Digital Economy and Sustainable Development in China: From the Perspective of High-Quality Development of Manufacturing

Abstract

This study investigates the role of the digital economy (DE) in advancing the high-quality development of manufacturing in China, with a particular focus on the moderating effects of manufacturing agglomeration and digital literacy. Using provincial panel data from 2013 to 2023, we find that the digital economy significantly enhances manufacturing development across three key dimensions: green transformation, innovation, and high-end industrial upgrading. Manufacturing agglomeration strengthens this effect, especially in the Eastern and Western regions, by facilitating digital spillovers and leveraging digital infrastructure. However, in the Central region, the impact of agglomeration is weaker, hindered by fragmented industrial clusters and underdeveloped digital infrastructure. The study also highlights significant regional differences in the moderating effect of digital literacy. In the Eastern region, digital literacy negatively moderates the relationship between DE and manufacturing development due to skill mismatches, while in the Western region, localized concentrations of digital skills have a positive but geographically constrained impact. Temporal analysis reveals a shift in the moderating role of digital literacy, with its negative effect becoming more pronounced after 2018, suggesting a growing need for targeted skill development policies. These findings underscore the importance of regionally tailored strategies to promote digital manufacturing integration, with a focus on sustainable development through digital transformation and green manufacturing practices.

1. Introduction

China’s manufacturing sector has undergone a remarkable transformation in recent decades, evolving from a labor-intensive, resource-driven model to a more complex, innovation-driven one. However, despite these advancements, the sector still faces several challenges, including low-end lock-in and insufficient innovation, particularly in traditional manufacturing industries [1]. As global competition intensifies, especially in high-end manufacturing [2], the need for a shift from a factor-driven growth model to an innovation-driven one has become urgent. Achieving high-quality development in manufacturing (HQDM) is critical not only for the sector itself but also for the overall sustainable economic growth in China.

The digital economy has emerged as a transformative force in this transition. The Digital China Development Report (2024) [3] highlights that core industries of the digital economy contributed approximately 10% to GDP in 2024. By year-end, China had deployed 4.25 million 5G base stations, covering 90% of administrative villages, and reached a computing power of 280 EFLOPS, ranking among the global leaders. As of the end of 2024, China has established over 30,000 basic-level, more than 1200 advanced-level, and approximately 230 excellent-level smart factories nationwide. Meanwhile, the penetration rate of digital R&D tools in key above-scale industrial enterprises reached 84.1%, reflecting the rapid digital transformation in major manufacturing sectors [3]. These developments underscore the critical role of the digital economy in enhancing manufacturing upgrading [4] and fostering green transformation [5], thereby paving the way for high-quality development.

This study builds on existing research by exploring the moderating roles of digital literacy and manufacturing agglomeration in the relationship between the digital economy and HQDM. While previous studies have examined these factors, this study distinguishes itself by exploring how these moderating effects evolve over time and vary across regions. Specifically, it investigates the temporal heterogeneity of these effects, highlighting how the influence of digital literacy and manufacturing agglomeration evolves as the digital economy matures. Additionally, it examines regional heterogeneity, addressing how varying levels of digital infrastructure, industrial concentration, and workforce skills in China’s Eastern, Central, and Western regions impact the digital economy’s influence on HQDM.

The study’s theoretical contribution lies in its comprehensive exploration of how manufacturing agglomeration and digital literacy influence the digital economy’s effectiveness in driving HQDM across different contexts. It expands beyond existing frameworks by treating agglomeration not only as a mediator but also as a moderator, offering new insights into how agglomeration affects the digital economy’s transformative power at various stages of industrial concentration. Similarly, it deepens the understanding of digital literacy by examining its evolving role in different regions. This approach offers a more nuanced understanding of how these two factors either amplify or hinder the digital economy’s impact on manufacturing development, providing region-specific policy recommendations to foster digital transformation tailored to each region’s unique context.

This study addresses the following research questions: (1) How does the digital economy influence the high-quality development of manufacturing, particularly through the mechanisms of innovation, green development, and industrial upgrading? (2) How do factors such as digital literacy and manufacturing agglomeration moderate the relationship between the digital economy and HQDM? (3) How do regional and temporal heterogeneity affect the moderating roles of digital literacy and manufacturing agglomeration in the digital economy’s impact on HQDM?

To answer these questions, this study makes the following contributions: (1) It establishes a comprehensive framework for evaluating HQDM, integrating dimensions such as green development, innovation, and high-end manufacturing, offering a holistic approach to assessing the sector’s transformation. (2) It utilizes data from administrative departments directly linked to the manufacturing industry, ensuring a more accurate and targeted evaluation of HQDM and addressing gaps in research relying on generalized data sources. (3) It employs advanced econometric models to explore the dynamic relationships between the digital economy and HQDM, with a particular focus on the moderating effects of digital literacy and manufacturing agglomeration. By considering the time-based and regional heterogeneity of these moderating effects, the study provides new insights into how the digital economy’s impact on manufacturing development evolves and varies across regions. Based on these findings, it offers region-specific policy recommendations aimed at fostering digital transformation and high-quality development tailored to the unique needs of Eastern, Central, and Western China. Figure 1 is the road map of this study.

2. Literature Review and Theoretical Hypothesis

2.1. Literature Review

2.1.1. The Definition and Measurement of Digital Economy

At the G20 Summit in Hangzhou in September 2016, the concept of the digital economy was defined as “a series of economic activities that use digitized knowledge and information as key production factors.” The OECD Digital Economy Outlook 2020 points out that the digital economy relies on or significantly benefits from digital inputs, including digital technologies, infrastructure, digital services, and data [6]. In 2021, the National Bureau of Statistics of China further categorized the digital economy into digital industrialization and industrial digitization. Despite these developments, the academic community has yet to reach a consensus on the definition of the digital economy.

Huang (2023) distinguishes between narrow and broad interpretations of the digital economy [7]. The narrow interpretation focuses on economic activities related to the production of information and communication technology (ICT) goods and digital services. The broader interpretation defines the digital economy as an emerging economic form centered around activities such as data acquisition, processing, computing, application, and storage.

The definition of the digital economy significantly influences its measurement. As a new economic paradigm, data on the digital economy lags behind its growth and application. There is no consensus on how to measure the digital economy, either within academia or industry. A common approach to quantifying the digital economy involves establishing an indicator system, with methods such as entropy and principal component analysis used to calculate digital economy indices.

Existing literature on digital economy evaluation systems demonstrates remarkable consistency in core dimensions but also notable differences in contextual emphasis. Most studies emphasize digital infrastructure as a fundamental pillar, highlighting physical and networked foundations [4,8,9,10,11,12]. For instance, Li et al. (2024) operationalizes this dimension with specific metrics, such as broadband internet users per 100 people and the number of cell phone users per 100 people [11], while Li et al. (2024) links infrastructure to transaction development and innovation, emphasizing its role in enabling digital transactions [12].

Digital industrialization and industrial digitization emerge as another pair of interconnected core dimensions, and many studies consistently include both [8,10,13,14]. Digital industrialization measures the direct economic contribution of the digital sector, while industrial digitization assesses the digital transformation of traditional sectors. Some studies simplify this framework to these two dimensions, emphasizing their reciprocal relationship [15]. Studies such as Hao et al. (2023) introduce “digital convergence ability” as a proxy for industrial digitization [16].

Innovative dimensions and contextual additions diversify evaluative frameworks. Some studies include digital technology innovation as a primary dimension [4,8,17]. Wang et al. (2024) and Zhou et al. (2024) expand the scope to include digital governance and digitization benefits, addressing policy effectiveness and societal impacts [10,18]. Study like Li et al. (2024) incorporates digital financial inclusion to assess accessibility to financial services via digital channels [11], while Sun et al. (2024) link financial transactions with industrial development and innovation capabilities [19]. These variations reflect differing research priorities—some studies focus on sustainable development [16], and others on policy environments [20].

Despite these shared foundations—digital infrastructure, industrial digitization, digital industrialization, and innovation—divergence arises from different research objectives. A notable issue in measurement is the disparity in the indicators used, even within the same secondary category. For example, the widely used indicator “digital infrastructure” is defined differently across studies. Zhang and Meng (2023) and Su et al. (2023) focus on cable length [8,14], while Wang et al. (2024) emphasize mobile phone base station density [10], and Hao et al. (2023) include digital reading rooms [16], demonstrating context-specific priorities. Domain names appear frequently, with Zhang and Meng (2023) specifying “domain names per thousand people” [8], and Li et al. (2024) using “number of domains” [15], highlighting differences in granularity.

Another typical issue is that most studies rely heavily on macroeconomic data, with insufficient use of meso-level data for industries related to the digital economy. This may lead to data biases and reduce the representativeness of digital economy indices.

2.1.2. The Connotation and Measurement of High-Quality Development of Manufacturing (HQDM)

Manufacturing is the cornerstone of the real economy and holds a pivotal position in economic development. High-quality development of manufacturing (HQDM) is, therefore, a key priority for sustainable economic growth in China. The 2023 government work report underscored the importance of the digital transition for traditional sectors and SMEs, highlighting the role of manufacturing in driving high-end, intelligent, and green development. HQDM has been a prominent research topic, with current studies focusing on its connotation and measurement. These two aspects are closely related, as measurement is a key manifestation of connotation.

Currently, two primary methods are used to measure HQDM. One uses Total Factor Productivity (TFP) [21] as a proxy for HQDM. However, many studies argue that it is biased to use a single indicator to represent HQDM, and thus, indicator systems have been developed to characterize and measure HQDM.

For example, Li et al. (2024) propose a comprehensive evaluation system for High-Quality Green Marketing in Manufacturing, which includes four primary indicators: operating profit, marketing revenue, product innovation, and energy consumption [12]. Chen (2025) outlines an evaluation index system with four first-level indicators: the scale of operation, development benefits, structural optimization, and development potential [22]. Wang et al. (2022) present an index system with five first-level indicators: economic benefits, technological innovation, green development, integration, and high-end development [23].

Green development emerges as a common core dimension, but its operationalization varies. Li et al. (2024) measure it via energy consumption (focusing on efficiency and recycling) [12]. Chen (2025) uses sustainable development indicators like total investment in industrial pollution control to emphasize pollution reduction [22]. Wang et al. (2022) combine both approaches, including energy consumption per industrial added value, solid waste utilization, and emissions per main business income, reflecting both efficiency and pollution intensity [23].

Measurement of HQDM faces similar challenges to those of the digital economy. The measurements are still divergent, and most data used to evaluate HQDM come from general sources that fail to capture the unique features of HQDM. A more comprehensive index system that reflects the specific development of HQDM is needed.

2.1.3. Effects of Digital Economy on HQDM

The digital economy has emerged as a key driver of growth in the digital age. As a core component of the real economy, manufacturing plays a critical role in economic development, with high-quality development of manufacturing (HQDM) being essential for achieving sustainable economic growth. Consequently, understanding the impact of the digital economy on HQDM is crucial. Existing studies consistently show that the digital economy positively influences HQDM.

The digital economy is pivotal in driving comprehensive innovation within manufacturing. It fosters technological advancements, facilitates the flow of knowledge [22], and encourages the adoption of new business models and management practices [24]. Collectively, these factors contribute to the transformation of the manufacturing industry by improving production efficiency and advancing technological progress. By promoting the efficient integration of production factors [24], the digital economy supports the sustainable development of the manufacturing sector.

Additionally, the digital economy plays a vital role in advancing the green development of manufacturing by promoting green technology innovation and facilitating industrial upgrading. It drives the green transformation through mechanisms such as resource optimization, data-driven decision-making [25], and the promotion of green technological advancements [26]. These contributions are crucial for fostering sustainable manufacturing processes and ensuring long-term environmental sustainability in the sector.

2.1.4. Role of Agglomeration in Manufacturing Digital Transformation

The relationship between digital economy, manufacturing agglomeration, and manufacturing development has become a central area of research, with scholars examining their bidirectional influences and transmission mechanisms.

Studies show that the digital economy has a nonlinear, U-shaped impact on manufacturing agglomeration. Lei et al. suggest that in the early stages, constraints such as digital talent shortages and resource mismatches inhibit collaborative agglomeration, as digital technologies mature and talent accumulates, the digital economy transitions to a facilitating role [27]. Further, the co-agglomeration of digital and manufacturing sectors introduces added complexity. It refers to an economic phenomenon where these sectors are highly concentrated and integrated in specific geographical or virtual spaces, forming synergies through industrial chain collaboration, technological penetration, and resource sharing as an advanced form of industrial agglomeration [28].

Digital technologies such as the industrial internet and modular technologies overcome geographical constraints, attract capital, and concentrate high-end workforces, illustrating diverse digital-driven agglomeration pathways. Agglomeration fosters manufacturing development through innovation spillovers, resource optimization, and industrial upgrading [29]. The spatial concentration of firms facilitates knowledge exchange, accelerates patent technology diffusion, and attracts labor and capital, intensifying competition and promoting resource reallocation from inefficient to efficient enterprises. These studies underscore a dynamic “digital empowerment—spatial agglomeration—industrial upgrading” nexus, where the digital economy reshapes agglomeration patterns, and agglomeration, in turn, drives manufacturing development through enhanced efficiency and structural transformation.

Existing research has established agglomeration as a mediator in this process, but treating it as a moderator allows for a deeper exploration of how the digital economy’s impact on industrial upgrading varies across different agglomeration stages. In highly agglomerated regions, dense firm networks and factor concentrations facilitate knowledge spillover and resource allocation efficiency, amplifying the impact of the digital economy. Conversely, in less agglomerated regions, infrastructure gaps or talent shortages might limit the digital economy’s transformative effects, highlighting agglomeration’s role as a critical contextual condition.

This perspective enriches theoretical frameworks by recognizing that the digital economy’s impact is not uniform but depends on the moderating influence of agglomeration, providing policymakers with nuanced insights. For example, investments in digital infrastructure could be tailored to regional agglomeration levels to optimize developmental outcomes.

2.1.5. Role of Digital Literacy in Manufacturing Digital Transformation

The integration of digital technologies in manufacturing has revolutionized operations and reshaped workforce dynamics, making digital literacy essential for workers to meet evolving job demands and contribute to industry sustainability. Digital literacy is a multifaceted construct, defined differently across academic and policy frameworks, but universally recognized as a cornerstone of industrial transformation.

Building on this foundational role, UNESCO’s Global Framework defines digital literacy as the ability to access, manage, and generate information safely using digital technologies, integrating computer literacy, ICT proficiency, and media literacy to enable employment and entrepreneurship [30]. The National Digital Literacy Initiative broadens this definition, emphasizing competencies for digital societies, including information creation, security assurance, and ethical practice, alongside technical skills [31]. In manufacturing, digital literacy drives operational optimization. It enables enterprises to leverage AI, big data, IoT, and robotics to streamline processes and foster innovation [32]. Furthermore, as digital technologies reach scale, their value increases, fueling the digital economy, particularly in environments with high digital literacy [33].

Exploring the moderating effect of digital literacy on the digital economy-manufacturing nexus is crucial both theoretically and practically. Theoretically, it reveals the contextual limits of the digital economy’s impact. Digital literacy determines how effectively manufacturing firms absorb and apply digital technologies (e.g., AI, IoT), shaping whether the digital economy accelerates or simply coexists with industrial development. Practically, it addresses the heterogeneity of digital economic outcomes in manufacturing, as disparities in digital literacy explain why some regions or firms thrive while others lag.

Clarifying how digital literacy influences the digital economy’s role will help stakeholders design targeted interventions, such as skill-upgrading programs or literacy-aligned infrastructure, to maximize the transformative potential of the digital economy, ensuring its benefits reach diverse manufacturing sectors.

2.1.6. Summary

The literature reveals a growing interest in the digital economy’s impact on high-quality manufacturing development, yet there remains room for exploration, particularly regarding how regional and temporal heterogeneity moderates this relationship. This study seeks to explore how factors such as manufacturing agglomeration and digital literacy influence the effects of the digital economy, highlighting that these impacts are context-dependent. While existing studies focus on broad trends, this study offers a more granular understanding of how digital economy integration interacts with regional dynamics and workforce capabilities, providing actionable insights for policy design. Furthermore, by examining the role of digital transformation in green manufacturing, our research aligns with global sustainability goals, offering critical implications for both economic growth and environmental responsibility.

2.2. Theoretical Hypothesis

2.2.1. Direct Effects

The digital economy serves as a transformative force for manufacturing, driven by two key engines: digital technologies such as big data analytics and the Internet of Things (IoT), and digital platforms like industrial Internet ecosystems. Together, these elements enable seamless data integration, process optimization, and agile innovation, reshaping manufacturing landscapes.

Big data analytics and industrial big data facilitate real-time monitoring of production processes by analyzing supply chain data, which reduces unplanned downtime and optimizes maintenance costs [34,35]. The Internet of Things (IoT) and the Internet of Robotic Things (IoRT) enable a shift from hardware-centric models to product-plus-service solutions. By leveraging sensor data, these technologies facilitate predictive maintenance and cloud-based production optimization, while enhancing supply chain transparency for flexible, customized manufacturing solutions [36,37]. Artificial intelligence-driven digital twin technologies accelerate R&D cycles through virtual prototyping and process simulation, improving both product quality and production agility [37]. Digital platforms equipped with simulation-based digital twins optimize multi-plant production planning through scenario analysis, enabling dynamic resource allocation and reducing workflow bottlenecks [38].

Digital solutions also play a significant role in promoting environmental sustainability and resource circularity. Technologies such as big data analytics enhance enterprises’ capacity for data management and talent development, thereby driving innovations in green products, processes, and management. These innovations, in turn, reduce pollution emissions and improve resource efficiency, leading to improved environmental performance [39]. Furthermore, big data analytics indirectly supports circularity by integrating sustainable manufacturing practices within supply chain networks [40].

Digital tools reshape manufacturing decision frameworks and industrial ecosystems through multi-criteria analysis and platform-driven integration. Digital twin platforms support KPI-driven performance management with real-time visualization, enabling the proactive identification of operational inefficiencies and supply chain risks [38]. Based on these analyses, the following hypothesis is proposed:

Hypothesis 1.

The digital economy enhances the high-quality development of manufacturing (HQDM).

2.2.2. Moderating Role of Manufacturing Agglomeration

Industrial agglomeration shortens the geographical distance between enterprises [41]. It facilitates cost reductions, specialization, and optimized resource allocation through the free flow of production factors such as knowledge, capital, and talent in agglomerated areas [42]. This proximity boosts knowledge spillovers [41] and technology diffusion [42], leading to technological innovations. The digital economy, unconstrained by geographical boundaries, further enhances the efficient flow of factors. Information exchange, resource sharing, reduced operating costs, and increased specialization become possible in agglomerated manufacturing areas. Therefore, manufacturing agglomeration amplifies the positive impact of the digital economy on manufacturing development. Based on this analysis, we propose the following hypothesis:

Hypothesis 2.

Agglomeration strengthens the positive impact of the digital economy on HQDM.

2.2.3. Moderating Role of Digital Literacy

In the digital economy, digitized knowledge and information are considered essential production factors [43]. Individuals with high digital literacy are better positioned to transform digital technology into productivity, innovation capacity, and green transformation. In contrast, individuals with low digital literacy may face difficulties in implementing digital technologies, diminishing their empowering effects. Highly skilled talent with superior digital literacy is crucial for both the growth of the digital economy and its integration with traditional industries [44]. Digital literacy encompasses the creative use of digital skills, emphasizing the application of digital tools and mastery of digital technologies [45]. Skilled talent with strong digital literacy provides the necessary knowledge and technical ability to facilitate manufacturing transformation [33]. Therefore, we propose the following hypothesis:

Hypothesis 3.

Digital literacy positively moderates the impact of the digital economy on HQDM.

Figure 2 is the mechanism of this study.

3. Study Design

3.1. Model Construction

3.1.1. Basic Model

Model (1) is constructed to test Hypothesis 1:

$$HQD{M}_{it}={\alpha }_0+{\alpha }_{1}D{E}_{it}+{\alpha }_2{x}_{it}+{\mu }_i+{\gamma }_t+{\epsilon }_{it},$$

(1)

where HQDM_it represents the degree of high-quality development of the manufacturing industry in province i (or Autonomous Region/Municipality) in year t. DE_it denotes the degree of digital economy development of province i in year t, and x_it represents control variables. µ_i is the region fixed effect, γ_t is the time-fixed effect, and ε_it is the error term.

3.1.2. Moderating Model

To examine the moderating mechanism, Models (2) and (3) extend Model (1) by incorporating manufacturing agglomeration (Agglo_it), digital literacy (Digi_it) and their interaction terms with digital economy, respectively:

$$HQD{M}_{it}={\eta }_0+{\eta }_{1}D{E}_{it}+{\eta }_{2}Aggl{o}_{it}+{\eta }_{3}D{E}_{it}\times Aggl{o}_{it}+{\eta }_4{x}_{it}+{\mu }_i+{\gamma }_t+{\epsilon }_{it},$$

(2)

$$HQD{M}_{it}={\beta }_0+{\beta }_{1}D{E}_{it}+{\beta }_{2}Dig{i}_{it}+{\beta }_{3}D{E}_{it}\times Dig{i}_{it}+{\beta }_4{x}_{it}+{\mu }_i+{\gamma }_t+{\epsilon }_{it},$$

(3)

where DE_it × Agglo_it and DE_it × Digi_it are interaction terms between the independent variable DE_it and the two moderating variables, and the remaining parameters follow the structure of the basic model.

3.2. Variable Description

3.2.1. Core Independent Variable

This study develops a four-dimensional index system to measure the digital economy (DE), as outlined in

Table 1. Index system for measuring DE.

Primary Indicators	Secondary Indicators	Data Source
Digital Infrastructure	Optical Fiber Length (km/10,000 people)	National Bureau of Statistics of the PRC
	IPv4 Address Count (Number/10,000 people)
Industrial Scale	Software and Information Technology Service Revenue as a Percentage of GDP (%)	National Bureau of Statistics of the PRC
Technology Output	Number of Authorized Digital Economy-Related Inventions (Pieces)	Chinese Research Data Services (CNRDS) Platform
Application Penetration	Big Data Industry Development Pilot and Demonstration Projects/National Green Data Centers/Manufacturing Internet Integration/Industrial Internet Pilot and Demonstration Projects/Cross-Industry and Cross-Domain Industrial Internet Platforms/Intelligent Manufacturing Enterprises	Ministry of Industry and Information Technology of the PRC

. This framework aligns with the OECD’s perspective, which emphasizes digital technologies, infrastructure, and innovation as central components of the digital economy.

Digital Infrastructure is measured using optical fiber length and the number of IPv4 addresses, which correspond to the OECD’s “digital foundation” pillar. Due to data constraints in the digital industry, the scale of digital-related sectors is unavailable. Consequently, this study uses the ratio of Software and Information Technology Service Revenue to GDP as a proxy for the industrial scale. Digital Economy-Related Inventions are used as a precise indicator of innovation activity within the digital economy sector. Application Penetration reflects the extent of digital technology adoption across various applications.

The principal component analysis method [19] is employed to quantify DE.

3.2.2. Dependent Variable

Building on existing research [12,22,23], this study identifies green development, innovation, and high-end development as core dimensions of high-quality manufacturing development (HQDM). Rather than solely relying on macro-level data, industry-specific data published by relevant authorities are also incorporated to measure HQDM, as detailed in

Table 2. Index system for measuring HQDM.

Primary Indicators	Secondary Indicators	Data Source
Green	Number of green factories/green supply chain management demonstration enterprises/green industrial parks/industrial green design demonstration enterprises (units)	Ministry of Industry and Information Technology of the PRC
	Number of green design products (items)
Innovation	Full-time equivalent of R&D personnel in Above-Designated-Size Industrial Enterprises (person years)	National Bureau of Statistics of the PRC
	R&D expenditure of Above-Designated-Size Industrial Enterprises as a percentage of GDP (%)
	Average number of valid invention patents per Above-Designated-Size Industrial Enterprises (items)
	Number of national technology innovation demonstration enterprises/national recognized enterprise technology centers (units)	Ministry of Industry and Information Technology of the PRC
High-end	Number of manufacturing single-champion products/demonstration/cultivation enterprises/specialized, refined, and new small giant enterprises (units)	Ministry of Industry and Information Technology of the PRC
	Number of national quality benchmarking enterprises (units)

.

The comprehensive HQDM index is calculated using the principal component analysis method.

3.2.3. Moderating Variables

Consistent with the theoretical hypothesis, manufacturing agglomeration (Agglo_it) and digital literacy (Digi_it) are employed as moderating variables. Manufacturing agglomeration is measured by the entropy of manufacturing location, as commonly applied in current research [20,29].

$$Aggl{o}_{it}={\displaystyle \frac{M{e}_{it}/T{e}_{it}}{\sum M{e}_t/\sum T{e}_t}}$$

(4)

where Agglo_it denotes the degree of manufacturing agglomeration in province i during year t. Me_it is the number of manufacturing employee in year t of province i. Te_it is the total employees in province i in t year. ∑Me_t and ∑Te_t represent the total number of manufacturing employees and total employees in China, respectively, during year t.

Digital literacy is measured using a three-dimensional index system (

Table 3. Index system for measuring Digi.

Primary Indicators	Secondary Indicators	Data Source
Daily Technology Penetration	Internet broadband subscription rate (%)	National Bureau of Statistics of the People’s Republic of China
Digital Talent Cultivation Potential	Ratio of digital economy-related majors in regular higher education institutions/approval total major numbers (%)	Ministry of Education of the People’s Republic of China
Digital Workforce Density	Employment in information transmission, software, and information technology services in urban areas as a percentage of total population (%)	National Bureau of Statistics of the People’s Republic of China

), based on the framework proposed by UNESCO [30]. This framework emphasizes the potential and skills of individuals in the digital domain. Daily Technology Penetration is represented by the internet broadband subscription rate, serving as the foundation for digital literacy development. Digital Talent Cultivation Potential is captured by the ratio of digital economy-related majors in higher education. Digital Workforce Density is defined as the employment rate in information transmission, software, and IT services in urban areas as a percentage of the total population.

The entropy method is used to measure digital literacy.

3.2.4. Control Variables

Both the digital economy and manufacturing growth are influenced by government infrastructure development. Government financial expenditures play a pivotal role in these processes. Therefore, government financial expenditure intensity (gover_it) is included in the model, measured as the ratio of government financial expenditures to GDP [20]. A higher level of industrialization supports manufacturing advancements. Industrialization (indus_it) is represented by the ratio of industrial value-added to GDP [25]. Regions with greater R&D expenditures establish a solid foundation for innovation, which, in turn, drives manufacturing transformation. R&D intensity (rede_it) is calculated as the ratio of R&D expenditures to GDP [26].

A summary of all variables is presented in

Table 4. Variables summary.

Category	Meaning	Variable	Measurement
Dependent Variable	The degree of high-quality development of manufacturing	HQDM	Based on Table 2, calculated with PCA approach
Independent Variable	The degree of digital economy development	DE	Based on Table 1, calculated with PCA approach
Moderating Variable	Manufacturing agglomeration	Agglo	Location entropy index (Equation (4))
	Digital literacy	Digi	Based on Table 3, calculated with entropy approach
Control Variable	Government financial expenditures intensity	Gover	Ration of government financial expenditures to GDP
	Industrialization	Indus	Ration of industry value-added to GDP
	R&D intensity	Rede	Ration of R&D expenditures to GDP

.

3.3. Data Source

The data for this study are collected from 30 provinces, autonomous regions, and municipalities in China, excluding Tibet, Hong Kong, Macao, and Taiwan due to missing data. The dataset covers the period from 2013 to 2023, resulting in 330 balanced observations for the provincial panel. The data are sourced from the National Bureau of Statistics of the People’s Republic of China, the Chinese Research Data Services (CNRDS) Platform, the Ministry of Industry and Information Technology of the People’s Republic of China, the Ministry of Education of the People’s Republic of China, and other relevant sources. All data are deflated by 1% to account for extreme values.

4. Trends and Regional Disparity Analysis

4.1. Key Variables Overview

Table 5. Variables description.

Category	Variable	Observations	Mean	Standard Deviation	Minimum	Maximum
Dependent Variable	HQDM	330	0.1194	0.1090	0.0097	0.5431
Independent Variable	DE	330	0.1381	0.1093	0.0163	0.5932
Moderating Variable	Agglo	330	0.8182	0.3420	0.2986	1.7946
	Digi	330	0.0030	0.0050	0.0005	0.0316
Control Variable	Gover	330	25.7706	10.6497	11.8874	67.5228
	Indus	330	32.0374	7.4409	11.2160	47.7002
	Rede	330	1.8908	1.1677	0.4750	6.4058

presents the descriptive statistics for all variables, deflated by 1%. For the dependent variable, high-quality development of manufacturing across provinces (330 observations), the mean is 0.1194 with a standard deviation of 0.1090. The values range from 0.0097 to 0.5431, indicating significant disparities in regional industrial development.

For the independent variable, digital economy development, the mean is 0.1381 (SD = 0.1093), with values ranging from 0.0163 to 0.5932, reflecting considerable inter-provincial variation. Among the moderating variables, manufacturing agglomeration has a relatively high mean of 0.8182 but a substantial standard deviation of 0.3420, suggesting varying impacts of regional policies and resource endowments on industrial clustering. Digital literacy, on the other hand, has a low mean of 0.0030 with a standard deviation of 0.0050, indicating both overall underdevelopment and significant regional disparities in digital capabilities.

Control variables such as fiscal expenditure intensity (mean = 25.7706), industrialization (mean = 32.0374), and R&D intensity (mean = 1.8908) also show notable dispersion.

4.2. Time-Series Analysis

To comprehensively understand the evolution of the digital economy, high-quality development of manufacturing, manufacturing agglomeration, and digital literacy from 2013 to 2023, a time-series analysis was conducted using mean of the raw data for these variables, as shown in Figure 3.

4.2.1. Trend Analysis for DE

The digital economy index (DE) demonstrates a consistent upward trajectory from 2013 to 2023. Beginning at 0.0359 in 2013, it increased to 0.2815 in 2023, reflecting the rapid expansion of the digital economy throughout the study period. This growth can be attributed to factors such as increased investment in digital infrastructure (e.g., 5G networks, data centers), the widespread adoption of digital technologies (e.g., AI, big data, cloud computing) across various sectors, and government-driven digital transformation policies. The exponential growth observed post-2017 indicates that the digital economy entered an accelerated development phase, driven by economies of scale and network effects.

4.2.2. Trend Analysis for HQDM

HQDM also shows a steady upward trajectory, rising from 0.0668 in 2013 to 0.2125 in 2023. This growth closely mirrors the expansion of the digital economy. As the digital economy grew (as reflected in the DE), manufacturing industries increasingly adopted digital technologies to optimize processes, drive product innovation, and promote green development. For example, digital twin technologies in manufacturing plants simulate and optimize production processes, while big data analytics are employed for quality control. The correlation between the growth of HQDM and DE highlights digitalization as a key driver of high-quality manufacturing development.

4.2.3. Trend Analysis for Agglo

The manufacturing agglomeration index (Agglo) shows a slight decline from 2013 to 2019, decreasing from 0.8350 to 0.8057, followed by a period of stability and slight increase. The initial decline may be attributed to the restructuring of the manufacturing sector, where some industries relocated from traditional agglomeration zones to areas with lower costs or better resource allocation. After 2019, Agglo exhibited a more stable or slightly upward trend, likely due to the integration of digital technologies into manufacturing clusters, facilitated by digital platforms that promote more efficient collaboration and resource sharing within agglomerated regions, thereby enhancing their competitiveness.

4.2.4. Trend Analysis for Digi

The digital literacy index (Digi) shows a gradual upward trend until 2022, followed by a slight decline in 2023. Starting at 0.0020 in 2013, it reached 0.0040 in 2022, reflecting a slow but steady increase in digital literacy. This growth is likely tied to the rising importance of digital skills in the labor market and the expansion of digital education and training programs. Overall, the relatively low level of Digi (compared to other indices) suggests that while digital literacy is improving, substantial progress is still needed to fully support the digital transformation of the economy, particularly given the rapid development of the digital economy and digitally driven manufacturing.

In conclusion, the time-series analysis reveals a strong positive correlation between the growth of the digital economy and the high-quality development of manufacturing. The degree of manufacturing agglomeration demonstrates a transitional pattern influenced by industrial restructuring and digital integration. While digital literacy is improving, further enhancement is needed to fully harness the benefits of digital transformation. These insights are critical for policymakers seeking to promote digital-driven economic development and manufacturing upgrades, as well as to enhance digital education and training systems.

4.3. Regional Differences Analysis

This section analyzes the regional disparities in four key variables across China. The 30 provincial-level administrative regions are grouped into three categories: Eastern, Central, and Western. Tibet, Hong Kong, Macao, and Taiwan are excluded from the analysis due to missing data.

Table 6. Regional differences summary.

Region	Province	DE	HQDM	Agglo	Digi
Eastern	Beijing	0.4113	0.1974	0.3899	0.0278
	Tianjin	0.1086	0.1324	1.2045	0.0033
	Hebei	0.0944	0.1087	0.7693	0.0013
	Liaoning	0.1323	0.0897	0.9089	0.0026
	Shanghai	0.1908	0.1757	0.9855	0.0114
	Jiangsu	0.2570	0.3054	1.4387	0.0032
	Zhejiang	0.2408	0.2796	1.2370	0.0033
	Fujian	0.1355	0.1550	1.2311	0.0023
	Shandong	0.1650	0.2884	1.1660	0.0017
	Guangdong	0.2717	0.3672	1.7756	0.0038
	Hainan	0.0694	0.0242	0.3218	0.0017
	mean	0.1888	0.1931	1.0390	0.0057
Central	Shanxi	0.0990	0.0637	0.5597	0.0013
	Jilin	0.0821	0.0389	0.8480	0.0019
	Heilongjiang	0.0967	0.0469	0.4205	0.0019
	Anhui	0.1312	0.1710	0.9528	0.0015
	Jiangxi	0.1116	0.0875	1.0367	0.0012
	Henan	0.0726	0.1604	1.0161	0.0014
	Hubei	0.1366	0.1360	0.9249	0.0022
	Hunan	0.1158	0.1405	0.7433	0.0013
	mean	0.1057	0.1056	0.8128	0.0016
Western	Inner Mongolia	0.1261	0.0519	0.5470	0.0016
	Guangxi	0.0923	0.0598	0.6236	0.0010
	Chongqing	0.1349	0.0917	0.7970	0.0016
	Sichuan	0.1552	0.1115	0.7307	0.0022
	Guizhou	0.0915	0.0470	0.4602	0.0009
	Yunnan	0.0967	0.0459	0.5474	0.0009
	Shaanxi	0.1342	0.0777	0.7318	0.0027
	Gansu	0.0833	0.0405	0.4886	0.0013
	Qinghai	0.1248	0.0184	0.6255	0.0013
	Ningxia	0.0813	0.0403	0.6382	0.0010
	Xinjiang	0.1242	0.0439	0.4301	0.0013
	mean	0.1131	0.0572	0.6018	0.0014
	National Mean	0.1389	0.1199	0.8184	0.0030

presents the raw values for these variables across each region, along with the mean values for each region and the national average. The data reveal substantial regional heterogeneity, which reflects underlying differences in economic structure, policy orientation, and industrial foundation. This sets the stage for the subsequent analysis of the interactive mechanisms at play.

4.3.1. Regional Analysis for DE

As shown in Figure 4, DE follows a clear gradient of “East > West > Central,” with the Eastern region leading (mean = 0.1888), followed by the Western (mean = 0.1131) and Central regions (mean = 0.1057).

Despite overall progress, the Eastern region exhibits significant internal dispersion in digital economy development. Table 6 reveals a wide range of values (0.0694–0.4113), highlighting considerable internal divergence. While regions such as Beijing and Jiangsu benefit from advanced technological infrastructure and a concentration of high-tech enterprises, peripheral provinces like Hainan lag behind.

In contrast, the Central region displays a narrow range (0.0726–0.1366), suggesting a relatively balanced development of the digital economy across provinces. With a mean DE of 0.1057, the Central region remains behind the Eastern region, but its growth is more evenly distributed. Provinces such as Anhui (0.1312) show higher DE values compared to others like Henan (0.0726), though they still fall short of the Eastern average. This disparity can largely be attributed to less advanced technology infrastructure, such as limited industrial internet coverage, and fewer high-tech enterprises, which hinder the region’s ability to replicate the digital agglomeration observed in the East.

The Western region shows a moderate range (0.0813–0.1552), reflecting localized pockets of digital development. With a mean DE of 0.1131, the region lags behind the East but slightly surpasses the Central region. The Western region benefits from policy-supported digital hubs, such as those in Chongqing (0.1349) and Sichuan (0.1552), driven by initiatives like the Chengdu-Chongqing Twin Cities Strategy. However, broader structural challenges, including geographical remoteness, outdated infrastructure, and a lack of high-tech resources, limit widespread digital growth, resulting in isolated hubs coexisting with digitally underdeveloped regions.

4.3.2. Regional Analysis for HQDM

Figure 5 illustrates that HQDM follows a distinct gradient of “East > Central > West.” This pattern reflects regional variations in industrial structure, policy support, and technological capabilities, providing a foundation for analyzing the interaction mechanisms between digitalization and manufacturing.

In the Eastern region, a pronounced polarization is evident. With a mean of 0.1931, the region exhibits a wide range (0.0242–0.3672), as shown in Table 6. Leading provinces such as Guangdong (0.3672) and Jiangsu (0.3054) are at the forefront of high-quality manufacturing, driven by concentrated high-tech industrial clusters. These areas possess advanced manufacturing sectors that promote automation, innovation, and efficiency gains. In contrast, peripheral provinces like Hainan (0.0242) lag significantly, with a tourism-dominated economy and limited manufacturing scale.

In the Central region, HQDM shows moderate progress, with a mean of 0.1056 and a narrower range (0.0389–0.1710). Provinces such as Anhui (0.1710) benefit from policy spillovers, such as the Yangtze River Delta Integration, and a growing science-and-technology-driven manufacturing sector (e.g., new energy vehicles in Hefei). However, other provinces, like Jilin (0.0389) and Heilongjiang (0.0469), remain heavily reliant on traditional industries, such as heavy machinery and resource processing, where digital adoption and R&D investment are limited. This transitional landscape reflects a delicate balance between emerging high-quality manufacturing hubs and persistent challenges in traditional sectors, limiting their upgrading momentum.

The Western region exhibits fragmented and low-level HQDM development, with a mean of 0.0572 and a range of 0.0184–0.1115. Core areas like Sichuan (0.1115) and Chongqing (0.0917) leverage state-driven initiatives to foster advanced manufacturing sectors (e.g., electronics in Sichuan, automotive in Chongqing). However, most provinces in the West, such as Qinghai (0.0184) and Gansu (0.0405), remain dependent on resource-based industries, including energy and minerals, which are low-tech and low-value-added. Geographical isolation, inadequate digital infrastructure, and persistent talent outflow further hinder development, creating a stark contrast between policy-driven hubs and stagnant resource-dependent sectors.

4.3.3. Regional Analysis for Agglo

Manufacturing agglomeration (Agglo) follows a distinct “East > Central > West” gradient, with clear regional dynamics, as depicted in Figure 6.

In the Eastern region, there is significant polarization in Agglo. Outliers such as Guangdong (1.7756) and Jiangsu (1.4387) anchor the industrial clusters along the Pearl and Yangtze River Deltas, benefiting from economies of scale and national policy support. These regions experience rapid industrial growth and innovation. However, provinces like Hainan (0.3218) lag behind, mainly due to their tourism-dominated economy. This highlights the uneven distribution of industrial agglomeration, even within the Eastern region.

The Central region shows moderate but fragmented agglomeration. Provinces like Jiangxi (1.0367) have emerging high-tech clusters, such as the Nanchang Optoelectronics and Communications Industry Cluster, and so on, while others like Heilongjiang (0.4205) remain heavily reliant on traditional heavy industry. This duality reflects a transitional phase where high-tech agglomeration coexists with lagging resource-based sectors, limiting the potential for cohesive industrial upgrading.

In the Western region, Agglo is more localized. Provinces such as Chongqing (0.7970) and Shaanxi (0.7318) benefit from state-led initiatives, like the Chengdu-Chongqing Twin Cities Strategy, which have fostered digital-manufacturing hubs. However, resource-dependent provinces like Guizhou (0.4602) lag behind, constrained by geographical isolation and weak industrial integration. The manufacturing agglomeration in the Western region is still in the early stages, marked by isolated growth in select areas.

4.3.4. Regional Analysis for Digi

Digital literacy (Digi) displays uniformly low levels across all regions (national mean = 0.0030, Table 6), as illustrated in Figure 7.

In the Eastern region, with a mean Digi value of 0.0057 (Table 6), there is considerable regional variation, even though it leads in digital literacy. Key hubs like Beijing (0.0278) and Shanghai (0.0114) benefit from policies promoting high-speed internet access, a concentration of digital-economy majors in universities, and a high proportion of ICT employment. These outliers exhibit relatively high digital literacy, while peripheral provinces like Hainan (0.0017) lag behind, hindered by limited digital job markets and insufficient focus on digital majors in universities, which keeps Digi at extremely low levels.

In the Central region, the mean Digi value is 0.0016, showing consistently low digital literacy across provinces. While broadband coverage is modest, it fails to translate into the development of digital skills, as traditional industries (e.g., heavy industry in Henan) have limited demand for technology-intensive tools. The supply of skilled labor is also constrained, with few universities emphasizing digital majors. The sparse ICT employment further reinforces a “low-level equilibrium.” Marginal differences between provinces like Hubei (0.0022) and Henan (0.0014) highlight stagnation in digital literacy in areas dominated by traditional industries.

The Western region, with the lowest mean Digi value of 0.0014, faces widespread deprivation, worsened by geographical constraints and deficiencies across all indicator dimensions. Rural broadband penetration remains limited, few universities offer digital majors, and skilled graduates often migrate eastward. Provinces like Guizhou (0.0009) remain entrenched in resource-dependent economies with few digital job opportunities. Localized improvements in Digi are typically short-term and driven by policy interventions rather than systemic changes, reflecting the dual constraints of geography and industrial structure.

These regional disparities in digital literacy highlight the need for targeted interventions, such as expanding broadband access, promoting digital education, and fostering industry-specific digital skills development. The patterns of digital literacy, geography, and industrial structure provide valuable insights for implementing digital transformation more effectively across regions.

5. Empirical Investigation and Interpretation

5.1. Benchmark Regression Analysis

The benchmark regressions are presented in

Table 7. Benchmark regression.

Variable	(1) HQDM	(2) Green	(3) Innovation	(4) High-End
DE	0.5734 ***	0.2432 **	0.1037 ***	0.2251 ***
	[0.1638]	[0.1007]	[0.0143]	[0.0738]
gover	0.0029 *	0.0010	0.0001	0.0017 **
	[0.0015]	[0.0008]	[0.0003]	[0.0007]
indus	−0.0040 ***	−0.0020 ***	−0.0001	−0.0019 **
	[0.0014]	[0.0007]	[0.0002]	[0.0007]
rede	0.0377 *	0.0034	0.0278 ***	0.0065
	[0.0186]	[0.0091]	[0.0030]	[0.0107]
constant	0.0545	0.0315	0.0077	0.0155
	[0.0794]	[0.0447]	[0.0157]	[0.0381]
Fixed Time	YES	YES	YES	YES
Fixed Region	YES	YES	YES	YES
N	330	330	330	330

Note: ***, **, and * indicate significance at 1%, 5%, and 10% level, with robust standard errors in square brackets.

. In column (1), where the dependent variable is the overall high-quality manufacturing development index (HQDM), the digital economy (DE) shows a significant positive coefficient of 0.5734 (at the 1% significance level). This suggests that the digital economy is a strong driver of the overall high-quality development of the manufacturing sector. From a theoretical standpoint, the digital economy fosters the integration of advanced technologies such as big data, artificial intelligence (AI), and the Internet of Things (IoT) into manufacturing processes. This integration facilitates smarter production planning, more efficient resource allocation, and improved quality control—all of which are crucial for advancing high-quality manufacturing. For instance, digital platforms can link different stages of the manufacturing supply chain, reduce information asymmetry, and enhance overall operational efficiency. Therefore, Hypothesis 1 is supported.

Further analysis is conducted to explore how the digital economy influences HQDM. In this context, the three dimensions of HQDM—green, innovation, and high-end—are used as core dependent variables to assess how the digital economy affects these aspects of high-quality manufacturing development in China.

For the green dimension of manufacturing, DE exhibits a positive and significant coefficient of 0.2432 (at the 5% significance level), as shown in column (2) of Table 7. Digitalization contributes to green manufacturing in multiple ways. On one hand, digital technologies enable real-time monitoring of environmental indicators, such as energy consumption and pollutant emissions, within the manufacturing process. This allows for timely adjustments to production methods, minimizing environmental impact. On the other hand, digital platforms facilitate the exchange of green manufacturing practices and technologies among enterprises. For example, through digital platforms, companies can adopt energy-efficient production techniques or green supply chain management models, promoting the green transformation of the manufacturing sector.

In the innovation dimension, column (3) in Table 7 demonstrates that DE has a positive and significant coefficient of 0.1037 (at the 1% significance level). The digital economy drives innovation in manufacturing by offering new tools and platforms for research and development (R&D). Big data analytics help companies more accurately identify market needs and technological trends, thereby guiding their innovation strategies. Furthermore, digital technologies such as cloud computing lower the cost and complexity of innovation by providing shared access to computing resources and R&D databases. Collaborative innovation platforms built on digital infrastructure also enhance cooperation among stakeholders in the manufacturing innovation ecosystem, including enterprises, research institutions, and universities.

Regarding the high-end dimension of manufacturing, column (4) in Table 7 shows that DE presents a positive and significant coefficient of 0.2251 (at the 1% significance level). The digital economy plays a crucial role in advancing high-end manufacturing. It supports the development of advanced manufacturing technologies such as intelligent manufacturing. Digitalization also facilitates the customization and personalization of high-end products, catering to the diverse and sophisticated demands of the market. Moreover, the digital economy enhances the global competitiveness of high-end manufacturing enterprises by providing better access to global markets, advanced technologies, and talent through digital platforms.

In conclusion, the digital economy exerts a significant positive impact on the overall high-quality development of the manufacturing sector and its three sub-dimensions: green, innovation, and high-end development. These effects are realized through various mechanisms, including technological integration, resource optimization, and ecosystem building. The findings provide a strong foundation for formulating policies aimed at promoting the digital transformation of the manufacturing sector for high-quality development.

5.2. Moderating Effect Analysis

Based on Model (2) and Model (3) in Section 3.1.1, this section aims to verify the moderating effects of manufacturing agglomeration (Agglo) and digital literacy (Digi), respectively. The test results are presented in

Table 8. Moderating mechanism tests.

Variable	Agglo		Digi
	(1)	(2)	(3)	(4)
DE	0.5701 ***	0.4521 ***	0.7047 ***	0.8627 ***
	[0.1564]	[0.0642]	[0.1525]	[0.1006]
Agglo	0.0534	0.0109
	[0.0604]	[0.0392]
Digi			−10.0752 *	4.6120 **
			[5.5782]	[1.7304]
DE × Agglo		0.3952 ***
		[0.0664]
DE × Digi				−19.3426 ***
				[2.5020]
Control variables	YES	YES	YES	YES
Fixed Time	YES	YES	YES	YES
Fixed Region	YES	YES	YES	YES
N	330	330	330	330

Note: ***, **, and * indicate significance at 1%, 5%, and 10% level, with robust standard errors in square brackets.

.

Column (1) of Table 8 shows the regression results incorporating manufacturing agglomeration (Agglo) into the benchmark model. In Column (2), both Agglo and its interaction term with the digital economy (DE) are included in the model. The same procedure is applied to digital literacy, with regression results for Digi shown in Columns (3) and (4).

Across all models, the coefficient of DE remains significantly positive, confirming the robust positive effect of the digital economy on manufacturing-related outcomes. This supports previous research, which suggests that digitalization promotes manufacturing upgrading through technological integration, efficiency gains, and the diffusion of innovation.

In Column (2), both the interaction term between Agglo and DE, as well as DE itself, are positive and significant at the 1% level. This indicates that agglomeration significantly moderates the impact of the digital economy on manufacturing development. Agglomeration enhances the digital economy’s effect by concentrating resources such as skilled labor and R&D facilities, reducing transaction costs, and facilitating knowledge spillovers. For example, firms within clusters can share digital infrastructure, such as industrial internet platforms, at lower costs, thereby accelerating digital technology adoption. This supports Hypothesis 2.

These findings align with existing literature. For example, Deng et al. (2025) highlight that manufacturing agglomeration not only offers large-scale implementation scenarios for digital technologies but also promotes resource sharing and cost reduction to enhance the digital economy [28]. However, Lei et al. (2024) argue that the impact of the digital economy on agglomeration is nonlinear, with early stage constraints, such as talent shortages, limiting collaboration [27]. This contrasts with our findings, where the moderating effect of agglomeration is significant. Future research should explore how the digital economy’s impact varies across different stages of agglomeration, particularly in less agglomerated regions where gaps in infrastructure and talent may limit its effects.

In Column (4) of Table 8, the interaction term between digital literacy (Digi) and the digital economy (DE) is negative and significant at the 1% level. This suggests that digital literacy negatively moderates the facilitating effect of the digital economy on high-quality manufacturing development. This unexpected finding contrasts with the prevailing view that digital literacy enhances the effective utilization of digital technologies in manufacturing.

Literature generally underscores the critical role of digital literacy in enabling workers to navigate and utilize digital technologies, thereby driving operational optimization and innovation in manufacturing. For instance, UNESCO’s Global Framework emphasizes the importance of digital literacy in equipping individuals with the skills to manage and generate information using digital tools, thus fostering the integration of advanced technologies such as AI, IoT, and big data [30]. Similarly, other research highlights that as digital technologies mature, their potential is maximized, particularly in regions with a digitally skilled workforce [33]. These perspectives suggest that digital literacy is a key enabler, facilitating the effective adoption and application of digital technologies to improve industrial processes.

However, our findings challenge this widely accepted view. The negative interaction effect observed in our study may be attributed to the current low levels of digital literacy in many regions, as discussed in Section 4.2 and Section 4.3. In regions where digital literacy remains limited, the pool of skilled workers is insufficient to meet the specialized needs of manufacturing’s digital transformation. As a result, firms face challenges in utilizing digital tools, such as industrial AI, leading to inefficiencies that undermine the positive effects of the digital economy. This issue is especially pronounced in regions with both low digital literacy and poor infrastructure, where even digitally literate workers struggle to fully leverage digital technologies.

5.3. Endogenous Check

To address potential endogeneity, this study employs the instrumental variable (IV) method. Following prior research [46,47], we construct an interaction term between the number of telephones owned by 100 people in 1998 and the number of national internet broadband access ports from the previous year. This serves as an instrumental variable (IV) for the digital economy. The 1998 telephone data, being a predetermined historical measure, is exogenous to subsequent manufacturing development. Both telephones and broadband, rooted in shared communication infrastructure, reflect the path dependence of digital connectivity, thereby ensuring their correlation with the digital economy. This design satisfies both the exclusion and relevance criteria for IVs, addressing concerns related to endogeneity.

A two-stage approach is used for the instrumental variable regression, with the results presented in Columns (1) and (2) of

Table 9. Instrumental variable regression.

Variable	(1) First Stage DE	(2) Second Stage HQDM
IV	0.0007 ***
	[0.0001]
DE		0.5459 ***
		[0.1028]
gover	0.0008	0.0030 ***
	[0.0012]	[0.0008]
indus	0.0003	−0.0040 ***
	[0.0011]	[0.0007]
rede	0.0590 ***	0.0396 ***
	[0.0106]	[0.0088]
F statistic	119.2300	71.5300
Fixed Time	YES	YES
Fixed Region	YES	YES
Kleibergen-Paap rk LM statistic		19.4210 ***
Kleibergen-Paap rk Wald F statistic		22.8540
Stock-Yogo weak ID test critical values (10%)		16.3800
N	330	330

Note: *** indicates significance at 1% level, with robust standard errors in square brackets.

.

In Column (1), the first stage shows that the IV exhibits a significantly positive correlation with the digital economy (DE), supported by a high F-statistic of 119.2300. In the second stage, the fitted DE has a significantly positive effect on HQDM. Additionally, the Kleibergen-Paap rk LM statistic is positive at the 1% significance level, rejecting the null hypothesis of under-identification. The Kleibergen-Paap rk Wald F-statistic (22.8540) exceeds the Stock-Yogo 10% critical value (16.3800), ruling out concerns regarding weak instruments. The IV passes all tests and reinforces the main conclusions of this study.

5.4. Robustness Checks

5.4.1. Robustness Checks for Benchmark Regression

Multiple tests support the hypothesis that the digital economy enhances high-quality manufacturing development (HQDM). To reinforce the results, two distinct robustness tests are conducted. First, factor analysis is applied to the original sample to recalibrate both the explained and explanatory variables. Second, we exclude the 2013 data and use the 2014–2023 sample to recalculate all variables. This adjustment helps mitigate any potential data anomalies present in the initial period, ensuring the stability of the results across different timeframes.

The robustness checks for benchmark regressions are presented in

Table 10. Robustness tests for benchmark regression.

Variable	Factor Analysis				Sample Replacement
	(1) HQDM	(2) Green	(3) Innovation	(4) High-End	(1) HQDM	(2) Green	(3) Innovation	(4) High-End
DE	0.5727 ***	0.2429 **	0.1033 ***	0.2249 ***	0.5498 ***	0.2280 **	0.1010 ***	0.2193 ***
	[0.1638]	[0.1005]	[0.0143]	[0.0738]	[0.1574]	[0.0951]	[0.0170]	[0.0745]
gover	0.0029 *	0.0010	0.0001	0.0017 **	0.0037 **	0.0012	0.0003	0.0021 **
	[0.0015]	[0.0008]	[0.0003]	[0.0007]	[0.0016]	[0.0009]	[0.0004]	[0.0008]
indus	−0.0040 ***	−0.0020 ***	−0.0001	−0.0019 **	−0.0043 ***	−0.0021 ***	−0.0002	−0.0020 **
	[0.0014]	[0.0007]	[0.0002]	[0.0007]	[0.0014]	[0.0007]	[0.0003]	[0.0008]
rede	0.0377 *	0.0034	0.0278 ***	0.0065	0.0316 *	−0.0001	0.0269 ***	0.0047
	[0.0186]	[0.0091]	[0.0029]	[0.0107]	[0.0184]	[0.0086]	[0.0040]	[0.0112]
constant	0.0547	0.0314	0.0080	0.0155	0.0544	0.0346	0.0104	0.0095
	[0.0794]	[0.0446]	[0.0158]	[0.0381]	[0.0812]	[0.0441]	[0.0215]	[0.0418]
Fixed Time	YES	YES	YES	YES	YES	YES	YES	YES
Fixed Region	YES	YES	YES	YES	YES	YES	YES	YES
N	330	330	330	330	300	300	300	300

Note: ***, **, and * indicate significance at 1%, 5%, and 10% level, with robust standard errors in square brackets.

.

In the factor analysis-based robustness test (Table 10, Columns (1)– (4)), the core variable, digital economy (DE), maintains significantly positive coefficients across all models. This consistency aligns with the benchmark regression results (Table 7), confirming that the digital economy’s positive effect on high-quality manufacturing development—across the green, innovation, and high-end dimensions—remains robust. The consistent significance across models further validates the stability of the digital economy’s driving role.

In the sample-replacement test (Table 10, Columns (5)– (8), excluding 2013 data), DE still demonstrates significant positive effects. By removing the 2013 sample, we mitigate potential data anomalies that might skew results. The consistent significance and coefficients across different sample periods indicate that the impact of the digital economy is not driven by outliers in the 2013 data, enhancing the credibility of the findings.

Both tests support the benchmark regression’s conclusion that the digital economy plays a significant role in promoting the high-quality development of manufacturing across various dimensions. The robustness across different variable measurement techniques and sample periods confirms that the findings are not sensitive to methodological or sample choices, providing robust evidence for the digital-manufacturing linkage.

5.4.2. Robustness Checks for Moderating Mechanism

After confirming the robustness of the benchmark regression results, we now extend this rigor to the analysis of moderating effects. To ensure the reliability of our findings regarding the moderating roles of manufacturing agglomeration (Agglo) and digital literacy (Digi), we apply the same robustness testing framework. Specifically, we use factor analysis to recompute the independent and dependent variables, addressing potential measurement biases. Additionally, we perform sample replacement (excluding the 2013 data) to assess the sensitivity of the results to sample composition. This dual-approach validation further reinforces the conclusions regarding the moderating effects of Agglo and Digi on the digital economy-manufacturing development linkage, ensuring consistency with the methodological rigor established in the benchmark tests.

The robustness checks for the moderating regressions are presented in

Table 11. Robustness tests for moderating regression.

Variable	Factor Analysis				Sample Replacement
	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
DE	0.5695 ***	0.4514 ***	0.7033 ***	0.8615 ***	0.5478 ***	0.4398 ***	0.6193 ***	0.7326 ***
	[0.1564]	[0.0642]	[0.1526]	[0.1007]	[0.1515]	[0.0635]	[0.1256]	[0.0922]
Agglo	0.0530	0.0105			0.0500	0.0084
	[0.0604]	[0.0392]			[0.0633]	[0.0426]
Digi			−10.0500 *	4.6268 **			−5.7475	4.3864 **
			[5.5749]	[1.7324]			[3.9857]	[2.0091]
DE × Agglo		0.3955***				0.3884 ***
		[0.0663]				[0.0703]
DE × Digi				−19.3363 ***				−14.9824 ***
				[2.5054]				[2.2905]
Control variable	YES	YES	YES	YES	YES	YES	YES	YES
Fixed Time	YES	YES	YES	YES	YES	YES	YES	YES
Fixed Region	YES	YES	YES	YES	YES	YES	YES	YES
N	330	330	330	330	300	300	300	300

Note: ***, **, and * indicate significance at 1%, 5%, and 10% level, with robust standard errors in square brackets.

.

Column (2) of Table 11 shows that for manufacturing agglomeration (Agglo) as a moderator, the interaction term DE × Agglo remains significant, with coefficients closely aligned to those in the original regression (Table 8, column (2)). Similarly, for digital literacy (Digi), the interaction term DE × Digi retains its negative significance (Column (4)), mirroring the original result (Table 8, column (4)). Factor analysis, by refining variable measurement, confirms the robustness of Agglo’s positive and Digi’s negative moderating roles.

In the sample-replacement test, excluding the 2013 data, the interaction terms remain statistically significant: DE × Agglo (Table 11, column (6)) and DE × Digi (Table 11, column (8)) show consistent signs and significance levels with the original results (Table 8, columns (2) and (4)). This adjustment mitigates concerns regarding result sensitivity to outliers from the initial period, further reinforcing the reliability of the moderating effects.

Both tests confirm that manufacturing agglomeration consistently strengthens the digital economy-manufacturing linkage, while digital literacy’s negative moderating role persists. This aligns with our earlier explanation regarding skill-ecosystem mismatches. The robustness across different methods further strengthens the conclusion that the moderating effects are intrinsic to the digital-manufacturing dynamics, rather than being artifacts of measurement or sampling.

5.5. Heterogeneity Analysis

5.5.1. Regional Heterogeneity Test

To further explore the spatial boundaries of the moderating effects, this study conducts sub-sample regressions for the Eastern, Central, and Western regions, as presented in

Table 12. Regional heterogeneity test for moderating mechanism.

Variable	Eastern		Central		Western
	(1) Agglo	(2) Digi	(3) Agglo	(4) Digi	(5) Agglo	(6) Digi
DE	0.6072 ***	0.9235 ***	0.4089 *	0.2637	0.2118 **	0.2220 **
	[0.0797]	[0.0831]	[0.1793]	[0.1984]	[0.0751]	[0.0899]
Agglo	0.0671 *		−0.0424		0.0009
	[0.0337]		[0.0740]		[0.0299]
Digi		−1.2834		31.1731 **		−5.7522 *
		[2.9262]		[10.4176]		[3.0952]
DE × Agglo	0.3992 ***		−0.0743		0.5565 **
	[0.0617]		[0.3499]		[0.2395]
DE × digi		−19.2083 ***		−77.8069		88.3599 **
		[2.9080]		[97.5536]		[28.9008]
Control variable	YES	YES	YES	YES	YES	YES
Fixed Time	YES	YES	YES	YES	YES	YES
Fixed Region	YES	YES	YES	YES	YES	YES
N	121	121	88	88	121	121

Note: ***, **, and * indicate significance at 1%, 5%, and 10% level, with robust standard errors in square brackets.

. This analysis investigates how manufacturing agglomeration (Agglo) and digital literacy (Digi) influence the digital economy-manufacturing linkage across distinct regional ecosystems.

As shown in Columns (1), (3), and (5) of Table 12, the moderating effect of manufacturing agglomeration (Agglo) on the digital economy-manufacturing linkage demonstrates significant regional divergence. The coefficients indicate that the interaction term DE × Agglo is significantly positive in the Eastern (0.3992) and Western (0.5565) regions, but insignificant in the Central region (−0.0743).

In the Eastern region, the highest level of agglomeration (mean = 1.0390, Table 6) and well-established industrial clusters are closely integrated with advanced digital infrastructure (e.g., Jiangsu: Agglo = 1.4387, DE = 0.2570). Digital spillovers are amplified through economies of scale and knowledge diffusion. In the Western region, despite having the lowest agglomeration mean (0.6018, Table 6), policy-driven high-density clusters in places (e.g., Chongqing: Agglo = 0.7970, DE = 0.1349) reinforce the positive moderating effect. In contrast, the Central region, with a moderate agglomeration mean (0.8128, Table 6), suffers from fragmented industrial distribution (e.g., energy-based clusters in Shanxi versus tech hubs in Wuhan) and inadequate digital infrastructure. In summary, Agglo’s moderating effectiveness depends on both industrial density and digital infrastructure.

Digital literacy (Digi) also exhibits significant regional heterogeneity in moderating the digital economy-manufacturing linkage, with universally low levels across all regions (Table 6: Eastern = 0.0057, Central = 0.0016, Western = 0.0014). The interaction term DE × Digi in Columns (2), (4), and (6) of Table 12 reveals three distinct patterns, each shaped by the interaction of limited digital skills with regional industrial contexts.

In the Eastern region, the moderating effect of Digi is significantly negative at the 1% level, with a coefficient of −19.2083 (Column (2), Table 12). Although Eastern Digi is the highest among the regions, its absolute level (0.0057) remains extremely low, only 1.9 times the national average (0.0030, Table 6). This creates a “constrained complementarity”: manufacturing powerhouses (e.g., Jiangsu, Guangdong) are pursuing aggressive digital transformation (high DE), but the limited digital talent pool (e.g., Shanghai: Digi = 0.0114) cannot support the complex digital tools required. Firms face bottlenecks in talent absorption, resulting in inefficiencies that dampen the digital economy’s potential.

The Central region shows an insignificant moderating effect of Digi (Column (4), Table 12). With the second-lowest Digi (0.0016, Table 6), digital skills are both scarce and misaligned with the industrial structure. Most provinces (e.g., Henan, Shanxi) are heavily reliant on traditional manufacturing (low HQDM, Table 6), which has minimal demand for digital talent. Neither Digi nor DE is sufficient to trigger meaningful synergy. Both digital skills and industrial digitalization are underdeveloped, preventing observable moderating behavior.

Despite Western China exhibiting the lowest digital literacy (Digi) in the nation (mean = 0.0014, Table 6), its moderating effect on the digital economy-manufacturing linkage is significantly positive (Column (6), Table 12). This anomaly arises from localized concentration: digital skills are more concentrated in provincial capitals and municipalities (e.g., Shaanxi: Digi = 0.0027, Chongqing: Digi = 0.0016). In these niches, even ultra-scarce Digi can support fledgling digital initiatives, generating short-term positive synergy. However, this effect is confined to isolated hubs, with most Western regions (e.g., Yunnan, Guizhou: Digi < 0.0010) remaining in a non-interaction state, similar to the Central region.

5.5.2. Time-Based Heterogeneity Test

Although the digital economy has experienced significant growth in recent years, it remains in its early stages in China. To explore how the moderating mechanisms evolve across different stages of digital economy development, this study conducts a time-based heterogeneity test. Recognizing the G20 summit in September 2016 as a milestone for the digital economy, this study treats 2017 as a watershed year. For the purpose of the time heterogeneity test, the research period is divided into two intervals: 2013–2017 and 2018–2023. The results of these time-based heterogeneity tests are presented in

Table 13. Time-based heterogeneity tests for moderating mechanism.

Variable	2013–2017		2018–2023
	(1) Agglo	(2) Digi	(3) Agglo	(4) Digi
DE	0.3610 ***	0.4327 ***	0.3691 ***	0.6382 ***
	[0.0723]	[0.1211]	[0.0852]	[0.1092]
Agglo	−0.0352		−0.0140
	[0.0582]		[0.0497]
Digi		0.2300		1.0757
		[3.1527]		[3.0978]
DE × Agglo	0.4936 **		0.4532 ***
	[0.1781]		[0.1350]
DE × Digi		−8.1232		−8.4613 **
		[7.8961]		[3.3478]
Control variable	YES	YES	YES	YES
Fixed Time	YES	YES	YES	YES
Fixed Region	YES	YES	YES	YES
N	150	150	180	180

Note: *** and ** indicate significance at 1% and 5% level, with robust standard errors in square brackets.

.

Columns (1), (2), (3), and (4) in Table 13 reveal that the moderating roles of manufacturing agglomeration (Agglo) and digital literacy (Digi) evolve significantly across the two periods. These changes are closely linked with China’s policy shifts in the digital economy, manufacturing, and talent development.

In both periods, the interaction term DE × Agglo remains significantly positive, reflecting the persistent role of spatial concentration in enhancing the synergy between digitalization and manufacturing. During the first period (2013–2017), Agglo (Figure 3) maintained a high baseline, and policies such as the early phase of “Made in China 2025” focused on upgrading industrial clusters (e.g., smart factory pilots in coastal regions). Concentrated industrial resources quickly absorbed nascent digital technologies, generating stronger marginal effects. In the second period (2018–2023), although the moderating coefficient for Agglo slightly declined, it remained significant. The digital economy (Figure 3) expanded rapidly, but agglomerated regions underwent structural adjustments (e.g., phasing out outdated capacities). Simultaneously, policies shifted towards high-quality development, emphasizing digital integration across more regions, which diluted Agglo’s relative advantage. Nevertheless, agglomeration’s inherent economies of scale and knowledge spillovers continued to support a positive moderating effect.

The interaction term DE × Digi transitions from insignificance in the first period (2013–2017: −8.1232) to a significantly negative effect in the second period (2018–2023: −8.4613), reflecting the chronically low levels of Digi (Figure 3) and the evolving policy-demand gaps. During 2013–2017, Digi was universally scarce, but both the digital economy (DE) and manufacturing digitization demands were still nascent. This resulted in minimal skill-demand mismatches, as both the supply and demand for digital skills were limited. Policies were focused on traditional manufacturing, and the cultivation of digital talent lagged behind. However, in the second period, the digital economy surged (DE’s rapid rise in Figure 2), and “Digital China” related policies mandated aggressive manufacturing digitization. Despite this, Digi remained stagnant at low levels, creating a sudden mismatch: the digital demand in manufacturing outpaced the slow growth of digital skills, leading to inefficiencies and a negative moderating effect.

Temporally, Agglo’s stable positive moderating role emphasizes the need to maintain cluster-digital integration (e.g., upgrading industrial parks to smart clusters). In contrast, Digi’s emerging negative moderating effect highlights the urgent need to address skill gaps (e.g., targeted vocational training in digital manufacturing). These findings suggest that policy must adapt to the evolving interplay between technological diffusion, industrial structure, and human capital, strengthening support for the enduring advantages of agglomeration while addressing the growing bottlenecks related to Digi as digital transformation deepens.

6. Findings and Implications

6.1. Findings

This study examines the pivotal role of the digital economy in driving the high-quality development of manufacturing, with a particular focus on the moderating effects of manufacturing agglomeration and digital literacy. Several key conclusions are drawn through robust econometric analyses:

(1)

The Digital Economy Drives High-Quality Manufacturing Development

The results confirm that the digital economy significantly enhances the high-quality development of manufacturing across three key dimensions: green, innovative, and high-end manufacturing. China’s 14th Five-Year Plan for Digital Economy Development (2021–2025) underscores the integration of big data, AI, and IoT to boost production efficiency. Locally, Jiangsu Province’s Three-Year Action Plan (2025–2027) aims to upgrade over 10,000 enterprises annually to smart factories, focusing on industrial internet integration. Zhejiang’s “Industry Brain” initiative, part of its broader digital economy plan, connects industrial clusters via AI-powered supply chain platforms for real-time production optimization. These policies highlight how digital technologies, supported by targeted frameworks, accelerate industry transformation.

(2)

Manufacturing Agglomeration Amplifies the Digital Economy’s Impact

Manufacturing agglomeration plays a crucial moderating role in the digital economy-manufacturing linkage. The positive moderating effect of agglomeration is most apparent in the Eastern and Western regions, where concentrated industrial clusters, supported by digital infrastructure and policy initiatives, amplify digital spillovers and foster synergies between manufacturing and digital transformation. Beyond economic factors, manufacturing agglomeration also plays an essential role in promoting social collaboration and regional integration. The spatial concentration of firms facilitates knowledge exchange, accelerates technology diffusion, and strengthens regional policy cooperation, which in turn enhances social infrastructure and promotes balanced regional development. This dynamic relationship contributes to social cohesion, improving the quality of life in agglomerated areas. However, the moderating effect of agglomeration is weaker in the Central region, where fragmented industrial distribution and lagging digital infrastructure impede the realization of its full potential.

(3)

Digital Literacy’s Role is Context-Dependent

This study highlights significant regional heterogeneity in the moderating effect of digital literacy on the digital economy-manufacturing linkage. While digital literacy remains universally low across regions, its impact varies. In the Eastern region, where digital transformation is advanced but the talent pool remains insufficient, the effect of digital literacy on the digital economy is negative, indicating a mismatch between the supply of skills and industrial demand. In contrast, in the Western region, the localized concentration of digital skills in certain hubs provides a temporary positive moderating effect. However, this effect is limited to specific areas and does not extend across the entire region.

(4)

Time-Based Heterogeneity and Policy Shifts

The analysis of time-based heterogeneity further elucidates how policy shifts and the evolving digital economy have shaped the moderating roles of agglomeration and digital literacy. In the first period (2013–2017), manufacturing agglomeration played a dominant role in driving the digital-manufacturing synergy, supported by early stage policies such as “Made in China 2025.” However, in the second period (2018–2023), although the influence of agglomeration slightly declined, it remained significant. The rapid expansion of the digital economy and the shift towards “high-quality development” policies diluted agglomeration’s relative advantage. Nevertheless, agglomeration continued to play a key role in fostering innovation and knowledge diffusion. In contrast, the mismatch between the digital demands of manufacturing and the slow growth of digital literacy became more pronounced in the second period, leading to negative moderating effects that highlight the need for targeted skills development.

6.2. Policy Implications

This study underscores the significant regional disparities in the impact of the digital economy on the high-quality development of manufacturing in China. Based on these findings, the following policy recommendations are proposed for the Eastern, Central, and Western regions to strengthen the linkage between the digital economy and manufacturing, thereby promoting high-quality, sustainable development:

(1)

Eastern Region

The Eastern region, with its well-established industrial clusters and relatively advanced digital infrastructure, is well-positioned to lead the digital transformation of manufacturing. However, despite significant advancements in digital technology adoption, a critical gap persists between the rapid growth of the digital economy and the availability of skilled labor. Manufacturing hubs in Jiangsu and Guangdong are actively pursuing digital transformation, but the talent pool remains insufficient to meet the demand for advanced digital tools such as AI, big data analytics, and IoT. To address this challenge, policies should prioritize the cultivation of digital talent through targeted education and training programs focused on high-end digital skills. Moreover, efforts should be made to upgrade existing industrial parks into “smart clusters” that integrate digital technologies with manufacturing capabilities, fostering synergies that enhance both innovation and sustainability. This approach will enable the region to maintain its competitive advantage while addressing skill shortages that hinder digital adoption.

(2)

Central Region

In the Central region, where industrial agglomeration is fragmented and digital infrastructure is underdeveloped, a more integrated approach to digital transformation is required. The region’s reliance on traditional industries, such as energy and heavy manufacturing, has resulted in limited demand for digital skills. To facilitate the transition to a high-quality manufacturing ecosystem, policies should focus on establishing digital transformation hubs in key cities, where industrial development can be combined with digitalization initiatives. Incentivizing the adoption of smart manufacturing technologies and fostering partnerships between local governments, businesses, and educational institutions will help bridge the digital skills gap. Additionally, targeted vocational training programs aligned with the region’s industrial needs are essential to develop a digitally capable workforce. This will enable the region to transition towards green manufacturing, advanced materials, and other high-tech sectors, supporting long-term sustainable economic growth.

(3)

Western Region

The Western region, which faces low levels of digital literacy and inadequate digital infrastructure, faces significant challenges in achieving widespread digital transformation. However, localized digital initiatives, such as those in Chongqing and Xi’an, have shown promise. To maximize the region’s potential, policies should focus on expanding digital infrastructure, particularly in rural and underserved areas, to ensure equitable access to digital benefits. Establishing digital manufacturing hubs in major cities, supported by digital literacy programs, will stimulate localized economic growth and develop a skilled workforce. Furthermore, incentivizing the digital adoption of traditional industries, such as resource extraction and manufacturing, could accelerate the region’s digital transformation, leverage its existing industrial base, and transition to a more sustainable digital economy. These initiatives will ensure that the region contributes to China’s broader sustainability goals while addressing its unique developmental challenges.

6.3. Research Limitations and Future Research Directions

(1)

Research Limitation

A limitation of this study is its primary focus on China, which may restrict the generalizability of the findings to other countries, particularly those at different stages of digital and industrial development. While the insights provided are highly relevant to China’s context, the absence of an international perspective limits our understanding of how global regions with diverse economic structures and varying levels of digital adoption experience digital transformation in manufacturing. Additionally, the study relies on secondary data, which may not fully capture the fast-paced nature of digital technologies and the evolving dynamics of digital literacy. The lack of real-time, granular data may impact the accuracy of capturing the rapid shifts in the digital economy and hinder the analysis of short-term technological disruptions and innovations.

(2)

Future Research Directions

Future research could address these limitations by expanding the scope to include cross-country comparisons, particularly focusing on countries with diverse economic structures, policy frameworks, and stages of digital adoption. Such comparative studies would provide valuable insights into global best practices and illustrate how institutional contexts shape digital transformation in manufacturing. Moreover, longitudinal studies that track the evolution of digital skills, the adoption of digital technologies, and their long-term impacts on manufacturing sustainability could deepen our understanding of these trends and their outcomes.

Further research could also explore sector-specific digital literacy gaps, examining how these variations influence digital adoption across different manufacturing industries, such as automotive, electronics, and textiles. Understanding the unique demands of each sector will enable policymakers to design more effective, tailored training programs and industrial policies that align with the specific needs of sustainable manufacturing.

Additionally, future studies could assess the effectiveness of regional policies in fostering digital integration and manufacturing transformation, particularly through comparisons of different strategies on a global scale. By identifying the most successful approaches, these studies can inform best practices for policy frameworks that support sustainable and inclusive digital manufacturing development. Finally, exploring the impact of emerging technologies such as artificial intelligence, blockchain, and digital twins will be crucial for understanding the future of digital manufacturing. These technologies have the potential to drive profound disruptions to traditional production systems, and their role in enhancing the sustainability of manufacturing processes warrants detailed investigation.