Spatiotemporal patterns and drivers of coupling coordination between digital technological innovation and economic resilience in the Yangtze river economic belt

Libin Guo; Kang Liu

doi:10.1038/s41598-025-16188-x

. 2025 Aug 22;15:30975. doi: 10.1038/s41598-025-16188-x

Spatiotemporal patterns and drivers of coupling coordination between digital technological innovation and economic resilience in the Yangtze river economic belt

Libin Guo ^1,², Kang Liu ^2,^✉

PMCID: PMC12373814 PMID: 40847111

Abstract

The synergistic evolution of digital technological innovation (DTI) and urban economic resilience (UER) has become an inherent requirement for the high-quality development of urban agglomerations. Based on panel data at the prefecture-level city scale from 2011 to 2022, this paper explores the spatiotemporal patterns and driving factors of the coupling coordination between DTI and UER across the three major urban agglomerations in the Yangtze River Economic Belt (YREB). The study yields the following key findings: First, during the study period, the coupling coordination degree between digital innovation and UER experienced a fundamental shift from disorder to coordination, but high-quality coupling coordination has not yet been fully achieved. Second, the spatial distribution of coupling coordination exhibits a clear upstream-downstream gradient, with higher coordination in the upper reaches and lower coordination in the middle and lower reaches, forming a distinct core-periphery structure within each agglomeration. Third, the overall inequality in coupling coordination shows a declining trend, but inter-agglomeration differences remain the primary source of inequality. Finally, economic development, fiscal pressure, innovation capacity, and scientific research support are identified as the key driving factors influencing coupling coordination. Based on these findings, the study recommends: promoting regional collaboration, enhancing fiscal support, and optimizing the spillover effects of core cities to foster more balanced and resilient development across the YREB.

Keywords: Digital technological innovation, Economic resilience, Coupling coordination, Urban agglomerations, Yangtze river economic belt

Subject terms: Socioeconomic scenarios, Sustainability

Introduction

In recent years, despite navigating the dual challenges of domestic economic restructuring and external uncertainty shocks, China has continued to demonstrate remarkable growth momentum—a testament to the resilience of its economy^1,2. However, given the sheer scale, multi-layered structure, and deep global embeddedness of China’s economy, the economic resilience across regions and industries exhibits significant heterogeneity^3,4. In response to increasingly complex domestic and international conditions, enhancing macroeconomic resilience has become an urgent priority⁵.

At this pivotal juncture marked by a new wave of technological and industrial revolutions, Chinese government underscores the imperative to accelerate digital development, cultivate new competitive advantages in the digital economy, strengthen innovation and application of key digital technologies, and promote the deep integration and application of digital technologies across the real economy, digital society, government governance, and urban management. The emergence and advancement of digital technologies have triggered profound transformations in urban economic activities and governance models^6,7. On one hand, digital technological innovation (DTI) serve as the foundation and driving force behind the digital economy, with their rapid development accelerating the expansion of digital industries⁸. On the other hand, DTI are deeply embedded in traditional industries, giving rise to new industries, business models, and economic paradigms, thereby injecting renewed vitality into traditional sectors^9,10.

Amid the growing complexity of urban systems and increasing uncertainty in the global economy, greater attention is being paid to the interplay between technological innovation and economic resilience. DTI, once viewed primarily as a technical input, now plays a broader role—shaping how cities absorb shocks, reconfigure their structures, and pursue sustainable growth^11,12. At the same time, the concept of urban economic resilience (UER) has expanded beyond its traditional focus on resistance to disruption, evolving into a dynamic capacity for adaptation and innovation^13,14. The relationship between DTI and UER has thus moved beyond a one-way influence, forming a more integrated, reciprocal, and co-evolving mechanism. This shift highlights the need to move from treating DTI and UER as isolated subjects toward a more holistic view that emphasizes their coordinated development. Therefore, a systematic investigation of the coupling coordination between DTI and UER, particularly under the constraints of regional disparities and industrial structure rigidities, is essential for promoting differentiated urban transformation pathways and enhancing the resilience of China’s urban economic systems.

As one of China’s earliest regions to embrace the digital economy—and home to some of the country’s richest technological resources—the Yangtze River Economic Belt (YREB) possesses unique advantages in leveraging digital technologies to unlock regional development potential, promote industrial transformation and upgrading, enhance regional coordination and cooperation, and ultimately strengthen the overall UER of the region^15,16. The YREB, as a core economic axis and a strategic focal point of China’s Three Major Strategies, plays a critical role as both a stabilizer and ballast for the national economy¹⁷. Nevertheless, cities along the YREB face pronounced disparities in economic development levels and persistent deficiencies in internal coordination and cooperation¹⁸. The eastern region’s export-oriented growth model remains vulnerable to shifts in global economic conditions; the central region’s role as a key connector remains underdeveloped; and the western region continues to struggle with the delicate balance between economic growth and environmental sustainability^19,20. These unresolved challenges indicate that the YREB’s economic and social development is still exposed to considerable uncertainty and systemic risks.

Existing studies on UER and DTI have provided valuable insights but largely follow independent analytical paths. Research on UER primarily focuses on measurement methods, such as core variable approaches and multi-index systems^19,21and on key influencing factors including industrial structure, population density, and disaster exposure²². Studies on DTI mainly examine its spatiotemporal evolution and its linkages with the digital economy, real economy, and industrial upgrading, often measuring innovation levels through patent data or composite indices^23,24. Although some works have confirmed that DTI enhances UER by promoting industrial transformation, improving total factor productivity, and stimulating innovation dynamics²⁵few studies have systematically explored the coupling coordination between the two, particularly from a spatial perspective. However, as technological transformation efficiency improves and market economies continue to evolve, this unidirectional relationship has gradually developed into a mutually reinforcing interaction. Both DTI and UER have become critical drivers of high-quality development. Insufficient technological empowerment or weak economic resilience can hinder their coordinated development. Furthermore, previous studies have predominantly concentrated on cross-country or inter-city comparisons. Yet, with the growing emphasis on regional integration and coordinated development, there is an urgent need to explore the coupling and coordinated development mechanisms within urban agglomerations.

In comparison with previous studies, this paper makes the following marginal contributions. First, it systematically explores the coupling coordination relationship between DTI and UER. While existing research has predominantly examined the one-way influence of DTI on UER^26,27this study highlights the mutual and dynamic interaction between the two, reflecting the evolution from a linear impact to a bidirectional coupling mechanism. By applying the Vertical and Horizontal Deviation Method and the Coupling Coordination Model, the study quantitatively measures both systems and analyzes their spatiotemporal evolution, spatial patterns, and coordination dynamics within a unified framework.

Second, this study deepens the analysis of spatial heterogeneity in coupling coordination within urban agglomerations. Existing literature has largely focused on inter-country or urban-rural comparisons, overlooking intra-agglomeration disparities. Taking the three major urban agglomerations of the YREB as the empirical context, the study reveals the core-periphery spatial structures and upstream-downstream gradients of coupling coordination. Analytical tools such as the Dagum Gini Coefficient and spatial autocorrelation analysis are employed to uncover the spatial differentiation and agglomeration patterns within and across the agglomerations.

Third, the study systematically identifies the driving mechanisms of coupling coordination and reveals their spatial spillover effects. By constructing a spatial econometric model, it clarifies how economic development, fiscal capacity, innovation capability, and scientific research support influence the coupling coordination degree, and highlights the differentiated spatial effects of these factors across regions. Based on these findings, the study proposes tailored policy recommendations aimed at narrowing regional disparities, enhancing innovation diffusion, and promoting high-quality, coordinated development of urban agglomerations. These insights provide both theoretical enrichment and practical guidance for fostering resilient and innovative urban systems in China’s evolving regional development landscape.

Literature review

Research on urban economic resilience (UER)

The concept of resilience, originally from engineering, was first used to describe the time required for systems to recover after disturbances²⁸. Holling later extended it to ecology, emphasizing systems’ capacity for adaptation and reorganization rather than simply returning to their original state²⁹. Since then, resilience theory has expanded into economics, sustainable development, and disaster management³⁰. In economic contexts, resilience refers to a region’s ability to withstand and adapt to shocks, incorporating concepts like path dependence and adaptability⁵.

Scholarly interest in economic resilience grew after the 2008 financial crisis, when researchers sought to understand why some regions rebounded faster than others. Early studies borrowed from ecological resilience, emphasizing a system’s ability to absorb shocks without altering its structure³¹. However, economic systems are dynamic and constantly evolving. Adaptive resilience, focusing on response capacity and flexibility, better reflects urban economic realities³². Martin³³ further defined ER as a regional economy’s ability to withstand and recover from external shocks, highlighting both resistance and recovery capabilities.

Current research on UER largely focuses on measurement and influencing factors. Measurement approaches include core variable methods and multi-index systems^19,21. Influencing factors frequently discussed include industrial diversity, population density, and disaster exposure. For instance, Brown and Greenbaum²² found that cities with diverse industrial structures better maintain economic stability amid external fluctuations. Overall, UER is seen as a dynamic process of maintaining stability, adapting to changes, and promoting sustainable growth in complex urban systems.

Research on digital technology innovation (DTI)

Research on DTI has evolved from early discussions of information systems to a broader focus on economic and social transformation. In its early stage, studies mainly addressed how ICT tools improved organizational efficiency and decision-making^34,35. Later, scholars highlighted organizational learning and structural adaptation as critical to realizing the productivity benefits of digital investment³⁶.

Since 2000, research has shifted toward a systemic perspective, examining how digital technologies reshape organizational capabilities, business models, and regional innovation ecosystems^37,38. Digital technologies are no longer seen as isolated tools but as enablers of dynamic capabilities and regional network formation³⁹. Since 2010, with the emergence of AI, big data, and cloud computing, DTI research has become highly interdisciplinary, focusing on digital ecosystems, platform economies, and sustainability transitions^8,40. Scholars increasingly explore how digitalization drives industrial upgrading, green innovation, and organizational transformation.

At the macro level, DTI exerts four major impacts. First, it fosters economic growth through the expansion of the digital industry and the enhancement of total factor productivity across sectors⁴¹. Second, it accelerates technological progress by enhancing innovation capacity, improving market responsiveness, and facilitating breakthrough innovations^42,43. Third, DTI promotes industrial upgrading, optimizing resource allocation and strengthening supply chain resilience⁴⁴. Finally, digital innovation improves government governance through smart infrastructure, digital service delivery, and enhanced public management capacity⁶. Overall, DTI plays a pivotal role in driving urban and regional transformation toward high-quality, resilient, and sustainable development.

Research on the relationship between DTI and UER

DTI plays a critical role in enhancing UER by optimizing resource allocation, improving adaptive capacity, and fostering structural transformation. On the one hand, DTI enables organizations to integrate diverse data, information, and heterogeneous resources to build internal digital ecosystems that respond to external uncertainty⁴⁵. On the other hand, the digitalization of organizational processes strengthens learning capabilities, supporting rapid recovery and adaptive growth in urban economies⁴⁶.

At the macro level, DTI contributes to economic resilience through two key pathways. First, it enhances government governance capacity by improving digital public services and interacting with industrial upgrading and innovation efficiency. Second, it improves resource allocation efficiency and factor productivity, helping cities recover from external shocks and explore new growth trajectories. The growth of the digital industry itself is an important source of resilience. During economic shocks, digital industries exhibit lower sensitivity to macroeconomic volatility, thus acting as a natural buffer⁴⁷. With characteristics of high technology intensity, strong spillover, and strategic leadership, the digital industry drives sustainable economic development and mitigates growth fluctuations⁴⁸. Moreover, digital industries enable regions to break path dependence and develop new growth paths, avoiding structural lock-in associated with traditional heavy industries⁴⁹.

Correspondingly, cities or enterprises with strong economic resilience also generate positive feedback on innovation. Economic resilience enables cities to maintain a stable level of investment even after coping with shocks, thereby ensuring that scientific research and innovation activities are not easily affected⁵⁰. A resilient economic system is typically more inclusive and able to accommodate failures and trial and error in the innovation process, providing innovators with a more relaxed experimental environment and helping to stimulate more risky innovation activities^51,52. Higher economic resilience often means more stable industrial chains and innovation networks⁵³which can promote more effective flow of knowledge and technology, making innovation more sustainable and systematic^54,55.

The coupling coordination mechanism between DTI and UER

The conceptual connotations of DTI and UER

Since John von Neumann revolutionized digital computing architecture, digital technology has undergone continuous evolution—from early hardware innovations (such as computers and industrial robots) to contemporary advancements (such as machine learning and big data analytics), which primarily exist in virtual forms^56,57. Broadly defined, DTI refers to the transformative changes in production, organization, distribution, and innovation processes driven by the adoption of digital technologies. In a narrower sense, it focuses on emerging technologies and applications directly related to digital industries and digitalization^58,59. Building upon existing literature and aligning with the realities of technological development, this study adopts the broader definition, conceptualizing DTI as the combination and application of information technologies, computing technologies, communication technologies, and connectivity technologies, which collectively drive innovation activities such as the development of new products, improvements in production processes, transformations in organizational models, and optimizations in business models. This definition captures three core dimensions: the foundation of digital innovation, the capabilities of digital innovation, and the tangible outcomes of digital innovation.

The concept of “Resilience” first emerged in the field of engineering, where it originally referred to a system’s ability to maintain stability when subjected to external shocks. In economic research, scholars have further refined this concept, distinguishing between two distinct forms of UER: resistance and reconstruction³¹. Resistance refers to an economic system’s capacity to absorb external shocks while maintaining its existing operational structure, whereas reconstruction denotes the system’s ability to rebuild disrupted components of its economic processes, formulate new operational models, and ultimately restore or even enhance economic growth following a shock^33,60. Building on this theoretical foundation, this study defines UER as a multidimensional concept rooted in economic development, driven by response capacity and recovery capability, and evaluated through resilience performance indicators. It emphasizes the capacity and mechanisms by which an economy maintains stability, restores functionality, and achieves sustained development in the face of both internal disturbances and external shocks.

The coupling and coordination mechanism between DTI and UER

DTI plays a pivotal role in enhancing urban economic resilience (UER), while resilient urban systems, in turn, provide a stable and supportive environment for the sustained advancement of digital technologies. This bidirectional and mutually reinforcing interaction between technological progress and economic adaptability constitutes an essential driving force for high-quality urban development^12,61. Specifically, DTI contributes to UER by fostering flexible supply networks, enabling adaptive industrial systems, and supporting institutional transformation, thereby improving the capacity of urban economies to absorb and recover from external shocks. Conversely, UER supports DTI by providing stable fiscal and institutional foundations, sustaining demand and market stability during crises, and fostering an environment conducive to long-term innovation and resource accumulation.

However, this interaction is not immune to external constraints stemming from economic, social, and institutional factors. Several challenges threaten to disrupt the synergy between digital innovation and UER, including the uneven distribution of digital technology resources, which exacerbates the “digital divide,” the persistent difficulty in translating technological breakthroughs into tangible productivity gains, and the entrenched industrial path dependence in certain regions^62,63. These obstacles highlight the complex and context-dependent nature of the coupling coordination between DTI and UER, underscoring the need for nuanced policy interventions that promote inclusive innovation diffusion, structural transformation, and institutional adaptability.

The pathways through which DTI enhances UER

DTI strengthens UER by improving adaptive capacity, enhancing flexibility, and creating new growth drivers. Its contribution can be explained through three interconnected pathways.

First, DTI supports the development of resilient and flexible supply networks. Technologies such as big data analytics and machine learning enable cities to identify potential vulnerabilities in supply chains and logistics systems at an early stage⁶⁴. This allows firms and governments to implement timely, targeted responses to external shocks. Moreover, digital platforms facilitate the real-time collection and intelligent utilization of diverse data resources, reducing information asymmetry and improving the coordination of urban production and distribution systems⁶⁵.

Second, DTI promotes adaptive industrial restructuring. Digital technologies facilitate dynamic adjustments in industrial systems, enabling firms to reorganize production processes, optimize resource allocation, and respond rapidly to shifting market conditions⁶⁶. The resulting industrial systems are more transparent, responsive, and adaptive to external shocks. In particular, digitalization reduces rigidities in industrial structures, making it easier for cities to shift toward more diversified and resilient economic systems⁶⁷.

Third, DTI drives the transformation toward high value-added industries. On one hand, emerging sectors such as artificial intelligence, big data, and the Internet of Things provide new sources of economic growth and employment⁶⁸. On the other hand, digital technologies accelerate the intelligent upgrading of traditional industries, improving production processes, reducing operational costs, and enhancing productivity⁶⁹.

The pathways through which UER supports DTI

UER lays the foundation for the sustained growth of DTI by offering financial support, stable demand, and an enabling institutional environment. This relationship can be explained through three key pathways.

First, UER provides the fiscal and institutional foundations for digital innovation. Cities with stronger economic resilience typically maintain robust fiscal capacity, which allows them to sustain public investment in critical digital infrastructure even during economic downturns⁷⁰. In addition, resilient urban systems are characterized by stable governance frameworks and predictable legal environments, both of which provide long-term policy support and institutional continuity for technological development⁷¹.

Second, UER sustains market demand and stabilizes investment environments. Economically resilient cities are better able to maintain stable levels of consumption and investment during external shocks, ensuring that the market for digital products and services remains viable⁷². Beyond maintaining existing demand, resilient cities are also more capable of generating new demand scenarios, as socio-economic shifts during crises often accelerate the adoption of digital solutions to meet emerging needs⁷³. At the firm level, strong UER mitigates financial risks by providing stable financing channels, allowing enterprises to continue investing in digital R&D and technology upgrades, even under volatile economic conditions.

Third, UER promotes resource accumulation and fosters innovation ecosystems. Technological breakthroughs depend on sustained investment in knowledge capital and physical resources⁷⁴. Cities with high levels of resilience are better positioned to accumulate capital over time and to channel these resources toward innovation activities. This includes funding for research institutions, talent development programs, and the construction of technology parks that serve as platforms for collaborative innovation⁷⁵. Moreover, resilient cities often cultivate open and inclusive business environments, which attract cross-regional collaboration and strengthen local digital innovation ecosystems⁷⁶.

The coupling and coordination process of DTI and UER is shown in the mechanism path diagram of Fig. 1.

Research methodology and data sources

Overview of the study area

The research scope of this study is shown in Fig. 2. The scope of this study covers the three major urban agglomerations in China’s Yangtze River Economic Belt: the Yangtze River Delta Urban Agglomeration, the Middle Reaches of the Yangtze River Urban Agglomeration, and the Chengdu-Chongqing Urban Agglomeration. These urban agglomerations not only serve as pioneering regions for the national digital economy, playing a leading role in digital economic development, but also act as key hubs and emerging highlands for resilient city construction. In 2015 and 2016, the National Development and Reform Commission of China issued the Development Plan for the Yangtze River Delta Urban Agglomeration, the Development Plan for the Middle Reaches of the Yangtze River Urban Agglomeration, and the Development Plan for the Chengdu-Chongqing Urban Agglomeration, marking the elevation of these urban agglomerations to national strategic status.

Note

Software version: ArcMap 10.8, URL: https://www.esri.com/zh-cn/arcgis/products/index. This map is produced based on the standard map with the review number GS(2024)0650, downloaded from the Standard Map Service website (http://bzdt.ch.mnr.gov.cn/index.html) and the base map has not been modified.

Variable selection and measurement

Selection of indicators for DTI

To assess the degree of coupling and coordination between DTI and UER, it is essential to conduct a comprehensive, objective, and accurate measurement of both variables. Drawing on official policy documents and existing academic research, and following the principles of scientific rigor and representative indicator selection, this study constructs a multi-dimensional indicator system to capture the full spectrum of DTI. The indicator system for measuring DTI is constructed around the following primary dimensions:

Digital infrastructure

This dimension focuses on the foundational infrastructure and policy environment necessary for the development and application of digital technologies. It assesses whether the essential infrastructure and policy conditions required to support digital technological advancement are sufficiently robust. Drawing on previous studies and relevant policy documents^77,78this dimension is represented by indicators capturing internet and mobile device penetration as well as the intensity of digital technology-related policies.

Digital technology application

This dimension emphasizes the practical deployment and utilization of digital technologies, encompassing the development of core digital industries and the application of digital technologies across other sectors. Based on prior research, this dimension is reflected through indicators measuring the scale of the digital industry, the development of digital financial services, and the public and media attention to digital technologies^79,80.

Digital innovation capacity

This dimension evaluates the R&D intensity and innovation potential of digital technologies, focusing on technological breakthroughs and the creation of new technologies. Therefore, this dimension is measured by the number of digital technology patent applications and granted patents, providing a comprehensive view of both innovation efforts and successful technological outputs⁴³.

Specifically, public attention to digital technology and the digital economy policy index jointly constitute the social and policy foundations of DTI. The former is measured by the Baidu Search Index for the term “digital transformation”⁸¹while the latter is captured by the frequency of digital-related terms in government work reports⁸². Indicators such as the number of mobile phones per 100 people and the number of internet users reflect the infrastructure foundation supporting DTI. The number of employees in the computer and software industry, used to represent the workforce engaged in digital-related industries, combined with per capita telecommunications business volume, captures the development level of digital industries. In addition, the digital inclusive finance index serves as a key metric for assessing the development of the digital finance sector. Finally, in the dimension of digital innovation capacity, digital technology patents are identified based on official classifications, ensuring that only patents explicitly linked to digital technologies are included in the measurement.

Table 1 presents the indicator system for measuring DTI.

Table 1.

Indicator system for DTI.

Main indicators	Primary indicator	Secondary indicator	Unit
Digital Technological Innovation	Digital Infrastructure	Index of Public Attention to Digital Technology	-
		Digital Economy Policy Index	-
		Number of Mobile Phones per 100 People	Units
		Number of Internet Users per 100 People	Units
	Digital Technology Application	Proportion of Workforce in Digital-related Industries	%
		Per Capita Telecommunications Business Volume	10,000 Yuan
		Digital Inclusive Finance Index	-
	Digital Innovation Capacity	Number of Digital Technology Patent Applications	Cases
	Digital Innovation Capacity	Number of Granted Digital Technology Patents	Cases

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Measurement of UER

UER refers to a city’s capacity to withstand external shocks, recover from disturbances, and undergo adaptive transformation in the aftermath of such shocks. The existing approaches to measuring UER can be broadly classified into composite indicator construction, single-indicator methods, and counterfactual simulation techniques. Among these, composite indicator methods tend to involve considerable subjectivity and often fail to capture the dynamic evolution of urban economies in response to shocks. Drawing on existing literature^13,26this study adopts the sensitivity-based measurement approach, which calculates UER as the difference between a city’s actual economic output and its expected output under normal conditions. Given that China’s employment and unemployment data are predominantly sampled from urban populations and often lack comprehensive coverage, this study opts to use real and expected economic output data as the basis for resilience calculation. The formula is as follows:

Specifically, Inline graphic denotes the actual economic growth rate of city i in year t, while represents the expected economic growth rate of city i in year t. The expected growth rate is calculated as:

where Inline graphic denotes the national average economic growth rate in year t.

For simplicity, the formula for UER can be expressed as:

This specification captures the extent to which a city’s actual economic performance deviates from the national average benchmark, thereby reflecting its relative resilience in response to macroeconomic shocks.

UER is defined as the difference between the actual economic growth rate of a city in a given year and the national average growth rate for that year. Specifically, if Inline graphic , it indicates that the city’s UER is stronger than the average resilience of cities across the country. Conversely, if , it suggests that the city’s UER is weaker than the national average. In this study, UER is measured by a city’s ability to resist and recover from significant external shocks. The U.S. subprime mortgage crisis of 2007 is selected as the primary shock for this analysis. We calculate the cities’ responses to this shock post-2011, and use this as a basis for assessing their UER.

Research methodology

Measurement methodology

This study employs the Longitudinal and Cross-sectional Ranking Method (LCRM) to measure DTI. Compared to other measurement methods, the LCRM reveals the differences, hierarchical structure, and trends of each indicator along both the vertical and horizontal dimensions, allowing for a more nuanced and layered analysis. This approach is particularly suited for handling panel data, thereby enhancing the comparability of results across different years. The specific steps of this method can be outlined as follows:

Before conducting the evaluation, the original dataset must undergo standardization via the range variation method, resulting in a normalized dataset. This step is essential to eliminate the comparability issues arising from differing dimensional units across indicators. In this method, the city index is denoted by i, the indicator index by j, and the year by t. The constructed evaluation function for the indicators is expressed as:

Where Inline graphic represents the weight of the indicator and denotes the calculated level of DTI or UER. To determine the value of , it is necessary to maximize the total squared deviation of . Since the dataset has already been standardized, . Then

Where Inline graphic , . Under the condition that , when reaches its maximum, the corresponding eigenvector of the largest eigenvalue of the real symmetric matrix represents the weights. In this case, the sum of the weights exceeds 1, thus requiring normalization to percentages. The calculation formula is as follows:

where Inline graphic represents the final indicator weight.

Coupling coordination model

The concept of coupling coordination originates from physics, where it describes the interaction between different systems under the combined influence of internal dynamics and external forces. In the context of regional economic analysis, the coupling coordination model is used to reveal the intrinsic relationships between subsystems, such as DTI and UER. The calculation formula for the coupling coordination degree can be expressed as:

In the formula, Inline graphic and represent the DTI system and the UER system, respectively. C denotes the coupling degree, which reflects the strength of the interconnection between the two systems. T represents the comprehensive development index, capturing the overall development level of both systems. Following existing literature and practical considerations, the weights Inline graphic and are both set to 0.5, ensuring that DTI and UER are equally weighted within the comprehensive index. The final indicator D represents the coupling coordination degree between the two systems. Drawing on the classification framework proposed by Gao⁸³the coupling coordination degree is further categorized into different coordination types, as shown in Table 2.

Table 2.

Coupling coordination type.

Range of coupling coordination degree	Types of coupling coordination	Range of coupling coordination degree	Types of coupling coordination
[0,0.1)	Extreme Disorder	[0.5,0.6)	Near Coordination
[0.1,0.2)	Severe Disorder	[0.6,0.7)	Primary Coordination
[0.2,0.3)	Moderate Disorder	[0.7,0.8)	Intermediate Coordination
[0.3,0.4)	Mild Disorder	[0.8,0.9)	Good Coordination
[0.4,0.5)	Near Disorder	[0.9,1]	Excellent Coordination

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Dagum gini coefficient

The Dagum Gini Coefficient Decomposition Method, proposed by Dagum⁸⁴not only measures the overall degree of inequality, but also decomposes total inequality into within-group inequality and between-group inequality. This decomposition offers a clearer analytical framework for understanding the sources and spatial distribution of inequality. In this study, the three major urban agglomerations within the YREB are used as the analytical units. The Dagum Gini Coefficient is employed to precisely identify both intra-group and inter-group spatial disparities in the coupling coordination degree between DTI and UER.

The calculation formula and decomposition framework for the Dagum Gini Coefficient can be found in references^84,85.

Spatial econometric model

Due to industrial linkages and knowledge spillover effects within urban agglomerations, changes in the coupling coordination degree between DTI and UER in one city may exert spatial spillover effects on neighboring cities. To account for these spatial dependencies, this study constructs a Generalized Nesting Spatial Model (GNS) to analyze the spatial spillover effects of the factors influencing coupling coordination. The model is specified as follows:

In the model, Inline graphic denotes the coupling coordination degree of city i in year t, represents the spatial weight matrix, and denotes a vector of explanatory variables capturing various influencing factors. and denote the city fixed effects and time fixed effects, respectively, controlling for unobserved heterogeneity across cities and common time-varying shocks.

Data sources

The year 2011 marks not only the beginning of the rapid development and widespread adoption of digital technologies in China, but also the post-crisis recovery period following the global financial crisis. Moreover, China’s 12th Five-Year Plan explicitly emphasized the promotion of informatization and intelligent development, underscoring the strategic importance of digital transformation. Against this backdrop, this study selects 68 cities from the three major urban agglomerations within the YREB as the research sample, covering the period from 2011 to 2022. Data related to digital technology is sourced from the patent database of the China National Intellectual Property Administration (CNIPA). The classification of digital technology patents follows the Classification Table of Core Industries in the Digital Economy and International Patent Classification (2023) issued by CNIPA. The Digital Inclusive Finance Index is obtained from the Peking University Digital Financial Inclusion Index Platform. Data on digital economy-related policies comes from official documents issued by governments and relevant institutions. Data on public attention to digital technologies is derived from the Baidu Search Index. Other economic, industrial, and environmental data are primarily drawn from the China Urban Statistical Yearbook and the China Environmental Statistical Yearbook, supplemented by local government statistical reports from individual cities. For minor missing data points, the study applies interpolation techniques to ensure data completeness.

Spatiotemporal characteristics of DTI and UER

By constructing a comprehensive indicator system and applying the LCRM, this study calculates the levels of DTI and UER for the 68 cities in the three major urban agglomerations of the YREB during the period 2011 to 2022. To illustrate the overall trends and inter-group differences, this study plots the average levels of DTI and UER for the YREB as a whole and for each of the three urban agglomerations separately, as shown in Fig. 3.

Analysis of UER levels

The temporal trend of UER is shown in the left panel of Fig. 3. From 2011 to 2022, the UER of cities within the YREB exhibited fluctuations around the zero baseline, reflecting four distinct phases of development following the shock of the global financial crisis. Phase 1 (2011–2013): Shock Resistance Phase. During this period, the U.S. subprime mortgage crisis evolved into a global financial crisis, significantly impacting the export-oriented economies of the YREB. As a result, UER declined markedly, reflecting the region’s initial vulnerability to external shocks.Phase 2 (2014–2016): Stable Recovery Phase. In response to the crisis, the Chinese government implemented a series of targeted macroeconomic stabilization policies, helping cities across the YREB gradually transition into the “New Normal” phase of economic development. UER began to recover as economic vitality was gradually restored. Phase 3 (2017–2018): Transition Phase. This period witnessed a further deterioration of the external environment, marked by increased global market volatility. Simultaneously, domestic structural reforms, including the shift between old and new growth drivers and supply-side reforms, introduced internal adjustment pressures, causing temporary setbacks in UER. Phase 4 (2019–2022): Second Shock Phase. The domestic economic environment became increasingly challenging after 2019. While resilience initially rebounded in 2019, it was soon disrupted by unprecedented external shocks. The pandemic-related production stoppages and economic disruptions severely weakened UER, though signs of gradual recovery emerged by 2022.

At the urban agglomeration level, different agglomerations exhibited distinct temporal evolution patterns in their UER, reflecting region-specific responses to external shocks and internal development dynamics. Yangtze River Delta Urban Agglomeration. This region recorded the highest average UER level over the sample period (0.101), with the smallest degree of fluctuation, indicating its strong capacity to withstand risks and adapt to changing conditions. Middle Reaches of the Yangtze Urban Agglomeration. This agglomeration exhibited the lowest average UER (0.036), coupled with the highest volatility. This reflects not only the region’s inherent economic fragility, but also its pronounced vulnerability to pandemic-related production halts and economic disruptions. Chengdu-Chongqing Urban Agglomeration. UER in this region was moderate (average value of 0.074), but with relatively large fluctuations, reflecting the ongoing structural transformation and policy-driven development adjustments in the region.

The Middle Reaches Yangtze River Urban Agglomeration experienced a sharp and distinctive decline in UER in 2020, exceeding the UER levels observed in both the upper and lower reaches. This was primarily due to the severe impact of the COVID-19 pandemic, as Hubei province, located in the Middle Reaches Yangtze River Urban Agglomeration, was the initial epicenter of the outbreak and subject to the most extensive and prolonged lockdowns nationwide. The suspension of production, disruption of logistics, and restrictions on consumption activities severely impaired the region’s economic functioning. However, the severity of the impact also reflected the region’s structural vulnerabilities. The middle reaches have long relied on traditional manufacturing and resource-based industries, sectors highly dependent on physical production and external markets. This dependence made them particularly fragile in the face of global supply chain disruptions and shrinking demand. By contrast, the Yangtze River Delta and the Chengdu-Chongqing region, despite facing similar health shocks, showed stronger resilience thanks to their more diverse economies and better-developed digital industries. Moreover, the DTI capacity of the middle reaches lags behind the other regions. Although digital transformation is underway, weak infrastructure and an immature high-tech sector limited the use of digital tools like remote work, smart logistics, and online consumption to soften the pandemic’s blow.

The spatial distribution of UER is shown in Fig. 4(a). At the overall regional level, the spatial distribution of UER exhibits significant regional disparities, characterized by a pattern of higher resilience in the Yangtze River Delta and Chengdu-Chongqing urban agglomerations, and lower resilience in the Middle Reaches of the Yangtze urban agglomeration.

At the urban agglomeration level, the Yangtze River Delta shows a distinct inland-coastal divergence. Cities with higher resilience are primarily located in inland areas, while coastal cities tend to exhibit lower resilience. This may be attributed to the export-oriented development model that dominates coastal cities, making them more vulnerable to external shocks. In contrast, many inland cities have seized the opportunity to accelerate industrial transformation, promoting strategic emerging industries and fostering new competitive advantages, thereby enhancing their resilience. In the Middle Reaches of the Yangtze urban agglomeration, UER is not only relatively low overall but also more evenly distributed across cities. This indicates the absence of a strongly dominant core city within the region. The leading cities such as Wuhan and Changsha have limited capacity to drive and anchor resilience for the entire agglomeration, highlighting the lack of effective regional leadership and coordination mechanisms. In the Chengdu-Chongqing urban agglomeration, the spatial disparity in UER is the most pronounced. The region exhibits a core-periphery structure, with Chongqing and Chengdu acting as dual economic cores. These two cities far outperform their surrounding cities in terms of economic size, population scale, and industrial development, generating a strong central radiating effect. However, this also exacerbates the resilience gap between the core cities and their peripheral counterparts.

The spatial disparity in UER levels highlights differentiated regional functions and development bottlenecks. Strategically, the Yangtze River Delta should consolidate its role as the national resilience core, promoting industrial chain stability and risk mitigation for the entire region. For the middle and upper reaches, the priority is to cultivate regional resilience hubs that can buffer external shocks and support surrounding areas. Policy-wise, fiscal transfers and targeted resilience-building programs should prioritize regions with weak self-recovery capacity. In urban planning, middle-reach cities should strengthen emergency infrastructure, improve redundancy in industrial systems, and optimize land-use layouts to reduce exposure to risks. Peripheral cities need to embed resilience thinking into industrial development and infrastructure investment, enhancing their capacity to withstand and recover from disruptions.

Note

Analysis of DTI levels

The temporal trend of DTI is shown in the right panel of Fig. 3. Over the study period, the level of DTI across the urban agglomerations of the YREB experienced significant improvement, rising from 0.095 in 2011 to 0.426 in 2022. However, the pace of growth slowed in recent years, and considerable room for further development remains. At the regional level, the Yangtze River Delta urban agglomeration consistently maintained a leading position, with a steeper growth trajectory compared to the YREB average. In contrast, the Chengdu-Chongqing urban agglomeration and the Middle Reaches of the Yangtze urban agglomeration both lagged behind and exhibited similar development levels and growth patterns, reflecting structural weaknesses in digital innovation capacity outside the core economic zones.

The spatial distribution of DTI is shown in Fig. 4(b). In terms of spatial patterns, DTI across the YREB demonstrates a “multi-point, multi-center” distribution, with high-value cities primarily concentrated in the Yangtze River Delta and the core cities of the other two urban agglomerations. This pattern highlights a clear “core-periphery” spatial structure, where digital innovation capacity is highly concentrated in leading cities, while peripheral cities face significant innovation deficits. At the urban agglomeration level, the Yangtze River Delta urban agglomeration ranks highest in DTI, benefiting from its early development in the digital economy, abundant innovation resources, and well-developed digital infrastructure, giving these cities a clear first-mover advantage. In the Middle Reaches of the Yangtze and Chengdu-Chongqing urban agglomerations, provincial capitals serve as regional innovation hubs, forming local innovation “highlands.” However, other cities within these agglomerations rely heavily on the spillover and radiating effects of their core cities. As a result, cities closer to provincial capitals tend to exhibit stronger digital innovation capacity, while cities located in peripheral, inland, and less developed areas display weaker digital innovation performance.

The spatial concentration of DTI in the Yangtze River Delta reveals a leading-edge digital economy cluster, but also exposes the innovation gaps in the middle and upper reaches. Strategically, the Yangtze River Delta should serve as a regional digital innovation source, fostering outward spillovers through industry cooperation and infrastructure interconnection. Policy prioritization should focus on enabling middle and upper reach cities to build digital industrial foundations, supported by national initiatives like “Eastern Data, Western Computing.” In urban planning, peripheral cities should prioritize smart infrastructure development, such as digital parks and industrial internet nodes, laying the groundwork for future industrial upgrading. These areas should also align their urban functional zoning with digital industry needs, integrating innovation districts into urban growth strategies.

Spatiotemporal characteristics of coupling coordination between DTI and UER

Temporal trends in coupling coordination

The boxplot analysis of coupling coordination degrees between DTI and UER (Fig. 5) across cities in the YREB from 2011 to 2022 reveals both the evolving trend and the dispersion dynamics over time. First, in terms of overall trends, the coupling coordination degree between DTI and UER has shown a continuous upward trend, with the mean and median values rising from 0.401 to 0.393 in 2011 to 0.682 and 0.669 in 2022, respectively. This reflects a notable transition from the category of “mild disorder” to “primary coordination.” This upward trajectory highlights the increasing role of digital technology in enhancing economic systems’ capacity to withstand and adapt to shocks, while a more stable economic environment simultaneously provides a favorable foundation for digital technology development. Second, regarding the dispersion of coordination degrees, the degree of dispersion was relatively large in 2011, indicating significant differences across cities at the early stage of digital transformation. However, over time, the dispersion gradually decreased, reaching its lowest point in 2018. This suggests a narrowing gap in coupling coordination levels across cities within the YREB, indicating a trend toward regional convergence. Nevertheless, in recent years, influences from domestic economic uncertainties have led to a widening of the dispersion again, highlighting emerging differentiation in cities’ digital-economic linkages. Finally, in terms of distribution shape, the annual boxplots generally exhibit a pyramid-like structure, with a wide base and narrow top. This indicates that while a small number of cities achieve significantly higher levels of coupling coordination (as seen from the presence of high-value outliers), most cities remain below the average coordination level, underscoring the fact that the majority of cities still face considerable room for improvement. Moreover, the limited spillover and diffusion effects from leading cities indicate that the driving role of high-performing cities in lifting overall regional coordination remains relatively weak.

Further analysis, based on the classification of coupling coordination types, reveals the evolving trajectory of coordination types over time (Fig. 6), highlighting three distinct stages: Initial Stage (2011–2014): Prevalence of Disorder. During this period, a large proportion of cities fell into the “disorder” categories, with some cities classified as “severe disorder.” This reflects the nascent stage of digital technology development, particularly in central and western cities, where digital infrastructure was underdeveloped and technological innovation capacity and penetration rates remained low. As a result, DTI had limited capacity to enhance UER, and the overall synergy between the two systems was weak. Transition Stage (2015–2018): Rapid Improvement in Coordination. This period witnessed a sharp increase in the share of cities classified as “primary coordination” or “near coordination,” while the proportion of disordered cities significantly declined, with severe disorder almost disappearing. This transformation coincided with the launch of the “Digital China” strategy and the “Made in China 2025” initiative under China’s 13th Five-Year Plan, which significantly accelerated the development of digital infrastructure and enhanced innovation capacity across cities in the YREB. As a result, digital industries, digital transformation, and technology-enabled upgrading significantly improved cities’ resilience to external shocks. Mature Stage (2019–2022): Gradual Maturity with Emerging Gaps. In this phase, “primary coordination” and “intermediate coordination” became the dominant coordination types, and a small number of cities advanced to “good coordination.” This reflects the deepening diffusion and application of digital technologies, which substantially enhanced the digital capabilities and adaptive capacity of most cities. However, no city reached the “excellent coordination” category, and “good coordination” cities remained relatively few, indicating that there is still considerable room for improvement in fully aligning DTI with UER. This underscores the need for further policy support, institutional innovation, and technology diffusion mechanisms to achieve a higher level of synergistic development.

Fig. 6 — Percentage of coordination types.

The temporal trends in the coupling coordination degree across different urban agglomerations are shown in Fig. 7. Among the three major agglomerations, the Yangtze River Delta Urban Agglomeration consistently exhibited the highest level of coordination, remaining above the regional average throughout the study period. Its coupling coordination degree improved from near disorder (0.479) in 2011 to intermediate coordination (0.709) in 2022, highlighting the strong mutual reinforcement between DTI and UER in the region. The Middle Reaches of the Yangtze Urban Agglomeration and the Chengdu-Chongqing Urban Agglomeration both exhibited lower levels of coordination but also experienced significant improvement over time. Their coordination degrees rose from mild disorder (0.350 and 0.358) in 2011 to primary coordination (0.673 and 0.652) in 2022. Notably, the gap between these two regions and the Yangtze River Delta gradually narrowed, indicating converging trends in regional digital-economic coupling. This improvement underscores the positive role of investments in digital infrastructure and targeted policy support for the digital economy in enhancing the alignment between digital innovation and UER in less-developed regions. However, the fact that these agglomerations have only reached primary coordination suggests that there remains substantial room for further progress, particularly in deepening digital-industrial integration and strengthening endogenous innovation capacity.

Spatial distribution of coupling coordination

Figure 8a and b, and 8c illustrate the spatial distribution of coupling coordination types between DTI and UER in 2011, 2018, and 2022, respectively. From a spatial perspective, the coupling coordination degree across the YREB reveals distinct regional disparities throughout the study period. In all three years, the Yangtze River Delta Urban Agglomeration consistently achieved the highest coupling coordination levels, while the Chengdu-Chongqing Urban Agglomeration and the Middle Reaches of the Yangtze Urban Agglomeration exhibited lower levels of coordination. Within each urban agglomeration, high coordination cities were predominantly concentrated in core cities located in the eastern and central parts of the region. In contrast, peripheral cities, which are less influenced by the spillover effects from core cities, maintained relatively low levels of coupling coordination. In the Yangtze River Delta Urban Agglomeration, coordination levels were relatively evenly distributed across cities, and the overall coordination level remained high. In particular, coastal cities near Shanghai exhibited notably high coordination degrees, indicating that Shanghai plays a strong radiating and driving role within the agglomeration. In the Chengdu-Chongqing Urban Agglomeration and the Middle Reaches of the Yangtze Urban Agglomeration, coordination gaps between core cities and surrounding areas were significantly larger, highlighting a marked internal imbalance within these regions. Core cities such as Chongqing, Chengdu, Wuhan, and Changsha have achieved relatively high coordination levels, while their surrounding cities lag behind, reflecting insufficient diffusion of digital innovation benefits and weaker UER spillovers.

Fig. 8 — The spatial distribution of coupling coordination degree.

From the perspective of the spatial evolution of coordination types, the YREB has undergone a notable transition from disorder to coordination. In 2011, the majority of cities remained in a state of disorder or low-level coordination, with only a few cities in the eastern region achieving primary coordination. By 2018, the number of cities reaching primary or intermediate coordination increased significantly, while the proportion of low-coordination cities declined sharply. By 2022, the coupling coordination degree had further improved, with all cities across the three urban agglomerations reaching at least primary coordination level. At the regional level, the spatial gap in coordination degrees among the three urban agglomerations gradually narrowed. The Yangtze River Delta Urban Agglomeration consistently maintained its position as the region with the highest coordination levels, with half of its cities achieving intermediate coordination or above by 2022. In the Middle Reaches of the Yangtze Urban Agglomeration, leading cities such as Wuhan and Changsha increasingly converged toward the higher levels observed in the eastern region, demonstrating a strong catch-up trend, while other cities successfully transitioned into primary coordination. In the Chengdu-Chongqing Urban Agglomeration, Chongqing and Chengdu consistently served as twin economic and innovation cores, actively driving improvements in the coupling coordination degree of their surrounding cities through spillover effects and regional economic integration.

Note

Spatial inequality and its decomposition in coupling coordination

This study employs the Dagum Gini Coefficient to measure the degree of spatial inequality in the coupling coordination degree across cities in the YREB, and further decomposes the spatial disparities into their sources. The between-group differences, within-group differences, and the contribution rates of each component are illustrated in Fig. 9.

Fig. 9 — Spatial inequality in coupling coordination.

Figure 9(a) depicts the overall spatial inequality in the coupling coordination degree between DTI and UER, as well as the within-group inequality for each urban agglomeration. Between 2011 and 2022, the overall Gini coefficient showed a fluctuating downward trend, decreasing from 0.155 to 0.036, indicating a clear convergence trend in the coupling coordination degree across the YREB. This reflects gradual narrowing of development gaps between cities. However, after 2018, the rate of decline slowed significantly, and in 2019 and 2020, the Gini coefficient even rebounded slightly. This temporary widening of inequality suggests the emergence of a “siphoning effect”, where cities with higher levels of coordination in digital innovation and UER begin to attract more innovation resources and development opportunities, thereby weakening the coordination momentum in cities with lower coordination levels. At the urban agglomeration level, the within-group Gini coefficients also displayed an overall downward trend, indicating declining internal inequality within each agglomeration. The Middle Reaches of the Yangtze Urban Agglomeration experienced the steepest decline (a reduction of 0.141), becoming the most internally equal agglomeration by 2022 with a within-group Gini of only 0.023. In contrast, the Yangtze River Delta Urban Agglomeration exhibited a more modest decline (0.063 reduction), remaining the agglomeration with the highest internal inequality by 2022 (Gini = 0.036). Nevertheless, the Yangtze River Delta maintained the smallest degree of fluctuation, demonstrating its strong systemic resilience in absorbing and adapting to external shocks.

Figure 9(b) reports the degree of disparity between urban agglomerations. Overall, the inter-group Gini coefficient showed a downward trend across all urban agglomeration pairs, indicating that the gaps in coupling coordination degree between different agglomerations have gradually narrowed. This suggests that the coordinated development strategy for the YREB has yielded positive effects in promoting regional convergence. Among the three agglomeration pairs, the gap between the Yangtze River Delta Urban Agglomeration and the Middle Reaches of the Yangtze Urban Agglomeration narrowed the most, with the inter-group Gini coefficient declining by 0.145. In contrast, the gap between the Chengdu-Chongqing Urban Agglomeration and the Middle Reaches of the Yangtze narrowed at a slower pace, with a decline of only 0.116. In terms of the absolute level of inter-group inequality, the gap between the Yangtze River Delta and Chengdu-Chongqing consistently ranked the largest contributor to inter-group inequality, with an average Gini coefficient of 0.072. This was followed by the Yangtze River Delta versus the Middle Reaches of the Yangtze (average Gini = 0.070), while the gap between Chengdu-Chongqing and the Middle Reaches of the Yangtze was the smallest (average Gini = 0.052). The persistently high inequality between the Yangtze River Delta and the other two agglomerations underscores the Delta’s clear leadership position in terms of coupling coordination between DTI and UER. However, the recent narrowing of these gaps indicates that the Yangtze River Delta has increasingly played a radiating and leading role, promoting spillover effects and helping to lift coordination levels in the other two agglomerations.

Figure 9(c) further reports the sources of inequality in the coupling coordination degree. Over the study period, the contribution rate of intra-group differences remained relatively stable, whereas the contribution rate of inter-group differences fluctuated significantly, showing an overall U-shaped trend. This suggests that the coordination and synergy between urban agglomerations still requires further enhancement. From the perspective of contribution share, inter-group differences consistently accounted for the largest share of total inequality, with an average contribution rate of 49.7%, significantly exceeding the contributions of intra-group differences (28.7%) and transvariation intensity (21.5%). This indicates that the primary source of spatial inequality in the coupling coordination degree stems from disparities between different urban agglomerations, rather than within individual agglomerations. These findings highlight the need for future regional planning efforts to further strengthen cross-agglomeration collaboration, promote deeper integration across regions, and avoid excessive concentration of resources within the most advanced regions. By fostering broader regional synergies, the YREB can move toward more balanced and resilient development.

The persistence of inter-agglomeration disparities signals the need to reconfigure regional development dynamics. Strategically, the YREB should shift from a core-periphery model to a more networked, multi-center system. Core cities should be tasked with building outward-oriented industrial and innovation networks, while peripheral cities focus on enhancing their absorptive capacity. In policy terms, differentiated fiscal incentives and inter-provincial collaborative mechanisms should be implemented to equalize development opportunities. Urban planning must reflect this rebalancing, with peripheral areas guided to develop complementary industries and logistics hubs that integrate with core regions, rather than competing on low-end manufacturing. Regional spatial planning should also prioritize innovation corridors and cross-agglomeration infrastructure links to reduce fragmentation.

Spatial autocorrelation characteristics of coupling coordination

Global spatial autocorrelation analysis

To assess the spatial dependence of coupling coordination degrees between DTI and UER across cities in the YREB, this study constructs a geographic weight matrix based on the inverse distance between city pairs. The Global Moran’s I index is then employed to evaluate the global spatial autocorrelation of the coupling coordination degree.

The results, presented in Table 3, show that the Global Moran’s I index is consistently positive across all years of the study period and statistically significant at the 1% level. This indicates the existence of significant positive spatial spillover effects, where neighboring cities tend to exhibit similar levels of coupling coordination, forming “high-high” or “low-low” spatial clusters. In terms of temporal trends, the degree of spatial clustering initially fluctuated and increased before peaking around 2017, followed by a gradual decline, forming an inverted U-shaped trajectory. By 2022, the spatial clustering degree (Moran’s I = 0.178) slightly exceeded its 2011 level (0.153), indicating a reinforced “clustering effect” among cities with similar levels of coupling coordination. This suggests that highly coordinated cities increasingly cluster together, while low-coordination cities also tend to group into lower-performing clusters. At the same time, this trend reflects a weakening of diffusion effects across the broader region, implying that the spillover benefits from leading cities to surrounding areas have diminished to some extent. This highlights the importance of strengthening cross-regional collaboration mechanisms to revive the diffusion and sharing of digital innovation advantages across all cities in the region.

Table 3.

Global moran’s I of coupling coordination degree.

Year	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022
Moran’ I	0.153	0.156	0.165	0.185	0.148	0.144	0.183	0.175	0.069	0.149	0.166	0.178
P-value	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000

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Local spatial autocorrelation analysis

The local Moran’s I index is employed to evaluate the local spatial autocorrelation of each city’s coupling coordination degree, with the results visualized through a Moran scatter plot (Fig. 10). Overall, the majority of cities fall within the first and third quadrants, indicating that most cities exhibit either “High-High” (H-H) promotion-type clustering or “Low-Low” (L-L) lagging-type clustering. Based on the quadrants, the spatial clustering types are classified as follows: H-H promotion type, L-L lagging type, H-L driving type and L-H transitional type.

Fig. 10 — Local Moran’s I scatter plot of coupling coordination degree.

In 2011, most cities were concentrated in the third quadrant, indicating that L-L lagging-type clustering was the dominant spatial pattern during the early stage of the sample period. This reflects the generally low level of coupling coordination across the region, where mutual reinforcement between cities was weak. By 2022, a significant portion of cities had shifted from the L-L lagging type to either the H-L driving type or the H-H promotion type. This shift signals the strengthening of spillover effects from cities with high coupling coordination, which increasingly radiated positive impacts to their neighboring cities. This trend underscores the growing role of leading cities in promoting the coordinated development of DTI and UER across the region.

Analysis of the driving factors behind coupling coordination

Spatial effects analysis of the driving factors of coupling coordination

As shown in the preceding analysis, the coupling coordination degree between DTI and UER has steadily improved over time, but remains constrained by several socio-economic factors. Moreover, the results of the Moran’s I index confirm the presence of significant spatial autocorrelation in the coupling coordination degree across the three major urban agglomerations of the YREB. To further identify the key drivers that contribute to the coupling coordination between DTI and UER, and to examine whether these drivers exert spatial spillover effects, this study conducts a spatial econometric regression analysis. Drawing on existing literature and the practical characteristics of the YREB, the analysis considers five key factors: Level of Economic Development (GDP), Fiscal Pressure (Fin), Industrial Structure (IS), Innovation Base (Inn), Scientific and Technological Support(Tec). The descriptive statistics for these key variables are presented in Table 4.

Table 4.

Descriptive Statistics.

Obs	Mean	SD	Max	Min
816	0.586	0.103	0.081	0.804
816	10.987	0.560	9.704	12.201
816	0.537	0.231	0.088	1.541
816	41.884	9.497	20.930	74.120
816	8.312	1.576	4.635	12.129
816	0.028	0.023	0.002	0.178

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First, the appropriate spatial econometric model is selected based on formal diagnostic tests. Results from the spatial LM tests show that both the spatial error LM statistic (1853.845) and the spatial lag LM statistic (1302.876) are highly significant at the 1% level. Similarly, the Robust LM tests for spatial error and spatial lag yield values of 663.661 and 112.692, respectively, both of which are also significant at the 1% level. These results confirm the presence of both spatial dependence in the dependent variable and spatial dependence in the error term, indicating that the Spatial Durbin Model (SDM) is the most appropriate specification for the analysis. Further, results from the LR tests yield values of 25.81 and 29.25, both of which reject the null hypotheses that the SDM can be simplified into either a Spatial Autoregressive Model (SAR) or a Spatial Error Model (SEM). This provides additional evidence supporting the use of the SDM.

Table 5 reports the regression results on the influencing factors of the coupling coordination degree between DTI and UER. Column (1) reports the regression results on the influencing factors of the overall coupling coordination degree in the YREB. From the overall perspective, the key drivers influencing the coupling coordination degree between DTI and UER include economic development level, fiscal pressure, innovation capacity, and scientific research support, all of which exhibit significant effects in the main regression results, at least at the 5% significance level. Regions with higher levels of economic development tend to have more diversified industrial structures, which facilitates deeper integration between digital technologies and the real economy. In addition, greater economic resources provide more stable funding for the continuous transformation and application of digital innovations. Lower fiscal pressure and higher scientific research support indicate that greater resources can be allocated to basic research and technological development, fostering the integration of digital technologies across multiple industries. This technological diffusion effect ultimately enhances the adaptive capacity and systemic stability of the regional economy. Cities with strong innovation capacity not only build the knowledge foundation for digital innovation but also accelerate the translation of digital technology into tangible productivity gains.

Table 5.

Analysis of factors influencing coupling coordination Degree.

	(1)	(2)	(3)	(4)
	YREB	Yangtze River Delta Urban Agglomeration	Middle Yangtze River Urban Agglomeration	Chengdu-Chongqing Urban Agglomeration
	0.0736^***	0.0567^***	0.0880^***	0.0726^**
	(0.0120)	(0.0110)	(0.0287)	(0.0367)
	0.0554^***	−0.0032	0.0956^***	0.0710
	(0.0198)	(0.0229)	(0.0341)	(0.0694)
	−0.0002	−0.0009	0.0015	−0.0002
	(0.0005)	(0.0006)	(0.0011)	(0.0013)
	0.0063^**	−0.0013	0.0157^**	0.0100
	(0.0031)	(0.0024)	(0.0073)	(0.0099)
	0.2765^**	0.4109^***	0.8722^***	−0.5175
	(0.1135)	(0.1211)	(0.2252)	(0.4037)
	−0.2257^***	−0.1276	−0.0178	0.3728
	(0.0713)	(0.0815)	(0.2027)	(0.2548)
	0.5338^***	−0.1009	0.8583^***	0.0744
	(0.1167)	(0.1509)	(0.2311)	(0.5595)
	−0.0045^**	0.0081^**	0.0210^***	0.0032
	(0.0020)	(0.0032)	(0.0065)	(0.0093)
	0.0247	0.0069	−0.0445	0.0306
	(0.0154)	(0.0172)	(0.0381)	(0.0644)
	2.5524^***	3.1052^***	8.1737^***	−1.7053
	(0.8448)	(1.0680)	(1.4752)	(2.7573)
	0.6764^***	−0.4871^*	0.3483^**	−0.8848^***
	(0.0783)	(0.2647)	(0.1444)	(0.3062)
Year	√	√	√	√
Individual	√	√	√	√
N	816	312	324	180

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Note: Standard errors in parentheses, * p < 0.1, ** p < 0.05, *** p < 0.01.

From the spatial perspective, economic development level and industrial structure both exhibit negative spatial spillover effects, significant at the 5% level. This suggests that high levels of economic development and advanced industrial structures in core cities tend to suppress the coupling coordination degree in neighboring cities. This is largely due to the “siphoning effect” whereby core cities absorb talent, capital, and technological resources from surrounding cities, hindering the latter’s ability to achieve coordinated development. Moreover, advanced industries tend to cluster in core cities, leaving peripheral cities trapped in lower-end industrial structures, making it difficult for them to leverage digital technology for industrial upgrading, thereby hampering broader regional coordination. In contrast, lower fiscal pressure and greater scientific research support generate positive spatial spillover effects. When fiscal pressure is low, cities are less reliant on competitive policies to attract external resources, allowing them to focus more on collaborative development and regional synergies. Cities with strong scientific research support often emerge as regional innovation hubs, and their technological breakthroughs can spill over to neighboring cities, promoting digital-economic coordination across the region.

At the urban agglomeration level, the primary drivers of coupling coordination in the Yangtze River Delta Urban Agglomeration are economic development level and scientific research support. In terms of spatial spillovers, economic development level does not exert a significant negative effect on neighboring cities’ coordination degrees, while industrial structure actually contributes positively to neighboring cities’ coordination. This reflects the mature regional collaboration mechanisms, robust technology diffusion channels, highly integrated industrial chains, and well-developed infrastructure within the Yangtze River Delta, all of which provide a favorable external environment for coupling coordination. For the Middle Reaches of the Yangtze Urban Agglomeration, the key drivers are consistent with the full sample analysis, including economic development level, fiscal pressure, innovation capacity, and scientific research support. Spatial spillover patterns in this agglomeration also mirror those observed in the Yangtze River Delta, indicating that the Middle Reaches region has gradually developed higher levels of internal coordination and diffusion capabilities. In contrast, in the Chengdu-Chongqing Urban Agglomeration, economic development level is the only significant factor influencing coupling coordination, and no significant spatial spillover effects are detected. This suggests that the Chengdu-Chongqing region is still in a foundational development phase, where basic economic development outweighs advanced innovation capacity, industrial upgrading, or institutionalized regional cooperation as a driver of coordination. Furthermore, the “dual-core” effect of Chengdu and Chongqing has yet to fully translate into a strong radiating effect capable of lifting coordination levels in surrounding cities.

The spatial spillover patterns suggest that while fiscal and innovation spillovers benefit wider regions, economic agglomeration effects risk deepening inequalities. Strategically, core cities must transition from isolated growth to regional leadership, proactively cultivating industrial and innovation linkages with surrounding areas. Policy priorities should shift from supporting only leading cities toward enhancing the spillover absorption capacity of peripheral cities—through innovation platforms, fiscal support, and industry alignment mechanisms. In urban planning, spatial structure should be adjusted to strengthen cross-regional industrial corridors and collaborative innovation spaces. Peripheral cities should plan for supporting functions, such as logistics hubs, technology service centers, and workforce training bases, to better integrate into the regional innovation-resilience system.

Table 6 presents the spatial effect decomposition results for the full sample. The results indicate that economic development level, fiscal pressure, innovation capacity, and scientific research support all exert significant positive direct effects on the coupling coordination degree between DTI and UER, with significance levels of at least 5%. However, economic development level and industrial structure exert negative indirect effects on coupling coordination, both significant at the 10% level. This suggests that higher economic development and more advanced industrial structures in one city tend to suppress the coupling coordination development of neighboring cities, likely due to siphoning effects where core cities attract resources at the expense of their neighbors. In contrast, fiscal pressure and scientific research support generate positive indirect effects, indicating that cities with lower fiscal pressure and stronger scientific research support contribute positively to coupling coordination development in neighboring cities, potentially through regional cooperation mechanisms and knowledge spillovers. From a comprehensive perspective, the positive and negative effects of economic development level essentially offset each other, resulting in an insignificant total effect. In contrast, fiscal pressure and scientific research support both produce significant positive total effects, indicating their net contribution to promoting coupling coordination. Conversely, industrial structure exerts a significant negative total effect, reflecting the concentration of advanced industries in core cities and the resulting structural imbalances across regions. Lastly, the total effect of innovation capacity is insignificant, implying that the localized benefits of innovation capacity have not yet fully translated into broader regional coordination gains.

Table 6.

Spatial effect decomposition.

Direct effects	Indirect effects	Total effects
0.0652^***	−0.5857^*	−0.5204
(0.0131)	(0.3259)	(0.3314)
0.0815^***	1.9569^**	2.0384^**
(0.0233)	(0.9336)	(0.9501)
−0.0004	−0.0167^*	−0.0171^*
(0.0006)	(0.0088)	(0.0089)
0.0080^**	0.1017	0.1097
(0.0033)	−0.0669	(0.0675)
0.3884^***	9.3231^*	9.7116^*
(0.1448)	(5.6106)	(5.6758)

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Robustness checks

To ensure the reliability and robustness of the empirical results, a series of robustness checks were conducted from multiple perspectives, including the spatial weight matrix, variable definitions, sample data, and model specification. The regression results are shown in Table 7.

Table 7.

Robustness checks.

	(1)	(2)	(3)	(4)	(5)
	Gravity-based Spatial Weight Matrix	Winsorization	Coupling Coordination Types	SAR Model	SEM Model
	0.0758^***	0.0657^***	0.9304^***	0.0687^***	0.0731^***
	(0.0121)	(0.0100)	(0.1560)	(0.0119)	(0.0121)
	0.0540^***	0.0641^***	0.4120	0.0735^***	0.0672^***
	(0.0198)	(0.0181)	(0.2569)	(0.0189)	(0.0197)
	−0.0001	−0.0002	−0.0039	−0.0003	−0.0001
	(0.0005)	(0.0004)	(0.0068)	(0.0004)	(0.0005)
	0.0068^**	0.0057^**	0.0675^*	0.0070^**	0.0070^**
	(0.0031)	(0.0026)	(0.0397)	(0.0028)	(0.0030)
	0.2950^***	0.2443^**	5.1222^***	0.2838^**	0.2642^**
	(0.1137)	(0.1076)	(1.4761)	(0.1132)	(0.1147)
	−0.2472^***	−0.1762^***	−2.8470^***
	(0.0723)	(0.0609)	(0.9276)
	0.5661^***	0.3880^***	6.7543^***
	(0.1071)	(0.1014)	(1.5273)
	−0.0043^**	−0.0028^*	−0.0508^**
	(0.0020)	(0.0017)	(0.0257)
	0.0241	0.0137	0.3497^*
	(0.0159)	(0.0130)	(0.2010)
	2.4324^***	2.3893^***	21.7681^**
	(0.8029)	(0.7407)	(10.9931)
	0.5847^***	0.7186^***	0.5588^***	0.7158^***
	(0.0942)	(0.0703)	(0.0984)	(0.0701)
lambda					0.7129^***
lambda					(0.0716)
sigma2_e	0.0011^***	0.0007^***	0.1775^***	0.0011^***	0.0011^***
sigma2_e	(0.0001)	(0.0000)	(0.0088)	(0.0001)	(0.0001)
Year	√	√	√	√	√
Individual	√	√	√	√	√
N	816	816	816	816	816

Open in a new tab

Note: Standard errors in parentheses, * p < 0.1, ** p < 0.05, *** p < 0.01.

First, we replaced the baseline spatial weight matrix with a gravity-based spatial weight matrix. Compared with the conventional adjacency matrix, the gravity matrix integrates both spatial distance and economic size, reflecting the dual influence of geographic proximity and economic interaction intensity on regional coupling coordination. The regression results are shown in Column (1), Table 7.The regression results under the gravity matrix remained largely consistent with the baseline findings, and the core explanatory variables continued to show significant impacts. The results remain largely consistent, confirming that the spatial dependence identified in the baseline model is robust to changes in spatial weights.

Second, to mitigate the potential influence of extreme values, we applied a 1st–99th percentile winsorization process to the core explanatory and explained variables. The regression results are shown in Column (2), Table 7. After excluding the top and bottom 1% of extreme observations, the regression results remained robust, with the direction and significance levels of the key variables largely unchanged. The estimation results remained stable, indicating that extreme values did not bias the core findings.

Third, we conducted an alternative explained variable test. In the baseline model, the coupling coordination degree was used as a continuous dependent variable. In the robustness test, we replaced it with the coupling coordination types, a categorical classification variable derived from the degree. The generation rules for this categorical variable are specified in Table 2. The regression results are shown in Column (3), Table 7. Regression results using coupling coordination types as the dependent variable remained largely consistent with the baseline results, confirming that the relationship between digital technological innovation and urban economic resilience holds true across different forms of measurement.

Finally, we tested the robustness of model specification by replacing the SDM with the spatial autoregressive model (SAR) and the spatial error model (SEM). Both SAR and SEM control for different spatial dependence structures—SAR captures endogenous spatial spillovers, while SEM accounts for spatial autocorrelation in the error terms. The regression results are shown in Column (4) and Column (5), Table 7. Across these alternative models, the key driving factors such as economic development level, fiscal capacity, innovation base, and technological support maintained their explanatory power, and spatial dependence remained statistically significant. This confirms that the spatial effects remain robust across models.

In summary, the results of these robustness checks confirm that the main empirical conclusions of this study are not sensitive to changes in spatial structure, variable selection, sample treatment, or model specification. The coupling coordination mechanism between digital technological innovation and urban economic resilience, along with its spatial spillover effects, exhibits strong explanatory stability across a variety of empirical conditions.

Conclusion and policy recommendations

Conclusion

This study investigates the interactive relationship between DTI and UER across the three major urban agglomerations of the YREB from 2011 to 2022, adopting a coupling coordination perspective. The key findings can be summarized as follows:

The level of DTI in the three urban agglomerations of the YREB exhibited a steady upward trend over the sample period, while UER fluctuated around the zero baseline. Both systems face imbalanced and insufficient development challenges, with a spatial pattern of higher levels in the eastern region and lower levels in the central and western regions. However, regional disparities have gradually narrowed in recent years.

The coupling coordination degree between DTI and UER has continuously improved, transitioning from “mild disorder” to “primary coordination.” However, fluctuations re-emerged in recent years, indicating periodic instability in the coordination process. At present, most cities remain at the “primary coordination” stage, meaning high-quality coordinated development has yet to be fully achieved.

The overall spatial inequality in coupling coordination degree exhibited a declining trend, with both inter-agglomeration and intra-agglomeration disparities narrowing. However, inter-agglomeration disparities remain the primary source of spatial inequality, with the Yangtze River Delta retaining its dominant advantage despite a gradual narrowing of its gap with the other two agglomerations.

Significant positive spatial spillover effects exist in the coupling coordination degree, forming clear “High-High” (H-H) and “Low-Low” (L-L) spatial clusters. Over time, spatial clustering exhibited an inverted U-shaped pattern, and the dominant clustering type gradually shifted from L-L to H-H and H-L, demonstrating the increasing radiating effect of high-coordination cities.

Key drivers of coupling coordination include economic development level, fiscal pressure, innovation capacity, and scientific research support, all of which exert significant direct effects. However, the “siphoning effect” of core cities causes economic development level and advanced industrial structure to produce significant negative spatial spillovers, constraining coordination in neighboring cities. The Yangtze River Delta Urban Agglomeration benefits from strong scientific research support and effective industrial structure upgrading, which positively contributes to neighboring cities’ coordination. Its mature regional cooperation mechanisms and robust technology diffusion capacity further enhance regional synergy. In the Middle Reaches of the Yangtze Urban Agglomeration, the driving factors largely align with those observed in the full sample, indicating emerging regional coordination capacity. In contrast, the Chengdu-Chongqing Urban Agglomeration remains primarily driven by basic economic development, with weak contributions from technological innovation capacity or the dual-core development strategy of Chengdu and Chongqing. Further enhancement of collaborative innovation and coordinated development mechanisms is urgently needed.

Policy recommendations

Based on the above findings, this study proposes the following policy recommendations to enhance the coupling coordination between DTI and UER across the YREB:

Promote cross-regional innovation platforms and industrial collaboration mechanisms

To narrow the regional disparities in coupling coordination between DTI and UER, YREB should prioritize the construction of cross-regional innovation platforms and industrial collaboration mechanisms. Currently, the Yangtze River Delta demonstrates strong technological spillover and industrial coordination capacities, while the middle and upper reaches lag behind in innovation diffusion and industrial upgrading. Bridging these gaps requires enhancement of regional linkages and innovation ecosystems. Building on established initiatives such as the Yangtze River Delta G60 Science and Innovation Corridor and the Chengdu-Chongqing Regional Innovation Cooperation Zone, the YREB should further expand its cross-agglomeration collaboration. Efforts should focus on strengthening the coordinated development of industrial chains and improving the flow of innovation resources from core areas to peripheral cities. This includes supporting the formation of joint technology R&D centers, integrated data exchange platforms, and collaborative industrial parks that facilitate the commercialization of digital technologies. In addition, institutional mechanisms such as the Middle Reaches City Cluster Cooperation Framework can be leveraged to promote collaborative innovation among central cities like Wuhan, Changsha, and Nanchang. By fostering industrial specialization and division of labor within the region, and by enhancing the connectivity of digital infrastructure, these mechanisms will support the formation of adaptive supply networks and flexible industrial systems, thereby enhancing the overall resilience of the region. To ensure sustained cooperation, it is necessary to establish a YREB-level digital innovation collaboration framework, align regional science and technology development plans, and facilitate cross-provincial industrial coordination agreements.

Strengthen fiscal and policy support to enhance innovation capacity in the middle and upper reaches

To address the structural weaknesses in the middle and upper reaches of the YREB, it is essential to strengthen fiscal and policy support, focusing on enhancing the regional capacity for DTI and improving UER. Compared with the Yangtze River Delta, the middle and upper reaches suffer from weaker fiscal capacity, lower levels of industrial diversification, and underdeveloped digital infrastructure, making them more vulnerable to external shocks and limiting their endogenous innovation momentum. National and regional policy instruments should be better coordinated to address these shortcomings. Priority can be given to leveraging major national initiatives such as the Eastern Data, Western Computing project, which channels data computing resources from eastern cities to western and central regions, promoting the construction of digital infrastructure in less developed areas. In addition, the Central Government Science and Technology Innovation Guidance Fund, as well as special funds under the “New Infrastructure” initiative, can be directed toward supporting digital R&D, platform construction, and industrial upgrading in key cities along the middle reaches and the Chengdu-Chongqing region. On the fiscal side, it is recommended to strengthen intergovernmental fiscal transfers and targeted subsidies, focusing on enhancing the innovation absorption and transformation capacity of peripheral cities. Pilot programs such as digital economy demonstration zones and intelligent manufacturing clusters can be promoted in cities like Wuhan, Changsha, and Chengdu, improving their ability to translate technological breakthroughs into practical industrial competitiveness. Furthermore, provincial-level governments should improve policy coordination across regions, jointly formulating incentive systems for industrial upgrading, talent attraction, and platform building.

Optimize the spillover effects of core cities and enhance the development capacity of peripheral areas

The spatial imbalance of coupling coordination within the YREB is closely related to the concentration of DTI and industrial resources in core cities, leading to resource siphoning effects on surrounding areas. To build a balanced and resilient regional economic system, it is essential to optimize the spillover mechanisms of core cities and enhance the development capacity of peripheral cities. In industrial layout, efforts should focus on promoting the gradient transfer of mid-to-high-end industrial segments from core cities such as Shanghai, Hangzhou, and Chongqing to peripheral areas including Hefei, Nanchang, and Mianyang. This process can be guided by regional initiatives such as the Yangtze River Delta Industrial Transfer Demonstration Zones and the Chengdu-Chongqing Advanced Manufacturing Collaboration Zone, supporting the formation of multi-layered and multi-centered industrial networks. At the policy level, preferential measures should be designed to foster distinctive industries and innovation capacity in peripheral areas. For instance, supporting cities in the middle and upper reaches to build smart manufacturing clusters, digital economy pilot zones, and regional innovation hubs, tailored to their comparative advantages. Moreover, peripheral cities should be encouraged to strengthen their urban innovation networks, improving inter-city collaboration in research, talent mobility, and industrial services.

Limitations

Compared to previous studies, this paper makes several innovations by embedding its analysis in the context of rising technological transformation efficiency and the construction of a unified domestic market. It highlights the two-way coupling relationship between digital innovation and UER and analyzes its spatial dynamics across urban agglomerations. Using the YREB’s three major urban agglomerations as the empirical setting, this study investigates both intra-agglomeration and inter-agglomeration differences in coupling coordination, and further identifies the key drivers and spatial spillover mechanisms shaping this relationship.

However, this study also has some limitations. The focus on the YREB’s three major urban agglomerations provides valuable insights, but the factors influencing the spatial differentiation of digital innovation and UER coupling coordination may differ in other regions. Future research could extend the analysis to other regions and urban clusters to test the robustness and generalizability of the conclusions. Additionally, as the understanding of new technology and economic development continues to evolve, more comprehensive and rigorous measurement methods for DTI and UER will be essential. Developing dynamic, multi-dimensional evaluation systems that capture emerging technologies and resilience pathways will be a key focus of future research.

Abbreviations

DTI: Digital technological innovation
UER: Urban economic resilience
YREB: Yangtze River Economic Belt
LCRM: Longitudinal and Cross-sectional Ranking Method

Author contributions

Libin Guo and Kang Liu wrote the main manuscript text, Kang Liu prepared figures and Tables. Libin Guo provided funding support. All authors reviewed the manuscript.

Funding

This work was supported by Major Special Project of Social Science Planning in Chongqing (No. 2022ZDSC11), Key Science and Technology Project of Chongqing (No. KJZD-K202301601) and Science and Technology Research project of Chongqing Education Commission (No. KJZD-K202502102).

Data availability

Interested researchers may request access to the data by contacting Kang Liu at lk_ctbu@163.com. Access to the data will be granted on a case-by-case basis.

Declarations

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

Interested researchers may request access to the data by contacting Kang Liu at lk_ctbu@163.com. Access to the data will be granted on a case-by-case basis.

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PERMALINK

Spatiotemporal patterns and drivers of coupling coordination between digital technological innovation and economic resilience in the Yangtze river economic belt

Libin Guo

Kang Liu

Abstract

Introduction

Literature review

Research on urban economic resilience (UER)

Research on digital technology innovation (DTI)

Research on the relationship between DTI and UER

The coupling coordination mechanism between DTI and UER

The conceptual connotations of DTI and UER

The coupling and coordination mechanism between DTI and UER

The pathways through which DTI enhances UER

The pathways through which UER supports DTI

Fig. 1.

Research methodology and data sources

Overview of the study area

Fig. 2.

Note

Variable selection and measurement

Selection of indicators for DTI

Digital infrastructure

Digital technology application

Digital innovation capacity

Table 1.

Measurement of UER

Research methodology

Measurement methodology

Coupling coordination model

Table 2.

Dagum gini coefficient

Spatial econometric model

Data sources

Spatiotemporal characteristics of DTI and UER

Fig. 3.

Analysis of UER levels

Fig. 4.

Note

Analysis of DTI levels

Spatiotemporal characteristics of coupling coordination between DTI and UER

Temporal trends in coupling coordination

Fig. 5.

Fig. 6.

Fig. 7.

Spatial distribution of coupling coordination

Fig. 8.

Note

Spatial inequality and its decomposition in coupling coordination

Fig. 9.

Spatial autocorrelation characteristics of coupling coordination

Global spatial autocorrelation analysis

Table 3.

Local spatial autocorrelation analysis

Fig. 10.

Analysis of the driving factors behind coupling coordination

Spatial effects analysis of the driving factors of coupling coordination

Table 4.

Table 5.

Table 6.

Robustness checks

Table 7.

Conclusion and policy recommendations

Conclusion

Policy recommendations

Promote cross-regional innovation platforms and industrial collaboration mechanisms

Strengthen fiscal and policy support to enhance innovation capacity in the middle and upper reaches

Optimize the spillover effects of core cities and enhance the development capacity of peripheral areas

Limitations

Abbreviations

Author contributions

Funding

Data availability

Declarations

Competing interests

Footnotes

References

Associated Data

Data Availability Statement

ACTIONS