during this entire process was
only mild disorder, without any instances of moderate disorder or severe
disorder. Upon calculating the average annual growth rate (refer to Table 3), it
is evident that the degree rose from 0.504 in 2011 to 0.658 in 2020, signifying
an average yearly growth rate of 3.01%. This suggests a notable mutually
reinforcing effect within the development system of China’s digital economy and
regional economic resilience. Upon closer examination of the results of coupling
coordination degree calculations, it is worth noting that Xinjiang and Tibet
demonstrated the lowest values during the research period, registering at 0.455.
These values placed them on the edge of the disorder level. Upon further
investigation of the sample size for this lowest value, it was found that in the
province of Tibet in 2011, the performance was 0.313, indicating a state of mild
disorder. However, the growth rate was 11.14%, signifying an ongoing improvement
in the coordination effect between the digital economy and regional economic
resilience. An observation of the growth rates in coupling coordination degree
for other provinces and municipalities reveals that the remaining performances
were positive apart from isolated negative values observed towards the end of
the study period. This indicates that during the study period, the coupling
coordination of development between the digital economy and regional economic
resilience shifted from disorder development to transitional development and
Coordinated development, displaying an upward trajectory. It underscores the
mutually beneficial dynamic between developing China’s digital economy and
regional economic resilience.

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Table 3. Comprehensive index proportion and coupling coordination degree growth
rate changes.



https://doi.org/10.1371/journal.pone.0296890.t003

Fig 2 also shows that the coupling and coordination levels between the digital
economic and regional economic resilience vary annually across provinces. To
expound upon this phenomenon, we further calculate the ratio of the development
indices for the digital economy and regional economic resilience. When the ratio
equals 1, it indicates synchronous development; if it is less than 1, it
signifies that the digital economy lags in comparison to the resilience
development of the regional economy. By scrutinizing the index weights (see
Table 3), we discern that the mean proportions for each province over the survey
period have risen from an initial value of 0.589 to a final value of 0.976.
Additionally, the ratios for different regions and cities have all experienced
an augmentation, ultimately approaching 1. This suggests a transformation from
the scenario where the digital economy lags in developing economic resilience to
a state of synchronized development. This indicates that under the backdrop of
orderly development in regional economic resilience, an advantageous environment
and support system have been provided to enhance the digital economy.
Simultaneously, the digital economy, relying on the resilience of the regional
economy, exerts a more robust impetus. With the rapid advancement of the digital
economy, a virtuous cycle of synchronous effects with regional economic
resilience is generated, leading to a continuous improvement in the coupling and
coordination degree between the two systems in each region.

Analyzing the chronological changes, it should be emphasized that in 2011, the
four areas of Beijing, Shanghai, Guangdong, and Jiangsu displayed the highest
degree of coupling coordination, indicating a primary level of coordination
between the digital economy and regional economic resilience in these regions.
This shows that the two systems in these areas have developed an appropriate
degree of coordination and interaction, with each system having a favorable
influence on the other. In contrast, the western and northeastern regions have
predominantly shown mild disorder and edge disorder. At the same time, the
central part of the country has mainly achieved a barely coordinated status in
terms of coupling coordination. In 2015, China experienced significant
development in the distribution of the coupling coordination degree. While
Beijing, Shanghai, and Guangzhou advanced to the intermediate coordination
stage, continuing to remain a key factor influencing the overall development
direction. In the western region, Xinjiang, Tibet, Qinghai, Yunnan, and Ningxia
remained at the edge disorder stage. However, Inner Mongolia, Gansu, and Shanxi,
which were initially in the same echelon, experienced rapid improvement in their
degree of coupling coordination, reaching the stage of barely coordinated. The
eastern coastal region, the Yangtze River Basin, and some central provinces
achieved a primary coordination stage in their coupling coordination degree.
This transition reflects a shift from transitional development to coordinated
development. In 2020, the coordination degree of linkage between the two systems
showed further advancement. By this point, a strong correlation between the
digital economy’s growth and local economies’ resilience was demonstrated as
more than two-thirds of China’s provinces had entered the stage of coordinated
development. Beijing, Shanghai, and Guangzhou achieved high-level coordination
in their development, while Shandong, Jiangsu, Zhejiang, Hubei, and Sichuan
reached the intermediate-level coupling coordination development stage.
Observing the inter-regional differences in coupling coordination degree, a
clear "east high, west low" ladder-type distribution is evident. In 2011, the
distribution showed characteristics of barely coordinated, edge disorder, and
mild disorder from east to west. By 2015, the distribution shifted to primary
coordination, barely coordinated, edge disorder, and mild disorder. In 2020, the
distribution further evolved to intermediate coordination, primary coordination,
and barely coordinated from east to west.


6.2 CHARACTERISTICS OF LOCATIONAL DIFFERENCES

China’s digital economic development and regional economic resilience exhibit a
distinct distribution of coordination from east to west, displaying notable
agglomeration characteristics. To further examine how the two systems’ spatial
connectivity and coordination are distributed, the concept of the eight
integrated economic zones, as proposed by Hong [53], is introduced. This
division allows for regional analysis of the 31 provinces, enabling a
comprehensive and concentrated investigation of how the coupling coordination
degree varies within each agglomeration. Based on the characteristics of the
coupling coordination degree, this method facilitates the development of
regionally tailored policies.

Through a comparative analysis of the coupling and coordination trends in each
comprehensive economic zone with the national average, we categorize the eight
regions into three levels: leading, average, and lagging. It is worth noting
that the general trend in all regions is steady improvement, with most falling
within the [0.5–0.8] range. Further details regarding the varying trends among
regions of different levels are illustrated in Fig 3.

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Fig 3. Development trend of coupling coordination degree at different levels.



https://doi.org/10.1371/journal.pone.0296890.g003

The leading region encompasses the eastern coastal region, the northern coastal
region, and the southern coastal region, with an average coordination coupling
value of 0.666. Over the study period, the eastern coastal region exhibits the
highest level of coupling coordination in the eight major economic complexes.
Its coupling falls primarily within the range of primary and intermediate
coupled development, representing a stage of coordinated development. Moreover,
this region’s degree of coordination coupling significantly surpasses other
comprehensive economic zones. Within the eastern coastal economic zone,
Shanghai, Jiangsu, and Zhejiang demonstrate higher digital economy development
indices, resulting in a narrower gap in regional economic resilience among them.
This, in turn, fosters a robust interaction between the digital economy
development system and the regional economic resilience system. Following
closely in second and third place are the northern and southern coastal regions,
characterized by development intervals ranging from barely coupled to primary
coupled coordination. Taking a comprehensive view of the top three regions, it
is evident that they are all coastal areas. Their development processes exhibit
a relatively balanced similarity, with geographic location and openness offering
distinct advantages for advancing the digital economy development system,
resulting in significant progress. Simultaneously, the concentration of talent
and industries further bolsters the innovation and transformative capabilities
of the economic resilience system. These regions exemplify both systems’
harmonious coupling, interaction, and coordination, fostering mutually
supportive development by reasonably allocating quality resources.

The second level encompasses the average regions, including the middle reaches
of the Yangtze River, the Northeast region, and the middle Yellow River. The
mean value of coupling coordination in these regions is 0.586. Compared to the
national average level of coupling coordination, the middle reaches of the
Yangtze River are above average. Within this region, the coupling coordination
is at a state of barely coupled coordination to primary coupled coordination.
The middle reaches of the Yangtze River region are influenced by the radiation
effects of the eastern and southern coastal areas regarding its location. It
receives the spillover of resources brought about by the agglomeration effect of
the digital economy industry. And this region complements the digital economy’s
development downstream of the Yangtze River. The digital industry’s orderly
development and relatively stable resilience support regional economic
resilience. It’s important to note that by the end of the research period, the
coupling coordination level in the middle reaches of the Yangtze River showed a
decline. This period coincided with the outbreak of COVID-19 in Wuhan,
indicating that the virus’s impact led to a disruption in the coordination
between the digital economy and regional economic resilience. The Northeast
comprehensive economic region has some overlaps with the national average
level—the coupling interval ranging from edge disorder to barely coupled to
primary coupled coordination. As a substantial base for China’s secondary
industry, the Northeast comprehensive economic region has experienced slow
industrial structure adjustments and faces challenges in reform. This has led to
the squeezing of the development of digital industries within the region, which
is difficulty in obtaining quality investment support, thereby limiting their
level of development. However, the region is characterized by large-scale
state-owned enterprises, providing a certain level of support for regional
economic resilience. Additionally, the innovation of internal digital systems
within these enterprises has also propelled the development of the digital
economy. The coupling coordination in the middle reaches of the Yellow River
falls into edge disorder, barely coupled coordination, and primary coupled
coordination. Although it did not stand out compared to other regions in terms
of coupling development levels during the research period, it achieved the
highest growth rate in coupling coordination at 28.8% among the eight
comprehensive economic regions. In the initial stages of the research period,
Inner Mongolia, Shanxi, and Shaanxi in the Yellow River middle reached the
economic region needed to catch up to the national level regarding digital
economic development. However, with the deepening of the "Central Rise" strategy
and the introducing of the high-quality development strategy for the Yellow
River basin, the economic resilience of the Yellow River middle reaches economic
region has been effectively enhanced. This, in turn, has driven the development
of the digital economy, leading to coordinated progress between the two systems
and a rapid increase in coupling coordination.

The third level comprises the lagging regions, including the Southwest and
Northwest regions, with a mean coupling coordination of 0.516. The coupling
development range in the Southwest comprehensive economic region falls into edge
disorder, barely coupled coordination, and primary coupled coordination. It
achieved a coupling coordination growth rate of 25.7%, second only to the Yellow
River middle reaches economic region. The rapid development of the
Chengdu-Chongqing economic zone has played a driving role in the entire
comprehensive economic region. Moreover, being conveniently located for
cooperation between the upper and lower reaches of the Yangtze River and
benefiting from the construction of the New Silk Road, the Southwest
comprehensive economic region has gained new vitality. The development
environment for the digital economy continues to improve, absorbing
transformation ideas from the eastern regions. This has enhanced the region’s
innovative and transformative capabilities of economic resilience. The level of
coupling development between the two systems in the northwest region
significantly lags behind other regions. The coupling coordination falls within
the range of mild disorder to Barely coordinate, with the study period failing
to break through to the coordinated development stage. Most areas within this
integrated region experience uneven economic development, characterized by weak
adaptive and regulatory capacity and limited acceptance of external resources.
The regional economic resilience and the level of digital economy development in
this region are below the national average. The scarcity of digital enterprises
hampers the formation of an effective digital transformation, imposing
significant constraints on the digital development level of Northwest China. As
a result, it is crucial to carefully consider local conditions and prioritize
the development of industries that leverage regional characteristics. By
stimulating economic vitality, we can strengthen the region’s economic
resilience and, in turn, promote the advancement of the digital economy through
a dual-system mechanism. It is imperative to avoid adopting a catch-up strategy
unquestioningly but rather tailor approaches to the specific circumstances of
the region.

To further explore the overall distribution dynamics and evolutionary patterns
of coupling coordination in the comprehensive economic regions, the kernel
density estimation method is employed to create dynamic evolution trend maps of
the coupling coordination between digital economic development and regional
economic resilience in 2011, 2015, and 2020 (see Fig 4). The analysis examines
the distribution trends, polarization tendencies, and distribution
extensibility. Fig 4. shows that the coupling coordination degree’s kernel
density curve’s center consistently shifts to the right. The data depicts a
consistent upward trend in the central range of the coupling coordination level
between regional economic resilience and digital economic development across
China’s eight Comprehensive Economic Zones (CEZs). Moreover, the change interval
of the kernel density curve decreases, suggesting a narrowing of the overall gap
in the coupling coordination degree. The curve’s "tip" flattens, and its "peak"
rises, showing a widening gap in the degree of coupling coordination between the
eight comprehensive economic zones. As time progresses, the density curve in
2020 begins to display a trailing phenomenon, indicating a tendency to form a
side peak. This shows that, among the eight comprehensive economic zones, there
is a growing disparity in the degree of linkage coordination between the regions
with a high level of digital economy development and those with a low level.
Regions with a coupling coordination degree of 0.7 or higher tend to be found in
Intermediate coordination, leading to a polarization phenomenon.

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Fig 4. Distribution curve of kernel density in the eight integrated economic
zones.



https://doi.org/10.1371/journal.pone.0296890.g004


6.3 CHARACTERISTICS OF THE SPATIAL EVOLUTION

6.3.1 GLOBAL SPATIAL AUTOCORRELATION ANALYSIS.

The degree of coupling coordination between digital economy development and
regional economic resilience across the 31 provinces and municipalities was
measured using the global Moran’s I index. During the study period, Table 4
shows that the global Moran’s I index for the coupling coordination degree is
greater than 0 for all provinces and municipalities. Moreover, the Z value
exceeds 1.96, and the P value is less than 0.01. These findings indicate the
rejection of the original hypothesis "there is no spatial autocorrelation" at a
significant level of 1%. The P-value of less than 0.01 further confirms the
rejection of the initial hypothesis of "no spatial autocorrelation" at a 1%
significant level. This suggests the presence of positive spatial correlation,
explicitly indicating the spatial characteristics of "areas with high values of
coupling coordination degree being surrounded by areas with high values" and
"areas with low values of coupling coordination degree being surrounded by areas
with low values." Upon observing the changes in Moran’s I index, it is apparent
that from 2011–2020, the index is mostly higher than 0.3, except for 2016, when
the value is lower at 0.285. This suggests that there are evident clustering
characteristics in the coupling coordination of the growth of the digital
economy and regional economic resilience. The clustering level starts low and
then increases, displaying a "U" type distribution.

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Table 4. Global Moran’s I Index.



https://doi.org/10.1371/journal.pone.0296890.t004

6.3.2 LOCAL SPATIAL AUTOCORRELATION ANALYSIS.

This work used the local Moran’s I index to analyze the spatial correlation
variations among areas in the 31 Chinese provinces and municipalities. during
the study period. Moran scatter plots were created using the 2011, 2015, and
2020 results, highlighting the coupling coordination characteristics of the
different regions concerning themselves and their neighboring regions. The
results of these plots are displayed in Fig 5.

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Fig 5. Local Moran’s I scatter distribution of the coupled coordination of
digital economy development and regional economic resilience in 2011, 2015 and
2020, the Chinese provinces in Fig 5 are represented by the codes of the "Rules
for the Compilation of Statistical Division Codes and Urban and Rural Division
Codes (2022)" issued by the National Bureau of Statistics (NBS), which are as
follows:11 Beijing, 12 Tianjin, 13 Hebei, 15 Inner Mongolia, 21 Liaoning, 22
Jilin, 23 Heilongjiang, 31 Shanghai, 32 Jiangsu, 33 Zhejiang, 34 Anhui, 35
Fujian, 36 Jiangxi, 37 Shandong, 41 Henan, 42 Hubei, 43 Hunan, 44 Guangdong, 45
Guangxi, 46 Hainan, 50 Chongqing, 51 Sichuan, 52 Guizhou, 53 Yunnan, and 54
Tibet,61 Shaanxi, 62 Gansu, 63 Qinghai, 64 Ningxia, 65 Xinjiang.



https://doi.org/10.1371/journal.pone.0296890.g005

From Fig 5, the first and third quadrants of the scatter plot, which represent
the H-H (high-high) agglomeration and L-L (low-low) agglomeration, respectively,
clearly show the main distribution of the digital economy development and
regional economic resilience in the 31 provinces and municipalities of China.
However, there is also a distribution in the second quadrant (L-H) and the
fourth quadrant (H-L). These distribution characteristics indicate a significant
positive local spatial correlation between regions, with bipolar agglomeration
being the dominant pattern. In 2011, there were 13 regions in the third
quadrant, but by 2020, the number of regions in the third quadrant decreased to
10. This suggests that the migration pattern is primarily characterized by
upstream migration. The number of regions in the first quadrant (H-H) remains
stable at 12, with the exception of Hainan, which experiences downstream
migration. Hubei, on the other hand, reaches this level through upstream
migration. This indicates a relatively stable spatial pattern among the regions.
By analyzing the migration-prone areas, one can study the spatial evolution of
the coupling coordination between the two systems in those regions.

 1. Upstream migration: Liaoning, Jilin, and Heilongjiang provinces transitioned
    from L-L (low-low) agglomeration in the early part of the study period to
    H-L (high-low) agglomeration in the later part. The evidence indicates an
    enhancement in the coupling coordination level between the local digital
    economy’s growth and the economy’s resilience. The Northeast Economic Zone’s
    neighborly structure has allowed consistent spillover effects to benefit
    their development. Henan Province moved from L-H (low-high) agglomeration to
    H-H (high-high) agglomeration, indicating an improvement in its level of
    coupling and coordination. This improvement can be attributed to the
    radiation effect of leading regions and the absorption of opportunities for
    growth through spillover effects. Hunan Province transitioned from H-L
    (high-low) agglomeration to H-H (high-high) agglomeration in 2015. Still,
    towards the end of the research period, it returned to H-L (high-low)
    agglomeration. This suggests instability in the inter-regional development,
    indicating that its spillover effect could not sustainably drive the growth
    of neighboring regions effectively.
 2. Downstream migration: Hainan Province transitioned from H-H (high-high)
    agglomeration at the beginning of the study period to L-H (low-high)
    agglomeration at the end. This indicates that Hainan Province did not
    maintain a consistent development trajectory between the digital economic
    development and regional economic resilience systems. Despite its geographic
    location in the southern coastal region and its adjacency to Guangdong
    Province, which provides a natural advantage for cooperation and opening up,
    Hainan Province needs to strengthen the utilization of resources further and
    improve its development situation to achieve significant breakthroughs.
 3. Local migration: During the study period, most regions in the first
    quadrant, such as Beijing, Shanghai, Jiangsu, Tianjin, Zhejiang, Shandong,
    Fujian, Anhui, Hebei, and Chongqing, experienced local migration
    characterized by coupling development in leading regions or core driving
    areas. These regions demonstrated a higher level of coupling coordination,
    with stable performance in both systems. Jiangxi Province, situated in the
    second quadrant, encountered industrial barriers that impeded the growth of
    the digital economy, leading to a delayed pace of advancement and challenges
    in enhancing its efficiency. The provinces in the third quadrant are
    predominantly located in the northwest and southwest regions. These regions
    exhibit lower values of coupling coordination, indicating a "collapse zone"
    in inter-regional development. Acquiring high-quality resources and
    benefiting from the radiation effects of leading regions pose challenges,
    making it difficult for these regions to reach a higher level of coupling
    and coordination. However, the distribution of each region displays a
    tendency to the right when comparing the changes throughout the entire study
    period, showing an improvement in the coupling coordination level. In the
    fourth quadrant, Guangdong, Sichuan, and Shaanxi are development engines
    within their respective comprehensive economic zones, demonstrating good
    growth. However, the impact on other regional provinces and municipalities
    is limited, resulting in a high-low (H-L) agglomeration pattern. During the
    study period, Hubei Province changed its agglomeration pattern. It started
    in the high-high (H-H) category, shifted to high-low (H-L) in the middle,
    and finally reverted to high-high (H-H) agglomeration by the end. This
    indicates that Hubei Province has influenced the development of surrounding
    regions through its spillover effect. Situated in the middle sections of the
    Yangtze River, Hubei Province benefits from convenient transportation and
    abundant high-quality science and education resources, providing good
    development opportunities for neighboring areas.


6.4 DRIVER ANALYSIS

Various driving forces influence the coupling between the digital economy and
economic resilience. In this study, geographic detectors are employed to analyze
these factors. Based on existing research, the following variables are
considered as detection factors: D1: GDP per capita (measured in 10,000 yuan),
D2: length of fiber optic cables (measured in kilometers), D3: online payment
capacity (measured in 10,000 yuan), D4: proportion of value added in the second
and third industries (%), D5: population density (measured in people/km2), and
D6: total amount of imports and exports of foreign-funded enterprises (measured
in million dollars).To process the independent variables, the natural break
method of ArcGIS software is utilized for type-volume transformation. The
detection calculations for each driving factor yield the corresponding P-values.
Due to space constraints, the detection results for 2011, 2015, and 2020 are
listed in Table 5.

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Table 5. Detection results.



https://doi.org/10.1371/journal.pone.0296890.t005

The results in Table 5 show that each influencing factor demonstrates a
significant relationship at different confidence levels across the three years.
Furthermore, all the factors positively influence the coupling coordination
degree between digital economic and regional economic resilience. Specifically,
the online payment capacity stands out as a significant factor at a 1%
confidence level throughout the study period, indicating its consistent and
stabilizing impact on the coupling and coordination degree. Apart from online
payment capacity, foreign-funded enterprises’ total amount of import/export also
exhibited significant influence at a 1% confidence level in 2015. Population
density and the value-added share of the second and third industries both
exhibited growing influence in 2015, while the influence of total import/export
of foreign-funded enterprises and fiber optic cable length decreased. In 2020,
the significance of the influence of GDP per capita and fiber optic cable length
increased, making them significant factors alongside online payment capacity.
However, the extent of its effect is diminishing, as evidenced by the amount of
value created in the second and third industries. These findings imply that
several variables influence how closely the growth of the digital economy and
regional economic resilience are coupled and coordinated, with online payment
capacity consistently having a significant impact. The influence of other
factors may vary across different years, reflecting the changing dynamics of the
two systems.

The most influential factor, with P-values of 0.6581, 0.8434, and 0.8635 across
the three years, is the total import and export amount of foreign-funded
enterprises. The suggested strong relationship exists between the level of
coupling coordination of the digital economy’s growth and regional economic
resilience and foreign-funded firms’ total import and export quantity. It
reflects the level of regional openness, as the growth of the digital economy
often relies on the degree of openness, and regional economic resilience
benefits from increased openness. Online payment capacity and fiber optic cable
length consistently increase their P-values, reaching 0.7792 and 0.6002,
respectively, in 2020, and are significant at a 1% confidence level. The growth
of the digital economy is closely linked to these two elements. They
significantly impact the overall coupling coordination between the two systems,
making them a critical factor in advancing the digital economy. Compared to
other factors, they exhibit a neighboring relationship, highlighting their
substantial impact on the development and interaction of the two systems. During
the three years, there were fluctuations in the value added by secondary and
tertiary industries, GDP per capita, and population density. The P-value of GDP
per capita and population density followed a "U"-shaped change, decreasing
initially and then increasing, while the proportion of value added of secondary
and tertiary industries showed an inverted "U" type of change, increasing
initially and then decreasing. The observed patterns indicate a threshold effect
in how these factors impact the coupling coordination of digital economic
development and regional economic resilience. However, with these factors’
continued accumulation and role, the original thresholds are broken, and they
begin to affect how the two systems are coupled and coordinated significantly.
The change in P-values reflects the ability to transform the industrial
structure. In the pre-transformation period, these factors had a more
significant coordinating effect on the two systems, indicating the importance of
industrial structure transformation. As the structural transformation
progresses, the degree of influence on the two systems weakens, leading to
changes in the P-values. This suggests that the coordinating effect on the two
systems is greater during the pre-transformation period, while its influence
becomes less significant as the structural transformation continues.


7. DISCUSSION

The emergence of the digital economy as a new economic form injects vitality
into contemporary socio-economic development and inevitably exerts a certain
influence on the development of economic resilience. Consequently, current
research predominantly focuses on the unilateral impact of the digital economy
on economic resilience, with limited consideration of how the two systems
develop in coordination, overlooking their mutual interdependence. Therefore,
this study adopts a new perspective on the collaborative development of the
digital economy and regional economic resilience. Firstly, we employ the
coupling coordination model for measurement and construct a new comprehensive
evaluation system for the two systems. We apply the entropy weight-TOPSIS method
for weighted evaluation. Additionally, we utilize data from China’s 31 provinces
and municipalities from 2011 to 2020 as research samples. Furthermore, to
thoroughly explore the evolution and distribution of the collaborative
relationship between the digital economy and regional economic resilience, we
conduct discussions on temporal-spatial distributions. After dividing the
regions, we also observe the differences in coupling coordination effects under
regional agglomeration effects. We further employ spatial autocorrelation
analysis to observe adjacent effects and leap features during the research
period. Finally, we use a geographic detector to investigate the driving factors
of the collaborative impacts between the two systems. The research findings
provide a basis for the collaborative development and joint governance of the
digital economy and regional economic resilience.

Through the comprehensive evaluation system constructed in this research period,
the Development Index of China’s digital economy and the Comprehensive
Development Index of regional economy resilience exhibit an annual upward trend.
However, the Comprehensive Digital Economy Index demonstrates a "leapfrog"
increase, while the Regional Economic Resilience Index undergoes a
"steady-state" transformation. This aligns closely with the conclusions drawn by
Zhang et al. (2017) [54] and Wang et al. (2023) [55]. While these perspectives
have delved further into regional disparities in China’s digital economy and
regional economic resilience as individual systems, this study focuses on the
synergistic development between the digital economy and regional economic
resilience. Zhang et al. posit that China’s digital economy is generally in a
state of growth but faces certain imbalances. Wang et al. believe that China’s
regional economic resilience is experiencing an overall improvement, though a
step-like pattern exists in the east-west direction.

Furthermore, this study observes that during the research period, the coupling
coordination between the development of China’s digital economy and the regional
economic resilience demonstrates a positive performance, showing continuous
improvement. However, there exists a disparity in the level of coupling
coordination between regions, manifesting as a step-like distribution of "higher
in the east and lower in the west." In conjunction with the findings of Zhang et
al. (2017) [54] and Wang et al. (2023) [55], we note that the distribution
characteristics of the synergistic development of the digital economy and
regional economic resilience exhibit similarities with the distribution
characteristics of individual systems, both displaying a certain degree of
imbalance, with the step-like distribution pattern persisting. Additionally,
this paper divides the observed economic comprehensive areas into levels of
aggregation disparities, revealing a hierarchical presentation of coupling
coordination. The first level encompasses the eastern, northern, and southern
coastal regions, constituting the leading areas. The second level comprises the
middle reaches of the Yangtze River, the northeastern region, the middle reaches
of the Yellow River, and the southwestern region, revolving around the national
average level, representing the average areas. The third level encompasses the
northwestern region, where the coupling development level of the two systems
lags.

In Li et al.’s (2021) study on the digital economic system [56], conclusions
were drawn regarding the distribution disparities among China’s eight major
comprehensive economic zones. It was found that the digital economy primarily
concentrates on coastal areas, the Yangtze River’s middle reaches, and the
Yellow River’s middle reaches. This aligns with the performance of the
synergistic coupling between the digital economy and regional economic
resilience discussed in this paper. Additionally, Li et al. observed a
significant positive spatial correlation in the level of digital economic
development, which is consistent with the spatial correlation findings in this
study. Under different regional transitions, past research has suggested that
the digital economy offers greater possibilities and flexibility for regional
economic development, resulting in a marginal increment effect and a penetrative
solid influence on enhancing resilience. These aspects can closely integrate
with the region’s ability to continuously adjust in the face of external
environmental changes, thereby maintaining overall economic stability [4,51].
Developed regions find it easier to embrace the digital economy’s new ideas and
technologies, facilitating collaborative adjustments in economic structure and
enhancing resilience against risks [57]. Consequently, the digital economy and
regional economic resilience exhibit a more pronounced synergistic effect.
Furthermore, this study conducts a spatial correlation analysis to validate the
research samples’ coupling coordination. It is observed that there is a
spill-over effect in coordination between neighboring areas, but the patterns
triggering leaps exhibit variations. However, for the most part, a spatially
stable pattern is characterized by " Local migration," indicating that a lag by
the spill-over effect from adjacent areas influences different regional
disparities.

Moreover, according to the results obtained from the geographical detector
analysis, all variables positively affect the coupling coordination level of the
two systems. Notably, online payment capability exerts the most significant
influence. Research by Du et al. (2023) [29] and Song et al. (2022) [58] has
previously indicated that the digital economy demonstrates unique elasticity,
with consumer spending levels being able to promote the impact of the digital
economy on economic resilience. Furthermore, this is correlated with improving
people’s online payment habits. Among other findings, the impact of fiber optic
cable length is relatively high, while per capita GDP and population density
have lower and fluctuating influences. Li et al.’s (2021) [56] research revealed
that material input has the most significant impact on digital economic
development, and investment in labor costs is crucial to digital economic
growth. However, based on the conclusions drawn in this paper, elements of a
similar nature, such as fiber optic cable length and population density, exhibit
disparities in their impact on the performance of both the digital economy and
regional economic resilience systems. Additionally, the level of external
cooperation plays a critical role in the synergistic development of the digital
economy and regional economic resilience while the influence of industrial
structure fluctuates.

The findings of this study provide a foundation for understanding the
collaborative development of the digital economy and regional economic
resilience. The comprehensive evaluation system established during the
analytical process for the digital economy and regional economic resilience has
meaningful implications for research in other regions worldwide. Furthermore,
the clear research conclusions on the evolutionary patterns and distribution
characteristics of the collaborative development of the two systems, obtained
through coupling coordination and spatial autocorrelation, offer valuable
references for the joint governance of the digital economy and economic
resilience in the sampled regions. These findings also hold illuminating
potential for areas beyond the sample. In light of the heterogeneity in the
synergistic state of China’s digital economy and regional economic resilience
identified in the conclusions, targeted development strategies can be formulated
for vulnerable regions, drawing reference from leading regions. This approach
can facilitate synchronized progress in the digital economy and regional
economic resilience, efficiently harnessing synergistic effects.

At the same time, this study has certain limitations. Firstly, the research
sample in this paper consists of Chinese provincial-level units, leaving ample
room for expanding the research data. Future studies could focus on more
granular regions, such as urban clusters and county-level areas, which would be
conducive to discovering the collaborative development patterns of the digital
economy and economic resilience within regions and proposing targeted governance
recommendations. Additionally, it would be beneficial to conduct separate
discussions on regions with different development statuses and categorize
discussions based on the characteristics of regional industrial development.
This would allow for a concentrated examination of the collaborative development
characteristics of the digital economy and economic resilience under the
dominance of different types of industries. Finally, there is significant room
for exploration regarding the factors influencing the collaborative development
of the digital economy and regional economic resilience. Critical factors for
the collaborative governance of the digital economy and regional economic
resilience from multidimensional and multi-perspective angles await further
investigation. The precise identification of favorable development factors as
regulatory targets can be a focal point for future research.


8 CONCLUSION AND RECOMMENDATIONS


8.1 CONCLUSION

Based on the analysis conducted, the following conclusions have been drawn:

China’s digital economy development and regional economic resilience of the
comprehensive development level between 2011 and 2020 showed an upward trend
year by year. Still, the regional economic resilience composite index showed a
"smooth" change, while the digital economy composite index showed a "leaping"
rise.

Throughout the study period, there was a strong coupling between the development
of China’s digital economy and the resilience of regional economies, leading to
continuous progress. However, there was a noticeable disparity in the level of
inter-regional coupling coordination, manifesting as a "high east, low west"
ladder-type distribution. The economic integration zones demonstrated
hierarchical levels of agglomeration. The first level comprised the eastern,
northern, and southern coastal regions, which were the leading regions. The
second level comprises average regions closer to the national average level.
These regions include the middle reaches of the Yangtze River’s northeastern
region and the middle reaches of the Yellow River. The northwestern and
southwestern regions, classified as backward regions due to the lagging coupling
coordination level of the two systems, fall under the third level. By utilizing
the kernel density estimation method, a widening gap in the coupling
coordination level between the eight economically integrated regions was
observed, suggesting a polarization trend.

Spatial analysis reveals that the coupling coordination level among China’s 31
provinces and municipalities exhibits a positive spatial correlation, displaying
clear spatial agglomeration patterns. The Moran’s I index follows a "U"-shaped
distribution, initially decreasing and increasing, indicating a shift from low
to high agglomeration levels. The local Moran’s scatter plot indicates a
prevalent pattern of local migration, with most provinces and municipalities in
the first and third quadrants. This suggests a spatial stabilization pattern
among the regions. Furthermore, upstream migration is observed in Liaoning,
Jilin, Heilongjiang, and Henan provinces, while downstream migration occurs in
Hainan province.

The results of the Geodetector analysis indicate a positive influence of each
variable on the coupling coordination level of the two systems. Among them, the
online payment capacity has the most significant effect. Moreover, the coupling
and coordination level is more significantly influenced by the total import and
export amount of foreign-funded enterprises, online payment capacity, and length
of fiber-optic cables. On the other hand, variables such as GDP per capita,
population density, and the proportion of value added in the secondary and
tertiary industries have a lower impact intensity and are more susceptible to
fluctuations.


8.2 RECOMMENDATIONS

Based on the aforementioned conclusions, it is evident that China’s digital
economic development and regional economic resilience are experiencing orderly
enhancements, and their coupling role has also been strengthened. However,
regional disparities in development levels persist and are likely to widen
further. The development trends of these two systems are challenging to
synchronize due to the influence of different economic environments.

In light of this, this study makes the case that the digital economy and
regional economic resilience have a significant synergistic relationship. As the
development of one system progresses, it is essential to consider the role and
mechanisms of the other system. Cities in the eastern, northern, and southern
coastal areas have demonstrated excellent performance in both systems and can
effectively combine and leverage their advantages. These cities should focus on
resource utilization within their regional engine area while gradually
establishing a leading role. They can strengthen inter-regional development and
cooperation by leveraging their coastal geographical locations.

Furthermore, by strengthening inter-regional cooperation and establishing a
complementary and mutually beneficial pattern, these coastal cities can fully
unleash their spillover effects, thereby radiating their influence to other
economic zones. East-West nexus regions should focus on upgrading their
industrial structure and efficiently harnessing the high-quality resources and
skills that overflow from the coastal region. They can break through the
barriers to their development by aligning with strategic initiatives such as
"the rise of the central part of the country," the Belt and Road Initiative, and
the Yellow River Basin’s excellent development. These regions can serve as
crucial hubs for exchanges and communication between the East and the West.

Given the relatively underdeveloped state of the Northwest and Southwest
regions, it is challenging to bring about rapid changes in the lagging
development of the two systems. Formulating development policies that align with
the local conditions and regional development policies is necessary. The
government should prioritize advancing the development of favorable industries
and creating appropriate initiatives to entice businesses. In the context of the
national development of the digital economy and the creation of new economic
models, this will facilitate the introduction and development of industries with
unique characteristics, aligning with the development of their economic
resilience. The government should prioritize developing advantageous industries
in the area, and customized strategies for introducing new businesses should be
created.


SUPPORTING INFORMATION

S1 Dataset -

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ABCDEFGHIJKLMNO1cityyearInternet broadband access subscribersAll-society
fixed-asset investment in the information transmission, computer services and
software industryExpenditures on science and technology)Number of digital
economy-related patents Level of online mobile paymentsWebsites per 100
businessesEmployed persons in information transmission, software and information
technology servicesemployed persons in urban unitsEmployed persons in
information transmission, software and information technology services/employed
persons in urban unitsPeking University Digital Inclusive Finance IndexNumber of
computers per 100 persons in industrial enterprisesE-commerce turnover of
industrial enterprisesthe main business income of industrial
enterprises2Beijing12011510.8436.291648537.84088879.45949.1685.90.0715847791223210479.41524357.7818902752.023Beijing12012473.69488.91973442.350511110.025952.6717.40.07332032339001952150.65565211.7524585033.624Beijing12013480.45942130617.562671136.535958.2742.30.0784049575643271215.62577467.628517239.485Beijing12014482.4686.672335010.174661196.256061.1755.90.08083079772456675235.36619012.431371853.976Beijing12015491.9923.362440874.594031243.236368777.30.0874823105622025276.386210530.534538854.577Beijing12016475.76593.082548433.3100578286.876469.2791.50.0874289324068225286.37048238278996612026.739409751.798Beijing12017541.94870.582690851.2106948303.126577.4812.90.0952146635502522329.9438033063126718385.744868872.029Beijing12018638.81755.462740102.7123496317.546484819.30.10252654705236178368.53698342964477018261.249578245.5910Beijing12019688.112681.992851858.7131716330.515885.9791.30.10855554151396438399.00257747083077223235.956952842.5711Beijing12020747.33247.632974156.5162824342.695892.3762.70.12101743804903631417.8753998304297725831.863161621.9712Tianjin22011186.7153.222107771.61398255.52552.1268.20.00782997762863534760.58241007.291693818.6813Tianjin22012204.78159.352558684.61978274.34573.3289.10.011414735385679694122.96251355.782323275.3114Tianjin22013188.4183.783000377.224856110.17533.6302.40.011904761904761906175.26271053.72761575.0815Tianjin22014208.81207.533228056.526351165.52543.8295.50.012859560067681894200.16301946.53885630.9916Tianjin22015249.842613526665.137342206.76544.4294.80.01492537313432836237.53313148.65034368.6517Tianjin22016283.93183.093499550.539734247.13534.82860.016783216783216783245.83820494833053330355526360.6918Tianjin22017339.35300.672411417.741675257.2525.3269.50.019666048237476808284.033712372482372629.45514410.5219Tianjin22018437.91737.82252876154680278.23476.42600.024615384615384615316.88465737133423731066855875.420Tianjin22019523.631194.62134320.257799292.57436.6269.40.0244988864142539344.1120785937147393266.39092549.4321Tianjin22020534.61584.542287717.375434291.874072790.025089605734767026361.455131353389343434210895598.1122Hebei32011824.5491.341586188.61111924.36505.9555.40.01062297443284119732.4212366.84262470.7623Hebei32012963.91538.71980850.31531543.52506.5619.90.0104855621874495989.3214743.26378178.124Hebei320131031.6651.662327418.31818664.92508.6653.40.013161922252831344144.98161166315580.7625Hebei320141127.64728.092606711.320132114.97568.6656.20.013105760438890581160.76171629.7292228.3826Hebei320151317.22866.462858050.630130161.59578.8643.60.013673088875077689199.53191440395437.727Hebei320161612.02623.23086607.631826214.64578.4639.60.013133208255159476214.3588258878981192416.1589959.2828Hebei320171910.11096.193509683.735348226.55577.5535.30.014010835045768728258.1665791018198212441.1889244.729Hebei320182159.772790.143819915.751894238.96568.4550.30.015264401235689626282.7720567868686252556.12759840.0730Hebei320192359.674741.944385826.357809258.65510.15760.017534722222222222305.0597074481554282726.33811904.4331Hebei320202534.45971.714854543.592196268.55110.4576.30.01804615651570363322.6968664345796314402.35549645.8832Shanxi42011416.1279.33895890.9497419.48454.9409.70.01195997071027581233.411580.03224824.6733Shanxi42012504.78308.491069589.7719637.43475.34360.01215596330275229392.9814209.25306087.6634Shanxi42013521.3358.161237697.8856561.874364640.01293103448275862144.2216315.3527680.9535Shanxi42014571.13394.571247027.38371117.06475.5452.10.0121654501216545167.6618506.9484595.0436Shanxi42015723.94472.81008949.710020159.41465.5440.30.012491483079718374206.319676.7512007.1437Shanxi42016747.19328.73976282.510062206.37455430.60.011611704598235021224.810729882810321680.3425621.6738Shanxi42017872.87584.211122322.911311216.39424.9428.70.011429904362024727259.954852225656222864.3941470.7739Shanxi42018991.031372.931312531.115060243.58394.9425.80.011507750117426023283.6542400191222232450.21507566.6140Shanxi420191126.062375.21380812.716598265.71345.2441.10.011788710043074132308.7259742040338242136.71095226.9341Shanxi420201252.13091.7156179027296272.44325.2450.80.011535048802129548325.7269511290497262322.6449790.742Inner
Mongolia52011231231.51701634.5226227.51484.2262.40.0160060975609756128.891241.98226719.0543Inner
Mongolia52012274.8258.51858476.8308445.63504.4270.80.01624815361890694491.6814127.341060961.7744Inner
Mongolia52013284.4293.711004405.6383673.66465.9303.80.019420671494404212146.5917212.7387389.6845Inner
Mongolia52014316.78316.571080287.44031117.47505301.50.01658374792703151172.5620392139393.1546Inner
Mongolia52015365.65377.071186260.55522154.71495298.30.016761649346295676214.55221340.1153871.5247Inner
Mongolia52016417.18249.6412798535846195.22504.8293.20.01637107776261937229.9258763150416251587.6120492.0448Inner
Mongolia52017494491.171082639.66271196.4514.8280.60.017106200997861722258.5040157651248281725.5196086.8149Inner
Mongolia52018628.281270.771033594.49625205.87444.9272.40.01798825256975037271.5666079300939311948.6198398.3150Inner
Mongolia52019682.52075.811183624.611059224.23384.7280.90.016731933072267714293.889210418314332568.1224792.6

Digital economyeconomic resilienceDriving factors

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S1 DATASET.

https://doi.org/10.1371/journal.pone.0296890.s001

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