3.1. Construction of the Indicator System

Academic research on the conceptualization and measurement of traditional trade facilitation has been relatively systematic, but studies on digital trade facilitation remain in their infancy. Regarding the indicator system for evaluating digital trade facilitation, most scholars have adopted Ismail’s ⁸ framework. For instance, Zhang and Ma ⁶, Li and Wang ²⁷, and Cao et al. ⁹ all measure the level of digital trade facilitation across three dimensions: digital infrastructure, digital usage, and digital security. Building upon this foundation, this paper constructs a comprehensive evaluation index system for digital trade facilitation based on the theoretical framework of digital trade facilitation, drawing upon the research contributions of Ismail ⁸ and Cao et al. ⁹.

1. (1)

   Digital infrastructures: This paper selects three indicators—fixed telephones, mobile cellular services, and broadband. Fixed telephones represent traditional telecommunications, while mobile cellular and broadband reflect modern digital characteristics in the information and communications technology sector. In the specific estimation process, the development level of digital infrastructure is measured using the number of fixed telephone subscriptions, fixed broadband subscriptions, and cellular telephone subscriptions per 100 people.

2. (2)

   Digital usage: This study employs the percentage of internet users relative to the total population as an indicator to reflect the prevalence of digital technology adoption at the societal level.

3. (3)

   Digital security: For a nation actively promoting e-commerce transactions, the state of digital security is undoubtedly a critical assessment factor. This study utilizes the indicator of safe internet servers per million people to measure the digital security level of a country or region.

3.2. Results of Digital Trade Facilitation Level Assessment

This study evaluates the degree of digital trade facilitation across RCEP nations and uses the entropy approach to determine the weights of assessment indicators based on a thorough evaluation methodology. The formula is as follows:

1. (1)

   Standardize the selected data:

   \begin{equation} S_{ij} = \frac{X_{ij} - \min\bigl(X_{ij}\bigr)}{\bigl(\max X_{ij} - \min X_{ij}\bigr)}. \label{eq:eq1} \tag{1} \end{equation}
2. (2)

   Calculate the proportion \(P_{ij}\) for each indicator:

   \begin{equation} P_{ij} = \frac{X_{ij}}{\displaystyle \sum_{i=1}^{n} x_{ij}^{n}}. \label{eq:eq2} \tag{2} \end{equation}
3. (3)

   Determine the \(j\)-th evaluation index’s entropy value:

   \begin{equation} e_{j} = -\frac{1}{\ln n} \sum_{i=1}^{n} p_{ij} \ln p_{ij},{\qquad} 0 \le e_{j} \le 1. \label{eq:eq3} \tag{3} \end{equation}
4. (4)

   Calculate the coefficient of variance:

   \begin{equation} g_{i} = 1 - e_{j}. \label{eq:eq4} \tag{4} \end{equation}

In Eq. (4), the higher the value of \(g_{i}\), the greater the emphasis should be placed on the role of this indicator within the comprehensive evaluation system.

1. (5)

   Calculate indicator weights:

   \begin{equation} W_{j} = \frac{g_{j}}{\displaystyle \sum_{i=1}^{m} g_{j}},{\qquad} j = 1, 2, 3, \dots, m, \label{eq:eq5} \tag{5} \end{equation}

   where \(W_{j}\) represents the final weighting coefficient for each secondary indicator.

2. (6)

   Determine the degree of digital commerce facilitation in a nation for a specific year:

   \begin{equation} \mathit{ditf}_{{\!}j} = \sum_{i=1}^{m} w_{j}x_{ij}^{\ast},{\qquad} \left(0 \le w_{j} \le 1,{\ }{\sum_{i=1}^{m} w_{j}=1} \right), \label{eq:eq6} \tag{6} \end{equation}

where \(\mathit{ditf}_{{\!}j}\) represents a country’s trade facilitation level in a given year, and \(w_{j}\) is the weighting coefficient for evaluation indicator \(x_{j}\).

Substituting the obtained data into the aforementioned formula results in the weight coefficients for each secondary indicator within the digital trade facilitation system. The calculation outcomes are presented in Table 1.

According to the aforementioned comprehensive assessment model for the facilitation of digital trade, specific scores for digital trade facilitation among RCEP states from 2010 to 2023 can be derived. Table 2 presents digital trade facilitation levels of each country, ranked from lowest to highest according to 2023 data.

The extent to which RCEP countries facilitate digital commerce largely rose between 2010 and 2023, as Table 2 illustrates. However, significant disparities exist among member states, resulting in a substantial “digital divide.” Analysis of growth trends reveals that Singapore, Japan, Australia, and China have made particularly significant strides in enhancing the level of digital trade facilitation. Specifically, Singapore, Japan, Australia, and South Korea have consistently maintained leading positions. In 2010, the digital trade facilitation levels of these countries generally exceeded 0.2, and by 2018, all had increased to above 0.3. However, countries such as Myanmar, Cambodia, and Indonesia lag behind in digital trade facilitation, with their levels remaining below 0.1 as of 2023.

4.1. Theoretical Analysis

The most direct impact of digital trade facilitation on China’s transboundary e-commerce exports is the reduction of trade costs, enhancement of transaction efficiency, and expansion of market reach. According to the transaction cost theory proposed by Coase ²⁸ and Williamson ²⁹, market transactions involve various costs such as information search costs, negotiation costs, and compliance costs. First, the improvement of digital infrastructure reduces information search costs. Through online platforms, enterprises can reach global consumers, significantly expanding market reach. For Chinese overseas e-commerce exporters, digital trade platforms enable access to international markets and greater business opportunities. Second, the application of digital technologies like electronic certification and digital signatures substantially enhances transaction efficiency by reducing intermediary steps and lowering negotiation and contract costs. Third, the widespread adoption of secure internet servers strengthens transaction security, diminishes performance risks, and reduces post-transaction monitoring costs, thereby boosting China’s cross-border e-commerce exports.

4.2. Construction of a Measurement Model

The dependent variable in the study is the transaction value of China’s international electronic commerce exports to other RCEP nations between 2010 and 2023. As China currently lacks official statistics on transnational electronic commerce exports to other countries, a review of relevant literature reveals that most scholars adopt the estimation methodology proposed by iResearch Consulting Group ³⁰,³¹. Therefore, this paper also adopts the calculation method of iResearch Consulting, as shown in Eq. (7): where China’s transnational electronic commerce export transaction value is sourced from the e-commerce big data database compiled by NetEase, while China’s export volume to a specific country and China’s total export volume data are sourced from the UN Comtrade database. This approach inevitably introduces some error relative to actual values. However, after reviewing relevant literature, we found this method to be a common practice in current academic research.

This paper builds upon the traditional gravity model, incorporating the methodology proposed by Ismail ⁸. It introduces the digital trade facilitation index (\(\mathit{DITF}_{j}\)) of the importing country, the distance between the two countries’ capitals (\(\mathit{DIS}\)), and the presence of a bilateral free trade agreement (\(\mathit{FTA}\)) with China. This enhanced gravity model looks at how China’s transnational electronic commerce export performance is affected by digital trade facilitation. The particular model is as follows:

The description of explanatory variables and data sources is shown in Table 3.

4.3. Regression Analysis Results

This study employed Stata 17.0 software to conduct regression analysis using the least squares method. The regression results are presented in Table 4.

Regression analysis results show that China’s exports of electronic commerce are greatly increased by the level of digital trade facilitation among RCEP member nations. This effect remains statistically significant even after controlling for other variables. This demonstrates that a favorable digital trade environment in partner countries is indeed a key external factor enabling Chinese transnational electronic commerce to gain competitive advantages in these markets. Enhanced digital trade facilitation reduces trade costs for electronic commerce exports, creates additional market opportunities, and consequently drives growth in the scale of electronic commerce exports.

The expansion of China’s international electronic commerce exports is also positively impacted by the GDP and population size of RCEP member nations. An importing nation’s domestic consumption potential and demand increase with its GDP level and population size. This undoubtedly creates highly favorable conditions for China’s exports of transnational electronic commerce, which propels the growth of export volume.

China’s large population has also significantly boosted the expansion of its electronic commerce exports. A substantial population base translates to abundant labor resources. Generally, a larger population correlates with relatively lower labor costs. This cost advantage enhances the price competitiveness of Chinese exports in international markets, thereby laying a solid foundation for the sustained expansion of cross-border e-commerce exports.

Geographical distance exhibits a significant negative correlation in regression models. Greater distances imply higher trade costs, and these increased costs are often partially or fully passed on to domestic consumers through price mechanisms, thereby reducing the competitiveness of China’s transnational electronic commerce exports.

The growth of China’s exports of electronic commerce has also been greatly aided by the signing of bilateral free trade agreements with RCEP countries. These agreements typically lower trade barriers, streamline customs clearance procedures, and enhance clearance efficiency, thereby driving an increase in China’s transnational electronic commerce export volume.

4.4. Robust Test

4.5. Analysis of Heterogeneity in Factors Facilitating Digital Trade

To examine the differential effects of digital infrastructure, digital applications, and digital security on promoting China’s cross-border e-commerce exports, a heterogeneity test was conducted on these three factors. The regression results are presented in Table 8.

As shown in Table 8, all three dimensions of digital trade facilitation significantly promote China’s cross-border e-commerce exports, though their impact varies markedly. Specifically, digital applications exert the most pronounced effect, followed by digital security, while digital infrastructure has a relatively smaller influence. The substantial promotional role of digital applications aligns closely with network externalities theory. Network externalities theory posits that the value of a product or service increases with the number of users. In the cross-border e-commerce sector, higher digital application penetration means more internet users, a larger potential consumer base for cross-border e-commerce, and broader market opportunities for exporting enterprises. The second-strongest impact comes from digital security, a finding consistent with institutional theory. Institutional theory emphasizes that a stable institutional environment promotes economic exchange by reducing uncertainty and enhancing trust. In cross-border e-commerce, digital security facilitates exports by lowering transaction risks and compliance costs while strengthening institutional trust. Digital infrastructure has a relatively minor promotional effect. Developed RCEP member countries possess relatively well-developed digital infrastructure with limited scope for further improvement, whereas developing countries have lower infrastructure levels with room for enhancement. Therefore, digital infrastructure still promotes China’s cross-border e-commerce exports, albeit to a lesser extent.

5.1. Conclusion

This paper constructs a comprehensive evaluation index system for digital trade facilitation across three dimensions—digital infrastructure, digital applications, and digital security—and uses the entropy method to measure the level of digital trade facilitation among RCEP member states from 2010 to 2023. Subsequently, an empirical analysis was conducted using an extended gravity model to examine the impact of digital trade facilitation levels in partner countries on China’s cross-border e-commerce exports. The study also explored the heterogeneous effects across three dimensions, drawing the following conclusions and proposing corresponding policy recommendations.

1. (1)

   Although the RCEP member states’ aggregate level of digital trade facilitation increased between 2010 and 2023, there was still a “digital divide” between them. Singapore, Japan, Australia, and China demonstrated significant improvements in facilitation levels, with Singapore, Japan, Australia, and South Korea consistently ranking among the top performers. In contrast, countries such as Myanmar, Cambodia, and Indonesia lag behind in terms of digital trade facilitation and still have significant room for improvement.

2. (2)

   The level of digital trade facilitation among RCEP member states has significantly boosted the export performance of China’s cross-border e-commerce sector. Among the contributing factors, digital applications have had the greatest impact, followed by digital security, while digital infrastructure has played a relatively minor role.

3. (3)

   Traditional gravitational variables—economic scale, population, geographic distance, and institutional arrangements—remain crucial factors influencing China’s cross-border e-commerce export competitiveness. Specifically, the GDP and population size of RCEP member countries, along with China’s bilateral free trade agreements with RCEP nations, significantly promote the growth of China’s transnational electronic commerce export volume. Conversely, the rise of China’s transnational electronic commerce exports is significantly impeded by the geographical distance between China and RCEP nations.

5.2. Policy Recommendations

1. (1)

   Building regional digital competitiveness and strengthening infrastructure cooperation. Given the significant gap between lagging and leading nations in digital trade facilitation within the RCEP region, China should collaborate with member states to advance infrastructure development in countries with delayed progress. Priority support should be given to constructing 5G base stations and data centers in nations like Myanmar and Cambodia to narrow the digital divide among members. Simultaneously, while ensuring data security, China should actively engage in in-depth discussions with RCEP members to jointly establish a facilitation mechanism for cross-border data flows, providing smoother data support for cross-border e-commerce operations.

2. (2)

   Deepen cooperation to advance mutual recognition of standards and security safeguards. China should continuously deepen cooperation with RCEP members, a key measure for enhancing coordinated development in regional digital trade facilitation. On one hand, actively promote mutual recognition of standards such as electronic authentication and electronic signatures among RCEP members to streamline cross-border e-commerce transaction processes and improve efficiency. On the other hand, establish a digital security cooperation mechanism within the RCEP region to jointly combat cyber fraud, data theft, and other criminal activities. This will create a trustworthy environment for cross-border e-commerce transactions, bolstering the confidence of both businesses and consumers in cross-border trade.

3. (3)

   Optimize logistics systems and formulate differentiated export strategies. To mitigate the impact of geographical distance on China’s transnational electronic commerce exports, Chinese transnational electronic commerce enterprises should be encouraged to strengthen cooperation with logistics companies to jointly optimize distribution systems. For instance, increased investment in logistics infrastructure could be directed toward establishing overseas warehouses in key cities across RCEP member states. This would enable local storage and distribution of goods, shortening delivery times and improving logistics efficiency. Additionally, technologies like big data and artificial intelligence can be leveraged to optimize logistics route planning, enhance the intelligence of transportation operations, and reduce logistics costs. Concurrently, China should fully leverage its strengths by developing differentiated transnational electronic commerce export strategies tailored to the distinct GDP and demographic characteristics of each country. For nations with higher GDP and stronger purchasing power, high-quality, high-value-added products can be promoted. For countries with large populations and significant market potential, diverse, cost-effective products can be offered.