Research on the Evolution of Global Electronics Trade Network Structure since the 21st Century from the Chinese Perspective

Abstract

With the development of technology and the widespread adoption of digital technology, the trade volume of electronic products keeps improving. For a country’s trade situation, it is important to study the global trade of electronic products. In this paper, the data on global trade in electronic products from 240–246 countries and regions from 2000 to 2021 are used to create complex network models. Characteristic indicators, such as the network density, average clustering coefficient, average path length, and centrality are used to analyze the evolution of the global electronic product trade network pattern. The results of the complex network analysis show the following: (1) Since 2000, global electronic products have shown a trend of fluctuating growth, showing a state of three-pole differentiation. In addition, the trade volume is unevenly distributed, with the United States and China in the leading positions. (2) The global electronics trade network has significant scale-free and small-world characteristics, with high network density and close ties between countries. (3) There are differences between the closeness centrality and the betweenness centrality of the global electronic product trade network. The core countries are mainly in Europe and North America, while the influence of Asian countries is rising. (4) The global electronic product trade network has a clear division of communities and undergoes dynamic evolution. (5) Global electronic product trade is influenced by natural resources, economic and technological strength, political culture, and other factors. Finally, three policy suggestions are made for the development of China’s electronics trade.

1. Introduction

With the rise of the digital economy and globalization, the information technology industry has become a strategic sector for measuring a country’s technological development and overall economic strength. The import and export trade of electronic products is a significant indicator of a country’s progress in the digital economy, and many nations place great importance on the trade of electronic information products. Since the beginning of the 21st century, the scale of global trade in electronic products has been expanding rapidly, and China has become the world’s largest producer and seller of consumer electronics, serving as a key global manufacturing base for such products [1]. Most of the world’s major electronic manufacturing and OEM enterprises have established their manufacturing bases and R&D centers in China [1]. China produces over 80% of personal computers and more than 65% of smartphones and color TVs globally, which directly creates around 4 million jobs and more than 10 million jobs in supporting industries [1]. Therefore, understanding the global trade of electronic products is of great significance for China’s electronic industry to further expand its global reach.

The study of the characteristics of global trade using complex networks has become a popular research direction. Previous studies have mainly focused on two aspects: first, the analysis of the topological characteristics of complex networks in a certain region, such as the “Belt and Road” trade networks among countries [2] and the trade of EU countries [3]; second, the use of complex networks to study specific industries, such as cultural trade [4] and coffee trade [5]. However, there is relatively little research on the trade of electronic products. With the increase in the scientific and technological strength of various countries, the cross-border trade of electronic products has become a new focus. Although Jiang and Wang [6] used complex network features to describe the evolution of the electronic product trade pattern on “the Belt and Road”, a global perspective is still lacking. Li [7] studied the effects and obstacles of cooperation between China and South Korea in electronic products and proposed suggestions, but the study lacked empirical research. Zhang and Cao [8] analyzed the trade creation and transfer effects of electronic products between China and Korea, combining practical and theoretical analysis to study the effect of the China–Korea FTA on the trade creation of electronic products between the two countries.

The existing studies are mostly limited to country-to-country or local areas and lack a worldwide analysis of the evolution of electronic product trade and the study of the characteristics of complex trade networks in a global context.

To address this, our paper utilizes global trade data between countries and regions since the 21st century (from 2000 to 2021) and applies complex network theory for analysis. Firstly, we examine the characteristics of trade volume and network density from a global perspective. Secondly, we utilize weighted degree values and centrality indicators to investigate the roles that individual countries play in the network. Thirdly, we divide the communities to illustrate the distribution of trade among countries. Finally, we identify the factors that contribute to the formation of the observed pattern. Our study analyzes the trade of electronic products from three dimensions: global, national, and regional. By enriching the practical study of global trade through complex networks, we provide insights into the overall development trend of global electronic products and offer recommendations for China when making relevant decisions.

2. Data Sources and Methodology

2.1. Data Sources

According to the names of industrial industries and commodities in China’s Industrial Classification of National Economy [9], referred to by Wang [10], eight product categories coded 751 (office machines), 752 (automatic data processing machines and units thereof), 759 (parts, etc. of and accessories for machines of headings 751 or 752), 761 (television receivers), 762 (radio-broadcast receivers), 763 (gramophones, dictating machines, and other sound recorders), 764 (telecommunication equipment, etc., parts and accessories, etc.), and 776 (thermionic, microcircuits, transistors, valves, etc.) [11] were selected according to the standard classification in SITC Rev. 3. from the UN Comtrade database.

In electronic product trade, a country typically engages in both import and export activities. Therefore, the total import and export volume of a country is referred to as its total trade volume of electronic products. During the pre-processing of UN Comtrade data, it was discovered that the reported import volume from a country or region was not always consistent with the export volume reported by its trading partners. This inconsistency may be due to various reasons, such as different valuations (import CIF and export FOB), specific goods, timing, and others.

Fagiolo, Schiavo, and Reyes [12] used export data to construct a network analysis of the world trading system and found that the resulting network structure was robust to changes in data sources. Head and Mayer [13] used export data to analyze trade networks between countries and regions, finding that the results were largely consistent with those obtained using import data. Freund and Weinhold [14] examined both import and export data of a sample of countries over time but concluded that either source can be used for studying international trade networks. Overall, these studies suggest that while there may be some differences between using import and export data for analyzing trade networks, they are generally minor enough to not significantly affect overall conclusions about the structure or dynamics of such networks. Therefore, this study focused on the reported trade exports of each country as the subject of analysis.

2.2. Research Methodology

2.2.1. E-Products Trade Network Building

A complex network model of global electronic products trade (G) was constructed using countries as nodes and inter-country trade as edges, with weights assigned to the edges.

$$\mathrm{G}= (\mathrm{V}, \mathrm{E}, \mathrm{W})$$

(1)

In the above equation, $\mathrm{V}=\left\{{\mathrm{v}}_1,{\mathrm{v}}_2,\cdots ,{\mathrm{v}}_{\mathrm{k}}\right\}$ denotes the set of nodes, where v_i represents a country involved in electronic product trade. $\mathrm{E}=\left\{{\mathrm{e}}_{\mathrm{ij}}\right\}$ denotes the set of edges, where edge e_ij exists if country v_i exports to country v_j, and edge e_ji exists if country v_i imports from country v_j. $\mathrm{W}=\left\{{\mathrm{w}}_{\mathrm{ij}}\right\}$ denotes the weight of the edge, where w_ij represents the total quantity of electronic products traded by country v_i to country v_j for export.

2.2.2. Analysis of the Overall Characteristics of the Electronics Trade Network

(1)

The network density (D) is a measure of the proportion of existing edges in the network to the maximum number of edges that could potentially exist between nodes. It reflects the degree of connectivity among nodes in the network [15]. The formula for calculating the network density is as follows:

$$\mathrm{D}=\frac{\mathrm{M}}{\mathrm{N} (\mathrm{N}-1)}$$

(2)

where M is the number of real edges in the network, and N is the total number of nodes. In electronics trade networks, a higher network density indicates stronger trade relationships among countries.

(2)

The average path length (L) is the mean number of edges traversed by the shortest path between every two nodes in the network, representing the network’s efficiency in transmitting information, goods, etc. [16]. The formula is given as:

$$\mathrm{L}=\frac{2}{\mathrm{N} (\mathrm{N}-1)}{\displaystyle {\displaystyle \sum }_{\mathrm{i}\ge \mathrm{j}}^{\mathrm{N}}}{\mathrm{d}}_{\mathrm{ij}}$$

(3)

where d_ij represents the number of edges on the shortest path from node v_i to node v_j. In electronics trade networks, the smaller the average path length of the network, the more efficient the trade transfer between countries.

(3)

The average clustering coefficient (CL) refers to the mean value of the clustering coefficients of individual nodes in a network. It indicates the degree of cohesion of the network [17]. The formula is as follows:

$$\mathrm{CL}=\frac{1}{\mathrm{N}}{\displaystyle \sum }_{\mathrm{i}=1}^{\mathrm{N}}\frac{2{\mathrm{M}}_{\mathrm{i}}}{{\mathrm{k}}_{\mathrm{i}} ({\mathrm{k}}_{\mathrm{i}}-1)}$$

(4)

where k_i is the degree of node v_i, and M_i denotes the sum of the respective number of edges connected between the nodes connected to node v_i. In electronic product networks, the higher the average clustering coefficient of the network, the stronger the network’s cohesiveness.

2.2.3. Individual Analysis of Country Nodes in the Electronics Trade Network

(1)

The node degree (K) is a measure of the number of connections per node, representing the total number of countries with which a country has direct trade relationships. Weighted degree (C) takes into account the edge weights, and in a directed network, the weighted degree is the sum of two indicators: the weighted out-degree C_out and the weighted in-degree C_in. The formulas are as follows:

$${\mathrm{C}}_{\mathrm{out},\mathrm{i}}={\displaystyle \sum }_{\mathrm{j}=1,\mathrm{i}\ne \mathrm{j}}^{\mathrm{N}}{\mathrm{w}}_{\mathrm{ij}}$$

(5)

$${\mathrm{C}}_{\mathrm{in},\mathrm{i}}={\displaystyle \sum }_{\mathrm{j}=1,\mathrm{i}\ne \mathrm{j}}^{\mathrm{N}}{\mathrm{w}}_{\mathrm{ji}}$$

(6)

$${\mathrm{C}}_{\mathrm{sum},\mathrm{i}}={\mathrm{C}}_{\mathrm{out},\mathrm{i}}+{\mathrm{C}}_{\mathrm{in},\mathrm{i}}$$

(7)

In the network of electronic products trade, a country’s out-degree represents the number of countries to which it directly exports electronic products, indicating the level of its export and the economic importance. On the other hand, a country’s in-degree represents the number of countries from which it directly imports electronic products, indicating the level of its import and the economic importance. Additionally, the weighted degree, which takes into account the weights of the edges, reflects a country’s position in the global electronic products trade market. A higher weighted degree indicates a greater level of involvement in the trade of electronic products.

(2)

Closeness centrality (CC) is a measure of the average length of the shortest paths from a node to all other nodes in the network. It is defined as the reciprocal of the sum of the distances from a node to all other nodes. The higher the CC, the more central the node is in the network. The formula is as follows:

$${\mathrm{CC}}_{\mathrm{i}}=\frac{\mathrm{N}-1}{{{\displaystyle \sum }}_{\mathrm{j}=1,\mathrm{i}\ne \mathrm{j}}^{\mathrm{N}}{\mathrm{d}}_{\mathrm{ij}}}$$

(8)

A country with a higher closeness centrality is more central in the global trade network and less vulnerable to control by other countries in the electronic product network.

(3)

Betweenness centrality (BC) refers to the proportion of shortest paths in the network that pass through a particular node for all pairs of nodes in the network. It indicates the importance of that node as a mediator in the network. The formula is as follows:

$${\mathrm{BC}}_{\mathrm{i}}={\displaystyle \sum }_{\mathrm{j}}^{\mathrm{N}}{\displaystyle \sum }_{\mathrm{k}}^{\mathrm{N}}\frac{{\mathrm{b}}_{\mathrm{jk}}\left(\mathrm{i}\right)}{{\mathrm{b}}_{\mathrm{jk}}}$$

(9)

where b_jk represents the number of shortest paths between the nodes v_j and v_k, and b_jk(i) represents the number of shortest paths between the nodes v_j and v_k that pass through the node v_i [18]. In the electronic product network, a country’s betweenness centrality measures its centrality in the trade network and the degree to which it controls the flow of electronic goods across borders. The higher the value, the more central the country is in the network.

2.2.4. Characterization of the Structure of the Electronics Trade Network Community

Modularity (Q) is often used as an indicator to measure the effectiveness of community detection in complex networks. In this network, a group of closely connected nodes is referred to as a community, and the process of community detection involves two steps: first, each node in the network is treated as an independent community, and modularity is calculated; second, some nodes are merged into communities to improve modularity, and the newly formed community is treated as a new node [19]. This process is then repeated. When the value of Q reaches its maximum, the effectiveness of community detection is considered optimal and the process is concluded. The value of modularity Q is calculated using the following formula:

$$\mathrm{Q}=\frac{1}{2\mathrm{m}}{\displaystyle \sum }_{\mathrm{i},\mathrm{j}}\left[{\mathrm{A}}_{\mathrm{ij}}-\frac{{\mathrm{k}}_{\mathrm{i}}{\mathrm{k}}_{\mathrm{j}}}{2\mathrm{m}}\right]\mathsf{\delta }\left({\mathrm{c}}_{\mathrm{i}},{\mathrm{c}}_{\mathrm{j}}\right)$$

(10)

where m represents the sum of the weights of all edges of the entire network. ${\mathrm{A}}_{\mathrm{ij}}={\mathrm{w}}_{\mathrm{ij}}+{\mathrm{w}}_{\mathrm{ji}}$ represents the sum of the weights of the edges between the nodes v_i, v_j. $\mathsf{\delta }\left({\mathrm{c}}_{\mathrm{i}},{\mathrm{c}}_{\mathrm{j}}\right)$ has only two values: 0 and 1; if the node v_i and the node v_j are in the same community, the value is 1, otherwise, it is 0.

Countries that belong to the same community in the electronic products trade network are closely connected, while those that belong to separate communities are more dispersed.

3. Results and Analysis

3.1. Spatial and Temporal Evolution of Trade Network Patterns

3.1.1. Overall Transaction Volume

Figure 1 illustrates the fluctuating growth trend of the total trade in electronic products for all countries from 2000 to 2021, with four distinct stages. The first stage covers the years 2000–2003, with the total trade volume fluctuating around USD 200 billion. The second stage occurred from 2004–2007, during which the total trade volume surged from USD 264.5 billion (2004) to USD 357.2 billion (2007). The third stage is characterized by a “V” pattern of total trade from 2008–2011, which resulted from the global financial crisis in 2008. The total trade volume sharply declined from USD 368.3 billion (2008) to USD 316.1 billion (2009) and then gradually rose to USD 393.8 billion in 2010. The fourth stage, spanning from 2012 to 2021, was marked by a steady increase in the total trade volume, which rose continuously from USD 416.8 billion (2012) to over USD 500 billion in 2018, and finally reached USD 540 billion in 2021.

This study examined the electronic product trade of all nations in the world using four time-nodes. The investigation started in the year 2000, which served as the first time-node. The year 2009, marked by a sharp decline in the total trade volume due to the global financial crisis, was considered the second significant time node. The third key time node was 2019, which experienced a slight dip in overall trade volume. In 2021, the global total trade in electronic products reached its peak, representing the most recent development in trade. Therefore, 2021 was regarded as the fourth important time node.

3.1.2. Increased Country Participation and Centralized Distribution of Trade Volume

Table 1. Top 10 countries and regions in total global trade in electronics, 2000–2021.

Ranking	2000	2009	2019	2021
1	USA	CHN	CHN	CHN
2	JPN	USA	HKG	HKG
3	SGP	HKG	USA	USA
4	GBR	SGP	Other *	Other *
5	DEU	DEU	KOR	DEU
6	HKG	JPN	SGP	NLD
7	Other *	KOR	DEU	JPN
8	KOR	NLD	VNM	MYS
9	NLD	Other *	NLD	MEX
10	MYS	MYS	JPN	SGP

* ‘Other’ in the table stands for ‘Other Asian countries’ in UN Comtrade.

presents the top 10 countries and regions involved in global electronics trade from 2000 to 2021, with predominantly Asian countries and regions. The United States consistently ranked among the top three North American countries, and Mexico only made the top 10 in 2021. The list includes only the United Kingdom, Germany, and the Netherlands from Europe, and Germany and the Netherlands consistently ranked in the top 10. No African, Oceanic, or South American countries or regions made the list. Over the period of 2000 to 2021, the top 10 countries accounted for 62.76% to 66.83% of total global trade, reflecting the increasing polarization of electronics trade worldwide. The number of countries and regions participating in trade increased slightly from 240 to 246, indicating a modest increase in country participation.

Since 2000, the global electronics trade pattern has been characterized by a “tripartite confrontation”, which is unevenly distributed across regions, with Asia, North America, and Europe dominating, while most countries in Africa, South America, and Oceania are under-represented. However, since then, developing countries, led by China, have gained momentum, with China and Malaysia appearing in the top 10 in 2009, and Mexico entering the top 10 ranking in 2021. Although the majority of the top 10 countries are developed countries, the proportion of developing countries has increased, indicating that developed countries no longer have an absolute advantage in dominating global trade in electronics, and developing countries have taken their place through their own development.

The dominance of China in the global electronics trade is noteworthy, with the country ranking first in 2009, 2019, and 2021. Hong Kong also consistently appeared in second or third place. The total trade volume between the two regions increased from USD 81.752 billion in 2000 to USD 196.855 billion in 2021, and its share of total trade rose from 25.86% to 36.45% in the same period. This trend indicates that China has established itself as a leader in the international market for electronic products, and its market share is continually growing.

3.2. Network Size and Network Characteristics

3.2.1. Overall Network Size

Table 2. Characteristics of global electronics trade networks, 2000–2021.

Year	Nodes	Edges	Average K	D	Average C
2000	240	9859	41.079	0.172	4,126,141,547.950
2009	242	12,706	52.504	0.218	5,856,188,667.008
2019	245	13,571	55.392	0.227	9,341,450,273.698
2021	246	11,436	46.492	0.190	9,751,075,087.069

presents the distinct features of the global electronics trade network during 2000–2021. The network’s number of nodes, edges, and average weighted degree exhibit a steady rise from 2000 to 2009. The number of nodes increased from 240 to 246, while the number of edges rose from 9859 to 11,436, and the average weighted degree rose from 412 million to 975 million. The average degree and network density show a gradual increase in 2000, 2009, and 2019, with the average degree increasing from 41.079 to 55.392, and the network density increasing from 0.172 to 0.227. In 2021, the average degree and network density were slightly lower than in 2019, at 46.492 and 0.190, respectively. This reduction could be due to the impact of the COVID-19 pandemic on global trade, although it still remained at high levels compared to the year 2000. These results suggest that the global electronics trade network has expanded, with more countries joining and strengthening the connections between countries.

3.2.2. Scale-Free, Small-World Features

Figure 2 shows the cumulative probability distribution of the weighted degree of nodes in the global electronics trade network for 2000, 2009, 2019, and 2021. The data were fitted by a power function, resulting in the following equations: y = 44.17x^−0.261, R² = 0.8551 for the year 2000; y = 48.029x^−0.255, R² = 0.8031 for the year 2009; y = 39.858x^−0.242, R² = 0.7968 for the year 2019; and y = 27.854x^−0.225, R² = 0.7634 for the year 2021. These fits are highly significant, indicating that the cumulative probability distribution follows a power-law distribution, meaning that the network exhibits a scale-free structure. This suggests that a small number of countries have a very large trade volume, while most countries have a small trade volume. In addition, Figure 3 shows the Lawrence curves of the cumulative probability of node weighting in the global electronic products trade network and the Gini coefficients of the four years were 0.922, 0.919, 0.927, and 0.931, respectively. Each of them is greater than 0.5, indicating an extremely wide gap in the trade volume of electronic products among countries. Thus, the global trade in electronic products is controlled by a few countries.

Between 2000 and 2021, the average clustering coefficient of the global electronics trade network increased from 0.72 to 0.745, and the average path length decreased from 1.767 to 1.589. These trends suggest that global trade has become more interconnected and efficient, with increased access among networks and more convenient transactions.

Table 3. Statistical characteristics of global electronics trade networks compared to random networks (background darken), 2000–2021.

Year	CL	L	Random Network Nodes	CL	L
2000	0.720	1.767	240	0.026	2.874
2009	0.723	1.704	242	0.027	2.806
2019	0.734	1.655	245	0.023	2.824
2021	0.745	1.589	246	0.026	2.827

shows that compared to a random network with the same number of nodes, the electronics trade network had a significantly larger average clustering coefficient and a smaller average path length, indicating that the global electronic product trade network exhibits small-world characteristics.

3.3. Unbalanced Trade Networks, Dominated by a Few Countries

Table 4. Top 10 countries or regions in terms of closeness centrality of global electronics trade networks, 2000–2021.

Ranking	2000		2009		2019		2021
1	BDI	1	USA	0.916	USA	0.910	DEU	0.904
2	ERI	1	DEU	0.906	DEU	0.907	GBR	0.904
3	USA	0.902	FRA	0.899	FRA	0.904	NLD	0.897
4	GBR	0.888	CHN	0.880	CHN	0.897	USA	0.891
5	FRA	0.882	GBR	0.876	GBR	0.897	CHN	0.881
6	DEU	0.866	NLD	0.867	NLD	0.891	FRA	0.875
7	NLD	0.854	Other *	0.867	DNK	0.875	ESP	0.872
8	ITA	0.833	KOR	0.855	BEL	0.868	DNK	0.872
9	BEL	0.827	CAN	0.855	IND	0.862	TUR	0.857
10	CHE	0.827	BEL	0.849	ESP	0.862	CAN	0.854

* ‘Other’ in the table stands for ‘Other Asian countries’ in UN Comtrade.

shows the top 10 countries and regions ranked by closeness centrality in the global electronics trade network from 2000 to 2021. The high representation of European and American countries suggests that they are at the center of the global electronics trade network, with frequent trade exchanges among themselves and better spatial accessibility to other countries in the network. In 2000, Burundi and Eritrea, two African countries, had the highest closeness centrality, indicating that they were the most efficient in trading with other countries and the least vulnerable to the influence of other countries’ control. However, in 2009, 2019, and 2021, the closeness centrality of the two countries was zero. Both countries have experienced political upheavals in the 21st century, such as protests and violence in Burundi’s presidential election in 2015, and restrictions on civil rights and freedoms in Eritrea in 2001 under a system of general mobilization. These are among the reasons for the sharp decline in the centrality of the two countries. Apart from 2000, China’s closeness centrality ranked in the top 10 in all other years, remaining at number 4 in both 2009 and 2019, and increasing from 0.88 to 0.897 in absolute terms. However, it slightly dropped to 0.881 in 2021, and fell to number 5. This drop can be attributed to the impact of the COVID-19 pandemic on China’s imports and exports. Among other Asian countries, only South Korea and India made it to the top 10 in 2009 and 2019, respectively, with 0.855 and 0.862 in absolute terms. None of the South American and Oceanic countries made it to the list due to their geographical locations.

Table 5. Top 10 countries and regions in terms of betweenness centrality of global electronics trade networks, 2000–2021.

Ranking	2000		2009		2019		2021
1	USA	3425.257	USA	2825.946	USA	1682.076	USA	1533.264
2	GBR	2172.528	FRA	1672.460	CHN	1542.691	GBR	883.560
3	FRA	2124.083	GBR	1508.359	GBR	1339.005	DEU	799.529
4	DEU	1565.765	CHN	1363.075	FRA	1169.540	NLD	783.903
5	CAN	1248.684	DEU	1164.689	NLD	1113.542	FRA	744.225
6	ITA	1061.292	NLD	1035.971	DEU	1056.536	CHN	702.931
7	NLD	1057.005	AUS	891.280	HKG	863.419	HKG	577.019
8	AUS	998.388	BEL	798.878	ARE	822.815	CAN	557.644
9	CHE	958.418	CAN	764.569	CAN	767.987	ARE	557.445
10	ZAF	929.406	DNK	744.849	ITA	698.669	BEL	522.302

shows that the top 10 countries and regions in the betweenness centrality ranking of the global electronic product trade network varied considerably by geographical distribution from 2000 to 2021. In 2000, North America, led by the United States and Canada, and Europe, led by the United Kingdom, Germany, and France, dominated the top 10 countries. Australia, an Oceanic nation, and South Africa, an African nation, were also on the list, with respective values of 998.388 and 929.406. However, no Asian nations were featured. In 2009, European and American nations remained relatively unchanged, but no African nations made the list. China, an Asian nation, ranked fourth with 1363.075, while Australia remained in the top ten. After 2019, the top 10 countries consisted entirely of North American, European, and Asian nations. In addition to China and Hong Kong, the United Arab Emirates joined the list in 2019 and 2021, with 882,815 and 557,445 inhabitants respectively. The preceding analysis demonstrates that European and American countries have always been at the center of the trade network and have strong control over global electronic product trade, while Oceanic and African countries have lost control, and Asian countries have gradually increased their influence, resulting in a triple balance of power.

By considering both the comprehensive closeness centrality and the betweenness centrality, the global electronics trade network exhibits a three-tiered trend. European and American countries have maintained a stable central position, while Asian countries have gradually increased their ability to trade in electronic products and are becoming less subject to control by other regions.

3.4. Community Division

Using Gephi to partition the global electronics trade network into communities for the period of four years, the results are presented in Figure 4, Figure 5, Figure 6 and Figure 7. Each community is represented by a distinct color, and the size of the node corresponds to its importance. The links between countries represent the trade activities, and the edge weight indicates the strength of the connection and transaction volume.

In 2000, the global electronic products trade network was classified into four communities: the first community was dominated by the United Kingdom, Germany, and the Netherlands, accounting for 61.27% of the total number of countries in the world. The countries within this community were mainly concentrated in Europe. The second community, led by the United States, Singapore, and Japan, accounted for 28.43%, with countries primarily located in North America and East Asia. The third community, led by China and Hong Kong, accounted for 9.31%. Finally, the fourth community comprised only two countries, Suriname and Guyana.

In 2009, the global electronics trade network was divided into five communities. The first community still included Germany, the Netherlands, and France, but its proportion dropped to 39.51%. The second community was mainly composed of developing countries, such as Malaysia, Thailand, and India, and accounted for 30.73% of the total number of countries in the world. The third community, led by the United States, accounted for 11.71%. The fourth community, led by China and Hong Kong, accounted for 11.71% and also included Singapore and Japan, which were in the second community in 2000. The fifth community had only one region, named the neutral zone, between Iraq to the southeast and Saudi Arabia to the northeast, and both countries had equal rights over the zone.

In 2019, the global electronics trade network was divided into three communities. Germany and the Netherlands dominated the first community, which comprised 75.25% of the world’s countries. China, Hong Kong, South Korea, and Singapore dominated the second community, accounting for 14.36%. The United States headed the third community, accounting for 10.4%. This community structure indicates a shift in global trade patterns, with developing countries gradually gaining more influence in the network.

In 2021, the number of communities remained the same as in 2000, but the proportion of countries in each community tended to be more balanced, with 43.48% (the first community), 28.99% (the second community), 15.94% (the third community), and 11.59% (the fourth community). The community led by China and Hong Kong ranked first, followed by the German- and Dutch-led community in second place. The third community included the United Arab Emirates, Saudi Arabia, and other Middle East nations. Although the proportion of the fourth community led by the United States had not changed significantly, it had the smallest proportion compared to the other societies.

In summary, the number of communities in the global electronics trade network follows a fluctuating “up-down-up” pattern. Communities display spatial continuity, with stronger trade links between countries on the same continent and weaker links between countries on different continents, which is more evident after 2019. It is noteworthy that countries with higher nodal weights, indicating greater trade volumes, do not necessarily belong to the largest communities. Instead, the geographical location plays a more significant role in community division, considering the variations in logistics costs [20,21], product acceptance [22], and national economic power [23,24,25] that influence trade activities among countries.

3.5. Analysis of Factors Influencing the Electronics Trade Network

The pattern of the global electronics trade network is the result of a complex interplay of natural, economic, technological, political, and cultural factors. The unique features of the global electronics trade network have varied significantly over time, with different factors playing varying roles.

3.5.1. Influence of Natural Factors

The initial flow of the global electronics trade network is determined by differences in natural endowments [26], while geographical location is the main factor in its evolution. The European region represents closer electronics trade between countries in close proximity. Since 2000, community divisions have become more pronounced with the emergence and development of Asian-European, South American, and East Asian communities, all conforming to the Law of Diffusion [27] (when a new thing or phenomenon appears, it usually spreads gradually from the source to surrounding areas).

3.5.2. Influence of Economic Factors

Changes in market supply, demand, and pricing fluctuations are among the economic factors that influence the pattern of the global electronics trade network [28,29], making economics the driving force behind its evolution [30,31]. The global economic environment significantly affects global electronic product trade, with the total trade volume dropping considerably in 2009 and shrinking again due to the COVID-19 pandemic in 2021. The number of global electronic product trades (or network edges) and the network density have decreased over time, particularly in 2021. In 2000, the top four countries in terms of GDP were ranked by the weighted degree value. The top 10 countries in the weighted degree value ranking in 2009, 2019, and 2021 all contained the top 4 countries in terms of GDP: the United States, China, Japan, and Germany, indicating a positive correlation between countries’ economic development and the weighted degree value of the global electronics trade network. Moreover, countries with different purchasing power have varying spending capabilities on electronics, which also impacts the global electronics trade [28].

3.5.3. Influence of Technological Factors

Electronics are products that heavily rely on technology, and a country’s ability to design, produce, market, and service electronics significantly impacts its electronics trade. The countries that have high centrality in the network nodes of the global electronics trade, such as the US, Germany, the UK, and France, all made significant progress during the Second Industrial Revolution [32], and their original accumulation of electrical technology has given them an inherent advantage in the development of high-tech and technology-intensive industries.

3.5.4. Influence of Other Factors

In addition to the natural, economic, and technological factors mentioned above that influence the development of global trade in electronic products, other factors, such as international politics, national policies, and regional cultures [33,34], play important roles in the formation and evolution of the trade pattern of electronic products. For instance, China’s “Belt and Road” initiative has led to increased trade and regional cooperation among countries along the route [35]. This initiative has had a significant impact on the global trade of electronic products by improving infrastructure, promoting investment, and enhancing connectivity among participating countries.

4. Conclusions and Further Discussion

4.1. Conclusions

This paper analyzed the trade data of electronic products from over 240 countries and regions worldwide between 2000 and 2021. We constructed a weighted directed network model based on complex network theory and investigated the global, national, and regional scales of the global electronic products trade network, including its spatial evolution pattern, topological structure, and community division. The following conclusions were drawn:

• The total global trade of electronic products has grown, with the top ten trading countries mainly located in Europe, America, and Asia, indicating a three-tier division. Developing countries also saw a significant increase in trade volume.
• The global trade network of electronic products is complex, with an increasing number of trade links and involved countries or regions. The network density has also risen, indicating closer trade relations between countries. Additionally, the average clustering coefficient of the global electronics trade network has increased, while the average path length has decreased, demonstrating the scale-free and small-world characteristics. The enhanced cohesion of the network has improved trade transmission efficiency and convenience.
• The positioning of countries within the global electronics trade network varies significantly based on their degree of control over resources and ability to escape from the control of other countries. European and American countries dominate the top ten ranking for closeness centrality, with the United States, Germany, France, and the Netherlands at the center of the network. The top ten countries for betweenness centrality vary greatly, with representation from Europe, the United States, and Asian countries, where China represents the Asian region for the expansion of the influence of electronic product resources.
• Between 2000 and 2021, the global electronics trade communities underwent a dynamic process of differentiation and integration. In 2000, the largest communities were the first community on the European continent and the second community led by the United States. Since then, the number of more stable communities has risen to three, namely the Asia–Europe community, the South American community, and the East Asian community, led by China. The communities are closely interconnected and characterized by spatial continuity.
• The formation of the global electronics trade network is the result of the interactions of various factors, including natural, economic, technological, political, and cultural factors, which influence the development of global trade among countries.

Based on the findings above, this article proposes the following measures and recommendations to enhance China’s position in the global electronic products trade network:

• To maintain the stability of the global market and China’s economic development, it is necessary to establish friendly relations and long-term stable trade cooperation with core countries in the global market, thus avoiding trade frictions.
• To fully utilize China’s geographical and political advantages, strengthening relationships with neighboring countries and increasing investment in electronic products in Belt and Road countries is important. By leveraging the comparative advantages of these nations, resource complementarity can be achieved to establish a mutually beneficial trade pattern. Additionally, enhancing people-to-people ties among “Belt and Road” countries, improving trade efficiency, and minimizing investment risks are crucial factors to consider.
• The “Revitalize China through Science and Education” strategy should be fully implemented in China. it is essential to adhere to the belief that science and technology are primary productive forces. Prioritizing science, technology, and education in economic and social development is crucial for China’s progress. To accelerate high-tech development, enhance independent innovation capabilities, and elevate its important position in the international electronic products market, China should focus on increasing research and development investment in the electronics industry.

4.2. Further Discussion

Firstly, by collecting the annual data of each country and analyzing it through a complex network research method, we can clearly see the global trade situation of electronic products in the above four periods (2000, 2009, 2019, and 2021). However, as for the economic principles behind the changes, this paper mentions briefly but lacks a systematic theory description. Thus, researchers can explain the causes of the phenomena described in this paper by analyzing the economic policies and trade activities of different countries at different times in detail.

Secondly, this paper studied the global trade of electronic products from the perspective of China, and the part on policy suggestions highlights the main body of China but does not involve other countries or regions. Therefore, researchers can put forward corresponding policy suggestions based on the specific conditions of other countries, such as the United States or Germany.

Finally, due to the limitation of data availability, our data cover up to the year 2021. Considering the global mitigation of the pandemic, trade may have an increased impact, and thus, it is necessary to focus on the post-pandemic data response.