Uncovering the Mechanism of Elevating High-Tech Export Competitiveness in China’s Sustainable Economic Development: Force of Digital Economy

Abstract

The accelerating growth of the digital economy is fundamentally transforming international trade patterns and increasingly contributing to sustainable economic progress. Despite this, there is still limited research on how it influences the competitiveness of high-tech exports. Addressing this research gap, the present study investigates the mechanisms through which the digital economy strengthens the competitiveness of high-tech exports—an essential engine for sustainable growth. China, recognized as a leader in digital innovation, serves as the focal point of this analysis. Drawing on panel data spanning from 2011 to 2021 across 30 provincial-level regions (including municipalities and autonomous areas), we develop a multidimensional index that captures the extent of industrial digitalization, digital industrialization, and the digital development environment. Our econometric analysis uncovers direct and indirect mechanisms through which digitalization reduces trade costs, accelerates innovation-led business models, and lowers market entry barriers, thereby promoting sustainable industrial upgrading. Moreover, we reveal that digital integration contributes to narrowing regional disparities, fostering inclusive and resilient economic growth. A double-threshold effect is identified, where the initial high costs of digital adoption yield substantial long-term sustainability benefits. These findings offer critical insights for policymakers seeking to align high-tech industrial strategies with sustainable development goals, ensuring an equitable and innovation-driven digital economy.

1. Introduction

Amidst accelerating technological advancements and increasing sustainability demands, the digital economy has emerged as a key driver in building resilient and globally competitive industries [1]. High-tech industries, characterized by intensive capital investment, high R&D intensity, and strategic integration into global value chains, are at the forefront of this transformation. In the context of China—where digital transformation is advancing swiftly under the framework of the dual circulation strategy—it becomes particularly important to examine the influence of the digital economy on the international competitiveness of high-tech exports. This topic holds both theoretical value and practical policy implications.

Previous studies have identified multiple beneficial effects of the digital economy on trade outcomes. Digital infrastructure development, data-driven production models, and online platforms have collectively enhanced productivity, lowered transaction costs, and supported product quality upgrading [2,3]. Other studies show that digitalization facilitates domestic value addition and export diversification, often through improved factor allocation and financial deepening [4]. However, these findings are mostly derived from analyses of the broader manufacturing sector, offering limited insight into high-tech industries’ specific dynamics [5].

Moreover, much of the existing literature suffers from two key limitations. First, it remains either overly generalized or limited to specific regions. For example, empirical evidence from areas like the Yangtze River Economic Belt and the Pearl River Delta suggest positive spillover effects from digital development on high-tech export performance [6], yet these findings lack generalizability at the national level. Second, there is a noticeable gap in theoretical clarity and mechanism-based analysis. Although digitalization is frequently associated with promoting innovation and driving industrial upgrading, there is still a lack of comprehensive empirical analysis of the extent to which these factors serve as intermediaries linking the digital economy with enhanced export competitiveness [7].

Moreover, research on the digital economy often lacks a unified conceptual framework, resulting in fragmented perspectives. Although frameworks measuring digitalization—encompassing infrastructure, industrial application, and innovation ecosystems—have advanced, they rarely align with theoretical models that explain trade competitiveness. The intersection of digital and sustainable development is also under-theorized, despite increasing interest in how digital technologies contribute to cleaner production, capital deepening, and inclusive growth [8,9].

Against this backdrop, this study aims to make three contributions. First, it addresses the lack of sector-specific analysis by focusing explicitly on China’s high-tech industries, offering a fine-grained view of sub-sectoral heterogeneity. Second, it enhances the theoretical rigor of existing research by identifying and empirically testing two mediating pathways—technological innovation and industrial upgrading—through which digital economy development influences export competitiveness. Third, it adopts a comprehensive multidimensional index to assess digital economy development—encompassing digital infrastructure, digital industrialization, and industrial digitalization, thereby improving measurement accuracy and construct validity.

Drawing on provincial panel data spanning the years 2011 to 2021, this study utilizes econometric techniques to investigate the digital economy’s influence—both direct and through intermediary mechanisms—on the competitiveness of high-tech exports. Framing this inquiry within the wider context of sustainable economic transformation, the research seeks to shed light on how digital technologies can foster innovation-driven, high-value-added, and environmentally resilient growth. Ultimately, this research contributes to theoretical refinement and offers actionable insights for policymakers navigating the digital and sustainable transition.

2. Mechanism Analysis

This section examines the mechanisms by which the digital economy affects the export competitiveness of high-tech products. Grounded in the factor endowment theory, the technological innovation theory, and the theory of technological advancement and industrial upgrading, we propose a conceptual framework comprising three distinct pathways, as follows: one direct channel and two indirect ones, through which the digital economy shapes the performance of high-tech exports. Each pathway is substantiated by peer-reviewed empirical studies and leads to a theoretically grounded hypothesis.

2.1. Direct Mechanism

According to factor endowment theory, countries possess varying relative abundances of production factors—such as capital, labor, and technology—which shape their comparative advantage in international trade [10,11]. As economies evolve, these endowments are increasingly defined not only by traditional inputs, but also by digital capabilities that influence productivity, innovation, and trade performance. The digital economy, encompassing industrial digitalization, digital industrialization, and the digital development environment, may thus reshape the comparative advantage by augmenting high-tech export competitiveness [12,13].

Industrial digitalization—the integration of digital technologies across primary, secondary, and tertiary sectors—can enhance productive efficiency, reduce transaction costs, and accelerate the time-to-market for high-tech goods. Empirical research demonstrates that digitized industrial processes correlate with higher productivity and innovation output, both of which are critical to export performance in technology-intensive industries [14].

Digital industrialization, characterized by the expansion of digital infrastructure and ICT industries, forms the backbone of digitally enabled trade ecosystems [12]. Prior work indicates that robust digital infrastructure reduces information friction and logistics barriers, facilitating access to global markets for high-tech producers [13]. The growth of the ICT sector itself also contributes directly to high-tech exports by increasing the supply and diversity of tradable digital goods and services [11].

The digital development environment, through investments in human capital and innovation systems, further supports the comparative advantage in high-tech sectors. Studies have shown that local innovation capacity—reflected in R&D spending, patent activity, and STEM education—positively correlates with the quality and complexity of export portfolios [14].

Together, these three dimensions can be seen as modern factor endowments that reinforce a country’s ability to compete in global high-tech markets. Building on this theoretical foundation, we propose the following hypothesis:

Hypothesis 1 (H1).

Advancements in the digital economy contribute directly to improving the export competitiveness of high-tech products.

2.2. Indirect Mechanism

2.2.1. Technological Innovation as a Mediator

In seeking to elucidate the underlying mechanisms through which the digital economy shapes international trade dynamics, we draw upon the technological innovation theory, which posits that technological advancement is both a driver and a product of economic transformation [15]. This framework suggests that digital infrastructure, platforms, and services do not merely enhance productivity directly, but also foster an ecosystem conducive to innovation [16].

The digital economy—comprising digital infrastructure, digital industrialization, and digital services—functions as a catalyst for knowledge diffusion and process upgrading. These components collectively reduce transaction costs [17], increase the efficiency of R&D activities [18], and enable the rapid reconfiguration of production networks [19]. The interplay between digital transformation and innovation capabilities has been empirically supported across multiple contexts, particularly in technologically intensive sectors [20].

Innovation-driven trade theory further contends that firms equipped with advanced technological capabilities exhibit a comparative advantage in high-value-added exports. As digital technologies enable faster design iterations, better alignment with global quality standards, and increased absorptive capacity for frontier knowledge, they indirectly promote export competitiveness through technological upgrading [21]. This innovation-mediated pathway is particularly salient for high-tech products, which are often sensitive to shifts in production technology and innovation intensity. Based on this synthesis of theoretical and empirical insights, we propose the following hypothesis:

Hypothesis 2 (H2).

The digital economy exerts an indirect positive effect on high-tech export competitiveness by stimulating technological innovation.

2.2.2. Industrial Upgrading as a Mediator

Grounded in the theory of technological advancement and industrial upgrading, the digital economy is increasingly recognized as a transformative force in industrial restructuring [22]. Prior research suggests that digital technologies—such as cloud computing, big data analytics, and intelligent manufacturing—facilitate the reconfiguration of industrial value chains by enhancing innovation capacity, streamlining resource allocation, and enabling dynamic integration across production networks [23]. These processes are instrumental in driving industrial upgrading, allowing firms to transition from labor-intensive, low-value-added operations toward knowledge-intensive, high-value-added activities [24]. Empirical evidence supports this transformation. For instance, studies have shown that digital adoption correlates with increased total factor productivity, particularly in high-tech and manufacturing sectors [25]. Additionally, digitally enabled firms demonstrate greater responsiveness to international quality standards and exhibit higher innovation efficiency, both of which are prerequisites for global competitiveness [26]. These developments suggest that digital technologies not only enhance domestic production capabilities, but also indirectly bolster the international competitiveness of high-tech exports through their role in industrial upgrading. Accordingly, we propose the following hypothesis:

Hypothesis 3 (H3).

The digital economy indirectly boosts the competitiveness of high-tech exports by facilitating industrial upgrading processes.

3. Methodology and Data

3.1. Model Construction

To empirically evaluate the impact of the digital economy on the export competitiveness of high-tech industries, this study constructs the following econometric model using panel data covering 30 provincial-level regions in China (excluding Tibet) over the period from 2011 to 2021:

$${Eci}_{it}={\alpha }_0+{\alpha }_1{De}_{it}+\alpha X_{it}+{\theta }_i+{\phi }_t{+\epsilon }_{it}$$

(1)

In this model, $i$ denotes the province and t refers to the year. The dependent variable, ${Eci}_{it}$, captures the export competitiveness of high-tech products in province $i$ during year t. The key explanatory variable, ${De}_{it}$, reflects the level of digital economy development, which is assessed through the following three key dimensions: industrial digitalization, digital industrialization, and the digital development environment. The vector $X_{it}$ includes a set of control variables. Following Han et al. (2023) [27], the selected controls are as follows: Edu, indicating educational attainment, is measured by the student-to-teacher ratio in regular higher education institutions; Open, representing openness to international trade, is calculated as the ratio of total trade (imports and exports) to GDP; Fdi captures the volume of foreign direct investment; and Fin reflects financial development, measured by the ratio of the year-end balance of financial institution loans and deposits to the provincial GDP. The terms ${\theta }_i$ and ${\phi }_t$ account for province-specific and time-specific fixed effects, respectively, while ${\epsilon }_{it}$ represents the stochastic error term and ${\alpha }_0$ is the constant.

The export competitiveness index is calculated using the following formula:

$${Eci}_i={\displaystyle \frac{\raisebox{1ex}{${Export}_i$}\!\left/ \!\raisebox{-1ex}{${\sum }_{i=1}^n{Export}_i$}\right.}{\raisebox{1ex}{${Gdp}_i$}\!\left/ \!\raisebox{-1ex}{${\sum }_{i=1}^n{Gdp}_i$}\right.}}$$

(2)

In this equation, ${Eci}_i$ denotes the export competitiveness index for province i, where ${Export}_i$ represents the export value of high-tech products, and ${Gdp}_i$ corresponds to the gross domestic product of that province.

To address potential issues of heteroscedasticity and improve model robustness, all variables are transformed into their natural logarithmic forms prior to regression analysis. By expanding Equation (1), we obtain the econometric model shown in Equation (3), as follows:

$${Eci}_{it}={\alpha }_0+{\alpha }_1{De}_{it}+{\alpha }_2{Edu}_{it}+{\alpha }_{3}Open+{\alpha }_4{Fdi}_{it}+{\alpha }_{3}Fin+{\theta }_i+{\phi }_t{+\xi }_{it}$$

(3)

Building on Equation (3), this study employs a mediation analysis framework to investigate the indirect pathways through which the digital economy influences the export competitiveness of high-tech products. This approach enables the empirical testing of Hypotheses 2 and 3, which propose that technological innovation and industrial upgrading serve as mediating mechanisms. The following mediation effect models are constructed:

$${Eci}_{it}={\alpha }_0+{\alpha }_1{De}_{it}+\alpha X_{it}+{\theta }_i+{\phi }_t{+\zeta }_{it}$$

(4)

$$M_{it}={\beta }_0+{\beta }_1{De}_{it}+\beta X_{it}+{\theta }_i+{\phi }_t+{\varsigma }_{it}$$

(5)

$${Eci}_{it}={\varnothing }_0+{\varnothing }_1{De}_{it}+{\varnothing }_2{M}_{it}+\varnothing X_{it}+{\theta }_i+{\phi }_t+{ϵ}_{it}$$

(6)

In this context, M denotes the set of mediating variables examined in this study, specifically including technological innovation ($Ti$) and industrial upgrading ($Upgra$). The term $X_{it}$ represents the vector of control variables, which remains consistent with those used in the fixed-effects model specification.

3.2. Variable Selection

(1)

Dependent Variable: Export Competitiveness of High-Tech Products (Eci)

In light of the global shift toward sustainable economic growth, evaluating the competitiveness of high-tech exports demands a multidimensional perspective. This measure should capture not only the scale and performance of a region’s trade activities, but also its underlying capabilities in technological innovation, value generation, and industrial transformation. This study centers on the export competitiveness of high-tech products—a sector that lies at the heart of the digital economy and plays a vital role in advancing a low-carbon, knowledge-intensive, economic transformation. To quantitatively assess this dimension, the export competitiveness index (Eci) is employed as the dependent variable. The Eci reflects the proportion of a region’s high-tech export value relative to the national total, adjusted by the region’s share of national GDP. By incorporating regional economic size into the calculation, this index effectively captures the interregional disparities and serves as a reliable metric for evaluating high-tech export competitiveness across provinces.

(2)

Core Explanatory Variable: Digital Economy Development Level (De)

In the context of global efforts to promote inclusive, innovation-driven, and sustainable economic development, the digital economy is increasingly recognized as a transformative force. It not only reshapes industrial structures and trade flows, but also enables resource-efficient production, low-carbon innovation, and balanced regional growth. To evaluate the influence of digitalization on the export competitiveness of high-tech industries, it is essential to develop a comprehensive, multidimensional metric that encapsulates the scope, intensity, and sustainability potential of digital economy development. In this study, a composite Digital Economy Development Index (De) is constructed to reflect the complex and interconnected nature of regional digital transformation. The index is built upon three core dimensions, as follows: digital industrialization, industrial digitalization, and the digital development environment. Collectively, these dimensions provide a holistic perspective on the progress and quality of digital economy expansion, as well as its alignment with the broader goals of economic resilience, environmental sustainability, and social inclusiveness. Serving as a key explanatory variable, the De index underpins the empirical analysis, allowing for an in-depth investigation of how the digital economy enhances high-tech export competitiveness while also supporting structural upgrading, regional economic convergence, and efficient resource utilization. Further details regarding the construction of the De index and its sub-indicators are presented in Section 4.2.

(3)

Mediating Variables

To investigate the mechanisms by which the digital economy influences the export competitiveness of high-tech industries, this study introduces two mediating variables: technological innovation and industrial upgrading. Both of these are critical levers for achieving sustainable economic development, as they facilitate the transition toward knowledge-intensive, low-carbon, and resilient industrial systems.

Technological Innovation (Ti): Innovation lies at the heart of sustainable and competitive economies. It enables firms and regions to increase productivity, develop cleaner and more efficient technologies, and reduce dependency on resource-intensive processes. Technological innovation is measured using an outcome-oriented indicator—total sales revenue from new high-tech products—aligned with the innovation capacity framework outlined by Brouwer and Kleinknecht (1999) [28]. This indicator reflects the commercialization of innovation, signaling both the capacity for technological advancement and the ability of firms to deliver market-relevant, high-value-added outputs. From a sustainability perspective, a robust innovation ecosystem is often linked to the adoption of eco-friendly technologies, enhancements in energy and resource efficiency, and the development of environmentally sustainable export capabilities in high-tech sectors. Thus, technological innovation not only enhances competitiveness, but also advances the broader agenda of environmentally sustainable trade and industrial transformation.

Industrial Upgrading (Upgra): Industrial upgrading refers to the transformation of an economy’s industrial structure—characterized by a shift from labor-intensive production toward capital- and knowledge-intensive industries, as well as a transition from low-value-added to high-value-added activities. In the context of high-tech exports and sustainable development, upgrading signals the capacity of an economy to transition from quantity-based growth to quality-driven, innovation-led growth that minimizes environmental impact while maximizing economic complexity. In this study, industrial upgrading is quantified using the natural logarithm of the ratio between the output value of high-tech industries and the total industrial output value. This metric captures the relative importance of high-tech sectors within the broader industrial landscape and serves as an indicator of structural advancement. This ratio captures the extent to which a region has pivoted toward advanced manufacturing and digital industries, which are central to green industrial policy, carbon neutrality goals, and sustainable trade competitiveness. Due to inconsistencies in the classification of industrial sectors across different editions of statistical yearbooks—especially post-2011—this study addresses data gaps by substituting missing values with the total industrial output of high-tech enterprises, as reported in the China Torch Statistical Yearbook. This proxy maintains consistency while enabling robust regional comparisons across the study period.

Together, these mediating variables—technological innovation and industrial upgrading—serve as both indicators of economic modernization and instruments of sustainable development. Technological innovation enables the creation of resource-efficient products, while industrial upgrading supports the structural transition toward cleaner, higher-value industries. As mediators, they help to clarify how digital transformation not only boosts export performance, but also drives progress toward equitable, resilient, and environmentally sustainable industrial ecosystems.

(4)

Control Variables

To ensure a robust analysis of the relationship between the digital economy and the export competitiveness of high-tech industries, this study incorporates a set of control variables that capture key socio-economic and structural factors shaping trade outcomes. Each of these factors—education, openness, foreign direct investment, and financial development—not only affect competitiveness, but also play a role in shaping a region’s capacity to pursue sustainable and inclusive economic growth. The inclusion of these variables enables a more nuanced interpretation of the findings by accounting for external conditions that may facilitate or constrain green, innovation-led trade growth.

Education Level (Edu): A highly skilled and digitally literate workforce is essential for the growth of knowledge-intensive and sustainable industries. The high-tech sector, in particular, relies heavily on human capital capable of driving technological advancement, innovation, and clean production processes. To capture this, this study employs the student–teacher ratio in regular higher education institutions as a proxy for regional education quality. While imperfect, this indicator reflects the density of instructional resources per student, which may influence the formation of specialized talent pools—a foundational component of sustainable industrial upgrading and export competitiveness.

Degree of Openness (Open): Openness to international markets facilitates not only trade, but also the exchange of green technologies, sustainable practices, and international standards. In the context of global sustainability transitions, regions with higher openness levels are better positioned to integrate into environmentally conscious value chains and adopt best practices in clean production and compliance. To account for the influence of external economic engagement on high-tech export performance, this study incorporates a regional openness index, measured as the ratio of total imports and exports to regional GDP.

Foreign Direct Investment (Fdi): Fdi is a key vector for technology transfer, industrial modernization, and sustainability diffusion. Multinational enterprises often bring advanced technologies and managerial practices that enhance the productivity and environmental performance of host firms. In high-tech sectors, Fdi can accelerate access to cleaner production methods and digital infrastructure, thus influencing both the competitiveness and sustainability of export-oriented growth. In addition, the total volume of actual foreign direct investment received by each region is included as a control variable, reflecting the role of international capital inflows in supporting export-oriented growth.

Level of Financial Development (Fin): Financial systems that support inclusive and innovation-oriented investment are critical to enabling firms—especially in high-tech industries—to scale, upgrade, and adopt cleaner technologies. A well-developed financial sector can reduce capital constraints, fund research and development, and support the transition to sustainable business models and green exports. Financial development is captured through the ratio of total loans issued by financial institutions to regional GDP, serving as an indicator of the capacity of local economies to mobilize financial resources for innovation and export expansion.

3.3. Measurement Method

The entropy method is a quantitative technique used to evaluate the degree of variability or dispersion in a set of indicators. In this context, greater dispersion implies a stronger influence of the indicator in the overall assessment. Hence, the entropy value serves as a basis for determining the relative weight of each indicator in constructing a composite index. Rooted in information theory, entropy reflects uncertainty, as follows: when the amount of useful information in an indicator is high, uncertainty is reduced, leading to lower entropy values. Conversely, limited information content corresponds to greater uncertainty and, therefore, higher entropy. The fundamental steps involved in calculating indicator weights using the entropy method are as follows:

Construct a matrix X with $i$ rows and j columns, where $i$ = 1, 2,…, m; j =1, 2, …, n

$$\mathrm{X}=\left[\begin{array}{ccc}{X}_{11}& \cdots & X_{1n}\\ ⋮& \ddots & ⋮\\ X_{m1}& \cdots & X_{mn}\end{array}\right]$$

(7)

1.

Standardization

Before performing calculations, it is necessary to standardize the indicators, classifying them as either positive or negative based on their impact on the overall index system.

For indicators that are considered positive, the formula can be expressed as follows:

$${\mathrm{x}}_{ij}={\displaystyle \frac{{\mathrm{x}}_j-{\mathrm{x}}_j^{\mathrm{m}\mathrm{i}\mathrm{n}}}{{\mathrm{x}}_j^{\mathrm{m}\mathrm{a}\mathrm{x}}-{\mathrm{x}}_j^{\mathrm{m}\mathrm{i}\mathrm{n}}}}$$

(8)

For indicators with negative effects, the formula is as follows:

$${\mathrm{x}}_{ij}={\displaystyle \frac{{\mathrm{x}}_j^{\mathrm{m}\mathrm{a}\mathrm{x}}-{\mathrm{x}}_j}{{\mathrm{x}}_j^{\mathrm{m}\mathrm{a}\mathrm{x}}-{\mathrm{x}}_j^{\mathrm{m}\mathrm{i}\mathrm{n}}}}$$

(9)

where $i$ represents each province (city, autonomous region) and j denotes the secondary indicators selected in this study.

2.

Calculate the characteristic proportion $P_{ij}$:

$$P_{ij}={\displaystyle \frac{X_{ij}}{{\sum }_{x=1}^m{X}_{ij}}}$$

(10)

3.

Determine the entropy value for the j-th indicator:

$$e_j=-\mathrm{L}\mathrm{n}{\displaystyle \frac{1}{n}}{\sum }_{i=1}^n{p}_{ij}\mathrm{L}\mathrm{n}{p}_{ij}$$

(11)

4.

Compute the information utility value for the j-th indicator:

$$g_j=1-e_j$$

(12)

5.

Calculate the weight assigned to the j-th indicator:

$$w_j={\displaystyle \frac{g_j}{{\sum }_{j=1}^n{g}_j}}$$

(13)

6.

Calculate the comprehensive evaluation index:

$$S_j={\sum }_{j=1}^n{w}_j\ast x_{ij}$$

(14)

3.4. Data

The dataset used in this study comprises 330 observations, covering the period from 2011 to 2021, with 30 provincial-level regions (including municipalities and autonomous regions) as the sample units. The data used to assess the export competitiveness of high-tech products are obtained from the China Statistical Yearbook of Science and Technology, China Statistical Yearbook, China Statistical Yearbook on High Technology Industry and relevant provincial statistical yearbooks. The development of the digital economy is measured through an indicator system that includes digital industrialization, industrial digitalization, and the digital development environment. The data used for these indicators are sourced from the National Bureau of Statistics of China, China Statistical Yearbook, China Industry Economy Statistical Yearbook, and China Statistical Yearbook on Electronic Information Industry. The control variable data are derived from provincial statistical yearbooks, the National Bureau of Statistics of China, and the DRCNET database. Information for the mediating variables comes from the China Torch Statistical Yearbook, the National Bureau of Statistics of China, and various provincial statistical yearbooks. Throughout the empirical analysis, data were transformed logarithmically, and missing values were estimated using interpolation techniques.

4. Descriptive Analysis and Indicator Measurement

4.1. Descriptive Analysis

Table 1. Descriptive analysis of variables.

Variable Type	Variable Name	Sample Size	Mean	Standard Deviation	Minimum	Maximum	Median
Dependent Variable	Eci	330	0.632	0.878	0.001	4.165	0.294
Core Explanatory Variable	De	330	0.130	0.131	0.004	0.880	0.092
Control Variables	Edu	330	17.748	1.229	13.98	22.7	17.79
	Open	330	0.265	0.291	0.008	1.548	0.142
	Fdi	330	8.381	8.092	0.003	35.760	5.815
	Fin	330	3.380	1.085	1.678	7.578	3.146
Mediating Variables	Ti	330	15.879	33.550	0.002	245.918	4.093
	Upgra	330	5.347	3.443	0.101	32.405	4.567

displays the descriptive statistics for the variables examined in this study, providing a comparison of the maximum, minimum, mean, median, and variance for each variable. The analysis highlights notable regional disparities. By comparing the coefficients of these variables, it becomes clear that the most significant regional variations are observed in the export competitiveness of high-tech products (Eci), the level of digital economy development (De), and industrial upgrading (Upgra).

The export competitiveness of high-tech products exhibits a maximum value of 4.165, a minimum value of 0.001, and a standard deviation of 0.878, suggesting considerable variation in export competitiveness across provinces (municipalities, autonomous regions). The digital economy development level shows a maximum of 0.880, a minimum of 0.004, and a standard deviation of 0.131, indicating significant disparities in the extent of digital economy development across regions. The industrial upgrading of high-tech industries has a maximum value of 32.405, a minimum of 0.101, and a standard deviation of 3.443, further underscoring substantial regional differences in the growth of high-tech industries.

4.2. Measurement of Digital Economy Development Level

For the measurement of the comprehensive index system, this study applies the entropy method to assign weights to the various indicators. By analyzing the existing literature on digital economy evaluations, it is evident that many scholars adopt indicator systems to assess this area. The development of the digital economy primarily comprises two key aspects: the digitalization of industries and digital industrialization. Digital industrialization serves as the cornerstone of the digital economy, while the digitalization of industries captures the integration of various sectors into the digital framework. Furthermore, the development environment of the digital economy plays a critical role in supporting and fostering its growth.

In light of these factors, this study develops an evaluation framework for digital economy development that spans the following three dimensions: digital industrialization, industrial digitalization, and the digital economy development environment. This framework includes a total of 22 specific indicators. The full set of indicators is outlined in

Table 2. Indicator weights for measuring digital economy development.

Primary Indicator (Weight)	Secondary Indicator (Weight)	Tertiary Indicators	Unit	Weight
Industrial Digitalization (0.4002)	Industry (0.1826)	Industrial changed value	CYN hundred million	0.0330
		Expenditure on technological transformation	CYN ten thousand	0.0340
		New product sales revenue from large industrial enterprises	CYN ten thousand	0.0583
		Full-time equivalent of R&D personnel in large industrial enterprises	Person-years	0.0573
	Agriculture (0.0679)	Changed value in agriculture, forestry, animal husbandry, and fisheries	CYN hundred million	0.0246
		Number of rural broadband access users	Ten thousand households	0.0433
	Tertiary Industry (0.1497)	Changed value of tertiary industry	CYN hundred million	0.0312
		Original insurance premium income	CYN hundred million	0.0292
		Express business revenue	CYN ten thousand	0.0893
Digital Industrialization (0.3792)	Infrastructure (0.0936)	Internet broadband access ports	Ten thousand locations	0.0268
		Postal service outlets	Locations	0.0284
		Year-end total of mobile phone users	Ten thousand households	0.0219
		Length of long-distance optical fiber cables	Ten thousand kilometers	0.0165
	ICT Industry (0.2756)	Main business revenue of computer industry	CYN hundred million	0.0895
		Telecom business volume	CYN hundred million	0.0548
		Software business revenue	CYN ten thousand	0.0818
		Urban employment in information transmission, software, and IT services	Ten thousand people	0.0495
Digital Development Environment (0.2304)	Digital Talent Cultivation (0.0371)	Number of students in regular higher education	Ten thousand people	0.0173
		Local government education expenditure	CYN hundred million	0.0198
	Innovation Environment (0.1933)	Internal expenditure on R&D in high-tech industries	CYN ten thousand	0.0815
		Number of domestic patent applications granted	Items	0.0642
		Local government spending on science and technology	CYN hundred million	0.0476

Notes: The data for these indicators are mainly derived from the National Bureau of Statistics of China, China Statistical Yearbook, China Industry Economy Statistical Yearbook, and China Statistical Yearbook on Electronic Information Industry. Considering data availability, Tibet is excluded from this study, and the observation period spans from 2011 to 2021, because at the time of conducting this study, the data for 2022 had not yet been released.

, with all indicators representing positive contributions.

(1) Industrial Digitalization. Industrial digitalization reflects the integration of the digital economy with agriculture, industry, and tertiary industry. This study uses several indicators to measure the level of digitalization across these sectors. For the industrial sector, the indicators include industrial value-added, investment in technological transformation, sales revenue from new products in large industrial enterprises, and the full-time equivalent of R&D personnel within these enterprises. In the agricultural sector, the selected indicators comprise the added value of agriculture, forestry, animal husbandry, and fisheries, alongside the number of rural broadband access users. For the tertiary sector, the chosen indicators are the value-added of the tertiary industry, the original insurance premium income, and the revenue from express business operations.

(2) Digital Industrialization. For the industrial sector, the indicators include industrial value-added, investment in technological transformation, sales revenue from new products in large industrial enterprises, and the full-time equivalent of R&D personnel within these enterprises. In the agricultural sector, the selected indicators comprise the added value of agriculture, forestry, animal husbandry, and fisheries, alongside the number of rural broadband access users. For the tertiary sector, the chosen indicators are the value-added of the tertiary industry, the original insurance premium income, and the revenue from express business operations.

(3) Digital Development Environment. The digital development environment plays a crucial role in supporting the growth of the digital economy. It is characterized by talent development and innovation. To measure the cultivation of digital talent, this study uses indicators such as the number of students enrolled in regular higher education institutions and local government expenditure on education. To assess innovation capabilities, this study includes indicators such as internal R&D expenditure in high-tech industries, the number of domestic patent applications granted, and local government spending on science and technology.

Table 2 presents the weights assigned to each level of indicators, calculated using the entropy weight method. The three primary indicators, ranked in descending order of their weights, are industrial digitalization, digital industrialization, and the digital economy development environment. Their respective weights are 0.4002, 0.3792, and 0.2304. The magnitude of the weights reflects the relative contribution of each indicator to the overall level of digital economy development. Industrial digitalization holds the highest weight of 0.4002, underscoring its pivotal role in driving the growth of China’s digital economy, especially through advancements in both industrial and tertiary sectors. Digital industrialization, with a weight of 0.3792, indicates strong penetration of digital technologies into the industrial sector. Meanwhile, the digital development environment, with a weight of 0.2304, underscores the importance of cultivating digital talent and fostering an innovation-friendly environment.

The secondary indicators derived from these primary categories further clarify the influence of key metrics. Within industrial digitalization (0.4002), the secondary indicators of industry (0.1826), agriculture (0.0679), and the tertiary industry (0.1497) reflect the varied contributions to the digital economy. For instance, within the industrial sector, tertiary indicators such as industrial added value (0.0330), expenditure on technological transformation (0.0340), and sales revenue from new products in large industrial enterprises (0.0583) significantly influence the sector’s development. These values highlight the significance of industrial output and innovation within the broader context of economic digitalization. Similarly, the tertiary industry indicator emphasizes metrics like express business revenue (0.0893), showcasing the growing importance of digital services and logistics.

In digital industrialization (0.3792), infrastructure (0.0936) and the ICT industry (0.2756) serve as key secondary indicators. Additionally, infrastructure-related tertiary indicators, including internet broadband access ports (0.0268) and the length of long-distance optical fiber cables (0.0165), highlight the fundamental importance of digital infrastructure in supporting broader industrial activities. Meanwhile, within the ICT industry, indicators like main business revenue of the computer industry (0.0895) and software business revenue (0.0818) underscore the driving force of technology sectors in advancing digital industrialization.

For the digital development environment (0.2304), digital talent cultivation (0.0371) and the innovation environment (0.1933) are critical secondary indicators. Tertiary metrics within this category, including local government education expenditure (0.0198) and internal R&D expenditure in high-tech industries (0.0815), point to the necessity of investment in education and research for sustainable digital growth. Additionally, the number of domestic patent applications granted (0.0642) further highlights innovation as a key component of a thriving digital economy.

Together, the weights calculated using the entropy method across primary, secondary, and tertiary indicators provide a comprehensive picture of the diverse factors shaping digital economy development, offering critical insights for policy formulation and strategic investment.

In recent years, China’s digital economy has seen continuous growth and notable achievements. This paper offers a thorough evaluation and analysis of the digital economy’s development across 30 provinces, municipalities, and autonomous regions in China from 2011 to 2021. Using the indicator system framework and corresponding weight values outlined earlier, the specific values were calculated. Table 2 presents the comprehensive scores for the digital economy development level in these 30 regions.

As depicted in

Table 3. Comprehensive scores of digital economy development in 30 regions from 2011 to 2021.

Division	Region	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021	Mean Value
North China	Beijing	0.0994	0.1101	0.1224	0.1365	0.1488	0.1627	0.1810	0.2019	0.2202	0.2467	0.2755	0.1732
	Tianjin	0.0355	0.0433	0.0488	0.0518	0.0569	0.0582	0.0574	0.0630	0.0676	0.0759	0.0808	0.0581
	Hebei	0.0796	0.0802	0.1006	0.1062	0.1138	0.1283	0.1423	0.1612	0.1841	0.2046	0.1988	0.1371
	Shanxi	0.0400	0.0458	0.0503	0.0492	0.0527	0.0534	0.0627	0.0688	0.0771	0.0880	0.0960	0.0622
	Inner Mongolia	0.0363	0.0385	0.0428	0.0452	0.0485	0.0514	0.0552	0.0596	0.0646	0.0700	0.0684	0.0528
Northeast China	Liaoning	0.0784	0.0843	0.0956	0.1009	0.0992	0.0960	0.1006	0.1088	0.1136	0.1215	0.1223	0.1090
	Jilin	0.0339	0.0372	0.0387	0.0445	0.0448	0.0486	0.0523	0.0540	0.0754	0.0619	0.0608	0.0502
	Heilongjiang	0.0447	0.0493	0.0534	0.0565	0.0588	0.0626	0.0694	0.0727	0.0797	0.0816	0.0777	0.0642
East China	Shanghai	0.0944	0.1042	0.1182	0.1320	0.1413	0.1628	0.1871	0.1958	0.2215	0.2432	0.2643	0.1695
	Jiangsu	0.2514	0.2932	0.3228	0.3443	0.3784	0.4073	0.4367	0.4682	0.5126	0.5765	0.6160	0.4189
	Zhejiang	0.1436	0.1663	0.1915	0.2037	0.2383	0.2528	0.2900	0.3409	0.3843	0.4207	0.4438	0.2796
	Anhui	0.0681	0.0755	0.0846	0.0924	0.1088	0.1231	0.1415	0.1668	0.1904	0.2149	0.2279	0.1358
	Fujian	0.0712	0.0826	0.0932	0.1020	0.1139	0.1272	0.1439	0.1669	0.1830	0.1861	0.1994	0.1336
	Jiangxi	0.0403	0.0454	0.0537	0.0600	0.0697	0.0781	0.0921	0.1085	0.1281	0.1465	0.1511	0.0885
	Shandong	0.1601	0.1821	0.2073	0.2174	0.2428	0.2606	0.2827	0.3003	0.3206	0.3545	0.3946	0.2657
Central China	Henan	0.0948	0.1059	0.1240	0.1330	0.1490	0.1649	0.1850	0.2129	0.2363	0.2653	0.2690	0.1764
	Hubei	0.0730	0.0846	0.0962	0.1072	0.1208	0.1345	0.1515	0.1744	0.1981	0.2095	0.2297	0.1436
	Hunan	0.0802	0.0918	0.1038	0.1033	0.1149	0.1227	0.1384	0.1517	0.1751	0.1961	0.1997	0.1344
South China	Guangdong	0.2756	0.3161	0.3662	0.3868	0.4457	0.5062	0.5780	0.6878	0.7764	0.8514	0.8799	0.5518
	Guangxi	0.0445	0.0510	0.0554	0.0577	0.0654	0.0718	0.0813	0.0968	0.1190	0.1343	0.1274	0.0822
	Hainan	0.0059	0.0075	0.0093	0.0099	0.0120	0.0139	0.0159	0.0190	0.0229	0.0255	0.0258	0.0152
Southwest China	Chongqing	0.0367	0.0428	0.0511	0.0570	0.0659	0.0756	0.0856	0.0938	0.1082	0.1220	0.1278	0.0788
	Sichuan	0.0961	0.1057	0.1244	0.1361	0.1535	0.1700	0.1949	0.2233	0.2586	0.2931	0.2965	0.1866
	Guizhou	0.0266	0.0325	0.0362	0.0412	0.0479	0.0534	0.0620	0.0735	0.0863	0.0968	0.0912	0.0589
	Yunnan	0.0358	0.0406	0.0467	0.0493	0.0555	0.0606	0.0701	0.0864	0.1033	0.1187	0.1075	0.0704
Northwest China	Shaanxi	0.0500	0.0563	0.0563	0.0631	0.0693	0.0766	0.0868	0.0958	0.1090	0.1252	0.1368	0.0917
	Gansu	0.0196	0.0248	0.0281	0.0298	0.0329	0.0352	0.0406	0.0477	0.0562	0.0621	0.0592	0.0397
	Qinghai	0.0043	0.0059	0.0064	0.0077	0.0088	0.0096	0.0110	0.0129	0.0148	0.0168	0.0142	0.0102
	Ningxia	0.0050	0.0050	0.0063	0.0087	0.0083	0.0093	0.0122	0.0162	0.0177	0.0194	0.0197	0.0116
	Xinjiang	0.0247	0.0278	0.0312	0.0337	0.0391	0.0406	0.0448	0.0520	0.0616	0.0676	0.0658	0.0444

, the digital economy across China has steadily progressed between 2011 and 2021, reflecting a national upward trend in digital economic development over the past decade. However, a regional comparison reveals significant variations in development levels. Guangdong, Jiangsu, and Zhejiang rank as the top three provinces in digital economy development. The economically advanced eastern regions consistently report high scores. For example, Guangdong’s digital economy score in 2021 was 0.8799, whereas Gansu’s score was only 0.0592, highlighting considerable regional disparities. A more detailed regional analysis of the digital economy development levels is provided in Appendix A.

4.3. Measurement of Export Competitiveness

The evaluation indicators for export competitiveness can be directly and objectively understood through import and export data. However, there is no unified international standard for these indicators. Based on existing research, several mainstream evaluation indicators have been developed, including the export competitiveness index (Eci) and trade competitiveness index (Tci). A detailed analysis of the trade competitiveness index is presented in Appendix B.

Evaluating the export competitiveness of high-tech industries requires a thorough approach that not only considers the structural dynamics within the sector, but also its alignment with broader objectives of sustainable economic development. It is insufficient to rely solely on import and export values for an objective assessment of export competitiveness. The export competitiveness index (Eci) is a more comprehensive measure, defined as the ratio of an industry’s share in the country’s total industrial exports to its share in the country’s total industrial sales revenue. This index incorporates the broader scale of industrial exports and provides a useful tool for assessing the export competitiveness of various provinces and sub-industries.

In this study, the export competitiveness index (Eci) is used to gauge the regional export competitiveness of high-tech products, taking into account regional disparities. The regional Eci is calculated by comparing the ratio of a region’s high-tech product export value to the national total, relative to the region’s GDP share within the national GDP. This methodology captures regional differences and provides a measure of export competitiveness across various regions. The average Eci for the 30 provinces (municipalities, autonomous regions) in China, spanning from 2011 to 2021, is shown in Figure 1.

The findings indicate that the overall export competitiveness of high-tech products in China follows a distinct regional pattern, where the eastern provinces outperform both the central and western regions, and coastal cities surpass inland cities. Eastern regions, such as Shanghai and Jiangsu, have experienced rapid economic development and have implemented substantial support for high-tech industries. Talent attraction policies have drawn a significant number of outstanding individuals to these areas. Coupled with favorable geographical advantages, this has led to the rapid development of high-tech industries in the eastern regions, resulting in strong product export competitiveness. According to the Ministry of Science and Technology of the People’s Republic of China, by September 2022, there were 173 high-tech parks in total across the country, with 77 located in the eastern region, making up nearly half of the total. This distribution underscores the greater concentration and scale of high-tech industries in the eastern region, which contributes to higher export values for high-tech products compared to those of the central and western regions.

5. Empirical Study

5.1. Baseline Regression Analysis

To empirically evaluate the influence of the digital economy on the export competitiveness of high-tech products within a sustainability-focused framework, we perform a baseline fixed-effects regression analysis. The suitability of the fixed-effects model is confirmed by the Hausman test. In light of the presence of outliers, all variable data are Winsorized at the 1% level prior to estimating the baseline regression model.

Table 4. Baseline panel regression results.

Variable	(1) LnEci	(2) LnEci	(3) LnEci	(4) LnEci	(5) LnEci
LnDe	0.728 *** (9.33)	0.640 *** (7.71)	0.836 *** (10.17)	0.835 *** (10.11)	0.656 *** (5.85)
LnEdu		2.031 *** (2.82)	1.471 ** (2.18)	1.479 ** (2.14)	1.640 ** (2.38)
LnOpen			0.813 *** (6.92)	0.813 *** (6.86)	0.814 *** (6.92)
LnFdi				0.003 (0.05)	−0.003 (−0.05)
LnFin					0.744 ** (2.35)
cons	0.348 * (1.79)	−5.705 *** (−2.64)	−2.160 (−1.04)	−2.195 (−1.01)	−3.951 * (−1.73)
Year	Yes	Yes	Yes	Yes	Yes
Province	Yes	Yes	Yes	Yes	Yes
N	330	330	330	330	330
R2	0.226	0.246	0.350	0.350	0.362

Note: Baseline results derived from panel regression analysis, accounting for both cross-sectional and temporal variations. *** p < 0.01, ** p < 0.05, * p < 0.1, with t-statistics in parentheses.

presents the baseline regression results. A stepwise approach is employed to incorporate control variables, allowing us to assess the impact of both the digital economy and these control factors on high-tech export competitiveness. Column (1) reports the effect of the digital economy on high-tech export competitiveness, while Columns (2) to (5) display the results after progressively adding control variables.

The regression results from Models (1) through (5) clearly indicate that the level of digital economy development—serving as the key explanatory variable in this study—yields coefficients of 0.728, 0.640, 0.836, 0.835, and 0.656, all statistically significant at the 1% level. This outcome validates Hypothesis 1 (H1) and highlights the digital economy’s pivotal role as a strategic driver of sustainable trade performance. In the absence of control variables, a 1% increase in digital economy development corresponds to a 0.728 percentage point improvement in the export competitiveness of high-tech products. This result suggests that advancements in digital infrastructure and integration enhance production and distribution efficiency, lower transaction and logistics costs, and reduce time-to-market—all of which foster more sustainable, innovation-driven, and competitive high-tech exports. These benefits are particularly relevant in the context of green growth strategies, as digitalization enables resource optimization, real-time supply chain management, and lower emissions associated with international trade.

As shown in Models (2) through (5), the regression coefficients for the education level are consistently positive and statistically significant at the 5% level, suggesting that higher education levels have a notable positive impact on the export competitiveness of high-tech products. This result reflects the central role of human capital in enabling technological innovation, digital transformation, and clean industrial upgrading. Investment in higher education cultivates a digitally skilled, innovation-ready workforce, which enhances product complexity and fosters eco-innovative solutions in the high-tech sector. These findings support the view that talent is a strategic resource not only for competitiveness, but also for sustainable development, reinforcing the importance of aligning talent development with digital economy and green economy goals.

From Models (3) to (5), a significant positive relationship is observed between the degree of openness and the export competitiveness of high-tech products, indicating that increased openness contributes to improved export performance in the high-tech sector. This suggests that open economic environments foster greater access to international markets, advanced technologies, and sustainability-related standards, reducing external informational barriers and enhancing integration into global green value chains. Such openness enables firms to benchmark against international best practices, which is critical for sustainable industrial transformation.

Interestingly, the results from Models (4) and (5) indicate that foreign direct investment (FDI) does not have a statistically significant effect on export competitiveness in the short term. This may reflect a lag in technology absorption and diffusion, where foreign technological inputs take time to translate into localized innovation and export performance. It may also indicate that FDI-driven growth in China’s high-tech sector still requires stronger domestic absorptive capacity, regulatory frameworks, and sustainability-aligned industrial policies to convert foreign investments into measurable gains in green and competitive exports.

In Model (5), the level of financial development is positively correlated with the export competitiveness of high-tech products, with a coefficient of 0.744, which is statistically significant at the 5% level. This highlights the critical role of financial institutions in enabling innovation, facilitating R&D, and supporting sustainable export growth. Developed financial markets reduce barriers to capital, enabling firms to invest in product development, clean technologies, and advanced digital infrastructure. This relationship reinforces the importance of inclusive and innovation-oriented financial ecosystems in sustaining long-term competitiveness in high-tech trade.

5.2. Heterogeneity Analysis

Building upon the baseline regression results, which demonstrated a significant positive relationship between the digital economy and high-tech export competitiveness, this section explores regional heterogeneity to examine whether the impact of digital transformation differs across various stages of economic development. Given the marked regional disparities within China, we divide the sample into eastern, central, and western regions. This enables a comparative analysis of how the digital economy influences sustainable trade growth across diverse economic and infrastructural contexts.

Table 5. Heterogeneity test results.

	Eastern Region	Central Region	Western Region
Variable	LnEci	LnEci	LnEci
LnDe	0.026	0.694 ***	1.060 ***
	(0.24)	(2.89)	(4.13)
LnEdu	−0.375	−0.002	1.437
	(−0.69)	(−0.00)	(0.89)
LnOpen	0.637 ***	0.709 ***	0.756 ***
	(4.14)	(3.27)	(4.48)
LnFdi	−0.029	−0.200 **	0.192 **
	(−0.57)	(−2.31)	(2.06)
LnFin	−0.369	1.458 ***	1.822 **
	(−1.29)	(2.88)	(2.29)
Cons	1.769	0.837	−3.770
	(1.05)	(0.23)	(−0.65)
N	121	88	121
R2	0.310	0.666	0.525
Province	Yes	Yes	Yes
Year	Yes	Yes	Yes

Note: *** p < 0.01, ** p < 0.05, with t-statistics in parentheses.

presents the regression results for the three regions. The findings highlight considerable variation in the effect of the digital economy on high-tech export competitiveness, with significant implications for developing regionally balanced and inclusive strategies.

The results show that the digital economy significantly boosts export competitiveness in the central and western regions, with coefficients that are statistically significant at the 1% level. Conversely, while the coefficient for the eastern region is positive, it does not reach statistical significance. Notably, the effect is most pronounced in the western region, suggesting that digitally-driven improvements in trade performance are more substantial in less-developed regions. This finding supports the notion that digital transformation can serve as a levelling mechanism, enhancing economic opportunity in areas historically limited by infrastructure, distance, and industrial capacity. The spillover benefits of digital platforms—such as reduced transaction costs, expanded market access, and greater supply chain integration—can help to offset regional disparities and promote inclusive, innovation-led growth. In the context of sustainable development, these findings highlight the potential of digital infrastructure investments to foster more equitable economic transitions in underdeveloped regions.

The effect of education on export competitiveness does not show statistical significance across the different regions. A likely explanation for this lies in the long gestation period required to cultivate mid- and high-level innovative talent, particularly in STEM and digital domains. As such, the absence of short-term effects highlights the need for long-term investments in education systems that are aligned with the requirements of sustainable industrial transformation. Accelerating the development of innovation-ready, sustainability-oriented human capital should remain a national priority to ensure the future competitiveness of high-tech exports.

Across all three regions, the degree of openness is positively and significantly correlated with export competitiveness, underscoring the importance of international engagement in sustaining high-tech export growth. This effect is consistent, regardless of a region’s development level, highlighting openness as a universal enabler of integration into global green value chains. These findings align with China’s broader commitment to the “dual circulation” development strategy, in which domestic innovation and market development complement international connectivity. In this context, regional openness supports not only trade expansion, but also the diffusion of global sustainability standards, access to clean technologies, and greater participation in environmentally conscious global supply chains.

Meanwhile, the influence of foreign direct investment (FDI) on high-tech export competitiveness exhibits considerable regional variation. In the western region, FDI has a strong positive effect, suggesting that foreign capital enhances export capacity by introducing new technologies and managerial practices. However, in the central region, FDI appears to exert a negative effect, potentially due to increased competition between foreign and domestic firms. In regions where domestic innovation ecosystems are still developing, foreign entrants may displace local firms without fully integrating their technological know-how, thereby undermining indigenous capacity building. These dynamics suggest that FDI alone is not sufficient to ensure sustainable export growth, rather it must be accompanied by policies that promote absorptive capacity, local innovation linkages, and inclusive technology transfer frameworks.

Finally, financial development plays a significant role in enhancing high-tech export competitiveness in both the central and western regions, with the most pronounced effect being observed in the western region. This reflects the growing capacity of regional financial systems to channel capital into high-tech sectors, enabling firms to invest in R&D, digital infrastructure, and clean technologies. Access to sustainable finance is essential for facilitating green industrial upgrading and the shift toward more resource-efficient and globally competitive production systems.

5.3. Endogeneity Test

When analyzing causal relationships, addressing the issue of endogeneity is essential. Common sources of endogeneity include measurement error, omitted variables, and bidirectional causality. This study takes steps to mitigate potential endogeneity concerns in model construction.

To minimize measurement error, all data utilized in this study are obtained from official statistical yearbooks, and the entropy method is employed to develop a comprehensive evaluation system for digital economy development levels, thereby reducing errors associated with raw data.

To address the omitted variables, four control variables—education level, degree of openness, foreign direct investment, and financial development—are incorporated into the regression analysis. The F-test, BP test, and Hausman test were conducted during model selection, with the fixed-effects model being chosen based on the test results.

Moreover, endogeneity can arise from reverse causality between variables. While the development of the digital economy enhances the export competitiveness of high-tech products, it is also possible that the increased export competitiveness of high-tech products may promote industrial digitalization and improve the digital economy environment, thus influencing the level of digital economy development.

To address this potential reverse causality, this study applies an instrumental variable approach, using the lagged term of the explanatory variable as the instrument. The panel two-stage least squares (2SLS) method is then employed to perform the regression and resolve the endogeneity issue. In the model examining the impact of the digital economy on the export competitiveness of high-tech products, the lagged term of the digital economy development level (De) is introduced as the explanatory variable for the test, with L.de representing the lagged term of the Lnde data.

Table 6. Endogeneity test results.

Variable	(1) First Stage	(2) Second Stage
L.De	0.976 *** (177.93)
LnDe		0.471 *** (5.77)
LnEdu	−0.042 (−0.78)	4.258 *** (5.51)
LnOpen	0.018 *** (3.10)	0.838 *** (9.98)
LnFdi	0.001 (0.19)	0.132 ** (2.54)
LnFin	−0.045 *** (−2.94)	0.267 (1.21)
Cons	0.245 (1.51)	−11.790 *** (−5.01)
N	300	330
R2	0.996	0.686

Note: *** p < 0.01, ** p < 0.05, with t-statistics in parentheses.

presents the regression results from the endogeneity test. The findings indicate that, even after addressing endogeneity concerns, the development level of the digital economy continues to have a significant positive impact on the export competitiveness of high-tech products, thereby affirming the robustness of the conclusions drawn in this study.

5.4. Robustness Test

To validate the reliability of the conclusions drawn from the baseline regression model, additional robustness tests are performed. This study employs two methods: substituting the dependent variable and replacing the core explanatory variable.

(1) Replacing the Dependent Variable

In the original baseline regression model, the export competitiveness of high-tech products is measured using the export competitiveness index (Eci). For the robustness test, this is substituted with the Revealed Comparative Advantage Index (Rca) as a proxy variable. The modified model is then estimated to further evaluate the stability of the regression results, following the formula outlined in Section 4. The results of this test are presented in

Table 7. Robustness test results with replaced dependent variable.

Variable	(1) LnRca	(2) LnRca	(3) LnRca	(4) LnRca	(5) LnRca
LnDe	0.728 *** (9.50)	0.600 *** (7.43)	0.563 *** (6.56)	0.572 *** (6.62)	0.440 *** (3.68)
LnEdu		2.836 *** (4.12)	2.902 *** (4.20)	2.734 *** (3.85)	2.864 *** (4.02)
LnOpen			−0.146 (−1.26)	−0.130 (−1.11)	−0.139 (−1.19)
LnFdi				−0.057 (−1.02)	−0.061 (−1.10)
LnFin					0.530 (1.57)
Cons	1.157 *** (6.04)	−7.308 *** (−3.54)	−7.851 *** (−3.72)	−7.117 *** (−3.19)	−8.438 *** (−3.55)
Year	Yes	Yes	Yes	Yes	Yes
Province	Yes	Yes	Yes	Yes	Yes
N	330	330	330	330	330
R2	0.232	0.273	0.277	0.279	0.285

Note: *** p < 0.01, with t-statistics in parentheses.

.

From the estimated results in Model (5) in

Table 8. Robustness test results with replaced core explanatory variable.

	(1)	(2)	(3)	(4)	(5)
Variable	LnEci	LnEci	LnEci	LnEci	LnEci
LnDe	0.515 ***	0.429 ***	0.493 ***	0.472 ***	0.363 ***
	(3.57)	(3.08)	(3.62)	(3.41)	(2.85)
LnEdu		3.728 ***	3.882 ***	4.030 ***	3.070 ***
		(5.16)	(5.52)	(5.57)	(4.56)
LnOpen			0.521 ***	0.508 ***	0.681 ***
			(4.35)	(4.20)	(6.04)
LnFdi				0.054	0.019
				(0.87)	(0.34)
LnFin					1.874 ***
					(7.65)
Cons	−0.618 ***	−11.472 ***	−10.878 ***	−11.552 ***	−10.736 ***
	(−2.60)	(−5.42)	(−5.28)	(−5.24)	(−5.32)
N	330	330	330	330	330
R2	0.041	0.119	0.172	0.174	0.311
Province	Yes	Yes	Yes	Yes	Yes
Year	Yes	Yes	Yes	Yes	Yes

Note: *** p < 0.01, with t-statistics in parentheses.

, the coefficient for the impact of digital economy development on the export competitiveness of high-tech products is 0.440, and this coefficient is statistically significant at the 1% level. This suggests that, even after replacing the dependent variable, the core hypothesis holds, reinforcing the robustness of this study’s conclusions.

(2) Replacing the Core Explanatory Variable

Following the approach of Zhao et al. (2020) [29], this study incorporates the Digital Financial Inclusion Development Index into the indicator system. This index is constructed using the following five key dimensions: internet penetration rate, number of internet practitioners, related output, number of mobile internet users, and digital financial inclusion development. The data for these dimensions are sourced from the China Statistical Yearbook and the Peking University Digital Financial Inclusion Index Research Report. To assess the level of digital economy development for the robustness test, the entropy method is applied. The regression results are presented in Table 8.

From the regression results in Model (5) in Table 8, it is evident that the coefficient for the effect of digital economy development on the export competitiveness of high-tech products is 0.363, which is statistically significant at the 1% level. This finding confirms that the development of the digital economy significantly enhances the export competitiveness of high-tech products. Thus, even after substituting the core explanatory variable, the hypothesis remains valid, further supporting the robustness of this study’s conclusions.

5.5. Mediation Effect Test

Having established the direct impact of the digital economy on high-tech export competitiveness, this section explores the indirect pathways through which digital transformation contributes to sustainable trade outcomes, specifically via technological innovation and industrial upgrading. These mechanisms are integral to the broader framework of sustainable economic development, as they signify an economy’s capacity to transition towards knowledge-based, low-carbon, and globally integrated production systems. To investigate these indirect effects, we use mediation analysis with stepwise regression models and perform robustness checks by substituting the core explanatory variable. The empirical results are reported in

Table 9. Mediation effect regression results.

Variable	(1) LnTi	(2) LnEci	(3) LnUpgra	(4) LnEci
LnDe	1.357 *** (10.53)	0.494 *** (3.64)	0.815 *** (7.63)	0.463 *** (3.70)
LnTi		0.106 ** (2.04)
LnUpgra				0.216 *** (3.47)
LnEdu	0.648 (0.85)	1.863 *** (2.70)	1.935 *** (3.04)	1.514** (2.19)
LnOpen	−0.214 * (−1.71)	0.849 *** (7.51)	−0.151 (−1.45)	0.859 *** (7.71)
LnFdi	−0.198 *** (−3.29)	0.058 (1.05)	−0.041 (−0.83)	0.045 (0.85)
LnFin	0.719 ** (1.99)	0.627 * (1.91)	0.705 ** (2.35)	0.551 * (1.70)
Cons	6.819 *** (2.67)	−5.632 ** (−2.42)	−3.016 (−1.42)	−4.255 * (−1.87)
Year	Yes	Yes	Yes	Yes
Province	Yes	Yes	Yes	Yes
N	330	330	330	330
R2	0.591	0.367	0.498	0.383

Note: *** p < 0.01, ** p < 0.05, * p < 0.1, with t-statistics in parentheses.

and

Table 10. Robustness test results for mediation effect.

	(1)	(2)	(3)	(4)
Variable	LnTi	LnEci	LnUpgra	LnEci
LnDe	0.390 **	0.289 **	0.327 ***	0.268 **
	(2.46)	(2.30)	(2.68)	(2.16)
LnTi		0.191 ***
		(4.19)
LnUpgra				0.290 ***
				(4.96)
LnEdu	3.318 ***	2.436 ***	3.476 ***	2.062 ***
	(3.96)	(3.62)	(5.38)	(3.03)
LnOpen	−0.556 ***	0.787 ***	−0.348 ***	0.782 ***
	(−3.97)	(6.99)	(−3.22)	(7.08)
LnFdi	−0.206 ***	0.059	−0.053	0.035
	(−2.92)	(1.04)	(−0.98)	(0.63)
LnFin	3.289 ***	1.246 ***	2.228 ***	1.228 ***
	(10.81)	(4.42)	(9.49)	(4.56)
Cons	−7.172 ***	−9.366 ***	−11.025 ***	−7.539 ***
	(−2.86)	(−4.70)	(−5.70)	(−3.68)
Year	Yes	Yes	Yes	Yes
Province	Yes	Yes	Yes	Yes
N	330	330	330	330
R2	0.449	0.350	0.413	0.364

Note: *** p < 0.01, ** p < 0.05, with t-statistics in parentheses.

.

Column (1) of Table 9 demonstrates a significantly positive regression coefficient for the relationship between the digital economy development level and technological innovation, confirming a positive link between digital economy growth and technological advancement. Column (2) presents the regression results for both the digital economy development level and technological innovation on the export competitiveness of high-tech products, with both coefficients being significantly positive. This indicates that the digital economy enhances high-tech export competitiveness by fostering technological innovation, with technological innovation serving as a partial mediator. The mediation effect is calculated as 22.5% (1.357 × 0.106/0.639).

Columns (1) and (2) of Table 10 reaffirm the positive relationship between the digital economy development level and technological innovation, even after substituting the core explanatory variable, thus confirming the robustness of the findings. In the context of the digital economy, improvements in technological innovation lead to more efficient resource use, reduced resource costs, and lower fixed asset utilization costs for enterprises. Additionally, the widespread application of digital technology facilitates knowledge sharing, enhancing human capital development. This, in turn, fosters innovation within high-tech industries and strengthens the export competitiveness of high-tech products, thereby supporting Hypothesis 2 (H2). This result underscores that digital transformation reduces resource consumption, lowers fixed asset costs, and facilitates efficient knowledge sharing—a crucial mechanism for elevating human capital and innovation capacity, particularly in high-tech sectors. This process reflects a core principle of sustainable development, as follows: doing more with less while empowering long-term productivity gains through innovation.

Column (3) of Table 9 shows a significantly positive regression coefficient for the relationship between digital economy development and industrial upgrading, with the result being significant at the 1% level. Column (4) reports the regression coefficients of digital economy development level and industrial upgrading on the export competitiveness of high-tech products, all of which are significantly positive at least at the 1% level, proving that the digital economy enhances the export competitiveness of high-tech products by facilitating industrial upgrading. This confirms the positive link between digital economy development and industrial upgrading. The analysis reveals that industrial upgrading serves as a partial mediator, with the mediation effect accounting for 27.5% (0.815 × 0.216/0.639). This result highlights the transformative role of digitalization in accelerating the transition to advanced manufacturing, improving process efficiency, and modernizing industrial structures. The digital upgrading of high-tech industries—through AI, cloud computing, and intelligent systems—enables cleaner production methods, leaner supply chains, and more responsive innovation cycles. In turn, these upgrades bolster the global competitiveness of exports while promoting environmental sustainability and economic resilience.

Columns (3) and (4) of Table 10 further validate this finding, showing that both the digital economy development level and industrial upgrading have significantly positive coefficients. This indicates that the conclusion holds even after substituting the core explanatory variable, confirming the robustness of the results. The findings suggest that, as intelligent infrastructure improves and the digital economy accelerates, traditional industries have increasingly embraced digital transformation. The integration of high-tech industries with digital technology has led to innovations in production methods and enhancements in operational systems, thereby fostering industrial upgrading within high-tech sectors. The industrial upgrading of high-tech industries forms a positive interaction with their product export competitiveness, ultimately showing that the digital economy empowers product export competitiveness by promoting high-tech industrial upgrading. Thus, H3 is validated. This result affirms the robustness of this mechanism, reinforcing the conclusion that the digital economy empowers export competitiveness not only directly, but also through systemic upgrades that align with national and global sustainability goals.

5.6. Further Analysis

While previous analyses establish a positive linear relationship between digital economy development and high-tech export competitiveness, the trajectory of digital transformation—particularly within the context of sustainable economic development—rarely follows a uniform path. The digital economy is characterized by network externalities, scale effects, and threshold-dependent dynamics, all of which evolve over time and vary across different stages of development. Understanding these nonlinearities is crucial for designing adaptive, inclusive, and resource-efficient policy interventions that maximize trade and sustainability benefits. To account for these complex dynamics, we adopt the threshold modeling framework proposed by Hansen (1999) [30] and develop a panel threshold regression model, where the digital economy development level (De) serves both as the core explanatory variable and as the threshold variable. This allows us to examine whether the marginal effect of digitalization on export competitiveness varies across different stages of digital maturity. The model is expressed as follows:

$$\begin{array}{c}\mathrm{l}\mathrm{n}{Eci}_{it}={\alpha }_0+{\alpha }_1\mathrm{l}\mathrm{n}{De}_{it}\ast \mathrm{I}\left(\mathrm{l}\mathrm{n}{De}_{it}\le {\theta }_1\right)+{\alpha }_2\mathrm{l}\mathrm{n}{De}_{it}·\mathrm{I}\left(\mathrm{l}\mathrm{n}{De}_{it}>{\theta }_1\right)+\cdots +{\alpha }_{1n}\mathrm{l}\mathrm{n}{De}_{it}·\mathrm{I}\left(\mathrm{l}\mathrm{n}{De}_{it}<{\theta }_n\right)\\ +{\alpha }_{2\mathrm{n}}\mathrm{l}\mathrm{n}{De}_{it}·\mathrm{I}\left(\mathrm{l}\mathrm{n}{De}_{it}>{\theta }_n\right)+\sum {\phi }_k\mathrm{l}\mathrm{n}{x}_{kit}+{\theta }_i+{\phi }_t{+\epsilon }_{it}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\end{array}$$

(15)

In this model, the digital economy development level (De) functions as both the core explanatory variable and the threshold variable. Function I(.) represents the indicator function, and $\theta$ denotes the threshold value. This threshold value divides the market into (n + 1) intervals, with the impact of digital economy development on the export competitiveness of high-tech products varying across these intervals.

Firstly, a threshold existence test is conducted, with 300 bootstrap samples drawn repeatedly. The results, presented in

Table 11. Threshold effect test results.

Variable	Thresholds	Threshold Value	F Value	p Value	10% Level	5% Level	1% Level
Digital Economy Development Level	Single Threshold	−3.107	55.81	0.013	30.370	35.389	56.081
	Double Threshold	−3.107	55.81	0.013	30.370	38.347	59.409
		−4.616	30.90	0.060	26.585	33.407	41.730

, show that the digital economy development level significantly passed both the single and double threshold tests, but the triple threshold test was rejected. This confirms the presence of a double threshold effect on the export competitiveness of high-tech products.

The estimation results for the threshold effect are presented in

Table 12. Threshold model estimation results.

Variable Name	Variable Interval	Regression Coefficient	Standard Error	T Value
Digital Economy Development Level	<−4.616	0.786 ***	0.202	3.89
	[−4.616,−2.663]	0.972 ***	0.229	4.24
	>−2.663	0.750 ***	0.193	3.88

Note: *** p < 0.01; control variables are controlled; provincial and time effects are fixed.

. Based on the observed double threshold effect, the digital economy development level is divided into three distinct intervals. When the logarithmic value of the digital economy falls below −4.616, the impact on the export competitiveness of high-tech products is represented by a fitted coefficient of 0.375. As the logarithmic value of the digital economy increases and falls between −4.616 and −2.663, the impact coefficient rises from 0.375 to 0.633, reaching its peak. However, once the logarithmic value crosses the threshold of −2.663, the fitted coefficient drops to 0.437, yet remains significant at the 1% level. This pattern suggests an inverted U-shaped effect, wherein the initial benefits of digital investment are moderate due to high infrastructure costs, fragmented adoption, and limited spillover effects. As digital capabilities mature, network externalities intensify—more enterprises engage in digital trade, digital platforms become more interoperable, and transaction costs decline. These effects accelerate industrial upgrading, technological innovation, and the expansion of sustainable export capabilities. Yet, beyond a certain point, the marginal gains taper off, likely due to diminishing returns to digital scale, institutional frictions, or saturation in early-stage adopters. This pattern aligns with the principles of Metcalfe’s Law, which posits that the value of a digital network grows with the square of its user base—up to a certain threshold, after which the growth in value levels off without further systemic transformation.

5.7. Discussion

To clarify the research architecture and empirical findings, we summarize the objectives, methods, and key results of each analytical stage in

Table 13. Summary of empirical stages and key findings.

Empirical Stage	Objective	Methodology	Key Findings
Baseline Regression	Estimate direct effect of digital economy on high-tech export performance	Fixed-effects panel regression	Digital economy significantly boosts export competitiveness (p < 0.01); supports H1.
Heterogeneity Analysis	Explore regional differences	Stratified regression by region	Stronger effects in central and western regions; highlights inclusivity potential of digital transformation.
Endogeneity Test	Address potential reverse causality	2SLS with lagged explanatory variable	Effect remains significant, confirming robustness and causality of the digital economy’s impact.
Robustness Tests	Test stability of results	Variable substitution (dependent and explanatory)	Findings hold under alternate metrics (RCA, digital finance index), reaffirming reliability.
Mediation Effect Analysis	Examine indirect pathways via innovation and upgrading	Stepwise regression + robustness checks	Technological innovation and industrial upgrading partially mediate the relationship; supports H2 and H3.
Threshold Effect Analysis	Identify nonlinear effects	Hansen’s threshold panel model	Double threshold effect found; impact follows an inverted U-shape—optimal at intermediate digital maturity.

. This structured overview aids in connecting each empirical test to the broader research narrative on how the digital economy fosters sustainable export competitiveness in high-tech sectors.

Our findings contribute to a growing body of literature exploring the role of digitalization in shaping economic competitiveness, particularly in high-tech sectors. While previous studies have documented the transformative potential of digital technologies on productivity and trade efficiency [31], our study extends this discourse by embedding the analysis within the framework of sustainable export competitiveness. This perspective not only foregrounds performance metrics, but also emphasizes innovation, inclusivity, and resilience—dimensions increasingly prioritized in the post-pandemic global economy.

Central to our analysis is the demonstration of a consistent and statistically robust link between digital economy development and enhanced high-tech export performance. This aligns with the existing findings on digital infrastructure as a productivity multiplier [32]; however, our evidence pushes the debate further by illustrating how such benefits are heterogeneously distributed across regions. The stronger effects observed in central and western regions support the hypothesis that digitalization may serve as a spatial equalizer—an insight that nuances dominant narratives, which often portray digital growth as exacerbating regional inequalities [33].

Moreover, our mediation analysis sheds light on the underlying mechanisms at play. While earlier research has highlighted the digital economy’s role in fostering innovation and upgrading [34], we provide empirical validation showing that these factors function as intermediate channels linking digital development to export competitiveness. This underscores the importance of system-level capabilities—not simply the presence of digital tools—in achieving sustainable growth outcomes.

The identification of a double-threshold effect adds an important inflection point in the policy discourse. Contrary to the assumption of linear returns, our results reveal a non-monotonic relationship, whereby the benefits of digitalization taper beyond a certain threshold. This finding complicates policy prescriptions that advocate for maximal digital investment, instead suggesting a need for calibrated strategies that consider institutional absorptive capacity and sectoral readiness [35].

Taken together, our study advances a more integrated understanding of how digital transformation interfaces with trade performance under the sustainability imperative. By empirically tracing the pathways—from infrastructure to innovation to upgrading—we illustrate not only that the digital economy matters, but how and under what conditions it delivers inclusive, resilient, and forward-looking competitiveness.

6. Conclusions

6.1. Main Results

The accelerating expansion of the digital economy is reshaping global trade patterns, offering new pathways for sustainable industrial growth. In China, where high-tech industries represent a crucial driver of economic modernization, digital transformation has emerged as a key enabler of export competitiveness. While the general connection between digitalization and trade is widely recognized, the specific mechanisms through which the digital economy enhances high-tech export competitiveness remain largely underexplored. While the general connection between digitalization and trade is widely recognized, the specific mechanisms through which the digital economy enhances high-tech export competitiveness remain largely underexplored. Our study systematically examines these mechanisms, revealing how the digital economy enhances competitiveness through technological innovation, industrial upgrading, and digital integration—factors that are critical to sustainable economic development.

First, baseline regression results consistently show that the development of the digital economy exerts a strong, statistically significant positive effect on high-tech export competitiveness across all model specifications. This relationship persists under a variety of robustness tests and remains stable even when controlling for endogeneity, suggesting that digitalization is a foundational enabler of green and innovation-driven trade performance.

Second, the digital economy’s impact is found to be uneven across regions, with the central and western provinces benefiting most significantly. This heterogeneity reveals the potential of digital transformation to act as an equalizing force, particularly in less-developed areas, where it can mitigate infrastructural constraints and unlock new avenues for sustainable industrial upgrading.

Third, our mediation analysis identifies two key transmission mechanisms—technological innovation and industrial upgrading—through which digital development enhances export competitiveness. These mechanisms are not only statistically significant, but also conceptually aligned with national and global sustainability goals, reflecting how digital tools amplify innovation, reduce resource intensities, and promote clean production models.

Finally, a threshold analysis uncovers an inverted U-shaped relationship between the level of digital economy development and high-tech export competitiveness. This nonlinear dynamic suggests that, while digital investments initially yield substantial returns, the marginal benefits eventually plateau. This points to the necessity of continuous institutional adaptation and system-wide innovation to sustain the gains of digitalization over time.

Together, these findings present a compelling case for integrating digital economy strategies into sustainable trade and industrial policies, especially in developing economies seeking to balance competitiveness with environmental stewardship.

6.2. Theoretical Contributions

This study contributes to theoretical development in three significant ways. First, it offers novel insights that contribute to the theoretical framework of sustainable development in high-tech industries. The present research broadens the digital economy literature by investigating the distinct dynamics within high-tech industries, an area that has been largely overlooked in prior research, which typically generalizes across broad manufacturing sectors or focuses on region-specific contexts. Unlike earlier studies that often fail to consider sectoral variations or the complexity of high-tech trade [36,37], this research presents a refined theoretical model that integrates the sector-specific idiosyncrasies of high-tech industries. By disaggregating provincial data and examining sub-sectoral heterogeneity, this study provides empirical evidence that deepens our understanding of the intricate relationship between digital economy and export competitiveness.

Second, the identification and empirical testing of mediating mechanisms—technological innovation and industrial upgrading—introduce a more structured explanation of how the digital economy affects export competitiveness. While earlier work has speculated on the relevance of innovation and upgrading, these have largely remained conceptual placeholders [38]. Our mediation analysis validates these pathways empirically, offering theoretical refinement by linking system-level digital development to sustainable trade performance through defined, operational mechanisms. This adds clarity to the literature that has too often treated digital transformation as a black box [39].

Third, by constructing and employing a multidimensional index of digital economy development, we address a persistent gap in measurement theory. Prior models often rely on narrow proxies such as internet penetration or ICT investment, which insufficiently capture the systemic nature of digital transformation [40,41]. Our inclusion of digital infrastructure, digital industrialization, and industrial digitalization reflects a more holistic framework. This approach enhances construct validity and supports a more theoretically coherent understanding of the digital economy’s influence—bridging the gap between conceptual ambition and empirical execution.

6.3. Managerial Implications

Our findings offer actionable insights for policymakers and corporate strategists seeking to harness digital transformation for sustainable export growth. First, the robust positive association between digital economy development and high-tech export competitiveness underscores the strategic imperative for firms and local governments to embed digital infrastructure and capabilities at the core of their operational models. Investments in digital platforms, data analytics, and intelligent manufacturing systems are not merely supportive tools, they are becoming prerequisites for global market relevance in high-tech sectors.

Second, the regional heterogeneity in digital dividends highlights the importance of localized digital strategies. Managers operating in central and western China, as well as similarly situated regions in other developing economies, should view digital transformation not as a uniform process, but as a catalytic force that can leapfrog traditional barriers to industrial development. Customized digital deployment—attuned to local needs and capacities—can yield outsized gains in export performance.

Third, the mediation effects of technological innovation and industrial upgrading suggest that digitalization is most effective when coupled with organizational learning and strategic repositioning. Firms must not only adopt new technologies, but also reconfigure their value chains, upskill their workforce, and pursue innovation ecosystems that align with green and sustainable principles. Digital tools should be leveraged to enable product differentiation, resource efficiency, and clean production, thereby enhancing both competitiveness and environmental performance.

Finally, the inverted U-shaped dynamic between digitalization and export competitiveness signals a cautionary note for managers and decision makers. Excessive or poorly coordinated digital investment may lead to diminishing returns. Sustaining the benefits of digital transformation will require continuous policy innovation, cross-sectoral coordination, and adaptive governance structures that ensure that the digital economy evolves in step with technological frontiers and market demands.

Collectively, these implications reinforce the necessity of treating digital transformation not as a discrete intervention, but as an evolving strategic trajectory—one that integrates technological, institutional, and sustainability dimensions to build resilient export-oriented industries.

6.4. Limitations and Future Research

While this study offers valuable insights into the influence of the digital economy on the export competitiveness of high-tech products, it is important to recognize several limitations that may affect the interpretation of the findings. Firstly, the official publication of the “China High Technology Industry Statistics Yearbook” for the year 2018 was not available, leading to gaps in high-tech industry data for that period. Additionally, some indicators for specific provinces (municipalities, autonomous regions) were missing and had to be interpolated, which might have affected the robustness of this study’s conclusions. This analysis primarily addresses the overall impact of the digital economy on the export competitiveness of high-tech products but does not examine specific sub-sectors due to limitations in data accessibility. Consequently, this study does not capture the potential industry-specific variations and nuances that could provide a more detailed understanding of how different high-tech sectors are impacted by digital advancements.

Future research should aim to address these limitations by obtaining more comprehensive and granular data that allow for an in-depth analysis of sub-sectors within the high-tech industry. This would enable the exploration of sectoral differences and provide more targeted insights. Additionally, future research could benefit from utilizing longitudinal data covering a longer time period to better understand the long-term effects and evolving trends of digital economy development on high-tech export competitiveness. Expanding the geographic scope to include international comparisons could also enhance the generalizability and applicability of the findings across different economic and technological contexts.