The Impact of Digital Trade on China’s Position in the GVC: An Empirical Analysis Based on Sino-Russian Cross-Border Panel Data

Abstract

This study analyzes OECD input–output data and UNCTAD database information to assess the impact of Sino-Russian digital trade on China’s position in the global value chain (GVC). The findings indicate that digital trade between China and Russia enhances China’s GVC status, especially in technology-intensive manufacturing sectors, while its impact is less pronounced in non-technology-intensive sectors. The digitalization level of the service industry significantly influences its effectiveness, with stronger effects in sectors that are less digitally mature. Mechanism analysis reveals that Sino-Russian digital trade boosts GVC positions through effective technology transfer, increased capital stock, and optimized human resources. Based on theoretical and empirical analyses, deepening the digitization of the manufacturing sector, expanding the layout of digital industries, strengthening Sino-Russian digital trade cooperation, and promoting the development of a digital “Belt and Road” initiative are beneficial for enhancing China’s position in the GVC and enhancing overall prosperity. These strategies not only enhance global competitiveness but also contribute to the broader goals of sustainable development by fostering economic resilience and innovation.

1. Introduction

In recent years, the global economy has been deeply affected by geopolitical tensions and uncertainties, with rising antagonism among major powers and ongoing trade disputes. The global trade landscape is undergoing dramatic changes, and regional cooperation frameworks are trending toward replacing multilateral cooperation frameworks represented by the World Trade Organization (WTO), leading to a slowdown in the process of economic globalization. The return of the manufacturing industry to developed countries, along with the migration of labor-intensive industries to new countries with cheap labor advantages, such as Thailand, Vietnam, and Mexico, seems to be shaking China’s status as the “world’s factory.” China is facing an unprecedentedly complex foreign economic situation. How to enhance its position in the global value chain under this circumstance is a question worth exploring in depth.

On one hand, amid a complex geopolitical landscape marked by intense competition among major powers, China must proactively adapt. It is essential for China to optimize its foreign trade framework and enhance its position in the value chain to manage the increasing uncertainties in international trade. Furthermore, bolstering cooperation within the Belt and Road Initiative (BRI) is a viable strategy. Particularly, the cooperation between China and Russia stands as a cornerstone; as a crucial conduit linking China with European markets, Russia’s role is pivotal in helping China navigate long-standing geopolitical tensions and in fostering new avenues for trade growth.

On the other hand, China has not yet completed the leap from being a major manufacturer to a strong manufacturing nation. Several technology-intensive industries still lack mastery of key technologies, and the phenomenon of bottlenecks in critical links of the GVC remains evident. Therefore, actively utilizing available external resources and fully leveraging its own advantages are crucial factors in achieving a leap in value chain position. The report of the 20th National Congress of the Communist Party of China clearly stated that economic development should not only focus on quantity but also pursue high-quality growth. Enhancing the position in the GVC is not only a crucial starting point for achieving high-quality economic development but also a cornerstone for the secure development of the industrial chain.

In 2020, the Chinese government at the policy level acknowledged the status of data as a key production factor, positioning the digitization of traditional manufacturing as a means to foster new development momentum, identify new growth points, and break away from the low-end constraints of the GVC. According to the “Digital China Development Report (2022),” the scale of China’s digital economy reached 50.2 trillion yuan in 2022, accounting for 41.5% of GDP.

Existing research often focuses on multilateral trade or bilateral trade between China and developed countries, which has limitations given the escalating trade frictions between China and the United States and the influence of various non-economic factors on bilateral trade with developed countries. However, trade between China and Russia has been flourishing, with rapid growth reaching $240.1 billion in 2023—an increase of over 25% from 2022.

Therefore, the main contributions of this paper are as follows: First, in the context of Sino-Russian digital trade, China acts as the main exporter of digital services, unlike its role as an importer in digital trade with developed countries. Focusing on bilateral trade between China and Russia fills a gap in existing research and enriches the understanding of how digital trade enhances a country’s position in the global value chain. Second, at a critical juncture where the digital economy intersects with the global value chain, the development of digital trade has a profound impact on promoting the digital transformation of manufacturing and improving the overall quality of the industry’s value chain. This research can provide new perspectives and solutions for enhancing the position of China’s manufacturing sector in the global value chain. Third, Russia serves as a significant geographical node and destination for the Belt and Road Initiative. Exploring the heterogeneity of various industries in Sino-Russian digital trade helps to deepen the understanding of the nature of trade between China and Russia and to grasp the cooperation process of digital trade within the Belt and Road Initiative.

2. Literature Review

In the era of globalization, economic activities transcend national boundaries, making understanding the GVC essential for grasping modern economies. Following the 2007 global financial crisis, multilateral cooperation under the WTO framework cooled, giving way to increased regional and sub-regional cooperation. This shift led to “fragmentation” and “re-centralization” of world trade. Enhancing GVC position has become a crucial task for economic development, drawing widespread attention in academic circles.

In the literature searches conducted on Web of Science and CNKI (in Chinese), a significant portion of studies utilize the degree of participation in or position within the global value chain as variables to investigate their effects on aspects such as employment (Yu et al., 2021) [1], foreign direct investment (Zhao, 2021) [2], productivity (Yanikkaya et al., 2022) [3], cross-border corporate activities (Li et al., 2022) [4], green total factor productivity (GTFP) (Ali et al., 2023) [5], and efficiency of green technology innovation (CTIE) (Hu et al., 2021) [6]. However, studies examining the factors influencing a country’s position in the global value chain, using it as a dependent variable, are relatively limited.

Exploring the impact of Foreign Direct Investment (FDI) on the position within the GVC is a primary focus and essential foundation of GVC position studies. The influence of FDI on China’s GVC position is complex, varying significantly across different trade modes. FDI enhances the GVC position of enterprises engaged in general trade by promoting reverse technology spillover, upgrading industrial structures, and expanding export scales. However, this finding is contrary in the context of China’s processing trade (Ren et al., 2024) [7]. Other scholars argue that the enhancement of a country’s GVC position through FDI is achieved through technological advancement and improved trade network positions, with this effect being more pronounced in high-technology industries than in medium- and low-technology industries (Li et al., 2021) [8]. Additionally, the influence of other factors on the GVC position varies significantly at different levels of FDI. When FDI levels are low, industry agglomeration tends to be detrimental to enhancing a country’s position in the GVC; however, as FDI increases, industry agglomeration promotes GVC advancement through scale effects and technology spillovers, and the positive impact of technological innovation becomes evident. On the other hand, the degree of economic openness, the level of physical capital, and the abundance of natural resources negatively affect the GVC position (Yang and Li, 2018) [9]. Additionally, some scholars have examined the effects of FDI within the framework of the China–Japan–Korea Regional Trade Agreement, finding that FDI has a negative impact on forward participation in the GVCs of member countries, while trade openness and economic freedom positively influence backward participation (Rahman et al., 2024) [10].

Moreover, China’s Overseas Economic and Trade Cooperation Zones (COCZs) have also been shown to impact host countries’ participation in and positions within the GVC, but this influence is not linear. The direction of COCZs’ impact on GVC positions is influenced by the host country’s factor endowments, level of innovation, and business environment. Particularly when the industrial structure of the host country where the COCZ is established is similar to China’s, its impact on the GVC position is more significant (Qin et al., 2023) [11]. Beyond economic and trade cooperation zones, non-traditional factors such as institutions, policies, and regulations also affect the GVC position. The influence of institutions on the GVC position can be realized by moderating the relationships between other influencing factors and the GVC position.

Previous research has indicated that the continuous optimization of national institutional environments has intensified both the positive and negative relationships between FDI and GVC positions (Ye and Jin, 2022) [12]. The effect of FDI on elevating firms’ positions within the GVC often stems more from policy incentives rather than purely market access; a well-developed institutional environment can enhance the role of foreign capital liberalization in advancing firms’ GVC upgrades, with property rights systems having a greater impact than contract systems (Guo et al., 2020) [13]. The influence of institutions is also evident in financial systems, where studies have shown that market-based financial structures are superior to bank-led ones in improving a country’s position in the GVC. Such structures can enhance GVC positions through channels such as human capital enhancement and R&D innovation incentives (Sheng and Jing, 2019) [14].

The economic policy barriers within different countries are correlated with their positions in the GVC based on their respective advantages. In other words, today’s GVCs are interconnected with policies in areas beyond traditional trade regulations (van der Marel 2016) [15]. For instance, China’s tax incentives for high-tech enterprises can enhance their positions in the GVC by stimulating increases in production efficiency. The positive impact of tax incentives on GVC positions is widespread, especially pronounced for labor-intensive, capital-intensive enterprises, and those located in Eastern China (Li et al., 2023) [16]. In terms of regulation, previous research examining the impact of environmental regulation on the GVC positions of the service sector found that environmental regulations significantly enhance the GVC positions of services. The impact of environmental health on the value chain position is significantly greater than that of ecosystem vitality. Furthermore, the impact of environmental regulation on the GVC positions of the service industry shows heterogeneity, with the degree of impact positively correlated with per capita income (Liu, 2022) [17].

The factors affecting the position in the GVC are complex, and this complexity is further illustrated by the varying impact of these factors across different stages of development. Between 2003 and 2013, among the many factors influencing GVC positions—including human capital, level of technological innovation, enterprise size, FDI, outward direct investment, productive services, and government subsidies—technological innovation had the greatest impact on improving GVC positions, with FDI and government subsidies being relatively strong, while the impact of productive services was relatively small (Feng and Wang, 2019) [18]. Today, with the rapid development of digital technology in recent years in China, there has been a significant push for iteration in the productive service industry, increasing its weight in influencing GVC positions. Previous studies have shown that artificial intelligence in the digital economy (Liu et al., 2024) [19] and internet technologies (Shi and Li, 2020) [20] can enhance productivity by reducing information costs, optimizing resource allocation, and increasing innovation frequency, thus impacting the GVC position. Digital trade, based on digital technology, involves the digitalization of trade modes and trade objects, both of which rely on the construction of digital infrastructure. Digital infrastructure improves the GVC position through channels such as enhancing the efficiency of new knowledge spillover and innovation (Li et al., 2020) [21] and can also promote the GVC position by driving the penetration of digital economy into traditional trade modes (i.e., digitalization of trade methods) (Qi and Ren, 2021) [22].

Currently, there are limited data in the literature on the pathways through which digital trade affects the position in the GVC. What is clear, however, is that digital trade influences GVC positions by improving resource allocation and enhancing innovation (Liu and Deng, 2023) [23]. The pathways through which digital trade impacts GVC positions exhibit industrial heterogeneity (Jin et al., 2023) [24] and display an “inverted U” pattern (Sha and Zhang, 2024) [25].

3. Theoretical Analysis and Research Hypotheses

Digital trade, crucial for the digital economy’s global spread, offers a modern twist to traditional trade. Broadly, digital trade includes two aspects: firstly, it involves applying digital technologies to traditional foreign trade, creating a new form of trade within an information-driven context. This encompasses cross-border trade activities facilitated by Information and Communication Technology (ICT), which penetrates both foreign trade enterprises and their trade methods. It is primarily manifested in supply chain management, smart manufacturing, customized production, and cross-border trade of these enterprises. The integration of digital technologies enhances supply chain efficiency. Technologies such as the Internet of Things, cloud computing, and artificial intelligence connect market and data advantages, offering transparency and cost efficiency, and overcoming geographical limits on production. Digitization of trade methods can continuously optimize and match supply and demand, significantly impacting trade processes and cycles. The integration of digital technology with logistics enables visualized and efficient transportation routes, improving transportation efficiency, reducing costs, and minimizing logistic risks (Venables, 2001) [26]. Digital trade drives the establishment of transnational internet trading platforms, facilitating effective connections between consumer demands and supply chain offerings, even enabling on-demand production. This aligns diverse demands with varied supplies, mitigates information asymmetry, and substantially reduces the hidden costs of trade (Jolivet, 2019) [27]. Thus, the digitization of trade methods enhances the global value chain position by reducing production, transaction, and logistic costs, including hidden costs, while the digitization of trade objects stimulates international demand for digital products and services, expanding market size.

The other aspect of digital trade involves cross-border transactions of digital products (such as software, music, e-books) and digitalized services (such as online education, cloud computing, electronic payments). It covers the sale of goods, provision of services, and transactions of intellectual property rights in various digital service trade areas. Digital technology has spurred the growth of new sectors such as online retail, online education, cloud computing, and online tourism and entertainment. Online education platforms and remote learning tools have made educational resources more accessible, increasing flexibility in learning and bringing revolutionary changes to the education sector. Cloud storage and software services have reduced hardware costs for businesses setting up data processing centers, enhancing production efficiency. The rise of digital trade provides new opportunities for businesses to expand into international markets, thereby promoting global economic growth. The increase in specialization and product diversity creates new competitive advantages for foreign trade, influencing participants’ positions in the GVC (Wang and Huang, 2023) [28]. In digital service trade, digital technology can reduce value chain costs (Chen and Liu, 2023) [29], improve value chain efficiency, and shorten value chain length through industrial agglomeration effects to enhance value chain position.

Based on the analysis above, it can be confirmed that digital trade has a pulling effect on the global value chain position. The following will use the tri-country model and production theory by Koopman et al. (2010) [30] to specifically analyze the impact pathways.

$${\mathrm{G}\mathrm{V}\mathrm{C}\_\mathrm{P}\mathrm{o}\mathrm{s}\mathrm{i}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}}_{\mathrm{i}}=\mathrm{Ln}\left(1+\frac{{\mathrm{I}\mathrm{V}}_{\mathrm{i}}}{{\mathrm{E}}_{\mathrm{i}}}\right)-\mathrm{Ln}\left(1+\frac{{\mathrm{F}\mathrm{V}}_{\mathrm{i}}}{{\mathrm{E}}_{\mathrm{i}}}\right)$$

(1)

Equation (1) defines the Global Value Chain position index (GVC_Position) of China’s sectoral level as the logarithmic ratio of the sector’s indirect value-added exports (IV) to foreign value-added exports (FV). If China is positioned upstream in the global value chain, it will supply more production materials to other countries, including raw materials, intermediate products, or both. In the equation, i represents different sectors, and E_i represents the total exports of sector i, where:

$${\mathrm{F}\mathrm{V}}_{\mathrm{r}}=\sum _{\mathrm{s}\ne \mathrm{r}}{\mathrm{V}}_{\mathrm{s}}{\mathrm{B}}_{\mathrm{s}\mathrm{r}}{\mathrm{E}}_{\mathrm{r}\ast }$$

(2)

$${\mathrm{I}\mathrm{V}}_{\mathrm{r}}=\sum _{\mathrm{s}\ne \mathrm{t}}{\mathrm{V}}_{\mathrm{r}}{\mathrm{B}}_{\mathrm{r}\mathrm{s}}{\mathrm{E}}_{\mathrm{s}\mathrm{t}}$$

Equation (2) defines B_sr and B_rs as elements of the Leontief inverse matrix $\mathrm{B}={\left(\mathrm{I}-\mathrm{A}\right)}^{-1}$, where A is the direct consumption matrix. B_sr represents the total output when the final demand in country r increases by one unit due to production in country s. Thus, B is a total demand matrix, and the sum $\sum _{\mathrm{r}}{\mathrm{V}}_{\mathrm{r}}{\mathrm{B}}_{\mathrm{r}\mathrm{s}}$ equals u, which is the unit vector. In this study, countries other than China and Russia are considered collectively as a “third country”, hence r, s, t = 1, 2, 3 correspond to China, Russia, and other countries, respectively. V_s and V_r are direct value-added coefficient vectors, representing the share of domestic direct value added in total output.

Next, we introduce a production function that incorporates technological progress:

$${\mathrm{Y}}_{\mathrm{i}}={\mathrm{A}}^{\mathrm{w}}·\mathrm{F}\left({\mathrm{L}}_{\mathrm{i}},{\mathrm{K}}_{\mathrm{i}}\right)$$

(3)

where, Y_i represents the total output of sector i, A stands for technological innovation, and L_i and K_i, respectively, signify labor and capital inputs in production. Combining Equations (1) and (2) with the input–output table, the equation for the domestic direct value added (v_i) of sector i can be derived:

$${\mathrm{v}}_{\mathrm{i}}{\mathrm{Y}}_{\mathrm{i}}={\mathrm{A}}^{\mathrm{w}}·\mathrm{F}\left({\mathrm{L}}_{\mathrm{i}},{\mathrm{K}}_{\mathrm{i}}\right)-\sum {\mathrm{X}}_{\mathrm{k}}-{\mathrm{x}}_0$$

(4)

$${\mathrm{v}}_{\mathrm{i}}=1-\frac{\sum {\mathrm{X}}_{\mathrm{k}}+{\mathrm{x}}_0}{{\mathrm{A}}^{\mathrm{w}}·\mathrm{F}\left({\mathrm{L}}_{\mathrm{i}},{\mathrm{K}}_{\mathrm{i}}\right)}$$

Assuming the production process is in an ideal state, the production function meets the Cobb–Douglas production function assumptions, and all production inputs are utilized in product production without any non-productive losses. In equation (4), X_k represents the intermediate inputs from sector k used in the production of one sector, x₀ represents the initial inputs. It is important to note that ${\mathrm{K}}_{\mathrm{i}}=\sum {\mathrm{X}}_{\mathrm{k}}+{\mathrm{x}}_0+{\mathrm{K}}_0$, where K₀ represents capital inputs other than production inputs.

3.1. Technology Transfer and Innovation

Technology transfer and innovation involve leveraging digital technology to optimize production, enhance product quality, and improve efficiency through the adoption and development of new technologies. Digitalization of trade methods promotes technology transfer and innovation in foreign trade enterprises through the following means:

In the technology information processing and storage phase, digital technology in foreign trade can more fully concentrate explicit and tacit technology in the technology market, promoting the integration of knowledge and technology in the technology transfer network. During the technology transmission phase, the application of digital technology in foreign trade practices changes the dispersed state of technology information caused by geographical and administrative boundaries, providing efficient channels for information transmission in technology transfer activities, reducing information friction and spatial barriers, thereby reducing the costs of computing and transmitting technology information. In the technology search phase, the network effects and economies of scale brought about by digital technology enable multi-channel technology transfer, which not only enhances the speed and frequency of technology transfer transactions but also reduces the search costs of technology, expanding the potential scope and quality of technology transfer. In the technology matching phase, the establishment of transnational trade platforms based on the internet and other technologies can overcome the heterogeneity of technology supply and demand sides, helping technology transaction entities match high-quality trading partners, thus forming a “competitive selection mechanism” for technology transfer. Additionally, the digitalization of trade methods can increase the attractiveness of the domestic investment market and improve the domestic investment environment. Based on the theory of firm heterogeneity, firms choose different production methods due to productivity differences; the highest productivity firms opt for OFDI, followed by product exports, while the lowest productivity firms serve the domestic market (Helpman 2004) [31]. Thus, the development of digital trade is conducive to attracting high-productivity, high-tech firms to invest in China, promoting technology transfer and innovation through the spillover effects of FDI.

Technology transfer and innovation enhance China’s GVC position. Equation (4) shows that, all else being equal, technological progress increases the domestic value-added ratio, v_i; thus, via Equation (2), elements in V_r (where r represents China) increase, positively affecting indirect value-added exports (IV), and according to Equation (1), elevating China’s GVC position. In reality, the accumulation of intangible assets from technology transfer and innovation correlates positively with GVC participation levels (Durcova, 2023) [32]. Additionally, technology transfer and innovation stimulate policy environment optimization, promoting an innovative-supportive social atmosphere, and creating a favorable policy setting for digital trade development. Therefore, technology transfer and innovation are key pathways by which digital trade can boost GVC positions.

Therefore, it can be proposed that:

Hypothesis H1.

Sino-Russian digital trade can enhance China’s position in the GVC through promoting technology transfer and innovation.

3.2. Human Resource Potential

The development of digital trade expands domestic demand for digital economic products, services, and technologies, fostering the growth of related industries within the digital economy. This drives a rapid evolution from information technologies to intelligent technologies, inevitably leading to a swift expansion in the scale of the digital economy workforce and diversifying professional roles, thereby continuously unlocking employment potential. The emergence of new business models provides more employment choices (such as autonomous vehicle safety operators, social media professionals, product testers, and video streamers). According to the “2023 White Paper on New Occupational Development Trends,” 17.5% of young people are exploring new professions outside traditional industries, and 58.5% express a strong interest in careers related to the digital economy.

The development of digital trade also stimulates labor potential through the income-pulling effect. Digital services trade, focusing on digital products, can positively impact labor income share through productivity effects, structural effects, and distribution effects. Moreover, exports of digital services trade are more beneficial than imports in increasing domestic labor income share (Yeerken, 2023) [33]. The rise in income share not only motivates higher work enthusiasm but more importantly, attracts a more skilled labor force, thus expanding the scale of human resources.

Additionally, the growth of digital trade also enhances the quality of labor by boosting the domestic digital economy. Unlike systematic learning in professional institutions, skill development in new occupational groups is individualized, primarily driven by interest and peer learning, breaking down strict divisions between learning, working, and trading environments, thus accelerating skill acquisition. Digital trade also promotes the societal application of digital technology, profoundly altering occupational structures, with new occupational groups exhibiting virtual and decentralized labor modes. Virtuality refers to a significant portion of labor processes, service exchanges, and innovation taking place in invisible virtual spaces. Decentralization indicates that the digital economy’s industry chain is continuously extending and becoming more finely divided, with market returns thus spread across different collaboration stages and regions.

Whether it is expanding the scale of the workforce or improving the quality of labor, both increase the labor input in production. In the Cobb–Douglas production function, the partial derivative of the production function with respect to labor is greater than zero. Therefore, in Equation (4), an increase in labor input can enhance the domestic value-added ratio of goods (v_i), which, as previously analyzed, ultimately leads to an improvement in China’s position in the GVC (Equation (1)). This conclusion is confirmed in studies on repatriated overseas personnel: an increase in overseas returnees can significantly increase the export probability and intensity of employing enterprises, strengthen trade between the home country and the study-abroad country, improve the export product quality of employing enterprises, promote the transformation and upgrading of export trade, and ultimately drive the home country’s GVC from the low end to the high end (Xu, 2018) [34].

Hypothesis H2.

Sino-Russian digital trade can enhance China’s GVC position through human resource optimization.

3.3. Expand the Scale of Capital Stock

The development of digital trade between China and Russia boosts GVC status through increasing capital stock in several ways. Firstly, it enhances international demand for digital services, promoting growth in China’s digital sector and further integrating digital technologies into traditional manufacturing. This growth attracts more domestic investment into digital production, while capital market preferences for digital sectors ease financing constraints, encouraging enterprises to aim for higher GVC tiers. However, only those enterprises with minimal financing constraints and high productivity can participate in higher GVC activities, yielding greater returns (Ma et al., 2017) [35]. The fusion of digital and traditional manufacturing sectors notably improves production efficiency and reduces financial barriers, thereby elevating GVC status.

Secondly, the expansion of Sino-Russia digital trade helps Chinese digital industries explore transnational models and gain international experience, producing substantial spillover effects. These effects exemplify the potential of Sino-Russia digital trade to serve as a model for digital economic cooperation with other nations, enhancing global demand and investment in China’s digital services from both Russian and international capital sources. Inflows of foreign direct investment (FDI) enhance GVC status through industrial agglomeration effects, employment quantity and quality effects, technology spillover effects, and product quality improvement effects (Yang, 2018; Luo, 2014) [36].

Expanding the scale of capital stock can enhance China’s position in the GVC. Under the assumption that all other conditions remain constant, capital accumulation can influence total output through the production function, which assumes a positive correlation between capital input and output. In Equation (4), an increase in capital input can enhance the proportion of domestic value added in goods (v_i). Subsequently, according to Equations (1) and (2), this increase elevates the GVC position.

In the context of Sino-Russia trade, China aims to spur technological innovation via increasing capital stock to upgrade its GVC position. By boosting R&D investment, China lays foundations for technological advancement, channeling funds into corporate R&D units and establishing R&D funds to foster innovation across research bodies and companies. This strategy also encourages the adoption and refinement of advanced technologies from Russia and beyond, uncovering new growth avenues. Moreover, increasing capital stock supports the creation of varied tech innovation platforms like incubators and industrial parks, which aggregate resources and foster an innovative climate, facilitating the progression and commercialization of technology. Capital empowers partnerships among businesses, academia, and research institutions, ensuring the practical application of technological advances and research outcomes, thus boosting China’s GVC competitiveness. Additionally, increasing capital stock attributes to developing labor input, attracting global innovators, and providing ongoing technical training to staff, thus strengthening the team’s innovative capabilities and technical skills. Through strategic trade interactions with Russia and focused capital deployment, China can lead in technological innovation and elevate its GVC standing.

Hypothesis H3.

Sino-Russia digital trade can enhance China’s GVC status through increasing capital stock.

4. Research Design

4.1. Model Construction

To empirically analyze the promotional effects of foreign trade on the improvement of GVC position, this study constructs the following benchmark econometric model:

$${\mathrm{G}\mathrm{V}\mathrm{C}}_{\mathrm{t}}={\mathrm{a}}_0+{\mathrm{a}}_1{\mathrm{D}\mathrm{S}}_{\mathrm{t}}+{\mathrm{a}}_2{\mathrm{C}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}}_{\mathrm{t}}+\mathsf{\delta }+{\mathrm{v}}_{\mathrm{t}}+{\mathsf{\epsilon }}_{\mathrm{t}}$$

(5)

where t represents the year. GVC_t indicates China’s position in the GVC in year t, and DS_t denotes the country’s digital trade abroad. Control_t includes control variables, following the research of Chen and Liu (2023), which selected export scale (Export), degree of openness (Opn), and national capital endowment (Ce) as control variables. δ, v_t, and ϵ_t represent country fixed effects, year fixed effects, and random error terms, respectively.

4.2. Indicator Selection and Data Sources

To ensure the quality and integrity of empirical analysis, this study examines sample data from China spanning from 1995 to 2017 after excluding missing and outlier data. The computation methods and data sources for each variable in the model are detailed as follows:

4.2.1. Dependent Variable

The measurement of GVC adopts the method proposed by Koopman (2010), as specified in Equation (1).

$$\mathrm{G}\mathrm{V}\mathrm{C}=\mathrm{l}\mathrm{n}(1+\frac{\sum {\mathrm{I}\mathrm{V}}_{\mathrm{i}}}{\mathrm{E}})-\mathrm{l}\mathrm{n}(1+\frac{\sum {\mathrm{F}\mathrm{V}}_{\mathrm{i}}}{\mathrm{E}})$$

(6)

Here, GVC represents China’s position index in the Global Value Chain, IV_i refers to the indirect value-added exports in specific industry i, FV_i indicates the foreign value added included in the exports of industry i, and E represents the total exports. A lower GVC index implies a higher proportion of foreign value added in imported intermediates in a certain industry of a country, indicating a lower position in the GVC (Wang and Huang, 2023) [28], sourced from the OECD database.

4.2.2. Explanatory Variables

Digital Trade (DS): It encompasses the logarithm of the total exports in seven industries, including insurance and pension services, financial services, intellectual property charges, information and communication services, other business services, personal, cultural, and recreational services, as well as government goods and services. This variable quantifies the scale of China’s digital trade. The measurement standard is derived from relevant data in the OECD database (Wen et al., 2021) [37].

4.2.3. Control Variables

• Degree of openness (Opn): This study employs the index of international trade freedom to measure the degree of openness, which is calculated as the ratio of total international trade (including imports and exports) to gross domestic product (GDP). Missing data in the database are imputed using linear regression to ensure data continuity and integrity. Data for this indicator primarily come from the WTO database and the World Bank;
• National capital endowment (Ce): In this study, national capital endowment is measured by the logarithm of the ratio of physical capital stock to total population. This method aims to reflect a country’s capital intensity and level of industrial development. Relevant data are obtained from the Worldwide Governance Indicators (WGI) database, providing comparable data on national capital endowment globally;
• Export scale (Export): The measurement of export scale is based on each country’s total export value (including goods and services) and is denominated in US dollars. This indicator reflects a country’s level of activity in international trade and the scale of its outward-oriented economy. Data on total exports are collected from the United Nations Conference on Trade and Development (UNCTAD) database.

The data sources are shown in

Table 1. Specific Meanings of Variables and Data Sources.

Variable Type	Variable Name	Code	Indicator	Data Source
Dependent Variable	GVC Position	GVC	GVC Position Index	OECD Database
Independent Variable	Digital Trade	DS	Logarithm of the sum of industry exports	OECD Database
Control Variables	Export Scale	Export	Total exports (goods and services) for each country	UNCTAD Database
	Openness to Foreign Trade	Opn	Trade total/GDP, adjusted with the consumer price index	WTO Database, World Bank Database
	National Capital Endowment	Ce	Logarithm of the ratio of physical capital stock to total population	World Governance Indicators Database

. In handling explanatory and control variables, logarithmic transformation is employed to reduce data volatility and skewness. Additionally, for missing values, this study utilizes the imputation method to ensure the completeness of the dataset. All empirical analyses are conducted using Stata 17.0 software. Descriptive statistics of the variables are shown in

Table 2. Descriptive Statistics of Variables.

Variable Types	Variable Names	Code	Mean	Std	Max	Min
Dependent Variable	GVC Position	GVC	2.17	0.53	4.38	−0.24
Independent Variable	Digital Trade	DS	8.79	1.76	12.36	3.79
Control Variables	Export Scale	Export	23.729	1.592	29.509	18.091
	Openness to Foreign Trade	Opn	5.24	0.63	10.79	4.01
	National Capital Endowment	Ce	7.31	1.32	12.95	5.27

.

5. Empirical Results Analysis

5.1. Stationarity Test

In this study, the Phillips–Perron (PP) test was employed to examine the stationarity of all variables. The test results (

Table 3. Stationarity Test ¹.

Variables	PP Test		Stationarity
	Test Statistic	p-Value
lnGVC	326.965 ***	0.000	stationary
lnDS	412.414 ***	0.000	stationary
lnExport	410.166 ***	0.000	stationary
lnOpn	501.317 ***	0.000	stationary
lnCe	338.289 ***	0.000	stationary
lnCN	508.760 ***	0.000	stationary
lnLA	335.810 ***	0.000	stationary
lnRD	550.162 ***	0.000	stationary

¹ *** denotes significance level of p < 0.001, respectively.

) indicate that all variables are stationary panel data, suggesting that these data can be directly used for regression analysis without the need for differencing or transformation, thus providing a solid data foundation for subsequent empirical analysis.

5.2. Baseline Regression

In the analysis of panel data, the suitability between fixed effects and random effects models is assessed using the Hausman test. This test specifically examines the correlation between fixed effects and other explanatory variables to determine the appropriate model. The null hypothesis of the Hausman test posits the random effects model as the appropriate specification. The test results, indicating a p-value less than 0.001, robustly reject the null hypothesis, suggesting a superior regression outcome via the fixed effects model compared to the random effects model. Consequently, employing the fixed effects model for subsequent analysis represents a justified selection to achieve optimal regression results.

To mitigate biases potentially introduced by temporal variations, this study employs a country-year dual fixed effects model for empirical analysis. The baseline regression outcomes, as presented in

Table 4. Benchmark Regression Results ¹.

Variables	(1)	(2)	(3)	(4)
DS	0.443 **	0.496 **	0.486 **	0.507 **
Ce		−0.054 *	−0.054 *	−0.052 *
Export			−0.059	−0.058
Opn				−0.054 *
Fixed Effects 2	yes	yes	yes	yes
N	693	693	693	693
Within-Group R-squared	0.2146	0.2211	0.2257	0.2416
Hausman Test	10.142 ***	12.875 ***	15.083 ***	18.105 ***

¹ *, **, and *** denote significance levels of p < 0.05, p < 0.01, and p < 0.001, respectively. ² “Fixed effects” in the table refer to country-year fixed effects, with the same notation applied in the subsequent table.

, detail the regression results with incremental inclusion of control variables across columns (1) to (4). These findings consistently indicate that digital trade has a positive coefficient and is statistically significant at the 1% level, illustrating a positive impact of digital trade on the elevation of GVC positions. Specifically, the results from column (4) demonstrate that a 1% increase in digital trade facilitates a 0.507% enhancement in China’s position within the GVC.

5.3. Robustness Checks

5.3.1. Substitution of Explanatory and Dependent Variables

To ascertain the robustness of our model, this study initially adopted the approach of altering the core explanatory variables and the dependent variables. Following the methodology of Hao et al. (2022) [38], the degree of ICT goods imports by a country was designated as the core explanatory variable and defined as the proportion of ICT goods trade imports to total trade exports (DS_change). For the dependent variable, the GVC production line length (GVCPs), as proposed by Wang et al. (2017) [39], was utilized for robustness analysis. GVCPs is calculated as per Equation (8), representing the ratio of forward linkage GVC production length (Plv_GVC) to backward linkage GVC production length (Ply_GVC). Both of them were assessed through Equation (8) to reflect the division of labor status within the GVC. The indicators used are sourced from the OECD database. Specifically, within Equation (7), this length is calculated by aggregating the added value created by all service sectors j in country r (here representing China), as well as the added value obtained from the final goods produced by service sectors i in country s (here representing Russia) from sector j in country r. A longer forward linkage production length indicates that the service sector’s value added contributes to a greater number of downstream production stages in the total economic output; conversely, a longer backward linkage production length suggests that a specific final product undergoes more upstream production stages in the economy (Zhou and Zhang, 2022) [40]. Hence, a higher GVCPs value signifies a higher position of a country in the division of labor within the GVC.

$${\mathrm{P}\mathrm{l}\mathrm{v}\mathrm{y}}_{\mathrm{i}\mathrm{j}}^{\mathrm{s}\mathrm{r}}=\frac{{\mathrm{v}}_{\mathrm{i}}^{\mathrm{s}}{\sum }_{\mathrm{t},\mathrm{k}}^{\mathrm{G},\mathrm{N}}{\mathrm{b}}_{\mathrm{i}\mathrm{k}}^{\mathrm{s}\mathrm{t}}{\mathrm{b}}_{\mathrm{k}\mathrm{j}}^{\mathrm{t}\mathrm{r}}{\mathrm{y}}_{\mathrm{j}}^{\mathrm{r}}}{{\mathrm{v}}_{\mathrm{i}}^{\mathrm{s}}{\mathrm{b}}_{\mathrm{i}\mathrm{j}}^{\mathrm{s}\mathrm{r}}{\mathrm{y}}_{\mathrm{j}}^{\mathrm{r}}}=\frac{\widehat{\mathrm{V}}\mathrm{B}\mathrm{B}\widehat{\mathrm{Y}}}{\widehat{\mathrm{V}}\mathrm{B}\widehat{\mathrm{Y}}}$$

(7)

$$\mathrm{G}\mathrm{V}\mathrm{C}\mathrm{P}\mathrm{s}=\frac{\mathrm{P}\mathrm{L}\mathrm{v}\_\mathrm{G}\mathrm{V}\mathrm{C}}{{[\mathrm{P}\mathrm{L}\mathrm{y}\_\mathrm{G}\mathrm{V}\mathrm{C}]}^{\prime }}$$

(8)

The results of the robustness checks are displayed in

Table 5. Robustness Test Results ¹.

Variables	(1) GVCPs	(2) GVCPs	(3) First Stage	(4) Second Stage	(5) Truncation	(6) Censoring
DSt	0.169 **			0.657 ***	0.194 ***	0.181 ***
DSt _change		2.290 ***
IVDSt			0.739 ***
control variables	yes	yes	yes	yes	yes	yes
fixed effects 2	yes	yes	yes	yes	yes	yes
N	652	652	652	652	652	652
within-group R-squared	0.2319	0.2661	-	0.2017	0.3018	0.3137

¹ ** and *** denote significance levels of p < 0.01 and p < 0.001, respectively. ² “Fixed effects” in the table refer to country-year fixed effects.

. Column (1) shows the regression analysis conducted with only the dependent variable substituted, revealing that the coefficient of digital trade is significantly positive at the p < 0.05 level. This indicates that digital trade has a significant positive impact on the length of GVC production lines. Furthermore, the regression results in column (2), which involve substitutions of both the dependent and independent variables, reinforce this finding. In this column, the coefficient for digital trade remains significantly positive at the higher significance level of p < 0.01. These outcomes not only corroborate the accuracy of the baseline regression conclusions but also attest to the robustness of the analysis results.

5.3.2. Endogeneity Issues

To address potential endogeneity issues arising from omitted variables, this study implemented a strategy involving the use of the lagged digital trade variable (IVDS_it) as an instrumental variable, coupled with a two-stage least squares (2SLS) approach for testing within a fixed effects model framework. This methodology aids in overcoming potential endogeneity biases, ensuring the accuracy of the estimation results. As indicated in the results presented in columns (3) and (4) of Table 5, the estimates derived from the 2SLS procedure are consistent with the baseline regression outcomes, showing no significant deviations. Specifically, the coefficient for digital trade remains significantly positive at the p < 0.01 level. This demonstrates that, even when accounting for endogeneity concerns and applying statistical methods for correction, the positive impact of digital trade on enhancing GVC positions is validated. These findings not only highlight the crucial role of digital trade in advancing GVC positions but also confirm the robustness of the baseline regression results.

5.3.3. Outlier Management

To ensure the robustness of model estimations, this study implemented outlier management techniques to examine the stability of model outcomes. Based on the methodology of Shi et al. (2023) [41], this study applies the shrinking and truncation treatment to the front and back 1% extremes of all continuous variables to minimize the possible bias of the regression results due to the extreme values. According to the analytical results presented in columns (5) and (6) of Table 5, the results obtained are consistent with the findings of the baseline analysis, whether the shrinking or truncation treatment is applied. This observation indicates that the positive impact of digital trade on the GVC position remains significant even after the exclusion of outliers, affirming the robustness of the baseline regression outcomes and ensuring that the research conclusions are not influenced by extreme data points.

5.4. Industry Heterogeneity Analysis

The rapid development of the digital economy has profoundly impacted the global industrial division of labor, manifesting distinct heterogeneity in its influence on the GVC positions across various industries. According to the classification methodology of Shi et al. (2023) and Gao et al. (2020) [42], this study categorizes manufacturing into two broad groups: technology-intensive manufacturing and non-technology-intensive manufacturing.

Technology-intensive manufacturing encompasses the following sectors: C11: Manufacture of chemicals and chemical products; C12: Manufacture of basic pharmaceutical products and pharmaceutical preparations; C18: Manufacture of electrical equipment; C19: Manufacture of machinery and equipment n.e.c.; C20: Manufacture of motor vehicles, trailers, and semi-trailers; C21: Manufacture of other transport equipment.

Non-technology-intensive manufacturing, also referred to as labor- and capital-intensive manufacturing, includes the following industries: C6: Manufacture of food products, beverages, and tobacco products; C7: Manufacture of textiles, apparel, leather, and related products; C8: Manufacture of wood, wood products, and cork products (except furniture); C9: Printing and reproduction of recorded media; Manufacture of coke and refined petroleum products; C13: Manufacture of rubber and plastic products; C14: Manufacture of other non-metallic mineral products; C15: Manufacture of basic metals; C16: Manufacture of fabricated metal products, except machinery and equipment; C22: Manufacture of furniture; other manufacturing; repair and installation of machinery and equipment.

Columns (1) and (2) of

Table 6. Heteroscedasticity Test Table ¹.

Variables	(1) Technology-Intensive	(2) Non-Technology-Intensive	(3)
DSt	0.1723 **	0.0492	0.1405 **
digital services			−0.0203 *
DSt * digital services			−0.0477 **
sample size	374	151	127
control variables	yes	yes	yes
year fixed effects	yes	yes	yes
country fixed effects	yes	yes	yes
industry fixed effects	yes	yes	yes
R2	0.2384	0.2167	0.2801

¹ * and ** denote significance levels of p < 0.05 and p < 0.01, respectively.

provide the results of a heterogeneity analysis regarding the impact of digital trade on the GVC positions of different types of manufacturing industries. The analysis reveals that technology-intensive manufacturing sectors significantly enhance their positions within the GVC through digital trade, demonstrating a statistically significant positive effect. In contrast, the influence of digital trade on the GVC positions of non-technology-intensive manufacturing sectors is not statistically significant. This disparity primarily stems from the inherent advantages of technology-intensive industries in the digital transformation.

Specifically, technology-intensive manufacturing sectors have a strong technological foundation and innovation capabilities and are able to effectively utilize digital technologies to optimize production processes and improve product quality, thereby more readily enhancing their positions in the GVC. Conversely, non-technology-intensive manufacturing sectors, characterized by weaker technological foundations and limited capacity to absorb and utilize digital technologies, derive minimal benefits from digital trade. Their slow digital transformation process hinders significant improvement in their GVC positions. Moreover, the products and services of non-technology-intensive manufacturing sectors typically have lower added value, and their competitive advantage relies mainly on cost control, which also limits their potential for GVC position enhancement through digital trade.

These findings underscore the critical importance of technology-intensive industries in the global economy and the pivotal role of digital transformation in enhancing industrial global competitiveness and GVC positions. Simultaneously, they highlight the challenges faced by non-technology-intensive industries in pursuing GVC elevation, emphasizing the urgent need for strategic adjustment and technological upgrading.

According to the classification methodology of Zhou and Zhang (2022) [40], the service sector is divided into two primary categories: digitally intensive and less digitally intensive services.

Digitally intensive services specifically refer to industries that heavily rely on digital technologies, ICT, and internet infrastructure for production and service provision. These sectors have a clear advantage in digital transformation and innovation, capable of effectively leveraging digital technology to enhance service efficiency, quality, and innovation capacity. This includes sectors such as film, video, and television program production, recording and music publishing services, programming and broadcasting services, telecommunications, computer programming, consultancy services, information services, financial, insurance, scientific research and development, advertising, and market research services, etc.

The fundamental distinction between digitally intensive and less digitally intensive services lies in the former’s ability to fully digitize the production, transaction, and delivery processes, which are completed by the internet. This characteristic enables digitally intensive services to establish a global internet-based industrial division of labor, or “global digital value chain” (Lund et al., 2019) [43]. In this value chain, service trade is not only the cross-border circulation of goods and services but also represents the global flow and redistribution of knowledge, technology, data, and capital. This process fosters deep integration of the global economy, offering new platforms and opportunities for synergistic development among economies.

By constructing dummy variables to differentiate between less digitally intensive services (assigned “0”) and digitally intensive services (assigned “1”), and incorporating interaction terms for regression analysis, this study reveals the heterogeneous regression results considering the digital intensity of the service sector in column (3) of Table 6. These results show that the interaction term coefficient between digital trade and the digital service sector is negative and significant at the 1% level, indicating that the intensity of digitalization in the service sector significantly impacts the role of digital trade in enhancing the GVC position of the service industry. Notably, for less digitally intensive services, digital trade plays a more pronounced role in improving their position in the GVC.

The underlying rationale for this phenomenon predominantly lies in digital trade’s provision of crucial technological support and innovative models for the digital transformation of traditional service industries. Digital trade facilitates the cross-border flow of information and data, opening new avenues for the service sector to access international markets, thereby enabling industries previously constrained by geographical and physical limitations to transcend spatial boundaries and expand their customer base. Concurrently, the application of digital tools such as cloud computing and big data analytics enhances operational efficiency and service quality in the service sector, thus maximizing value creation. These technologies not only assist enterprises in more accurately discerning market demands and refining service positioning but also promote the widespread dissemination of knowledge and technology, further augmenting the innovation capacity and overall level of the service industry.

Hence, the positive impact of digital trade is particularly pronounced on service industries with weak digitalization, underscoring the pivotal role of digital transformation in elevating these industries’ positions within the GVC.

6. Mechanism Analysis

The empirical findings previously discussed demonstrate that digital trade significantly facilitates the enhancement of China’s position within the GVC. However, the mechanisms through which digital trade influences this elevation remain unclear. To examine the mediating roles of technological upgrading and innovation, capital stock, and human resource optimization in the impact of foreign trade on GVC positioning, this study constructs the following model, referencing the mediation effect analysis method from Jiang Ting (2022) [44]:

$${\mathrm{G}\mathrm{V}\mathrm{C}}_{\mathrm{t}}={\mathsf{\alpha }}_0+{\mathsf{\alpha }}_1{\mathrm{D}\mathrm{S}}_{\mathrm{t}}+{\mathsf{\alpha }}_2{\mathrm{C}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}}_{\mathrm{t}}+\mathsf{\delta }+{\mathrm{v}}_{\mathrm{t}}+{\mathsf{\epsilon }}_{\mathrm{t}}$$

(9)

$${\mathrm{M}}_{\mathrm{t}}={\mathsf{\beta }}_0+{\mathsf{\beta }}_1{\mathrm{D}\mathrm{S}}_{\mathrm{t}}+{\mathsf{\beta }}_2{\mathrm{C}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}}_{\mathrm{t}}+\mathsf{\delta }+{\mathrm{v}}_{\mathrm{t}}+{\mathsf{\epsilon }}_{\mathrm{t}}$$

(10)

$${\mathrm{G}\mathrm{V}\mathrm{C}}_{\mathrm{t}}={\mathsf{\gamma }}_0+{\mathsf{\gamma }}_1{\mathrm{D}\mathrm{S}}_{\mathrm{t}}+{\mathsf{\gamma }}_2{\mathrm{M}}_{\mathrm{t}}+{\mathsf{\gamma }}_3{\mathrm{C}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}}_{\mathrm{t}}+\mathsf{\delta }+{\mathrm{v}}_{\mathrm{t}}+{\mathsf{\epsilon }}_{\mathrm{t}}$$

(11)

In Equation (10), M denotes the mediating variables, Research and Development input (RD), labor input (LA), and capital stock (CN). Specifically, RD_t, CN_t, and LA_t represent China’s technology investment, capital stock, and labor input in period t, respectively. Control_t corresponds to the control variables, consistent with those in the baseline regression model. δ, v_t, and ε_t signify the country fixed effects, year fixed effects, and the random error term, respectively.

Research and Development (RD) Input: A nation’s technological level, often reflected through its value-added capability, is indicative of higher value-add potential in technologically advanced nations. Enhancements in technological standards can be achieved via R&D investments, showcasing a country’s commitment to technological advancements and potential for technological upgrading. This study measures R&D input as the ratio of R&D expenditure to GDP, with data sourced from the World Bank database. Missing data within the database were interpolated to ensure continuity and completeness in the analysis.

Capital Stock (CN): This study uses the stock of fixed assets to represent physical capital to measure a country’s industrial foundation and structure. The scale and quality of fixed assets reflect the level of national industrial development, influencing its position and role within the GVC. This indicator’s data, sourced from the World Bank, provide a quantitative basis for analyzing the state of national physical capital.

Labor Input (LA): LA is used in this study to assess the impact of labor force participation on divisional status within the GVC. The proportion of the working population to the total population serves as a proxy for the labor capital indicator. This ratio reflects the potential of the labor market and the degree of utilization of a nation’s labor resources, which is significant for understanding its position in the division of labor within the GVC. The data for this indicator come from the United Nations Conference on Trade and Development (UNCTAD) database, offering a quantitative basis for analyzing national human resource conditions.

The handling and interpretation of data for intermediary variables adhere to the same principles as those for explanatory and dependent variables. The descriptive statistics of the mediating variables are shown in

Table 7. Descriptive Statistics of Mediator.

Variable Type	Variable Name	Code	Mean	Std	Max	Min
Mediating Variables	Labor Capital	LA	56.941	8.874	86.21	38.29
	Capital Stock	CN	16.399	1.699	29.415	12.147
	Research and Development Input	RD	7.837	2.641	14.893	0

.

6.1. Facilitating Technology Transfer and Innovation

This study examines how digital trade fosters China’s position in the GVC through its impact on technological inputs. The analytical results presented in columns (1) and (2) of

Table 8. Mediation Regression Results ¹.

Variables	RD		CN		LA
	(1)	(2)	(3)	(4)	(5)	(6)
DSt	6.339 **	0.183 **	4.092 *	0.025 *	3.096 *	0.104 *
RDt		0.146 **
CNt				0.019 *
LAt						0.041 **
Control variables	yes	yes	yes	yes	yes	yes
Time fixed	yes	yes	yes	yes	yes	yes
Sample size	693	693	693	693	693	693
R2	0.3593	0.4112	0.3482	0.3758	0.3485	0.3992

¹ * and ** denote significance levels of p < 0.05 and p < 0.01, respectively.

reveal that the coefficient of digital trade is positive and significant at the 1% level when considering digital trade alone (column (1)), indicating that an increase in digital trade effectively enhances technological inputs within industries. When both digital trade and technological inputs are considered simultaneously (column (2)), the coefficients of both are significantly positive, further corroborating that an increase in digital trade not only significantly boosts technological inputs but also elevates the GVC position through enhanced technological inputs. This underscores the critical mechanism by which digital trade promotes the elevation of GVC positions through increased technological investment.

As the world’s second-largest economy, China has experienced rapid growth in its digital economy. In 2022, China’s data production and storage reached 8.1 ZB and 724.5 EB, respectively, accounting for 10.5% and 14.4% of the global totals. This digital technology advantage forms the foundation for Sino-Russian digital trade cooperation. The collaboration in digital economy and digital trade between China and Russia primarily relies on cooperation platforms such as BRICS and the Belt and Road Initiative.

Table 9. The History of BRICS Cooperation in Digital Economy and Digital Trade.

Development Stage	Year (City)	Achievements in Digital Economy and Digital Trade
Early Stage	2009–2014	Although the concept of “digital economy” did not exist, topics such as ICT, internet economy, and e-commerce were already hot topics.
Official Launch	2015 (Ufa)	Included the development of the digital economy in the agenda.
	2016 (Goa)	Emphasized the importance of “strengthening e-commerce cooperation” for formulating the “BRICS Trade, Economic, and Investment Cooperation Roadmap until 2020”.
	2017 (Xiamen)	Formulated the “E-commerce Cooperation Initiative”, decided to establish an e-commerce working group, and fully launched e-commerce cooperation.
Rapid Development	2018 (Johannesburg)	Recognized the internet’s role in promoting the global economy in the “Joint Declaration” and decided to enhance cooperation and governance among countries while developing the digital economy. Proposed the “PartNIR” (New Industrial Revolution Partnership) and created a new platform to promote the exchange of knowledge and experiences in digital economy construction. Signed the “Memorandum of Understanding on Joint Research on Distributed Ledger Technology and Blockchain Technology under the Background of Digital Economy Development”.
	2019 (Brasília)	Acknowledged the significant progress made in building the “New Industrial Revolution Partnership” (the integration of the internet and manufacturing). Promoted the establishment of the Digital BRICS Working Group.
	2020 (Virtual)	Formulated the “BRICS Economic Partnership Strategy 2025”, explicitly listing digital economy cooperation as one of the three pillars of BRICS economic partnership.
	2021 (Virtual)	Recognized the importance of digital platforms, artificial intelligence, and big data in promoting development and improving the efficiency of pandemic response. Established the BRICS New Industrial Revolution Partnership Innovation Base in China. Compiled the “Social Infrastructure: Financing and Digital Technology Application Technical Report”. Formulated the “E-commerce Consumer Protection Framework”.
	2022 (Virtual)	Recognized the “vitality of the digital economy in mitigating the impact of COVID-19 and achieving global economic recovery”. Launched the “BRICS Digital Economy Partnership Framework” and the “BRICS Manufacturing Digital Transformation Cooperation Initiative”, pushing BRICS digital economy cooperation deeper, including port digitalization, digital infrastructure, and capacity building for SMEs.
	2023 (Johannesburg)	Recognized the vitality provided by the digital economy in promoting global economic growth, and digital economy and digital trade cooperation became regular topics at BRICS summits.

illustrates the history of BRICS cooperation in digital economy and digital trade.

Additionally, bilateral cooperation between China and Russia in the digital economy and digital trade has steadily advanced. In 2019, the “Digital Economy—Development Without Borders” China-Russia Business Forum was held in Moscow, marking the beginning of bilateral digital economy cooperation. Since 2020, the annual “China-Russia Digital Economy Summit Forum” has been held four times, where scholars from both countries have collaborated to develop cooperative models and development strategies for the digital economy, resulting in multi-sector, multi-field project cooperation agreements. In 2023, the completion of the Liaoning China-Russia Digital Trade Port in Shenyang marked a significant milestone in the practical progress of China-Russia digital trade cooperation. Furthermore, the establishment and operationalization of the e-commerce live streaming base at the Jinzhou Port China-Mongolia-Russia Logistics Park in 2024 signified substantial advancements in China-Russia digital trade. Cooperation between China and Russia opens up new market opportunities, facilitating the internationalization of Chinese digital products and services, and promoting the expansion and influence of Chinese firms in the global market. The Chinese government’s strong support for technological innovation plays a crucial role in advancing the country’s digital economy development. Through policy incentives, financial support, and market incentives, the government provides abundant resources and a conducive environment for technology research and application, significantly accelerating China’s elevation in the GVC within the global digital trade.

Additionally, although digital trade between China and Russia has only recently begun, it has been developing rapidly. Since 2021, China and Russia have signed memorandums of understanding on investment cooperation in the digital economy, promoting projects such as the China-Russia Digital Economy Research Center. In 2023, Alibaba’s AliExpress held a 25.8% share in the Russian e-commerce market, while Russia’s largest e-commerce company, Ozon, partnered deeply with China Post to create a robust China-Russia cross-border e-commerce logistics system. China is leveraging its new advantages in digital technology to transform its trade dynamics with Russia. As the empirical results demonstrate, the expansion of digital trade contributes to increased technological investment, enhances innovation capacity, and effectively promotes China’s position in the global value chain.

6.2. Increasing Capital Stock

The analysis examines the role of material capital in enhancing China’s position within the GVC division of labor through digital trade and the findings are presented in columns (3) and (4) of Table 8. Preliminary results indicate that, when solely accounting for digital trade, its coefficient is positive and statistically significant at the 1% level, suggesting that an increase in digital trade effectively raises the level of material capital in the industry. Further analysis, incorporating both digital trade and material capital factors, reveals that the coefficients for both are positive and significant, affirming that digital trade contributes to elevating China’s position in the GVC by enhancing material capital levels.

This phenomenon can be attributed to China’s growing influence in global manufacturing and the digital economy. As an emerging trade model, digital trade facilitates information flow and reduces transaction costs, thereby efficiently improving the utilization and productivity of material capital. Leveraging its vast market size and established manufacturing base, China more effectively integrates global resources, thus enhancing its competitiveness and position in the GVC. Moreover, the Chinese government’s high emphasis on the digital economy and its substantial investments in digital infrastructure and policy support lay a solid foundation for the robust development of digital trade. These efforts not only foster rapid growth in digital trade but also significantly enhance the efficiency and output of material capital, further strengthening China’s division of labor position within the GVC. Consequently, digital trade plays a crucial role in elevating material capital levels and thus further bolstering China’s position in the GVC division of labor.

6.3. Human Resource Optimization

The analysis of digital trade’s impact on China’s divisional status within the GVC, particularly concerning labor input, is detailed in columns (5) and (6) of Table 8. Preliminary findings indicate that, when focusing solely on digital trade, its coefficient is positive and significant at the 1% level, suggesting that an increase in digital trade effectively enhances labor input. Upon concurrently considering both digital trade and labor input, their coefficients remain significantly positive, aligning with expectations. This outcome suggests that digital trade facilitates the elevation of labor input levels, thereby enhancing China’s divisional status within the GVC.

The rise of digital trade, as a novel trade model, not only broadens market access but also opens new avenues for labor input enhancement by promoting global information flow and streamlining transaction processes. For instance, it fosters the internationalization of education and training services, promoting the improvement of labor skills and knowledge to meet the demands of the digital economy. Moreover, digital trade enhances the global exchange of knowledge and skills, providing opportunities for the Chinese workforce to learn advanced skills and managerial knowledge, thereby improving the quality of labor input. This enhancement not only bolsters the competitiveness of China’s labor market but also facilitates Chinese enterprises in achieving higher positions within the GVC. Therefore, by fostering the development of labor input, digital trade significantly supports the elevation of China’s status within the GVC, highlighting its pivotal role in driving global economic integration and the advancement of the knowledge economy.

7. Conclusions and Discussion

This study delves into how Sino-Russia digital trade shapes China’s positioning within the GVC and its specific impact mechanisms, yielding the following key findings: Firstly, Sino-Russia digital trade enhances China’s position in the GVC, a conclusion that remains robust after tests for variable substitution, endogeneity removal, and outlier exclusion. Secondly, the impact of Sino-Russian digital trade on the GVC position exhibits industry heterogeneity. In technology-intensive manufacturing industries, the effect of digital trade on enhancing the GVC position is significant, whereas in non-technology-intensive manufacturing industries, the effect is less pronounced. The degree of digitalization in the service sector varies, and thus digital trade’s pull on the GVC position differs across service industries. In less digitally advanced service sectors, the role of digital trade in enhancing the GVC position is more pronounced. Finally, a mediation mechanism analysis indicates that Sino-Russian digital trade enhances China’s position in the GVC through pathways that strengthen technology transfer and innovation, expand the stock of material capital, and optimize human resources.

The sample size selection in this study’s empirical analysis was constrained by multiple factors. Temporally, post-2018, the COVID-19 pandemic and regional conflicts have caused issues with the authenticity of data statistics and reporting delays among countries and international organizations. To minimize the impact of low-probability events, it was not feasible to obtain the most recent sample data with coherent statistical trends, which may prevent some details in this study from fully aligning with the latest realities of international trade. However, this does not skew the theoretical analytical conclusions and developmental trend forecasts revealed by this study.

Additionally, due to the lack of mature quantitative methods for assessing the impact of digital trade methods, this study’s focus on digital service trade (digitalization of trade objects) to represent digital trade in the empirical research section is somewhat one-sided and does not fully reveal the impact of digital trade on the position in the global value chain. Exploring a quantitative index system for digital trade methods and studying the pathways through which digital trade affects the global value chain position in larger, more coherent samples represent future research directions.

Grounded in the reality of Sino-Russian digital trade interactions, this research incorporates the rapidly evolving digital trade into the analytical framework of the GVC, exploring a systematic approach to examine how digital trade propels the swift development of China’s trade. The fast-paced digital economy has transformed digital trade from merely a rapid means of trade into an indispensable segment of service trade, even the most rapidly growing segment in terms of trade volume. Moreover, the spillover effect of digital trade is fundamentally reforming the composition of traditional transnational manufacturing industry chains. Future studies on modern service trade, modern commodity trade, industry transfer, and foreign investment can build upon this research. In factual analyses such as GVC studies, dynamic analyses of China’s foreign trade advantages, and analyses of China’s domestic industrial layout, this study provides valuable theoretical references.

Based on the research results presented herein, we propose the following policy suggestions:

• Deepen the integration of the digital economy with traditional manufacturing, expand the scope of manufacturing digitization, and increase investment in digital infrastructure. Centered around digital technologies, strengthen intellectual property protection, diversify digital trade types, actively attract high-quality international capital, and create a secure and reliable international investment environment;
• Fully capitalize on the explosive growth benefits of the digital economy by using it to drive the development of digital manufacturing supply chains in regions rich in new energy resources. Integrate digital economic development with regional development strategies to open new economic growth opportunities for underdeveloped areas;
• Fully leverage the exemplary role of Sino-Russia digital trade cooperation to advance digital trade development in regional economies, make full use of China’s advantages in infrastructure construction and digital industry development, promote digital technology exchanges, intensify efforts in digital technology innovation and transfer, and establish a digital trade center characterized by digital manufacturing and high-end digital services;
• Expand the training of professionals relevant to digital economy development and enhance the attractiveness for global talents to ensure a talent pool for digital economic growth. Improve employment terms and conditions for industry personnel to stimulate intrinsic motivation for technological innovation. Proactively plan for the evolution of digital technologies, establish an environment conducive to technological and innovative activities. Explore and develop next-generation computing and communication technologies, building a healthy development ecosystem with sufficient drive, talent, capital, and resources to ensure the sustainable momentum and global competitiveness of the digital economy.

By implementing these strategies, China can not only strengthen its position in the global value chain but also contribute to sustainable development goals. These measures foster economic resilience, drive innovation, and ensure long-term economic sustainability, aligning with broader global efforts to achieve a balanced and sustainable growth model.