The Impact of Digital Economy on International Trade - A Cross-Border E-Commerce Based Perspective

With the rapid development of digital technology and the popularization of the global Internet, the digital economy has gradually become an important force in promoting economic growth and international trade [1-2]. For example, literature [3] and literature [4] examined the impact of China’s digital economy on the high-quality development of the economy using methods such as the mediation effect model, revealing that the level of China’s digital economy development has been increasing year by year, and that the digital economy promotes the high-quality development of the brigade economy, and the spatial spillover effect is significant. Digital economy refers to economic activities based on the Internet and digital technology, including cross-border e-commerce, Internet finance, big data, artificial intelligence and other fields [5-6]. These emerging digital technologies and economic models have had a profound impact on international trade, especially cross-border e-commerce. Cross-border e-commerce realizes the transaction of goods and services on a global scale through the construction of online platforms [7-9].

With the innovation and upgrading of technological means such as electronic payment, logistics and customs clearance, cross-border e-commerce has greatly simplified the process of trade and reduced transaction costs [10-12]. Literature [13] examined the impact of cross-border e-commerce on international trade and economic growth in China, showing that cross-border e-commerce has had a significant positive impact on China’s international trade and economic growth in both the long and short term and encouraging the government’s much-needed support for cross-border e-commerce development. Through cross-border e-commerce consumers can conveniently purchase goods directly from around the world without being restricted by geography and time, which provides greater convenience for international trade [14-15].

The emergence of the digital economy has had a far-reaching impact on international trade, both providing greater opportunities for trade and bringing new challenges. Through the digital economy, cross-border trade has become more convenient and efficient, accelerated the flow and sharing of information, and promoted the transformation and upgrading of the trade structure, etc., but it also brings problems such as data privacy and security [16-19]. Literature [20] analyzed the role of digitization and related technologies in international trade, pointing out that digitization has brought new forms and opportunities for international business, and the expansion of cross-border commerce and intangible employment has contributed to the rapid growth of e-commerce with companies, businesses and households. Literature [21] shows that the digital economy has transformed global trade by facilitating cross-border transactions of goods, services and data, while at the same time creating multiple regulatory barriers as it generates issues such as data privacy and cybersecurity risks. Therefore, countries should actively address these challenges and strengthen international cooperation to promote the sustainable development of the digital economy and realize a more open, inclusive and mutually beneficial international trading system. Only with good interaction between the development of digital economy and international trade can we better utilize the dividends of digital economy and promote international trade to achieve more prosperous and sustainable development [22-25].

The digital economy has a profound impact on global trade and international economy by connecting various countries and regions using digital technology and the Internet. Literature [26] examined the impact of the digital economy on the international trade of cities using a fixed-effects model for a sample of major prefecture-level cities, showing that the digital economy significantly improves the development of international trade in cities, with a greater impact on exports than on imports. Literature [27] examines the determinants of economic growth in five Central Asian republics, especially the digital economy including e-commerce and others, describes the growth status of Central Asian economies, and provides practical insights for policy makers to effectively apply the digital economy to mitigate the negative effects of geographic location in order to achieve sustainable development. Literature [28] systematically introduces digital trade and emphasizes its impact on existing international trade, especially the traditional division of competitive advantage and global value chain models of international trade, and describes the challenges to the development of digital trade. Literature [29] examined the impact of digital economy tools on international trade based on EU panel data and used cluster analysis to identify homogeneous groups of countries based on digitization levels, and adopted multiple regression analysis to examine the impact of digital economy indicators on imports and exports. Literature [30] explored the impact of digital economy on inter-provincial trade based on Chinese inter-provincial panel data, showing that the digital economy promotes inter-regional trade outflows and inflows, while the promotion of inter-regional trade is more prominent in less developed regions or has greater potential. Literature [31] analyzed the impact of the development of digital economy in importing countries on China’s exports based on international trade efficiency and trade uncertainty, and examined China’s export data to 115 countries and regions, revealing that the development of the global digital economy brings opportunities for international trade, and challenges for China’s exports. Literature [32] explored the transformation, development and upgrading of international trade in the context of the digital economy, elaborated on the impact of the digital economy on the international trade pattern, rules, and international trade structure, and put forward international trade development strategies in the context of the digital economy from various aspects.

With reference to relevant studies, this paper adopts the TOPSIS entropy value method to construct the digital economy index, and uses the index to measure the current status of the level of digital economic development in multiple countries. On this premise, for the RCEP trade partner countries digital economy development level measurement, build the digital economy development level measurement index system to analyze the economic development level of RCEP countries. Taking the open trade data of RCEP countries as a sample, we design a set of evaluation index system for the level of high-quality development of foreign trade to assess the level of high-quality development of foreign trade of RCEP countries. Then, based on the RCEP countries, we optimize the traditional trade gravity model, select variables, and establish a model for evaluating the impact of digital economy on international trade. Finally, China-RCEP trade is taken as the experimental object, and the benchmark regression test, heterogeneity analysis and stability test of this paper’s model are carried out.

2

Measurement of the Digital Economy Index

This paper combs through the adopted perspectives and index compilation methods of relevant scholars in the dissertation research, and finds that the current digital economy index has not yet formed a unified assessment system, and there are still differences in the statistical methods and selected indexes, and due to the short time of the development of the digital economy, there are not many years and sample sizes of the acquired data, so the accuracy and coverage of the digital economy assessment are slightly insufficient. In order to improve the objectivity, accuracy and uniformity of digital economy assessment as much as possible. Based on the definition of digital economy by OECD (Organization for Economic Co-operation and Development), this paper combines relevant literature and public data to assess the development level of digital economy from four perspectives: digital infrastructure, digital application, digital payment and digital innovation input.

Given that the entropy value method is more objective and credible than principal component analysis, hierarchical analysis and other dimensional analysis methods, the entropy value method is more objective and credible than principal component analysis and hierarchical analysis. And entropy value method calculation process is relatively simple, at the same time can objectively distinguish the weight of the indicators, but entropy weight method can not accurately reflect the gap between the evaluation program, and TOPSIS method can supplement this defect, so this paper adopts TOPSIS entropy value method as the construction of the digital economy index. TOPSIS entropy value method of the computational process is shown below: 1)

Normalization

Due to the different scale of each index, the calculation results do not have practical comparative significance, and the calculation is complicated, therefore, this paper first standardizes each sample index, and the processing method adopts the maximum-minimum value method. Since the selected indicators are all positive, the standardization formula is shown in equation (1): (1) $r_{i j} = \frac{x_{i j} - \min x_{i j}}{\max x_{i j} - \min x_{i j}}$

Where max x_ij represents the maximum value of the initial indicator, minx_ij represents the minimum value of the initial indicator, and i, j refers to each province and indicator. 2)

Calculate the proportion

The weight value of each indicator after standardization is shown in equation (2): (2) $p_{i j} = \frac{r_{i j}}{\sum_{i = 1}^{n} r_{i j}}$

3)

Calculate the information entropy as in equation (3): (3) $E_{j} = - \ln \frac{1}{n} \sum_{i = 1}^{n} p_{i j} \ln (p_{i j})$

Where, E_ij represents the value of information entropy, n represents the number of samples, because this paper collects data from 39 countries, so n takes 39. 4)

Calculate the redundancy as in equation (4): (4) $D_{j} = 1 - E_{j}$

5)

The weights are obtained as in equation (5): (5) $W_{i} = \frac{D_{j}}{\sum_{i = 1}^{n} D_{j}}$

6)

Calculate the index as in equation (6): (6) $S_{i j} = W_{i} * r_{i j}$

7)

Calculate the weighting matrix as in equation (7): (7) $Z_{i j} = x_{i j} * S_{i j}$

where Z_ij denotes the weighted value of x_ij under entropy weighting. 8)

Calculate the Euclidean matrix as in equation (8): (8) $D_{i}^{+} = \sqrt{\sum {(z_{i j} - z_{i}^{+})}^{2}} D_{i}^{-} = \sqrt{\sum {(z_{i j} - z_{i}^{-})}^{2}}$

where $z_{i}^{+}$ and $z_{i}^{-}$ are the maximum and minimum values of z_ij, respectively. 9)

The scoring index is obtained as in equation (9): (9) $C_{i} = \frac{D_{i}^{-}}{D_{i}^{-} + D_{i}^{+}}$

C_i indicates the composite score of the index.

The value of the digital economy index calculated based on the TOPSIS entropy method ranges from [0, 1], with the larger value representing the higher level of the country’s digital economy development, and vice versa.

Based on the OECD’s definition of digital economy index, as well as data availability, accuracy and timeliness, this paper selects a total of 39 countries in the OECD and China from 2010 to 2020, and statistical data of 9 sub-indicators from 4 levels as samples of digital economy index. Among them, secure Internet servers, fixed broadband subscriptions per 100 people, fixed telephone subscriptions per 100 people, mobile traffic subscriptions per 100 people, Internet penetration rate and school enrollment rate are chosen as sub-indicators of digital infrastructure, and the above data are selected from the World Bank database. The share of digital payments in trade was chosen as an indicator of the extent of digital payments, frontier technology investment as an indicator of digital R&D investment, and the industry application index of frontier technology as an indicator of digital applications, and the above indicators were selected from the UNCTAD database.

This paper combines the above digital economy sub-indicators and the TOPSIS entropy weight measurement method. Since the data of digital application and digital R&D input are directly adopted as the scoring results, and the digital payment is presented in the form of percentage, in order to guarantee the objectivity and accuracy of the data, this paper firstly assigns weights to the digital infrastructure with the entropy weight TOPSIS method, and then carries out the four indexes of digital infrastructure, digital application, digital payment, and digital R&D input for the entropy weight TOPSIS calculation, to get the digital economy index of a total of 39 countries in OECD and China from 2010 to 2020. Among them, the U.S. has the highest level of digital economy development, with an average value of 0.71 over the past 11 years. The digital economy of developed European countries such as the U.K., the Netherlands, Germany, Switzerland, France, Ireland, Luxembourg, and Iceland maintains a stable and strong development trend, which may be attributed to the support of EU member states in terms of industrial policies, industrial cluster effects, and industry cooperation, which has made the EU member states a number of Internet giants’ investment preferred place. However, the digital economies of the UK and Iceland are declining, probably due to the economic recession associated with the UK’s political instability and Brexit, as well as the fact that the UK’s exit from the EU has resulted in the loss of important imports of raw materials and technology, which has increased the UK’s labor and material costs, thus inhibiting the development of the digital economy. Iceland, on the other hand, has a high degree of digital economy development, but since the financial crisis, the Icelandic economy has been struggling, and the ongoing recession has created a huge problem for Iceland’s digital economy development.

Iceland is the country with the highest average digital infrastructure score over a ten-year period among the 39 countries in the sample, but Iceland’s score is on a continuous downward trend. Other European countries such as the Netherlands, Switzerland, Denmark, Luxembourg, Germany, the United Kingdom, and France also have a high level of digital infrastructure development, with a growing trend overall. And the United States is ranked 7th overall, but the U.S. score has grown rapidly from 0.51 in 2010 to 0.73 in 2020, showing a strong trend. From the average infrastructure score, it can be seen that the EU economies, the UK and the United Kingdom attach great importance to the development of digital infrastructure and occupy a leading position in the world.

China’s digital payment accounted for 3.76% of trade, ranking sixth among the sample countries and in the first tier of global digital payment, which is closely related to China’s vigorous development of digital Alipay, WeChat Pay, online banking and other payment applications in recent years, with a rapid transformation of the payment environment towards digitization, and a maturing of the digital payment system. However, due to the impact of anti-globalization and trade protectionism, China’s share of digital payments has continued to decrease in recent years. Overall, the top countries in digital payments are mainly trading countries, and the United States occupies a monopoly in global digital payments, indicating that global trade has been transformed into digital trade, and the integration of the digital economy and the real economy is accelerating.

3

Measuring the level of development of the digital economy in RCEP countries

Based on the research in Chapter 2, this chapter adopts the index compilation method to construct the index system for measuring the level of digital economy development, and applies the system to measure the level of digital economy development in RECP countries.

3.1

Determination of indicator weights

The weights of the indicators for the measurement of the level of development of the digital economy are shown in Table 1.

Table 1.

The Weight of Index of the Development Level of the Digital Economy

Primary index	Secondary index	Comentropy	Weight (%)	Weight total (%)
(A) digital infrastructure	(A1) Fixed broadband penetration	0.935	13.788	35.278
	(A2) Mobile broadband penetration	0.957	11.018
	(A3) Mobile cellular subscription rates	0.968	1.984
	(A4) Internet penetration rate	0.925	5.755
	(A5) Degree of Internet access in schools	0.954	2.733
(B) Digital development environment	(B1) Higher education enrolment rate	0.964	9.058	31.523
	(B2) Availability of the latest technology	0.913	2.526
	(B3) Availability of venture capital	0.92	2.211
	(B4) Protection of intellectual property rights	0.991	5.248
	(B5) Government purchases of high-tech products	0.903	2.280
	(B6) innovation ability	0.967	3.715
	(B7) Enterprise R&D expenditure	0.936	3.387
	(B8) Efficiency of dispute resolution in the legal system	0.926	3.098
(C) Digital industry competitiveness	(C1) The proportion of ICT products exported	0.929	15.428	33.199
	(C2) Proportion of ICT service exports	0.946	9.286
	(C3) Proportion of high-tech exports	0.935	8.485

3.2

Analysis of the results of the evaluation of the level of economic development

Table 2 shows the composite score of the level of digital economy development of RCEP countries in some years. It can be seen that the comprehensive score of the level of digital economy development of RCEP countries varies greatly. From the average value of the 2012-2021 comprehensive score, the annual average of the 2012-2021 comprehensive score of the 15 RCEP countries is 50.75, and there are 8 countries whose 2012-2021 average value exceeds the average score, and all the developed countries’ scores exceed the average score and rank high, with the developed countries Singapore, South Korea, and Japan ranked No. 1, No. 2, and No. 3, respectively, and Australia and Developed countries Singapore, South Korea and Japan ranked 1st, 2nd and 3rd respectively, Australia and New Zealand ranked 5th and 6th, while developing countries only Malaysia ranked high at 4th, China ranked in the middle at 7th, and the rest of the developing countries ranked at the back of the composite score of the level of development of the digital economy, with Laos, Cambodia and Myanmar at the bottom of the list.

Table 2.

Score of the Digital Economy Development Level of RCEP Countries

Country	2012	2017	2021	Average value 2012-2021	Ranking
Singapore	88.99	96.28	100	92.08	1
Korea	75.77	82.92	95.40	79.73	2
Japan	67.97	77.72	85.37	71.42	3
Malaysia	63.27	76.55	82.59	69.91	4
Australia	61.95	71.21	74.57	65.59	5
New Zealand	62.19	68.62	75.06	63.65	6
China	52.38	70.11	81.49	59.03	7
Philippines	46.75	60.73	73.68	54.56	8
Vietnam	31.96	53.65	77.67	42.49	9
Thailand	31.96	47.23	58.52	41.75	10
Brunei	34.56	44.83	48.06	35.37	11
Indonesia	26.97	39.26	47.39	32.37	12
Laos	18.42	29.92	46.58	24.84	13
Cambodia	14.44	19.23	33.25	15.75	14
Myanmar	2.35	17.02	26.62	12.72	15
Average	-	-	-	50.75	-

Table 3 illustrates the average annual growth rate of the composite score of RCEP countries’ level of digital economy development from 2012-2021. Developing countries have great potential for digital economy development. From the score of developing countries comprehensive score from the developed countries there is still a certain gap, but from Table 2 can be seen, from the average annual growth rate, developing countries comprehensive score growth rate is much higher than the developed countries, Cambodia and Vietnam’s average annual growth rate of more than 10%, ranked in the top two, China’s annual growth rate of 5.97%, the rest of the developing countries in addition to Thailand and Malaysia, the growth rate of the developed countries, except for Thailand and Malaysia, are higher than the developed countries, New Zealand is the highest annual growth rate of 4.71%, Singapore and other developed countries. New Zealand is the developed country with the highest average annual growth rate of 4.71%, Singapore and other developed countries due to the better foundation of the digital economy, the average annual growth rate is generally lower, while the developing countries started late in the digital economy, the foundation is relatively weak, the development potential is huge.

Table 3.

Average annual growth rate of digital economy development level

Country	2012-2021 Annual growth rate (%)
Singapore	1.54
Korea	3.05
Japan	4.18
Malaysia	2.03
Australia	2.48
New Zealand	4.71
China	5.97
Philippines	4.69
Vietnam	11.45
Thailand	3.48
Brunei	7.12
Indonesia	4.96
Laos	8.26
Cambodia	15.38
Myanmar	4.75

This chapter discusses the level of development of the digital economy in RCEP countries and the reality of China’s export trade interests to RCEP countries, mainly combined with data.

4

Measurement and evaluation of the level of high-quality development of foreign trade

This chapter first measures the level of high-quality development of foreign trade of RCEP countries, constructs the indicator system of high-quality development level of foreign trade, and adopts the upper-value method to measure and evaluate.

4.1

Data sources and processing

The relevant data for each indicator come from 21 years of statistical yearbooks, statistical bulletins on national economic and social development, customs websites, and annual reports on the work of national governments on public information. Similarly, there are missing statistics for individual years when the data were tabulated, which were supplemented in this paper using mean interpolation and multiple interpolation.

4.2

Construction of the indicator system

On the basis of following the principle of indicator selection, through the reading of relevant literature and the research and study of the existing indicator system, this paper refers to the past indicator system and constructs the evaluation indicator system for the level of high-quality development of foreign trade as shown in Table 4. The indicator system includes two levels, with five first-level indicators and eight second-level indicators, of which the first-level indicators follow the connotation of high-quality development, namely: innovation, coordination, green, openness and sharing. , openness, and sharing. 1)

Innovation. Innovation includes many aspects, such as institutional innovation, organizational innovation, scientific and technological innovation, educational innovation, etc., and these innovations will have a positive impact on the high-quality development of foreign trade, and it can be said that if the whole process of foreign trade can be re-innovated and re-improved, then the high-quality development of foreign trade will be further up. Therefore, the innovation factor is one of the indicators that must be considered to measure the level of high-quality development of foreign trade, and its secondary indicator is: R&D input rate.

2)

Coordination. The coordination of high-quality development of foreign trade is manifested in four aspects: the coordination of imports and exports, the coordination of trade in goods and trade in services, the coordination of trade and two-way investment, and the more coordinated development of trade and industry. Combined with the availability of the principle of indicator selection, this paper chooses the coefficient of deviation of the regional structure of foreign trade and the balance of trade and imports and exports as the second-level indicators.

3)

Green. General Secretary Xi Jinping has said that green water and green mountains are only gold and silver mountains, which is reflected in all aspects of the economy and society, of course, including foreign trade. The whole process of foreign trade will produce industrial waste gas and water, and will also inevitably produce carbon dioxide, and the green requirements of high-quality development of foreign trade is to minimize pollution and protect the ecological environment. Therefore, this paper selects carbon emissions as an indicator to measure the green development of foreign trade.

4)

Openness. Foreign trade is to buy and sell business out of the country, looking at the international market, relying on the open national policy, in other words, there is openness to the outside world to have foreign trade, so the indicators to measure the level of high-quality development of foreign trade is not less openness, and its secondary indicators for trade competitiveness and foreign trade dependence.

5)

Sharing. High-quality development is the development that makes sharing the fundamental purpose, and sharing is sharing with the people, sharing and giving back the benefits to the society, which is the meaning of sharing. The high-quality development of foreign trade is also the same, to share as the fundamental purpose, so this paper selected the per capita foreign trade volume and trade employment contribution rate as the secondary indicators to measure the level of high-quality development of foreign trade.

Table 4.

Foreign trade high-quality development indicators measurement

Primary index	Secondary index	Index description	Units	Weight
Innovate	R&D investment rate	R&D expenditure /GDP	%	7.218
Coordinate	Foreign trade regional structure deviation coefficient	\|Yi-Y\|,Yi:Total import and export trade of the city /GDP,Y:Total import and export trade of the province /GDP	%	0.793
Coordinate	Trade balance between imports and exports	Import/export value	%	13.31
Green	Carbon emission	Carbon emission	10,000 tons	2.195
Open	Trade competitiveness	(Export value - import value)/Total import and export value	%	2.641
Open	Ratio of dependence on foreign trade	Total import and export value/GDP	%	18.834
Share	Per capita foreign trade volume	Total imports and exports/total population	$10,000 per person	28.149
Share	Contribution rate of trade employment	Total employment * Total exports /GDP	%	26.860

4.3

Measurement results and evaluation analysis

According to the evaluation index system of high-quality development level of foreign trade, the actual data of RCEP countries are substituted, and the comprehensive index of comprehensive indicators is synthesized according to the entropy method, and the comprehensive index of the high-quality development level of foreign trade of RCEP countries from 2012 to 2015 is calculated in Table 5, the comprehensive index of the high-quality development level of foreign trade of RCEP countries from 2016 to 2019 is shown in Table 6, and the comprehensive index of the high-quality development level of foreign trade of RCEP countries from 2020 to 2021 is shown in Table 7.

Table 5.

RCEP foreign trade quality development level index and ranking (2012-2015)

Country	2012	Ranking	2013	Ranking	2014	Ranking	2015	Ranking
Singapore	0.993	1	0.982	1	0.945	1	0.988	1
Korea	0.946	2	0.929	3	0.847	2	0.65	5
Japan	0.93	4	0.98	2	0.72	3	0.823	3
Malaysia	0.926	5	0.875	4	0.719	4	0.763	4
Australia	0.946	3	0.704	6	0.509	8	0.523	8
New Zealand	0.868	6	0.676	7	0.462	9	0.637	6
China	0.623	8	0.733	5	0.655	7	0.906	2
Philippines	0.467	10	0.674	8	0.197	11	0.541	7
Vietnam	0.493	9	0.589	9	0.418	10	0.465	9
Thailand	0.361	11	0.541	10	0.147	12	0.332	12
Brunei	0.336	12	0.086	15	0.709	5	0.216	15
Indonesia	0.794	7	0.249	13	0.659	6	0.272	13
Laos	0.108	14	0.5	11	0.032	15	0.232	14
Cambodia	0.194	13	0.459	12	0.081	14	0.337	11
Myanmar	0.039	15	0.157	14	0.117	13	0.452	10

Table 6.

RCEP foreign trade quality development level index and ranking (2016-2019)

Country	2016	Ranking	2017	Ranking	2018	Ranking	2019	Ranking
Singapore	0.99	1	0.979	1	0.919	1	0.899	1
Korea	0.963	3	0.829	4	0.839	3	0.746	2
Japan	0.702	6	0.834	3	0.859	2	0.614	4
Malaysia	0.764	4	0.846	2	0.574	6	0.736	3
Australia	0.966	2	0.598	8	0.679	5	0.505	5
New Zealand	0.698	7	0.718	7	0.52	9	0.403	8
China	0.74	5	0.822	5	0.558	7	0.454	7
Philippines	0.691	8	0.517	9	0.549	8	0.354	9
Vietnam	0.599	10	0.724	6	0.716	4	0.489	6
Thailand	0.464	13	0.323	11	0.511	10	0.27	10
Brunei	0.564	11	0.284	13	0.223	11	0.228	11
Indonesia	0.687	9	0.312	12	0.06	14	0.114	15
Laos	0.478	12	0.066	15	0.146	12	0.144	14
Cambodia	0.397	14	0.119	14	0.11	13	0.202	12
Myanmar	0.233	15	0.494	10	0.055	15	0.152	13

Table 7.

RCEP foreign trade quality development level index and ranking (2020-2021)

Country	2020	Ranking	2021	Ranking	2022	Ranking
Singapore	0.917	1	0.968	1	0.983	1
Korea	0.902	2	0.967	2	0.788	4
Japan	0.709	5	0.922	3	0.916	2
Malaysia	0.537	7	0.814	4	0.785	5
Australia	0.743	4	0.597	7	0.772	6
New Zealand	0.705	6	0.75	6	0.625	8
China	0.826	3	0.801	5	0.839	3
Philippines	0.213	13	0.412	10	0.628	7
Vietnam	0.491	9	0.309	11	0.488	12
Thailand	0.406	10	0.247	13	0.552	10
Brunei	0.341	11	0.515	8	0.483	13
Indonesia	0.493	8	0.304	12	0.618	9
Laos	0.224	12	0.474	9	0.445	14
Cambodia	0.154	15	0.201	14	0.532	11
Singapore	0.182	14	0.059	15	0.316	15

As shown in Tables 5, 6 and 7, the development trend of Singapore’s high quality development level of foreign trade has gradually increased over the past 11 years, and the country has been ranked steadily in the first place. The composite index of high quality development level of foreign trade of the Republic of Korea, Malaysia and New Zealand has almost declined year after year, indicating that the high quality development level of foreign trade of these three countries is also gradually declining, and it is necessary to clarify the reasons for the decline as soon as possible, and to turn the negative into positive. In comparison, the trend of China’s foreign trade high-quality development level is not bad, but the increase is not big. Combined with the weight columns in Table 4, if RCEP countries want to promote the high-quality development of foreign trade, they need to focus on the sharing dimension of high-quality development of foreign trade, which has the greatest impact.

Overall, RCEP countries as a whole have a long way to go in terms of high-quality development of foreign trade, and it is not possible for just one country to improve the level of high-quality development of foreign trade in order to boost the development of the organization as a whole, but it is necessary for each country to comprehensively improve the level of high-quality development of foreign trade in order to promote the RCEP towards the goal.

5

Model for assessing the impact of the digital economy on international trade

In order to explore the impact of the level of development of the digital economy on the bilateral services of international trade, this paper selects the countries of the RCEP organization with a wider influence and a larger scope as the object of study, and first optimizes the traditional trade gravity model and selects the variables in this chapter, and then sets up the model and selects the variables based on the RCEP countries, so as to complete the construction of the assessment model of the impact of the digital economy on international trade.

5.1

Optimization of the traditional trade attraction model and variable selection

The traditional trade gravity model is a common model used to study the relationship between trade flows between countries and the size of their economies, which assumes that bilateral trade flows are positively correlated with the total economic volume of the two countries and negatively correlated with geographical distance. The traditional trade gravity model was first introduced to the field of trade in services and was borrowed to analyze the main factors affecting the pattern of bilateral trade in producer services. Since then, the traditional trade gravity model has been widely used in services trade, such as measuring the total import and export of services trade between the United States and other countries in the form of deformation. Based on the previous research, this paper adjusts the traditional trade gravity model, selects the sample data of RCEP organization countries from 2012 to 2021, and constructs an empirical model for empirical analysis to objectively and directly illustrate the impact of the level of development of the digital economy on the bilateral trade in services. There is equation (10): (10) $\begin{array}{rcl} \ln T S_{i t} & = & C_{0} + C_{1} \ln T I M G_{i t} + C_{2} \ln E_A G_{i t} + C_{3} \ln C_A G_{i t} \\ + C_{4} \ln D I S T_{i t} + C_{5} \ln E_O P E N_{i t} + C_{6} \ln C_O P E N_{i t} \\ + C_{7} \ln F D I_{i t} + C_{8} \ln O F D I_{i t} + C_{9} \ln C P I_{i t} + ε_{i t} \end{array}$

In equation (10), subscripts i and t denote the country and year respectively. lnTSi_it denotes the logarithmic value of bilateral services trade transactions between i countries and China in the tth year, which is the explanatory variable of this paper. C_o is the constant term. lnTIMG_it represents the logarithm of the global digital economy development index of country i in year t, which is the explanatory variable of this paper. lnE_AG_it, lnC_AG_t, lnDIST_it, lnE_OPEN_t, lnC_OPEN_t, lnFDI_it, lnOFDI_it, lnCPI_it are logarithms of the control variables in this paper, respectively representing the per capita GNP of i countries in t, the per capita GNP of China, the geographical distance between i countries and China, the service trade openness of i countries, the service trade openness of China, and the service trade openness of China. Foreign direct investment to China from i countries, foreign direct investment from China to i countries, Consumer price index of i countries. C1 ~ C9 represents the influence coefficient of each variable, and ε_it is the random disturbance term.

The details of the selected indicators for empirical analysis are shown in Table 8.

Table 8.

Empirical analysis index selection list

Index classification		Name of index	Data sources
Variable being explained	TS	Bilateral trade in services	OCED Database
Explaining variable	TIMG	Global Digital Economy Development Index	Chinese Academy of Social Sciences
	E_AG_it	Gross national product per capita of EU countries	the World Bank
	C_AG_it	GDP per capita of China’s service sector	the World Bank
	DIST_it	geographical distance	CEPII Database
Control variable	E-OPEN_it	Openness of EU countries to trade in services	Calculated from UNCTAD database
	C-OPEN_t	China’s openness to trade in services	Calculated by Chinese Statistical Yearbook
	FDI_t	Foreign direct investment in China from EU countries	China Statistical Yearbook
	OFDI_t	China’s foreign direct investment in EU countries	China’s outward direct Investment Communique
	CPI_t	Consumer price index of EU countries	UNCTAD Datebase
Mechanism variable	HC_it	Human capital intensity	the World Bank
	INN_it	Innovation ability	the World Bank
	TF_it	Ease of trade	the World Bank

Explained Variables. In this paper, the bilateral service trade turnover TS is selected as the explanatory variable to reflect the development level of bilateral service trade between trading countries and China.

Explanatory variables. This paper utilizes the TIMG index to measure the level of global digital economy development, and chooses the global digital economy development index TIMG as the core explanatory variable. The TIMG index consists of three level 1 indicators, namely (A) digital infrastructure, (B) digital development environment, and (C) digital industry competitiveness, of which level 1 indicator (A) digital infrastructure consists of (A1) fixed broadband penetration rate, (A2) mobile The primary indicator (A) digital infrastructure consists of five secondary indicators: (A1) fixed broadband penetration rate, (A2) mobile broadband penetration rate, (A3) mobile cellular subscription rate, (A4) Internet penetration rate, and (A5) degree of Internet accessibility in schools, while the primary indicator (B) digital development environment consists of five tier-one indicators: (B1) enrollment in tertiary education, (B2) availability of the latest technology, (B3) availability of venture capital, (B4) strength of intellectual property rights protection, (B5) governmental purchasing of hi-tech products, (B6) governmental procurement of high-tech products and (B7) governmental purchasing of high-tech products, and (B9) governmental procurement of high-tech products. procurement, (B6) innovation capacity, (B7) corporate R&D expenditure, and (B8) efficiency of dispute resolution in the legal system are composed of eight secondary indicators, and the first-level indicator (C) digital industry competitiveness is composed of three secondary indicators, namely (C1) ICT product export share, (C2) ICT service export share, and (C3) high-tech export share.

Control variables. With reference to previous studies and the specific situation of the research object of this paper, the control variables are adopted as follows: 1)

Gross national product per capita. Gross national product per capita is the ratio of gross national product and population size, this paper uses gross national product per capita to measure a country’s level of economic development, which can reflect the demand and supply capacity of services.

2)

Population size. The larger the size of a country, the broader the market for trade in services and the more likely it is that bilateral trade in services will take place. However, when the population size is enlarged, it is possible that imports will be reduced because of increased domestic demand, which in turn affects the scale of bilateral trade.

3)

Geographical distance. Geographic distance has a direct impact on trade, for trade in goods, generally the further the geographic distance, the higher the cost of trade, resulting in a decline in trade flows, but the established literature shows that the correlation between distance and trade flows in services is not yet clear. In the traditional gravity model, geographic distance does not change, which affects the strength of the explanation of trade costs. In this paper, we borrow the fuel price of the current year to do the weight adjustment, i.e., the distance between the two capitals multiplied by the fuel price of the current year to indicate the geographic distance, so as to improve the accuracy.

4)

China’s outward foreign direct investment and foreign direct investment, foreign investment can make up for the lack of a country’s domestic capital, promote the optimization of industrial structure, bring foreign advanced technology, but also reflect the close degree of cooperation between the two sides, these factors have a role in promoting the development of trade in services. Since the scale of foreign investment flows may be negative due to disinvestment, this paper converts FDI and OFDI, and the conversion formula is equation (11): (11) $Y = \ln [X + \sqrt{X^{\land} 2 + 1}]$

5)

Openness of trade in services (OPEN), the higher the degree of openness of a country’s trade in services to the outside world, the more it tends to trade in services with other countries, which in turn affects the scale of trade in services. In this paper, the total amount of import and export of service trade/gross domestic product is chosen as a proxy variable for the openness of service trade.

6)

Consumer Price Index (CPI), CPI can reflect the trend of changes in the prices of consumer goods purchased by residents, CPI rise will promote exports and inhibit imports, and vice versa will promote imports and inhibit exports. Given that changes in the CPI may have a different impact on the strength of imports and exports, its impact on the size of bilateral trade in services cannot be completely offset and needs to be taken into account.

The mechanism variables are selected as follows: 1)

Human capital intensity (HC). This paper uses the percentage of a country’s higher education labor force as a proxy variable for human capital intensity. With the rise of digital transformation and the increasing impact of information technology on life, and the deepening dependence of many modern occupations on the Internet and digital technology, the labor force must keep up with the changing demand for skills. It is only then that innovation can be driven and competitiveness maintained. Intensive human capital can increase the depth of production of service products and produce differentiated, high value-added products that can meet market needs.

2)

Innovation capacity (INN). This paper uses the ratio of R&D expenditure to GDP as a proxy variable for innovation capacity. The stronger the innovation capacity, the more it can promote technological progress, promote productivity enhancement, improve trade structure, increase the added value of service trade products, and promote the growth of the scale of bilateral service trade.

3)

Trade Facilitation (TF). This paper uses the “cross-border trade facilitation score” indicator in the “Doing Business” report released by the World Bank to measure trade costs, i.e., the degree of cross-border trade facilitation is used as a proxy variable for the cost of trade in services, and the higher the degree of cross-border trade facilitation, the lower the cost of trade in services.

5.2

Modeling based on RCEP countries

This paper takes the index of digital economy development level of RCEP countries measured in Chapter 4 as the core explanatory variable, and the bilateral trade volume of China-RCEP as the explanatory variable, while adding the difference of per capita GDP, geographic distance, trade openness, and economic freedom as the control variables. Combined with the improved traditional trade gravity model, the impact assessment model of digital economy on international trade is constructed to study the impact of the development level of digital economy of RCEP countries on China-RCEP bilateral trade from 2011 to 2021. Where i denotes China, j denotes RCEP countries, and t denotes the year, the impact assessment model of digital economy on international trade is constructed as equation (12)-(14): (12) $\begin{array}{rcl} \ln T R A_{i, t} & = & β_{0} + β_{1} D^{2} G_{i, t} + β_{2} \ln G D P_{i, t} + β_{3} \ln D I S_{i, t} \\ + β_{4} O P E N_{i, t} + β_{5} E C O_{i, t} + ε_{i, t} + μ_{i} \end{array}$ (13) $\begin{array}{rcl} \ln I M_{i, t} & = & β_{0} + β_{1} D I G_{i, t} + β_{2} \ln G D P_{i, t} + β_{3} \ln D I S_{i, t} \\ + β_{4} O P E N_{i, t} + β_{5} E C O_{i, t} + ε_{i, t} + μ_{i} \end{array}$ (14) $\begin{array}{rcl} \ln E X_{i, t} & = & β_{0} + β_{1} D I G_{i, t} + β_{2} \ln G D P_{i, t} + β_{3} \ln D I S_{i, t} \\ + β_{4} O P E N_{i, t} + β_{5} E C O_{i, t} + ε_{i, t} + μ_{i} \end{array}$

Where TRA is the trade volume between the two countries, IM is China’s imports to other countries, EX is China’s exports to other countries, DIG is the level of development of the digital economy, i is the country, t is the year, and the control variables include the difference in per capita Gross Domestic Product (GDP), geographic distance (DIS), openness to trade (OPEN) and economic freedom (ECO).

5.3

Selection of variables based on RCEP countries

The explanatory variables of the model for assessing the impact of digital economy on international trade are the import and export trade volume between RCEP countries and China. The core explanatory variable is the level of digital economy development of RCEP countries. The control variables include the difference in GDP per capita (GDP), geographical distance (DIS), economic freedom (ECO), and trade openness (OPEN) of RCEP countries. See Table 9 for a description of the specific variables.

Table 9.

Variable meaning and data source

Variable	Meaning	Data sources
TRA_i,t	Total import and export trade between China and ASEAN countries in year t	UN Comtrade
IM_i,t	China’s import volume from RCEP i countries in year t
EX_i,t	China’s export volume to RCEP i countries in t year
DIG_i,t	The development level of digital economy in RCEP i countries in t	Be calculated
GDP_i,t	The difference in GDP per capita between China and RCEP i countries in t years	World Bank database
OPEN_i,t	Trade openness of RCEP i countries in t year	World Bank database
ECO_i,t	RCEP i countries in t years of economic freedom	The Heritage Foundation
DIS_i,t	Geographical distance between RCEP i countries and China	CEPII Database

For the explanatory variable trade openness: trade openness refers to the degree and openness of a country or region to foreign trade. Generally speaking, a country’s trade demand and possibilities increase with its trade openness, i.e., a country with a high degree of trade openness means that it is more involved in international trade. In this paper, trade openness is expressed as a country’s total imports and exports as a proportion of its GDP.

For the explanatory variable economic freedom: the level of economic freedom will directly affect the degree of openness of the country’s foreign trade and the convenience of trade activities, which will have a far-reaching impact on international trade. In the context of globalization, differences in the level of economic freedom of different countries will directly affect the international trade pattern and the depth and breadth of international economic cooperation.

For the explanatory variable geographic distance: in the trade process, transportation is an essential link. Generally speaking, longer geographic distance usually means higher transportation costs, including transportation costs, time and risk, and countries with longer geographic distances may have information asymmetry, including market information, regulations and standards, etc., while there are also often cultural differences, including language, customs, values, etc., which can negatively affect trade. This paper uses the product of the distance between the capitals of the two countries and the international oil price of the year as the geographic distance variable. Capital distance data are from the CEPII database, and international oil price data are expressed as the annual average price of WTI crude oil futures from the Investing website.

6

Empirical analysis of impact assessment models

For the impact assessment model of digital economy on international trade designed above, this chapter takes the international trade situation between China and RCEP countries as the research sample, and successively carries out the baseline regression analysis, heterogeneity analysis and stability test to verify the validity of the model.

6.1

Data descriptive statistics

Data sources for this paper: National Bureau of Statistics, China Science and Technology Statistical Yearbook, China Industrial Statistical Yearbook, Information Network of Development Research Center of the State Council (DRC), International Trade Research and Decision Support System, and China Research Data Service Platform. Due to the existence of some missing data, this paper carries out linear interpolation on some of the missing data. The results of the expressive statistics of all variables are shown in Table 10.

Table 10.

Descriptive statistical analysis of each variable

Variable	Observed value	Average value	Standard deviation	Minimum value	Maximum value
TC₁	360	0.209	0.335	-0.747	0.828
TC₂	360	0.0905	0.326	-0.708	0.793
de	360	0.319	0.209	-0.0538	1.019
fdi	360	0.0188	0.0162	-0.0187	0.0818
is	360	0.251	0.165	0.0114	1.037
sca	360	8798	7815	300.61	39398
rd	360	0.0168	0.0112	0.00348	0.0646
en	360	0.00127	0.00122	0.00087	0.0111
gtfp	360	1.468	0.398	1.014	3.766
gtp	360	3652.730	5196.645	23	35826

6.2

Benchmark regression

6.2.1

Stability tests

In order to avoid pseudo-regression, this paper first performs the following three unit root tests on the panel raw data. When the p-value is at the 1% level, the data are smooth, i.e., there is no unit root, and Table 11 shows the test results.

Table 11.

Unit root test

Variable	LLC	FisherADF	Hadri
TC₁	-9.9771***(0.0000)	-9.3532***(0.0000)	9.3577***(0.0000)
TC₂	-6.5425***(0.0000)	-9.0281***(0.0000)	13.8558***(0.0000)
de	-1.2333(0.1088)	3.6272***(0.0000)	14.8976***(0.0000)
is	-7.2208***(0.0000)	-8.5168***(0.0000)	7.0508***(0.0000)
en	-5.3355***(0.0000)	-7.6271***(0.0000)	8.9593***(0.0000)
fdi	-5.7138***(0.0000)	-5.6060***(0.0014)	7.9480***(0.0000)
rd	-8.0412***(0.0000)	-2.9841***(0.0000)	17.7094***(0.0000)
sca	-12.7881***(0.0000)	-8.7710***(0.0000)	14.6703***(0.0000)
gtfp	6.6553*(0.0511)	2.8029***(0.0000)	13.6017***(0.0000)
gtp	-3.4548***(0.0004)	1.8039***(0.0000)	16.9318***(0.0000)

Note: p values in parentheses, ***p<0.01,*p<0.1

6.2.2

Impact of the digital economy on trade competitiveness

This subsection proposes hypothesis H1: the digital economy has a direct positive impact on the improvement of industrial trade competitiveness. Comparison of this paper’s model and the benchmark model for the direct impact effect of the digital economy on trade competitiveness, the use of trade competitiveness index (TC), the comprehensive index of digital economy development (de), the regional industry size (sca), the level of industrial structure (is), environmental regulation (en), foreign direct investment (fdi), R & D investment (rd) a total of seven indicators to analyze the two models of the The specific analysis results are shown in Table 12.

Table 12.

Analysis of the direct effect of digital economy on trade competitiveness

	Benchmark Model		Textual model
Variable	TC₁	TC₁	TC₂	TC₂
lde	0.138***	0.122***	0.122**	0.108***
lde	(0.0214)	(0.0231)	(0.0276)	(0.0298)
is		0.467***		0.404**
is		(0.145)		(0.188)
en		-21.03***		19.61**
en		(7.043)		(9.149)
fdi		1.324*		1.827**
fdi		(0.684)		(0.889)
rd		1.566		1.265
rd		(3.168)		(4.118)
lsca		-0.0211		0.0523
lsca		(0.0412)		(0.0537)
Constant term	0.663***	0.635*	0.538***	-0.142
Constant term	(0.0872)	(0.341)	(0.114)	(0.442)
Observed value	342	342	342	342

Note:***p<0.01,**p<0.05,*p<0.1, standard deviation in parentheses

According to the data characteristics, in order to ensure the accuracy of the empirical results, in the process of regression analysis, this paper took the logarithm of the comprehensive index of digital economic development (de) and regional industry scale (sca) respectively. And it was tested that in the benchmark model, the average VIF of all variables including the mediating variable=3.39<10, that is, there is no multicollinearity problem. According to the Hausman test, the p-value was 0.0000, which rejected the original hypothesis H1. In view of this, the model of this paper was used for the analysis.

From the perspective of control variables, firstly, the coefficient of influence of industrial structure level (is) on the trade competitiveness of Chinese industries is 0.467, and it is significant at the 1% level. Chinese industries have gained new opportunities for high-end transformation of industrial structure under the impetus of the new generation of technological and industrial changes. In the era of globalization, cross-border layout has become the key to the global development of industries. Behind the rapid growth of global trade is the facilitation of cross-border flows on a global scale by multinational corporations through the integration of various types of factors such as capital, technology and talents. Second, the coefficient of environmental regulation (en) on the trade competitiveness of Chinese industries is -21.03 and significant at the 1% level. However, in the model of this paper, the coefficient of environmental regulation (en) is significantly positive, and the gap is unusually obvious. The study suggests that there is variability in the impact of environmental regulation on different industries. For industries, overly stringent environmental regulations will hinder the enhancement of their trade competitiveness, which will lead to the phenomenon of environmental race to the bottom. On the one hand, overly stringent environmental regulations may force industries to spend more manpower and resources on governance and regulation or to finance the upgrading of more advanced equipment to reduce pollution, which may lead to an increase in the production costs of the industry, thus affecting the productivity of the industry. Subsequently, the industry’s international competitive advantage is weakened because of its high cost relative to similar products in the international market, which ultimately affects the industry’s exports. On the other hand, due to the different environmental regulations set by different countries, industrial enterprises may pay higher environmental taxes or fines due to strict environmental regulations. Third, from the coefficient and significance of foreign direct investment (fdi), foreign direct investment will have a promoting effect on industrial trade competitiveness but its influence on the whole industry is greater. In all sectors of the national economy, the fundamental position of industry is irreplaceable. The United Nations Conference on Trade and Development report and the OECD have pointed out that in recent years, China is the country with the largest inflow of foreign direct investment, and the inflow of foreign capital has enhanced the allocation of various industries in China. Similarly, the rapid development of China’s industries and foreign direct investment inflows are inseparable, but when FDI and industry industry technology-intensive features combined, there is obvious heterogeneity, and the current industry-related industries based on FDI technology spillover transmission mechanism efficiency there is a lack of situation. Fourth, the coefficient of the impact of R&D investment (rd) on the trade competitiveness of Chinese industries is 1.566, but the result is not significant. R&D investment can stimulate the innovation of advanced technology, and the development of high-end equipment industry will certainly enhance the competitiveness of Chinese industries.

6.3

Heterogeneity analysis

According to the income status of individual countries, the World Bank has categorized RCEP countries into three groups: high-income countries, upper-middle-income countries, and lower-middle-income countries. In order to further investigate whether countries with different income levels are heterogeneous in terms of the impact of their digital economy development on the size of trade with China, this paper conducts regressions for the RCEP organization countries divided into three groups according to the classification criteria mentioned above, where the control variables include the level of digital economy development (DIG), the difference in per capita Gross Domestic Product (GDP), the geographic distance (DIS), the degree of openness to trade (OPEN) and the degree of economic freedom (ECO). According to the data characteristics, to ensure the accuracy of the empirical results, the logarithm of the difference in per capita gross domestic product (GDP) and geographic distance (DIS) were taken separately during the regression analysis.

The results of subgroup regression are shown in Table 13.

Table 13.

Grouping regression result

	High income	Upper middle income	Low and middle income
	lnTRA	lnTRA	lnTRA
DIG	1.165*	5.009***	0.894
DIG	(1.88)	(3.77)	(0.55)
lnGDP	0.718	0.0430	1.188***
lnGDP	(1.36)	(1.31)	(5.67)
lnDIS	-0.435	0.185	-0.0932
lnDIS	(-0.88)	(1.62)	(-0.86)
ECO	0.00557	-0.00927	-0.0156
ECO	(0.13)	(-0.52)	(-1.39)
OPEN	0.00915*	0.00108	0.00827***
OPEN	(1.95)	(0.24)	(2.98)
_cons	-1.647	4.331***	-4.525**
_cons	(-0.37)	(3.27)	(-2.42)
N	23	34	56
R²	0.369	0.764	0.815
F	1.751	16.07	39.28
P	0.184	0.00000413	0.000000011

t statistics in parentheses

*p<0.1,**p<0.1,***p<0.01

6.4

Stability testing

This subsection uses the bilateral service trade turnover between China and RCEP countries as the explanatory variables, adopts the model of this paper, and tests the robustness of the model by endogeneity problem treatment and replacing the core explanatory variables.

6.4.1

Endogeneity test

Based on the previous empirical findings, it can be deduced that the development of digital economy in RCEP countries can promote China-RCEP bilateral trade. However, the development of digital economy may take some time to adapt and adopt new technologies and platforms, market demand and consumer behavior may take time to adapt and change, and enterprises may take time to adjust their business models and strategies, in addition, relevant policies and institutions for digital economy may take some time to adjust and improve to the new digital environment. Therefore, the development of digital economy may not have a significant impact on the scale of trade until some time later, i.e. there may be a lag. In this paper, the lagged period L.DIG of RCEP countries’ digital economy development index is introduced into the model of this paper to re-regress, and the regression results are obtained as shown in Table 14.

Table 14.

Lag one stage regression results

	High income	Upper middle income	Low and middle income
	lnTRA	lnIM	lnTX
L.DIG	1.898***	2.815***	0.942*
L.DIG	(4.18)	(3.46)	(1.69)
lnGDP	0.192***	0.145	0.274***
lnGDP	(3.28)	(1.39)	(3.83)
lnDIS	-0.06520	-0.238*	0.0442
lnDIS	(-0.83)	(-1.69)	(0.46)
ECO	0.0232***	0.0134	0.0348***
ECO	(2.69)	(0.88)	(3.28)
OPEN	0.0101***	0.0182***	0.00376***
OPEN	(6.77)	(6.74)	(2.03)
_cons	1.553	1.964	-0.498
_cons	(1.61)	(1.14)	(-0.43)
N	100	100	100
R²	0.602	0.485	0.410
F	25.78	16.02	11.85
P	0.000000000096	0.0000000049	0.00000010

t statistics in parentheses

*p<0.1,**p<0.1,***p<0.01

The results after adding the new variable L.DIG with one period lag of the core explanatory variables into the model show that the significance of the regression results and the sign of the coefficients of the level of digital economic development are the same as those of the original regression results, and they pass the significance test of 1% in terms of total trade and China’s imports to the RCEP, and the significance test of 10% in terms of China’s exports to the RCEP, which indicates that the inclusion of one period lag of the data into the The results show that the regression results are still significant after incorporating the data of the lagged period into the model, indicating that the original model and the regression results have strong robustness.

6.4.2

Core variable substitution

This paper uses principal component analysis to assess the level of digital economy development of RCEP countries again, and replaces the previous DIG with the new calculated value DIG2 as the regression variable, and the specific regression results are shown in Table 15. The study finds that the level of digital economy development in RCEP countries has a significant contribution to promoting China-RCEP trade, a view that is consistent with the study above that the impact is not altered by change, regardless of the measurement method used.

Table 15.

The core variable replaces the regression result

	High income	Upper middle income	Low and middle income
	lnTRA	lnIM	lnTX
DIG2	1.521***	2.132***	0.876*
DIG2	(3.77)	(3.12)	(1.79)
lnGDP	0.161***	0.127	0.235***
lnGDP	(3.04)	(1.42)	(3.65)
lnDIS	-0.116	-0.254*	-0.0518
lnDIS	(-1.33)	(-1.72)	(-0.48)
ECO	0.0122	-0.00323	0.0291**
ECO	(1.18)	(-0.19)	(2.33)
OPEN	0.00888***	0.158***	0.00346*
OPEN	(5.84)	(6.18)	(1.88)
_cons	2.784***	3.194*	0.802
_cons	(2.68)	(1.82)	(0.64)
N	110	110	110
R²	0.588	0.473	0.436
F	27.21	16.98	14.63
P	0.0000000000055	0.0000000059	0.00000012

7

Conclusion

In this paper, by combing and referring to the relevant literature on the impact of digital economy on international trade competitiveness, analyzing the level of development of digital economy and the level of development of foreign trade in RCEP countries, and combining with the traditional trade gravity model, we put forward a model for evaluating the impact of digital economy on international trade. At the same time, the empirical analysis method is used to analyze the impact of China-RCEP digital economy on international trade competitiveness. Among them, the coefficient of influence of industrial structure level on China’s industrial trade competitiveness is 0.467, and the coefficient of influence of R&D investment on China’s industrial trade competitiveness is 1.566, which indicates that the digital economy is able to promote the improvement of the level of national industrial structure and the increase of R&D investment, and it has a more significant promotion effect on international trade.

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The Impact of Digital Economy on International Trade - A Cross-Border E-Commerce Based Perspective

Haojie Wang

Pubblicato online: 29 set 2025

Ricevuto: 31 gen 2025

Accettato: 10 mag 2025

DOI: https://doi.org/10.2478/amns-2025-1129

Parole chiaveDigital economy, TOPSIS entropy method, Foreign trade evaluation system, International trade competitiveness

© 2025 Haojie Wang, published by Sciendo.

This work is licensed under the Creative Commons Attribution 4.0 International License.

Parole chiave
Digital economy, TOPSIS entropy method, Foreign trade evaluation system, International trade competitiveness