An Empirical Investigation into the Effects of the Digital Economy on Regional Integration: Evidence from Urban Agglomeration in China

Abstract

Based on the urban panel data of Beijing-Tianjin-Hebei from 2009 to 2021, this article constructs an indicator system for the development level of the digital economy and regional integration, evaluates the impact of the digital economy on the integration development levels of different types of cities. The study found that (1) The digital economy significantly promotes the integration level of Beijing, Tianjin, and Hebei. The study divides Beijing-Tianjin-Hebei into two categories: large cities and small and medium-sized cities. Large cities and small and medium-sized cities have different effects on the relationship between the digital economy and integration level. (2) From the analysis of different dimensions of the digital economy, digital network infrastructure improvements, industrial digitization, and digital society can all promote regional integration. However, different digital economy contents have different promotional effects on different types of cities. (3) From the results of the mechanism analysis, it can be seen that the optimization of the allocation of human and capital elements, the increase in patent innovation, and the reduction of transaction costs will help enhance the driving force of the digital economy for regional integration. Among them, innovative development significantly impacts large cities’ integration levels. The promotion effect is more significant, and small and medium-sized cities are mainly affected by reduced transaction costs and the optimized allocation of capital factors. This study further expands the impact mechanism of the digital economy on the level of regional integration development. It provides a more in-depth analysis of the use of the digital economy to achieve coordinated regional development in regions with excessive economic and technological differences through heterogeneity research.

1. Introduction

The coordinated development strategy in Beijing-Tianjin-Hebei is a prime example of regional integration efforts in developing countries. Over the past decade, this strategy has achieved significant breakthroughs, with the region’s GDP reaching 10.4 trillion yuan in 2023, a 1.9-fold increase compared with 2013. In terms of digital industries, the Beijing-Tianjin-Hebei region achieved software business revenue of 2.9827 trillion yuan in 2023, accounting for 24.2% of the country, a year-on-year increase of 17.1%, 3.7 percentage points higher than the national average; it issued the “Beijing-Tianjin-Hebei Industrial Internet Collaborative Development Demonstration Zone Construction Plan (2021–2023)” and signed the “Beijing-Tianjin-Hebei Big Data Development Strategic Cooperation Agreement”. The synergy of industrial development was strengthened.

Currently, data are the fifth largest factor of production, playing a crucial role in enhancing global economic vitality and promoting world economic growth. According to calculations by the China Academy of Information and Communications Technology (CAICT), in 2023, the total digital economy of five countries, including the United States, China, Germany, Japan, and South Korea, exceeded 33 trillion USD, with a year-on-year growth of over 8%. Compared with that in 2019, the digital economy’s share of GDP reached 60%, an increase of approximately eight percentage points [1]. In 2022, Beijing’s digital economy will achieve an added value of 1733.02 billion yuan, accounting for 41.6% of the city’s GDP; Tianjin’s digital economy will exceed 870 billion yuan, accounting for more than 50% of GDP; Hebei’s digital economy will reach 1.51 trillion yuan, accounting for 35.6% of GDP. In practice, the digital economy has become a key driving force for regional economic growth and integrated development. Still, academia has yet to reach a consensus on the relationship between the digital economy and regional integrated development. On the one hand, digital technology promotes the development of new business forms and the transformation of traditional industries through digital industrialization and industrial digitalization. Through the introduction of new factor inputs, new resource allocation methods, and new total factor productivity, digital technology fosters regional economic development. In this process, the digital economy can directly influence regional coordinated development through spatial spillover effects, technology generalization effects, and driving transmission effects. It can also indirectly narrow regional development gaps by improving marketization levels and optimizing labor resource allocation [2]. The digital economy can also facilitate the flow and dissemination of information, reduce cultural differences between regions, and promote regional economic integration [3]. On the other hand, the digital economy may exacerbate the flow and concentration of factors such as capital, talent, technology, and innovation toward economically developed regions, even leading to a trend of “reverse penetration” of tertiary, secondary, and primary industries. Issues such as information silos, data chimneys, and digital divides are also becoming increasingly prominent, widening the economic development gap between regions. With the increasing economic gap between northern and southern China, whether the digital economy, as an essential component of new productive forces, can strengthen Beijing’s development advantages while promoting the development of Tianjin and Hebei and driving the improvement of Beijing-Tianjin-Hebei integration has become a key research topic in the construction of a pilot zone and demonstration zone for Chinese-style modernization in Beijing-Tianjin-Hebei, which is the focus of this study. Therefore, at this historical juncture of the tenth anniversary of the elevation of the Beijing-Tianjin-Hebei coordinated development strategy to a national strategy and the critical moment of China’s accelerated efforts to form new productive forces, thoroughly analyzing the development level and spatial evolution characteristics of the digital economy within Beijing-Tianjin-Hebei holds significant theoretical and practical implications for optimizing the spatial layout of the modern economic system in this region, building a world-class urban agglomeration, and playing a leading and exemplary role in regional coordinated development.

This paper focuses on the current gaps in the research on the development level and spatial structure of the digital economy in Beijing-Tianjin-Hebei. Indeed, research based on the “Statistical Classification of the Digital Economy and Its Core Industries (2021)” [4] issued by the National Bureau of Statistics of China to construct a comprehensive development index for the digital economy is lacking. Further, in-depth research on the spatial evolution characteristics of the digital economy in Beijing-Tianjin-Hebei is needed. This paper constructs a comprehensive development index based on the classification of the digital economy. It demonstrates that the digital economy significantly improves the level of integration in Beijing-Tianjin-Hebei. The impact of the digital economy on integration is influenced by different dimensions of the digital economy, different mechanisms of regional development, and different types of cities. This paper is structured as follows: Section 2 presents the theoretical hypotheses and research design, Section 3 explains the empirical regression analysis, Section 4 presents the extended analysis, and Section 5 concludes with policy recommendations.

2. Theoretical Hypotheses and Research Design

This section utilizes economic principles and existing research to investigate the degree of integration of digital economy development among cities in the Beijing-Tianjin-Hebei metropolitan area. It outlines the specific research design of this paper.

2.1. Theoretical Hypotheses

Based on synthesizing the literature on the relationship between digital economy development and integration within the Beijing-Tianjin-Hebei urban agglomeration and analyzing the underlying mechanisms, this paper conducts a theoretical analysis and proposes the following three hypotheses.

2.1.1. The Impact of the Digital Economy on the Integration in Beijing-Tianjin-Hebei

Cities within the Beijing-Tianjin-Hebei urban agglomeration exhibit varying levels of development, with significant disparities in digital investment, infrastructure, and industrial advancement [5]. The literature presents varying perspectives concerning the crucial question of how the digital economy influences regional development.

One group of scholars has argued that the digital economy can narrow regional economic disparities [6,7]. They contend that it expands the boundaries of the industrial division of labor, reduces transaction costs, and eliminates barriers related to time and space for labor. Innovating the flow of production factors enhances the utilization efficiency of production factors in less developed regions and strengthens interregional collaborative innovation capabilities [8,9].

Conversely, another group of scholars has highlighted the existence of digital inequality across regions [10,11,12]. They argue that factors such as infrastructure disparities, preferential policies, and platform monopolies exacerbate the core-periphery economic gap, which leads to the further concentration of production factors and the ultimate widening of regional development disparities [13,14,15].

As a critical engine of new quality productivity, the digital economy enhances overall total factor productivity, promotes economic development, and empowers other industries while strengthening environmental protection [16]. On the one hand, it promotes the integrated development of data elements with traditional production factors such as labor and capital, thus facilitating the establishment of complete and efficient industrial chains and supply chains. This expands the boundaries of production possibilities and the market space. On the other hand, the development of the digital economy breaks through geographical limitations and lowers barriers to industrial development, effectively bridging the “gap” in industrial transfer. For example, with its robust digital economy foundation, Beijing can develop cities with relatively slower digital economies within the region through industrial transfer, capital investment, technology spillover, and organizational alliances, thereby increasing the overall level of Beijing-Tianjin-Hebei integration. Considering that the digital economy encompasses both the digital transformation of traditional industries and the cultivation of new industries, it can bring new opportunities for regional development. For example, by leveraging Beijing-Tianjin-Hebei’s market demand potential and natural resource advantages, Zhangjiakou has gradually formed a data industry cluster centered on the Huailai Big Data Industrial Base and Zhangbei Cloud Computing Base. In this case, the city’s core big data industry has attained an added value of 15.1 billion yuan, accounting for 8.2% of the city’s GDP. Therefore, we propose Hypothesis 1: The digital economy can significantly enhance Beijing-Tianjin-Hebei integration. It can effectively empower economically underdeveloped regions and narrow regional economic disparities.

2.1.2. Differences in the Impact of Different Content in the Digital Economy

While a comprehensive and mature index for assessing the overall development of the digital economy has yet to be established in academia, most of the literature focuses on indicators such as digital infrastructure construction, digital financial development, digital industry innovation, and internet usage without forming a standardized system [17,18,19,20]. Starting from the fundamental definition and considering data availability, this paper acknowledges that the digital economy encompasses not only digital knowledge and information but also economic activities conducted through information and communication technologies (G20 Summit, 2016). Therefore, we define the digital economy indicators as encompassing four aspects: digital infrastructure, industrial digitalization, digital industrialization, and digital society integration.

The first aspect is digital infrastructure. Previous studies have often defined digital infrastructure as an information infrastructure, including hardware, software, networks, data centers, and other computer technology and information management systems that provide technical support and guarantees for information construction [21]. The state actively promotes digital infrastructure construction, and different stages have varying economic impacts [22]. When digital technology capabilities are relatively weak in the early stages, digital infrastructure construction can provide basic technical conditions for improving productivity. Then, as digital technology advances, investment in digital infrastructure can drive better economic development through an ecological development model [23,24].

The second aspect is industrial digitalization, the development resulting from integrating digital technology with traditional industries. It mainly includes two aspects: upgrading and transforming traditional industries through digital technology and integrating digital technology and data elements with conventional industries to create new industries and services [24]. Industrial digitization can promote upgrading industrial structures and significantly promote urbanization and economic development. Cities with better industrial digitization transformation, especially manufacturing, have more developed economies [25]. Meanwhile, industrial digitization can narrow regional income gaps and promote common prosperity [26].

The third aspect is digital industrialization. According to the “Statistical Classification of Digital Economy and Its Core Industries (2021)”, [4] the core industries of the digital economy correspond to the digital industrialization part, which mainly includes computer, communication, and other electronic equipment manufacturing; telecommunications; broadcasting; television; satellite transmission services; and internet and related services, software and information technology services. These industries are the foundation of digital economy development. Research has shown that digital industrialization can improve productivity in the industrial sector, promote labor transfer to the service sector, and increase the share of service output, all of which ultimately drive industrial structure transformation [27]. Through industrial structure upgrading, the development of digital industrialization can significantly increase regional tax revenue and lead to common development across the entire region [28,29].

The fourth aspect is digital society integration, which represents the digital development in social life and communication, including residents’ digital literacy and media promotion of digital development. In a rapidly developing digital society, improving residents’ digital literacy is beneficial for information acquisition, social capital accumulation, and market participation, thereby increasing human capital, strengthening social networks, and increasing labor income [30,31].

Therefore, this paper proposes Hypothesis 2: The digital economy, in terms of infrastructure, industrial digitalization, digital industrialization, and digital integration, can promote the integration of Beijing-Tianjin-Hebei.

2.1.3. Mechanisms through Which the Digital Economy Affects Beijing-Tianjin-Hebei Integration

Promoting regional integration is closely intertwined with the balanced allocation of factors within a region, the reduction in transaction costs between regions, and the advancement of technological innovation. Research has demonstrated that the digital economy, as a source of new elements, technologies, and driving forces, can facilitate regional collaborative innovation, optimize factor allocation, and lower transaction costs [32,33,34]. Currently, there are significant disparities between Hebei and Beijing-Tianjin in terms of technological innovation, economic development, and market integration. Considering the strong agglomeration characteristics of innovation factors and the need for systemic changes driven by regional policies to reduce transaction costs, this paper suggests that the digital economy primarily drives the integration of Beijing-Tianjin-Hebei through factor allocation.

First, according to the new economic geography theory, reducing transaction costs can help narrow regional disparities. Traditional transaction costs refer to transportation costs, whereas modern transaction costs encompass all expenses incurred to facilitate transactions beyond direct production, including transportation fees and costs arising from regional market segmentation [35]. Through online trading platforms based on unified national or even global platforms, the digital economy reduces the institutional barriers between regions, thereby lowering transaction costs and narrowing regional disparities [36].

Second, according to neoclassical theory, capital and labor factors can influence economic development. Through electronic payments, the digital economy can accelerate cash flow and reduce the geographical distance costs of investment through various social software. Additionally, human resources can provide knowledge and skills to less developed regions through online services facilitated by the digital economy. In summary, the digital economy can promote the interregional transfer of capital and human resources, optimizing factor allocation and narrowing regional economic disparities [37,38,39,40].

Third, patent innovation, an essential source of growth in the Solow model, is a driving force for regional economic development [41]. In academia, it has been widely recognized that the digital economy significantly enhances the innovation level of economic activities. This is achieved through the improvement of profitability, the shortening of industrial chains, the increase of independent research and development frequency, and the strengthening of innovation capabilities, thereby promoting local economic growth. Innovation spreads through interactions between industrial chains, within industrial parks, and in technology markets. Digital platforms facilitate this spread, often having a positive effect on neighboring regions and increasing the overall economic growth level of the entire region [42,43,44].

Therefore, this paper proposes Hypothesis 3: The digital economy can enhance Beijing-Tianjin-Hebei integration by reducing regional transaction costs, optimizing regional factor allocation, and promoting innovation.

2.2. Data Sources and Variable Definitions

As integrated manifestations of digital economy development and a crucial means for China’s digital exploration, smart cities are considered in selecting the data time frame for this study [44]. Following IBM’s introduction of the “smart city” concept in 2008, China explored smart city development. Combined with the availability of data, this paper utilizes data from 2009 to 2021, focusing on 13 cities within the Beijing-Tianjin-Hebei urban agglomeration in China (city-level data). China’s digital economy is developing rapidly and growing fast. In 2012, Chinese netizens’ growth rate slowed to 9.92% for the first time since 2003. The arrival of the mobile era has prompted China’s digital economy to enter a mature development period. Therefore, although the research stage selected in this article does not include the complete development process of the digital economy, it includes different stages of China’s digital economy development and has important reference significance. The data primarily originate from the “China City Statistical Yearbook”, “China Regional Statistical Yearbook”, “China Urban Construction Statistical Yearbook”, and statistical yearbooks of Beijing, Tianjin, and Hebei Provinces from 2010 to 2022. The Digital Financial Inclusion Index is sourced from the Peking University Digital Finance Research Center. Missing data are supplemented via trend extrapolation or interpolation methods. Additionally, all monetary variables are deflated to the base year of 2013.

2.2.1. Dependent Variable

Beijing-Tianjin-Hebei Integration Level. Regional economic integration includes the phenomenon of regional economic space where the additional cost of the cross-regional flow of factors in economic, social, and cultural dimensions gradually approaches zero [45]. Concerning the existing literature [46,47], and based on the principles of data availability, rationality, and scientificity, this paper constructs an evaluation index system for the integrated development of Beijing-Tianjin-Hebei from the three dimensions of market integration, spatial integration, and social integration (as shown in

Table 1. Integration Level Index.

Dimension	Measurement Indicators	Variable	Unit
Market Integration	The Flow of Goods	Total Freight Volume	10,000 tons
	Economic Development Level	Per Capita GDP	Yuan
	Trade Dependence	Total Import and Export	USD 100 million
	Industrial Structure	Proportion of Secondary and Tertiary Industries	%
Spatial Integration	Highway Network Construction	Total Highway Length	km
	Information Flow	Total Postal and Telecommunications Services	100 million yuan
	Population Flow	Total Passenger Traffic	10,000 people
Social Integration	Ecological Sustainability	General Public Expenditure	100 million yuan
	Public Education	Education Fiscal Expenditure	100 million yuan
	The Flow of Goods	Total Freight Volume	10,000 tons

). Market integration is measured through four aspects: the flow of goods, economic development level, trade dependence, and industrial structure. Spatial integration encompasses highway network construction, information flow, and population flow. Social integration includes two dimensions: ecological sustainability and public services, including healthcare and education. Based on this evaluation index system, the panel data entropy weight method is employed to assign weights to the indicators, and the Beijing-Tianjin-Hebei integration level is thereby calculated. The entropy weight method is derived from the concept of information entropy [48]. In information theory, entropy measures the uncertainty of random variables. The smaller the information entropy, the greater the amount of information and the higher the corresponding weight. Due to the differences in the dimensions and amplitudes of the level of integrated development, this paper standardizes the values of the variables. After standardization, different observed variables have the same scale, which can eliminate the differences caused by the numerical dimensions of different observed variables, accelerate the convergence of weight parameters, and make the final synthesized evaluation indicators more reasonable.

2.2.2. Explanatory Variable

Digital Economy Development in Beijing-Tianjin-Hebei. A large number of studies in the literature have examined the impact on regional development from the perspectives of digital infrastructure, digital industrialization, industrial digitization, and digital society [22,27,30,32]. Taking into account the comprehensive characteristics of the digital economy itself, based on the analysis in Section 2.1.2 and comparing the existing literature [24,47], this article constructs a digital economy development indicator system that includes four aspects: digital infrastructure construction, industrial digitization, digital industrialization, and digital society (digital integration), which more comprehensively measures the comprehensive development of the digital economy in multiple dimensions such as industry, society, and infrastructure construction. Taking into account the principles of data availability, rationality, and scientificity, digital infrastructure is measured from three aspects: the number of mobile phone users, the number of Internet broadband access users, and the density of long-distance optical cable lines; digital industrialization includes information transmission, computer services and software industry employment, and the total amount of telecommunications business; industrial digitalization is represented by smart industrial parks and digital inclusive finance; digital society includes the number of Internet users per 100 people and the frequency of digital words. Similar to the integration level indicator system, this paper uses the panel data entropy weight method to weigh digital economy indicators and then calculate the digital economy level of Beijing-Tianjin-Hebei (

Table 2. Digital Economy Index.

Dimension	Variable	Unit
Digital Infrastructure	Number of Mobile Phone Users	10,000 households
	Number of Internet Broadband Access Users	10,000 households
	Long-distance Optical Cable Line Density	/
Digital Industrialization	Employment in Information Transmission, Computer Services and Software Industry	10,000 people
	Total Telecommunications Services	100 million yuan
Industrial Digitalization	Smart Industrial Parks	Units
	Digital Financial Inclusion Index	/
Digital Society	Number of Internet Users per 100 People	Households
	Digitalization Word Frequency	Units

).

2.2.3. Control Variables

To enhance the scientific rigor and reliability of the estimated effects of the digital economy on Beijing-Tianjin-Hebei integration and its mechanisms, the following indicators are chosen as control variables for this paper [47], based on reference to frequently used variables in the literature: (1) Financial development (${\mathrm{d}\mathrm{e}\mathrm{p}\mathrm{o}\mathrm{s}\mathrm{i}\mathrm{t}}_{it}$), measured by the total year-end deposits of financial institutions in the region; (2) Basic education level (${\mathrm{p}\mathrm{r}\mathrm{i}\mathrm{m}\mathrm{a}\mathrm{r}\mathrm{y}}_{it}$), represented by the number of primary school students enrolled; (3) Industrial level (${\mathrm{i}\mathrm{n}\mathrm{d}\mathrm{u}\mathrm{s}\mathrm{t}\mathrm{r}\mathrm{i}\mathrm{a}\mathrm{l}}_{it}$), measured by the number of industrial enterprises above a designated scale; and (4) Foreign capital dependence (${\mathrm{o}\mathrm{u}\mathrm{t}}_{it}$), proxied by the actual amount of foreign capital utilized in the current year.

2.2.4. Mechanism Variables

The mechanism variables include capital factor allocation (${\mathrm{a}\mathrm{s}\mathrm{s}\mathrm{e}\mathrm{t}}_{\mathrm{i}\mathrm{t}}$), human capital allocation (${\mathrm{s}\mathrm{e}\mathrm{n}\mathrm{i}\mathrm{o}\mathrm{r}}_{\mathrm{i}\mathrm{t}}$), technology factor allocation (${\mathrm{p}\mathrm{a}\mathrm{t}\mathrm{e}\mathrm{n}\mathrm{t}}_{\mathrm{i}\mathrm{t}}$), and market transaction costs (${\mathrm{m}\mathrm{a}\mathrm{r}\mathrm{k}\mathrm{e}\mathrm{t}}_{\mathrm{i}\mathrm{t}}$). Capital factor allocation is measured by total floating capital and fixed capital investment [47], human capital allocation is measured by the number of students enrolled in higher education [27], and technology innovation is represented by the number of patents granted [49]. Market transaction costs are represented by the inverse of the marketization index [50].

Table 3. Descriptive Statistics of the Variables.

Variable Symbol	Variable Meaning	N	Mean	Standard	Min	Max
regional	Integration Level	169	0.241	0.167	0.027	0.751
digital	Digital Economy	169	0.277	0.144	0.055	0.778
deposit	Financial Development	169	1.485	3.29	0.09	19.21
primary	Basic Education Level	169	5.718	2.674	1.661	11.5
industrial	Industrial Level	169	2.384	2.01	0.292	8.326
out	Foreign Capital Dependence	169	2.418	4.823	0.016	30.83
patent	Technology Factor Allocation	169	4.567	13.074	0.007	79.21
senior	Human Capital Allocation	169	1.881	1.9	0.08	6.323
asset	Fixed Capital Allocation	169	3.395	2.912	0.361	13.05
asset1	Floating Capital Allocation	169	3.293	4.562	0.073	25.1

shows the descriptive statistics of the variables.

2.3. Econometric Model

This paper employs panel data from 13 cities over the period 2009–2021 to construct a fixed effects model to examine the impact of the digital economy on urban integration levels.

$${regional}_{it}={\alpha }_0+\beta {digital}_{it}+\sum _{n=1}^4\left({\gamma }_n{Z}_{nit}\right)+{\mu }_i+v_t+{\epsilon }_{it}$$

where ${regional}_{it}$ and ${digital}_{it}$ represent the level of regional integration and digital economic development in city $i$ in year $t$, respectively. $Z_{nit}$ represents a series of control variables; $\beta$ and $\gamma$ are the estimated coefficients of the respective variables; ${\mu }_i$ and $v_t$ represent regional and time fixed effects, respectively; and ${\epsilon }_{it}$ is the random error term.

Additionally, this paper employs threshold regression to examine the heterogeneous effects of the digital economy on urban integration across different cities within a region.

$${regional}_{it}={\alpha }_0+\beta {digital}_{it}+{\gamma }_n{Z}_{nit}+{\beta }_1\tau \times \mathrm{I}\left(\mathrm{thv}\le \phi \right)+{\beta }_2\tau \times \mathrm{I}\left(\mathrm{thv}>\phi \right)+{\mu }_i+v_t+{\epsilon }_{it}$$

where $\mathrm{thv}$ is the threshold variable; $\phi$ is the threshold value to be estimated; $\mathrm{I}\left(.\right)$ is an indicator function; $Z_{nit}$ represents control variables not used as threshold variables; $\tau$ is the interaction term between the explanatory variable and the threshold variable; and ${\epsilon }_{it}$ is the random disturbance term. Based on the Hansen threshold model, the proportion of secondary industry-added value to GDP and the per capita GDP gap between the city and Beijing are used in this paper as threshold variables.

To further empirically test the impact mechanism of the digital economy on urban integration and explore the mediating effect of factor allocation, following Wen Zhonglin et al. (2014) [51], this paper establishes the following mediating effect model:

$${regional}_{it}={\alpha }_1+{\beta }_1{medi}_{it}+{\sum }_{n=1}^4\left({\gamma }_n^{\prime }{Z}_{nit}\right)+{\mu }_i+v_t+{\epsilon }_{it}$$

(1)

$${digital}_{it}={\alpha }_2+{\delta }_1{medi}_{it}+{\sum }_{n=1}^4\left({\gamma }_n^{″}{Z}_{nit}\right)+{\mu }_i+v_t+{\epsilon }_{it}$$

(2)

$${regional}_{it}={\alpha }_3+{\beta }_2{medi}_{it}+{\delta }_2{digital}_{it}+{\sum }_{n=1}^4\left({\gamma }_n^{‴}{Z}_{nit}\right)+{\mu }_i+v_t+{\epsilon }_{it}$$

(3)

$${\beta }_1={\beta }_2+{\delta }_2\ast {\delta }_1$$

(4)

(a) Coefficient ${\beta }_1$ in Equation (1) is tested (i.e., test H0: ${\beta }_{1 }$= 0, which examines the total effect of the digital economy on regional integration).

(b) Coefficient ${\delta }_1$ in Equation (2) (i.e., H0 is tested: ${\delta }_1$ = 0) and Coefficient ${\beta }_2$ in Equation (3) are tested sequentially (i.e., H0 is tested: ${\beta }_2$ = 0). The significance of the product of the coefficients is effectively tested in the second step. If (a) the coefficient ${\beta }_1$ is significant and (b) both coefficients ${\beta }_1$ and ${\delta }_1$ are significant, then the mediating effect is significant.

(c) If the coefficient ${\delta }_2$ in Equation (3) is not significant, then it is a full mediating effect (the third step test is used to distinguish between full or partial mediation).

3. Empirical Regression Analysis

3.1. Annual Trend Analysis

The Beijing-Tianjin-Hebei region, the Yangtze River Delta, and the Pearl River Delta are among China’s three largest urban agglomerations. Compared with the other two regions, the Beijing-Tianjin-Hebei region has a relatively large gap in regional integration, with problems such as a single regional development value orientation, low market integration, and poor Hebei’s ability to undertake transfers [52,53]. As the economic development gap between the north and the south of China widens, the economic development momentum of prefecture-level cities in the north, except Beijing, has slowed, exacerbating the development gap between Beijing-Tianjin-Hebei cities. Considering the digital economy’s two-sided nature on the region’s overall development, it will have different impacts on the regional integration of cities at various development levels [7,12]. Therefore, this paper uses the indicator system in Table 1 to calculate the regional integration level of the 13 cities in Beijing-Tianjin-Hebei from 2009 to 2021 and classify the regional cities. This paper calculates the regional integration levels of 13 cities in Beijing-Tianjin-Hebei from 2009 to 2021 via the indicator system in Table 1.

As shown in Figure 1, first, the regional integration level of each city increased annually from 2009 to 2021. In 2009, the integration level in the Beijing-Tianjin-Hebei region ranged from 0.0271 to 0.5329; by 2021, it had grown to a range of 0.0748 to 0.7514. Second, the gap in regional integration levels within Beijing-Tianjin-Hebei has widened, with a trend of cities other than Beijing continuously declining in relative position. The number of cities in the first tier has decreased, with only Beijing remaining in 2021. The second tier is composed mainly of Shijiazhuang and Tangshan, with Tianjin joining in 2021. Baoding has consistently ranked fifth in terms of overall integration level, maintaining a level of approximately 0.25 since 2017, placing it in the third tier. Moreover, the number of cities in the fifth tier has increased. Before 2017, it included only Qinhuangdao and Hengshui, but owing to the slow growth of integration levels in Chengde and Langfang, they were also included in the fifth tier.

Based on the changes in regional integration levels across cities and considering the impact of city size on empirical results, this paper classifies cities into two categories to support the study of the relationship between integration levels and the digital economy in different types of cities. The first large cities category includes Beijing, Tianjin, Shijiazhuang, Tangshan, and Baoding. The second category of small and medium-sized cities includes Langfang, Qinhuangdao, Handan, Xingtai, Zhangjiakou, Chengde, Cangzhou, and Hengshui.

3.2. Baseline Regression

Based on panel data from 13 cities in the Beijing-Tianjin-Hebei urban agglomeration in China from 2013 to 2021, a fixed effects model is employed in this paper to evaluate the impact of digital economy development on Beijing-Tianjin-Hebei integration.

Table 4. Baseline Regression Results.

	(1)	(2)
Variable	Regional	Regional
digital	0.386 ***	0.171 ***
	(0.024)	(0.029)
deposit		0.044
		(0.030)
primary		0.267 ***
		(0.030)
industrial		−0.085 ***
		(0.024)
out		0.051 **
		(0.020)
Constant	0.134 ***	0.099 ***
	(0.007)	(0.009)
Observations	169	169
Number of id	13	13
R-squared	0.621	0.780

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

reports the estimation results of the baseline regression. Columns (1) and (2) present the estimated effects of the digital economy on urban integration levels in the Beijing-Tianjin-Hebei region with and without control variables, respectively. Regardless of including control variables, digital economy development positively affects Beijing-Tianjin-Hebei integration. In Model 1, the regression coefficient of the core explanatory variable, i.e., digital economy development, is 0.386, which is significant at the 1% level, indicating that digital economy development plays a positive role in enhancing the level of Beijing-Tianjin-Hebei integration. After a series of control variables are incorporated, as shown in Regression 3, the digital economy development regression coefficient is 0.171, which is still significant at the 1% level. This suggests that digital economy development significantly promotes the improvement of Beijing-Tianjin-Hebei integration, providing initial validation for the core hypothesis of this paper.

The regression results of the control variables reveal that financial development, basic education level, and foreign capital dependence contribute to the enhancement of Beijing-Tianjin-Hebei integration. However, the industrial level negatively impacts the integration of Beijing-Tianjin-Hebei, which may be related to the poor performance of the steel industry concentrated in Hebei. While the scale and concentration of the steel industry in Hebei Province have been increasing, its profitability has declined significantly, hindering local economic development [54].

3.3. Robustness Checks

This paper conducts robustness checks to validate further and enhance the reliability of the estimation results; key explanatory variables and dependent variables are replaced, the data are winsorized, and endogeneity issues with instrumental variables are addressed. The results are presented in

Table 5. Robustness Check Results.

	(1)	(2)	(3)	(4)	(5)
	Replacing the Dependent Variable		Replacing the Explanatory Variable		Winsorizing the Data
Variable	Regional	Regional	Regional	Regional	Regional
digital	0.227 ***	0.170 ***	0.197 ***	0.171 ***	0.164 ***
	(0.031)	(0.028)	(0.027)	(0.029)	(0.034)
Constant	0.066 ***	0.097 ***	0.113 ***	0.098 ***	0.106 ***
	(0.020)	(0.009)	(0.009)	(0.009)	(0.010)
Control	YES	YES	YES	YES	YES
Observations	169	169	169	169	169
Number of id	13	13	13	13	13
R-squared	0.8098	0.785	0.800	0.779	0.668

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

.

3.3.1. Replacing the Dependent Variable

Model (1) replaces the integration level evaluation indicator, as per capita GDP is substituted with urban residents’ per capita disposable income. Model (2) uses an integration level indicator calculated with equal weights. The results are shown in Columns (1) and (2) of Table 5. Regardless of the method used to calculate the integration level index, the estimated coefficient of the digital economy on the integration level remains significantly positive, which is consistent with the results of the baseline regression. This confirms the robustness of the results to a certain extent.

3.3.2. Replacing the Explanatory Variable

Model (3) presents the results of replacing the calculation method of the digital economy evaluation indicator. Model (4) uses an equally weighted digital economy index. The results show that regardless of the method used to calculate the digital economy index, the estimated coefficient of the digital economy on the integration level remains significantly positive, which is consistent with the results of the baseline regression and further validates the robustness of the results.

3.3.3. Winsorizing the Data

Model (5) presents the results after the variable data are winsorized at 10% on both tails. The results indicate that the estimated coefficient of the digital economy on the integration level remains significantly positive, which is consistent with the baseline regression results and further confirms the robustness of the results.

3.4. Endogeneity Issues

The rise in integration levels within urban agglomerations can significantly promote knowledge diffusion and innovation [55], creating conditions for the development of the digital economy, which relies on knowledge and technology. Therefore, a reciprocal causal relationship between the digital economy and urban integration may exist. Additionally, despite including control variables in the econometric model, unobservable variables may still affect the degree of urban integration, leading to potential omitted variable bias in the model estimating the impact of the digital economy on integration.

To address this, we follow the approach of Nunn and Qian (2014) [56] by constructing an interaction term between the national internet penetration rate in the previous year and the number of fixed telephone lines per 10,000 people in each city in 1984 as an instrumental variable for the digital economy. Then, we employ a panel instrumental variable approach to address potential endogeneity issues. The rationale is as follows: First, the historical accumulation of telecommunications infrastructure lays the foundation for developing the digital economy centered on the internet [24], satisfying the exogeneity requirement. Second, the widespread use of mobile internet has significantly weakened the role of traditional telephones in urban networks, satisfying the relevance requirement. Third, since the number of fixed telephone lines per 10,000 people in each city in 1984 does not change over time, it cannot be used as an instrumental variable in a panel data model. Therefore, we multiply it by the national internet penetration rate to obtain an instrumental variable that meets the requirements of panel data.

The instrumental variable method and the system GMM method of panel data were used for regression, respectively. The results are shown in

Table 6. Endogeneity Issues Results.

	(1)	(2)
Variable	Regional	Regional
L.regional		1.024 ***
		(0.123)
digital	0.582 ***	0.100 *
	(0.153)	(0.048)
Control	YES	YES
Constant	0.582 ***	−0.017 *
Observations	(0.153)	(0.008)
	169	156
Number of id	13	13
R-squared	0.482	/
P_Hansen	/	0.687

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

. The impact of the digital economy on the degree of urban integration is still significant after considering the endogeneity problem. The regression coefficient of the digital economy is 0.582. Significant at the 1% statistical level. Furthermore, the underidentification test, overidentification test, and weak instrument test all confirm the validity of the instrumental variable selection in this study.

3.5. Heterogeneity Analysis

Taking into account the large economic and technological gaps between cities in the Beijing-Tianjin-Hebei region and the fact that the digital economy will have different impacts on the regional integration of cities at different development levels, this paper calculates the characteristic results of the spatial evolution of the integration index based on Section 3.1, divides the cities in Beijing-Tianjin-Hebei into large cities and small and medium-sized cities, and then studies the specific mechanism of the digital economy’s promotion effect on urban integration. Large cities include Beijing, Tianjin, Shijiazhuang, Tangshan, and Baoding, while small-medium cities include Langfang, Qinhuangdao, Handan, Xingtai, Zhangjiakou, Chengde, Cangzhou, and Hengshui.

First, based on the Hausman test results, we conduct baseline regressions via fixed-effects or random-effects models for the two categories of cities. The results are presented in

Table 7. Heterogeneity Analysis Results.

	Large Cities		Small-Medium Cities
	(1)	(2)	(3)	(4)
Variable	Regional	Regional	Regional	Regional
digital	0.610 ***	0.150 *	0.555 ***	0.610 ***
	(0.046)	(0.075)	(0.035)	(0.046)
deposit		−0.010
		(0.046)
primary		0.324 ***
		(0.049)
industrial		−0.079 **
		(0.038)
out		0.059 **
		(0.028)
Constant	0.115 **	0.150 ***	0.103 ***	0.115 **
	(0.049)	(0.024)	(0.037)	(0.019)
Observations	65	65	104	104
Number of id	5	5	8	8
R-squared	0.691	0.869	0.334	0.865

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

.

The results in Table 7 reveal that the digital economy positively affects the integration of both large and small–medium cities. Columns (1) and (2) in the table list the results of regression without and with control variables for large cities, respectively, and columns (3) and (4) list the two results for small and medium-sized cities, respectively. By comparison, we can find that if financial development, basic education level, industrial level, and foreign capital dependence are held constant, a 1% increase in the digital economy can promote integration by approximately 15% for both types of cities. The difference mainly exists in that small and medium-sized cities are more limited in the promotion effect of the digital economy, which is significantly positive at the 1% level. In comparison, large cities are significant at the 10% level. This shows that the digital economy has generally promoted the integrated development level of all cities in Beijing-Tianjin-Hebei. Among them, the integrated development of small cities is more credible due to the promotion effect of the digital economy than that of large cities.

3.6. Threshold Regression

Given the distinct stages of development across cities in the Beijing-Tianjin-Hebei region, a threshold effect model is employed in this paper, and the proportion of secondary industry-added value to GDP (${\phi }_1$) and the per capita GDP gap between each city and Beijing (${\phi }_2$) are used as threshold variables. This approach allows us to observe the effect of the digital economy on economic growth at different stages of urban development. The rationale for selecting these two variables as thresholds is as follows: First, the Beijing-Tianjin-Hebei region exhibits poor industrial coordination, with Beijing and Tianjin having high-end industrial structures, whereas Hebei’s industrial structure is relatively low-end [57]. We select the proportion of secondary industry to GDP as an indirect measure of industrial structure to investigate the impact of this threshold on the relationship between the digital economy and Beijing-Tianjin-Hebei integration. Second, per capita GDP reflects regional development, and the gap with Beijing’s per capita GDP can essentially represent the development gap between other cities and Beijing. Beijing’s per capita GDP is a benchmark as the core of Beijing-Tianjin-Hebei integration and a leading area in digital economy development. A larger gap with Beijing’s per capita GDP indicates a more significant development disparity, which can influence the relationship between the digital economy and Beijing-Tianjin-Hebei integration. After testing, we found that each selected model exhibits only one threshold value. The regression results of the threshold effect model are presented in

Table 8. Threshold Regression Results.

Variable	(1)	(2)
Threshold	10,556,821	0.3270
	(0.0067)	(0.0167)
P_thresh	0.0167	0.0067
$\mathrm{digital}\left(\mathrm{thv}\le {\phi }_1\right)$	0.146 ***
	(0.039)
$\mathrm{digital}\left(\mathrm{thv}>{\phi }_1\right)$	0.217 ***
	(0.048)
$\mathrm{digital}\left(\mathrm{thv}\le {\phi }_2\right)$		0.220 ***
		(0.050)
$\mathrm{d}\mathrm{i}\mathrm{g}\mathrm{i}\mathrm{t}\mathrm{a}\mathrm{l}\left(\mathrm{thv}>{\phi }_2\right)$		0.151 ***
		(0.032)
Control	YES	YES
Constant	0.124 ***	0.108 ***
	(0.018)	(0.014)
Observations	169	169
Number of id	13	13
R-squared	0.807	0.806

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

.

Table 8 presents the relationship between the digital economy and Beijing-Tianjin-Hebei integration under different threshold values. The results indicate the presence of a single significant threshold value for both the proportion of secondary industry and the per capita GDP gap with Beijing, with threshold values of 0.3270 and 105,568 yuan, respectively. Model (3) uses the per capita GDP gap with Beijing as the threshold variable, whereas Model (4) uses the proportion of secondary industry. On the one hand, when the proportion of secondary industry is less than 0.3270, the correlation coefficient between the digital economy and Beijing-Tianjin-Hebei integration is 0.22. When the proportion of secondary industry exceeds 0.3270, the coefficient decreases to 0.151. This suggests that the smaller the proportion of secondary industry in a city is, the stronger the promoting effect of the digital economy on urban integration. In other words, the digital economy significantly impacts economic growth in less developed regions, narrowing regional economic disparities. This indirectly demonstrates that cities with a high proportion of secondary industry in the Beijing-Tianjin-Hebei region may need more digitalization in the secondary sector, leading to a lower level of digital development and a minor impact from the digital economy. On the other hand, when the per capita GDP gap between a city and Beijing in the same year is above 105,568 yuan, the relationship between the digital economy and Beijing-Tianjin-Hebei integration is positive, with a coefficient of 0.217. When the per capita GDP gap narrows to below 105,568 yuan, the coefficient decreases to 0.146. This shows that the greater the development gap between a city and Beijing, that is, the lower the overall development level of a city, the more significant the digital economy’s promotion effect on the city’s integration level, further proving that the digital economy narrows the economic development gap between regions. Thus, Hypothesis 1 is validated.

4. Extended Analysis

4.1. Heterogeneous Effects of Different Aspects of the Digital Economy

To deeply analyze the differences in the impact of the digital economy on different cities, this article studies the impact of four aspects of the digital economy on different cities based on the classification of different dimensions of digital economic indicators in the previous article. The regression results are shown in Table 8. First, the development of each part of the digital economy promotes the integration of Beijing-Tianjin-Hebei, and Hypothesis 2 is verified. Developing In regressions 1, 2, 3, and 4 in

Table 9. Segmentation dimension analysis results.

	(1)	(2)	(3)	(4)
Variable	Regional	Regional	Regional	Regional
base-dig	0.046 **
	(0.020)
dig-indu		0.240 ***
		(0.036)
indu-dig			0.128 ***
			(0.023)
soc-dig				0.064 ***
				(0.018)
Constant	0.093 ***	0.082 ***	0.110 ***	0.104 ***
	(0.010)	(0.021)	(0.022)	(0.022)
Model	FE	FE	FE	FE
Control	YES	YES	YES	YES
Observations	169	169	169	169
Number of id	13	13	13	13
R-squared	0.738	0.7952	0.7691	0.7450

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

, the regression coefficients for digital infrastructure (base-dig), digital industrialization (dig-indu), industrial digitalization (indu-dig), and digital society (soc-dig) are 0.046, 0.24, 0.128, and 0.064, respectively. The coefficient for digital infrastructure is significant at the 5% level, whereas the coefficients for the other regression models are positive at the 1% level. These findings indicate that all four dimensions of digital economy development play a significant and positive role in promoting the improvement of Beijing-Tianjin-Hebei integration. Among them, the impact coefficient of digital industrialization is the largest, suggesting a more pronounced effect on Beijing-Tianjin-Hebei integration.

Next, this paper analyzes how different aspects of the digital economy affect the integration levels of large and small–medium cities. The results are presented in

Table 10. Heterogeneity Analysis Results by Segmentation Dimension.

	Large Cities				Small-Medium Cities
	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
Variable	Regional	Regional	Regional	Regional	Regional	Regional	Regional	Regional
base-dig	0.012				0.037
	(0.036)				(0.024)
dig-indu		0.347 ***				0.056 **
		(0.084)				(0.023)
indu-dig			0.276 ***				0.113 ***
			(0.059)				(0.026)
soc-dig				0.055				0.079 ***
				(0.047)				(0.018)
Control	YES	YES	YES	YES	YES	YES	YES	YES
Constant	0.170 ***	0.143 ***	0.145 ***	0.173 ***	0.125 ***	0.115 ***	0.122 ***	0.119 ***
	(0.026)	(0.020)	(0.019)	(0.021)	(0.016)	(0.018)	(0.016)	(0.017)
Observations	65	65	65	65	104	104	104	104
Number of id	5	5	5	5	8	8	8	8
R-squared	0.860	0.893	0.900	0.863	0.871	0.869	0.922	0.923

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

.

The results in Table 10 reveal heterogeneous effects of different aspects of the digital economy on the regional integration levels of large and small–medium cities. First, digital infrastructure does not significantly impact the regional integration of either large or small–medium cities. Second, digital industrialization positively affects both types of cities, but the effect is more pronounced in large cities. Third, industrial digitalization significantly promotes the integrated development of both large and small–medium cities, with a more substantial impact observed in large cities. Fourth, digital society significantly enhances regional integration in small–medium cities.

A comparative analysis between large and small–medium cities indicates that the integration level of large cities is significantly correlated with industrial digitalization and digital industrialization. The Beijing-Tianjin-Hebei urban agglomeration has a relatively strong foundation in secondary industries, but the overall industrial structure is not high-end except for Beijing. Therefore, digital industrialization is more conducive to upgrading industrial structures and promoting economic development in these regions. In contrast, small–medium cities in Hebei Province have limited foundational levels in terms of digital infrastructure construction, industrial bases, and residents’ digital literacy. The structural upgrading effect of digital industrialization is not as significant as the improvement in industrial economic efficiency achieved through industrial digitalization. Furthermore, integrating a digital society promotes improving residents’ digital literacy, contributing to increased urban integration.

4.2. Mechanism Analysis

To further investigate the mechanisms through which the digital economy influences Beijing-Tianjin-Hebei integration, a stepwise regression approach is employed in this paper, as a fixed effects panel data model is utilized to examine whether transaction costs, factor allocation, and innovation affect the regional integration effect of the digital economy.

The results presented in

Table 11. Results of the Impact of Digital Economy on Mechanism Variables.

	(1)	(2)	(3)	(4)	(5)
Variable	Market	Senior	Asset	Asset1	Patent
digital	−1.098 ***	0.339 ***	0.411 ***	0.056 *	−0.060 *
	(0.108)	(0.121)	(0.067)	(0.032)	(0.031)
deposit	0.386 ***	−0.033	−0.406 ***	0.654 ***	0.580 ***
	(0.113)	(0.126)	(0.070)	(0.033)	(0.032)
primary	−0.834 ***	0.129	0.815 ***	0.162 ***	−0.061 *
	(0.115)	(0.128)	(0.071)	(0.033)	(0.033)
industrial	−0.063	0.104	−0.439 ***	−0.051 *	0.092 ***
	(0.090)	(0.100)	(0.055)	(0.026)	(0.026)
out	−0.123	−0.069	0.350 ***	0.128 ***	−0.120 ***
	(0.076)	(0.085)	(0.047)	(0.022)	(0.022)
Constant	1.011 ***	0.123 ***	−0.094 ***	0.002	0.043 ***
	(0.034)	(0.038)	(0.021)	(0.010)	(0.010)
Observations	169	169	169	169	169
Number of id	13	13	13	13	13
R-squared	0.786	0.176	0.779	0.892	0.724

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

indicate that the digital economy significantly reduces transaction costs and promotes innovation while increasing human capital and capital resources in cities. All estimated coefficients are significant at the 1% level.

Transaction costs, human factors, capital factors, and patent innovation all have mediating effects on the impact of the digital economy on regional integration. The estimated coefficient of transaction cost is significantly negative at a 1% level, the estimated coefficient of human factor and total circulating capital is significantly positive at a 1% level, and the estimated coefficient of fixed capital investment and patent innovation is significant at 10%, respectively. It shows that the reduction of transaction costs and the increase of human factors and capital factors are beneficial in promoting the process of regional integration. At the same time, the influence of the digital economy on regional integration will change with the change in transaction cost, human factor, and capital factor. Digital economy promotes the influence of the digital economy on regional integration by reducing transaction costs among regions and optimizing the allocation of regional human factors and capital factors. In other words, the driving force of the digital economy to regional integration can be improved by optimizing the allocation of capital and human factors and reducing transaction costs. This finding verifies Hypothesis 3. Comparing other results estimated by the impact mechanism, it can be obtained that market costs have the greatest impact on the regional integration effect of the digital economy, followed by the impact of capital and human factors. The possible reason is that the Beijing-Tianjin-Hebei region still has a large degree of market integration. There is still room for growth. The improvement in factor allocation and the increase in patent innovation brought about by the development of the digital economy are still affected by additional costs. There are still inconsistent policies within the region and uncoordinated industrial structures; therefore, reducing transaction costs and optimizing capital allocation have become the key factors for the development of the digital economy at this stage. Among them, the effect of patent innovation is inconsistent with expectations. According to existing research [49], patent innovation can promote the impact of the digital economy on regional development, and the results show that the increase of patent innovation inhibits the increase of the digital economy on the regional integration level. This paper thinks that this may be due to the low industrial coordination in the Beijing-Tianjin-Hebei region, and the digital economy still stays at a relatively low level for the industrial development of other cities. Therefore, although Beijing is a highland of science and technology, the diffusion scope of patent innovation is limited, and the digital economy reduces the promotion of patent innovation to regional integration.

4.3. Heterogeneous Mediating Effects

Furthermore, this article continues the classification method of cities in Section 3.1. It explores the impact mechanism of the digital economy in cities with different levels of integration in the Beijing-Tianjin-Hebei urban agglomeration. In large cities, the digital economy significantly influences industrial enterprise floating capital, patent innovation, and transaction costs. A 1% increase in the digital economy leads to a 0.568 increase in patent innovation, a 0.631 increase in fixed capital stock, and a 0.93 decrease in transaction costs, all of which are significant at the 1% level. In small-medium cities, the digital economy significantly affects human resources, patent innovation, industrial enterprise floating capital, and transaction costs. A 1% increase in the digital economy leads to a 0.262 decrease in human resources, a 0.166 increase in patent innovation, a 0.412 increase in industrial enterprise floating capital, and a 0.565 reduction in transaction costs.

From the analysis of the results, there are intermediary effects of transaction cost, industrial and enterprise circulating capital, and patent innovation in both big cities and small cities, but there is no intermediary effect of human factors in big cities. Therefore, it can be proved laterally: (1) The digital economy promotes the improvement of the regional integration degree of the Beijing-Tianjin-Hebei region’s digital economy by reducing transaction costs within the region and optimizing the allocation of capital elements. This mechanism not only acts in the Beijing-Tianjin-Hebei region but also is reflected in big cities and small cities respectively; (2) the optimal allocation of human factors promotes the influence of the digital economy on the degree of regional integration, but for the interior of big cities, this effect is not significantly positive, which shows that digital economy can not increase the increase of human factors in Beijing, Tianjin, Shijiazhuang, Tangshan, and Baoding, reflecting that other big cities except Beijing still have low industrial structure and limited attraction to highly educated talents; (3)Third, patent innovation in large and medium-sized cities promotes the improvement of digital economy to the degree of regional integration, but patent innovation has not played a corresponding role as a whole. This shows that the limited diffusion effect of patent innovation between cities leads to the lack of patent innovation ability in some big cities, thus affecting the increase of integration level (

Table 12. Mediating Effect Regression Results for Urban Heterogeneity.

Variable	Market	University	Capital	Capital1	Patent
Large cities
Digital	−0.930 ***	−0.125	0.018	0.631 ***	0.568 ***
	(0.209)	(0.096)	(0.094)	(0.222)	(0.178)
Constant	1.175 ***	0.073 **	0.078 **	−0.106	0.181 ***
	(0.067)	(0.031)	(0.030)	(0.071)	(0.057)
Control	YES	YES	YES	YES	YES
Observations	65	65	65	65	104
Number of id	5	5	5	5	8
R-squared	0.903	0.735	0.900	0.895	0.819
Small–medium cities
Digital	−0.565 ***	−0.262 **	0.065	0.412 ***	0.166 ***
	(0.105)	(0.126)	(0.050)	(0.072)	(0.060)
Constant	0.729 ***	0.427 ***	−0.030	0.108 ***	−0.195 ***
	(0.054)	(0.064)	(0.026)	(0.037)	(0.031)
Control	YES	YES	YES	YES	YES
Observations	104	104	104	104	104
Number of id	8	8	8	8	8
R-squared	0.804	0.565	0.916	0.906	0.939

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

).

From the empirical according to the analysis of empirical results, the reduction of internal transaction costs and the optimization of factor allocation in the Beijing-Tianjin-Hebei region have promoted the digital economy and regional integrated development. From the comparison of different cities, the industrial foundation, innovation ability, and factor endowment of small cities are limited. The development of a digital economy promotes the reduction of transaction costs, optimization of factor allocation, and patent innovation in small cities, thus improving the regional integration level of small cities. Compared with small cities, big cities have better digital economy development levels, lower transaction costs, and factor endowment. The development of the digital economy also promotes an increase in regional integration levels through the reduction of transaction costs and the optimization of capital factor allocation. However, in terms of patent innovation and human factors, the patent innovation ability of big cities except Beijing is weak, and human capital is small, which affects the development of integration level brought by patent innovation and human capital improvement in the digital economy, which often represents the development of higher-level industries brought by the digital economy. In the next step, Beijing-Tianjin-Hebei needs to strengthen the ability of Tianjin, Shijiazhuang, Tangshan, and Baoding to innovate. Through talent and innovation exchanges with Beijing, it is suggested that the local industrial structure be improved by adopting various methods such as industry-university-research cooperation.

5. Conclusions and Policy Implications

With the advancement of the Beijing-Tianjin-Hebei integration strategy, effectively mobilizing the driving force of the digital economy for regional integration has become a crucial issue that China urgently needs to address. Using panel data from 13 prefecture-level cities in the Beijing-Tianjin-Hebei region from 2009 to 2021, this paper investigates the impact of the digital economy on regional integration and its mechanisms through theoretical analysis and empirical testing. The results show the following: (1) The digital economy has a significant positive promoting effect on regional integration, and the lower the proportion of secondary industry and per capita GDP is, the more pronounced the promoting effect of the digital economy. This conclusion remains valid when the dependent and key explanatory variables are replaced, and the instrumental variable 2SLS regression method is used. (2) The analysis of different dimensions of the digital economy reveals that improvements in digital industries, industrial digitalization, and digital society can promote regional integration. However, digital infrastructure has yet to show significant effects. In this study, Beijing-Tianjin-Hebei cities are classified into two categories: large cities and small-medium cities. The regional integration levels of both types of cities are significantly and positively correlated with digital economy development. Still, the promoting effects of different digital economy components vary across city types. Digital industrialization and industrial digitalization have a more substantial promoting impact on regional integration in large cities. In contrast, small-medium cities are driven mainly by industrial digitalization, with more minor effects from digital industrialization and the digital society. (3) The mechanism analysis indicates that optimizing the allocation of human and capital resources, increasing patent innovation, and reducing transaction costs contribute to enhancing the driving force of the digital economy in regional integration. This is in line with the views of Chen et al., who emphasized that the development of the digital economy may be affected by optimizing the allocation of human and capital resources and increasing patent innovation [58]. Furthermore, our study verifies that reducing transaction costs enhances the driving force of the digital economy on regional integration. (4) Among these, innovation plays a more significant role in promoting integration in large cities. In contrast, small–medium cities are affected mainly by reduced transaction costs and the optimization of capital allocation.

Based on the above findings, this paper proposes the following four policy recommendations: (1) From the overall growth perspective, the digital economy can significantly improve the integrated development level of Beijing-Tianjin-Hebei. It is recommended that the importance of the digital economy be more emphasized in the coordinated development policy of Beijing-Tianjin-Hebei in the future from the perspective of funds, talents, policies, infrastructure construction, and other aspects to guide the development direction of the digital economy, strengthen the overall coordination of regional digital economic development, and explore the institutional system and top-level design compatible with the digital economy. (2) From the perspective of industrial development, except for large cities such as Beijing and Tianjin, Beijing-Tianjin-Hebei’s overall industrial structure level could be higher, and the industrial foundation of small and medium-sized cities could be better. On the one hand, we should focus on advantageous areas such as artificial intelligence, high-end software, intelligent manufacturing, transportation and logistics, medicine and health, and autonomous driving to enhance the competitiveness of digital economic industry clusters; on the other hand, through platform construction, financial funds, talent introduction, etc. The form promotes the deep embedding of digital technology into the real economy and improves the digitalization level of traditional industries and the ability to undertake supporting industries. (3) From the perspective of transaction costs, the improvement of the regional integration level is related to the digital economy. Still, it is also affected by the reduction of intra-regional transaction costs and the optimization of capital and human factors allocation. In addition to developing the digital economy, the Beijing-Tianjin-Hebei region should also focus on reducing regional transaction costs, promoting the flow of factors, reducing regional institutional policy barriers, and increasing the flow of factors between regions. In particular, as the political center, Beijing needs to pay more attention to policy coordination with Tianjin and Hebei to reduce the additional costs of intra-regional factor flow. Due to the significant differences in industrial structure within the region, the effect of innovation promotion is limited. Therefore, improving the industrial structure in Tianjin and Hebei is recommended to be promoted through the diffusion of industry-learning-research institution cooperation and regional industrial coordination. (4) From the perspective of spatial layout, the Beijing-Tianjin-Hebei region has significant economic differences, and it is necessary to provide targeted policies based on the development conditions of different cities. Big cities should make use of the advantages of Beijing’s surrounding areas to strengthen patent innovation cooperation from the perspective of industry, academia, and research, and use policies to attract Beijing’s industrial transfer and undertake the scientific and technological implementation results, and promote the industrial upgrading of Tianjin, Shijiazhuang, Tangshan, and Baoding through the industrial upgrading effect of the digital economy; small and medium-sized enterprises Cities can reduce barriers with Beijing through policies, seek new economic growth points by promoting the interconnection and open utilization of computing power, networks, new technologies, and other infrastructure, and at the same time do an excellent job in environmental optimization, urban infrastructure construction, etc. Attract human and capital resources, promote the improvement of urban residents’ digital literacy, and strengthen industrial digitalization’s development. Beijing-Tianjin-Hebei’s limited overall industrial structure level was reduced.

The main contributions of this paper are as follows: First, a digital economic indicator system has been created based on four dimensions: digital network infrastructure, digital industry, industrial digitalization, and digital society level. Compared with other documents, the content of the digital economic indicator system is more comprehensive, and the research found that different cities have heterogeneity in the relationship with different dimensions of the digital economy. Secondly, this paper considers a particular gap in the level of urban integration within the Beijing-Tianjin-Hebei region. Based on heterogeneity research, it is found that the relationship between the digital economy and different types of cities is different. Large cities in Beijing-Tianjin-Hebei, except Beijing, have problems such as low industrial structure and weak innovation capabilities, affecting the digital economy’s role in promoting regional integration and providing specific references for similar regions with significant economic and technological differences. To prove that the digital economy is the leading regional coordination policy, we still need to emphasize the role of technology transfer, optimal allocation of factors, and reduction of regional barriers. The research in this paper is based on data from Beijing-Tianjin-Hebei. It has yet to be analyzed and compared with other urban agglomerations. It has certain limitations, which is an important issue that needs further research.