Study on the Characteristics and Evolution Trends of Global Uranium Resource Trade from the Perspective of a Complex Network

Abstract

With consensus on the climate issue, most countries have successfully put forward their carbon emission targets. Due to low carbon and mature technology, nuclear energy has become the focus of scholars. The development of nuclear energy is inseparable from the support of uranium resources. Due to the geographically uneven distribution of uranium resources, the flow of resources across the globe satisfies both supply and demand. Therefore, research on the characteristics of the global uranium resource trade and its evolution trends can provide a reference for decision-makers to formulate relevant uranium resource trade policies to ensure the security of the national uranium resource trade. Based on the perspective of the industry chain, this paper constructed an upstream natural uranium trade complex network (upstream) and an enriched uranium trade complex network (downstream) to analyze the characteristics and evolution trends of GURTNs at the global, community, and national levels. The results show that: (1) The trade of enriched uranium is mainly concentrated between developed countries such as European and North American countries. Natural uranium is a raw material, and its trade characteristics are greatly affected by uranium price fluctuations. (2) The evolution of the global natural uranium trade community is dominated by the significant uranium-resource-demanding countries. The global natural uranium trade pattern will be difficult to change in the short term. (3) With the expiration of the USA–Russia enriched uranium trade agreement, this will become an uncertain factor affecting the evolution of the global enriched uranium trade pattern. (4) Since the United States and France are deeply involved in the global uranium resources trade, both have a higher ability of anti-control and control in GURTNs, which is inseparable from the uranium resource trade strategies of the two countries. The paper concludes by suggesting corresponding policy recommendations that can inform policymakers in formulating relevant trade policies.

1. Introduction

Climate change has attracted increasing attention due to the continuous rise in energy demand [1]. As one of the most potent and practical sources of low carbon energy, nuclear power has an irreplaceable advantage in terms of low carbon dioxide emissions and operating stability compared to traditional fossil and renewable energy [2]. Nuclear power is the largest source of energy with low carbon dioxide emissions after hydropower [3]. The Fukushima nuclear accident had a significant negative impact on the development of the global nuclear power industry. Although many countries with nuclear power have adopted a cautious attitude to supporting the development of their nuclear power industries [4], the growth of the global nuclear power industry is still sluggish. The global nuclear power generation has not yet recovered to the level of 2010. As countries begin to pay attention to climate change and put forward their own carbon neutrality goals one after another, nuclear energy has once again become the focus of policymakers due to its low carbon and stable technical economy [5,6]. In the case of France, nuclear energy is a massive driver of economic growth while also bringing about a lower carbon footprint. The importance of addressing climate change increases the future global role of nuclear power [7]. Besides, global uranium resource demand will increase due to the shift towards more sustainable and reliable low-carbon energy sources [8]. There is no denying that the development of nuclear power will be affected by policy-making in the futures [9,10].

Due to the geographically uneven distribution of uranium resources, the flow of resources across the globe satisfies both supply and demand. From 2000 to 2019, the number of natural uranium trading countries remained at around 70, and the number of enriched uranium trading countries remained at around 45. The global financial crisis in 2008 and the Fukushima nuclear accident in 2011 seriously negatively impacted the international uranium resources trade. In particular, the Fukushima nuclear accident brought the development of the global nuclear power industry into a trough, resulting in a downward trend in the worldwide trade of uranium resources. With the gradual transfer of nuclear power industry centers from European–American regions to the Asia–Pacific region, the major natural uranium importers have also changed from being dominated by developed countries to a situation where China and developed countries are “neck and neck.” Due to breakthroughs in mining technology, the production of natural uranium in Kazakhstan has risen rapidly, gradually replacing Canada as the world’s largest exporter of natural uranium.

The enriched uranium trade is mainly concentrated between developed countries. Due to its lack of ability to convert and concentrate uranium, Spain solely relies on imports for enriched uranium. Affected by the global financial crisis, the price of uranium resources fell. From 2008 to 2013, Spain mainly imported a large number of enriched uranium resources from the United Kingdom for storage, which caused great fluctuations in global trade volume (Figure 1). Uranium is a highly sensitive strategic mineral resource, and there are uncertain political risks in international trade cooperation, such as embargoes caused by political factors. With the rise of resource nationalism, trade protectionism, and the increasingly complex global geopolitical environment, the outlook of global uranium resource trade security is not optimistic [11].

The world’s largest importers of natural uranium in 2019 were China—36% of the world imports (USD 1297 million) and Canada—21% (USD 745 million). At the same time, the country with the highest export value of natural uranium was Kazakhstan, accounting for 43% of global exports (USD 1531 million). Canada was the world’s second-largest exporter of natural uranium, at 19% (USD 681 million). In 2019, the United States imported the most enriched uranium, a total of 34% (USD 1716 million), which is followed by Korea with 14% (USD 707 million). The top two exporters of enriched uranium were Russia an Netherlands, at USD 1723 and 823 million. These accounted for 34 and 16 percent shares of enriched uranium exports worldwide (Figure 2).

Currently, the research on uranium resource utilization mainly focuses on the following aspects: (1) Research fields on uranium resource demand. Once a power plant is being built, the demand for nuclear power is highly inelastic [12]. An insufficient supply of uranium resources will restrict the development of the nuclear power industry to a certain extent [13]. The global uranium resources can meet the medium- and long-term demand for global nuclear power [14,15]. According to the empirical model combined with China’s nuclear power development plan, under the medium scenario, by 2030, China’s uranium resource demand will increase to 20,113 tU. According to the empirical model, and [16] combined with China’s nuclear power development plan, under the medium scenario, China’s nuclear power industry’s demand for uranium resources will increase to 20,113 tU in 2030. By then, uranium resources will replace crude oil as an energy resource with the highest external dependence in China [17,18]. (2) Research fields of uranium resource supply. The global supply of uranium resources is mainly affected by prices [19], natural factors such as climate [20], supplier country policies [21], and other factors. [22] predicted China’s uranium resource supply by improving the Hubbert peak model and concluded that in the medium scenario, the peak of China’s uranium production will come in 2065. By then, China’s uranium mine supply will increase to 4605 tU. The supplies of unconventional uranium resources, such as phosphate rock co-associated uranium resources, are in low supply [23], as are uranium resources extracted from seawater [24] and tailings [25]. Due to its high supply cost, it is difficult to achieve large-scale production in the short term, so traditional uranium resources will still be an essential source to meet nuclear power demand. (3) Cost and price analysis of uranium resource. There is a relationship between uranium resource consumption and its supply cost. The Fukushima nuclear accident in 2011 seriously impacted the nuclear power industry, resulting in weak global demand for uranium resources. In recent years, increased supplies from Russia, Kazakhstan, and Uzbekistan have further led to a drop in uranium prices [26]. Ref. [27] concluded that the increase in the price of uranium resources has little impact on the cost of nuclear power but will affect the demand for uranium resources to a certain extent.

However, scholars have rarely conducted systematic research on the uranium resource trade, and there is a lack of analysis of the uranium resource trade from the perspective of the industrial chain. Trade links are one of the essential crisis transmission channels [28]. To a certain extent, changes of the trade network structure affect policymakers’ choices; for more information about networks to better understand some key features of the uranium industry, see [29]. Therefore, a comprehensive understanding of the evolution of global uranium resource trade and its influencing factors is crucial for policymakers to develop better national uranium resource security policies. To remedy the deficiencies above, we constructed global uranium resources trade networks (GURTNs) during 2000–2019. Based on complex network theory and an industrial chain perspective, we used bilateral trade data collected from the UN Comtrade database to analyze the characteristics of the uranium resources trade and its evolution trends. Complex network theory is a suitable method to describe international trade relations and study the characteristics of trade patterns [30]. The rise of “complex network science” and its application in international economic issues provide a more scientific research method for understanding the global trading system [31]. Guided by the complex network theory, there are growing numbers of studies analyzing international resource trade from the perspective of a complex network with examples of natural gas, crude oil, rare earth [32], and antimony ores [33]. At the same time, resource trade research based on the industrial chain has gradually become the focus of scholars, such as the cobalt industrial chain [34] and nickel industrial chain [35].

The contributions of the present study are threefold: first, to systematically study the global uranium resource trade by building a complex network model of uranium resource trade; second, based on the perspective of the industry chain, to analyze the evolution trend of the global uranium resource trade pattern from the global level (macro), the community level (medium), and the national level (micro). The remainder of this paper is organized as follows: Section 2 is devoted to the data sources, the process of constructing the global uranium trade networks, and the network topology indicator description. Section 3 presents the main findings of this research work, while the conclusions and policy implications of the study are drawn in Section 4.

2. Method and Data

2.1. Method

2.1.1. Construction of the Network

The core theory of complex networks is to abstract the relationships between individuals in the real world into a network and use this to describe the connections between individuals in the real world. In this paper, the global uranium resource trade network can be abstracted as a connected network G = (V, E), where V = {v_i: i = 1, 2, …, n}, n = |V| is the number of nodes and E = {e_i: i = 1, 2, …, m}, m = |E| is the number of edges. The nodes represent countries, and the edges represent the fact that there are relationships between each country. The direction of the edge represents the trade flow, and the weight of the edge represents the trade volume.

In the global uranium resource trade network, any two nodes (i, j) and (j, i) do not correspond to the same edge, and each edge in the network has a corresponding weight. Hence, the network is a directed weighted network. From the perspective of the industry chain, the global uranium resources trade network (GURTN) includes two sub-networks: the global natural uranium trade network (representing the upstream industry) and the global uranium-enriched product trade network (representing the downstream industry).

2.1.2. Network Topology Analysis

This paper constructs global indicators, community indicators, and national indicators to analyze the topology of the uranium resource trade network. Global indicators include average degree, which measures the connectivity of trade networks; average path length, which measures trade network efficiency; network density, which measures trade network prosperity; and average clustering coefficient, which measures trade network tightness. The community indicators are based on the network’s community division. The modularity value measures the extent of regionalization or globalization of uranium resource trade. The normalized mutual information measures the stability and deviation of the network. National indicators include closeness centrality, which measures the country’s anti-control ability in the trade network; betweenness centrality, which measures the country’s control ability in the trade network; and eigencentrality, which measures the importance of the country in the trade network.

Global-Level Indicators

(1)

Average degree (AD)

Degree refers to the number of edges of nodes in the network, including the in-degree and out-degree. The in-degree is the number of edges towards the node, and the out-degree is the number of edges that the node points towards other nodes. Degree is the number of direct trade relationships of a country. The higher value of out-degree or in-degree indicates a wider range of the country’s direct impact [36]. A country’s in-degree $D_i^{in}$, out-degree $D_i^{out}$, and degree $D_i$ are calculated as follows [37]:

$$D_i=D_i^{in}+D_i^{out}$$

(1)

$$D_i^{out}={\sum }_{j=1}^n{a}_{ji}\left(t\right)$$

(2)

$$D_i=D_i^{in}+D_i^{out}$$

(3)

$D_i^{in}$ represents the input degree of node i, $D_i^{out}$ represents the output degree of node i, and a_ij represents whether node i and node j are edge connected. If the nodes are connected, the degree of a_ij is 1, otherwise, it is 0.

The average degree refers to the average number of edges of nodes in the network, which can be used to measure the overall connectivity of the global uranium resource trade network. The higher the value of the average degree, the smoother the trade between countries [38]. The average degree is defined as follows:

AD = D_i/N.

(4)

(2)

Average path length (APL)

This paper defines average path length as the average number of steps along the shortest paths for all possible pairs of network nodes in the global uranium resources trade network, which can be used to measure the trade transmission efficiency of the network. The higher the value, the more edges the trade between the two countries needs to pass through, resulting in a decrease in the efficiency of trade transmission and an increase in trade costs [39]. The average path length is defined as follows [40]:

$$APL=\frac{1}{N\left(N-1\right)}{\displaystyle \sum }_{i,j}d\left(i,j\right)$$

(5)

where N is the number of ports in the network and d (i, j) denotes the shortest distance between economy i and j in the uranium resource trade network. Assume that d (i, j) = 0 if i = j or j cannot be reached from i.

(3)

Network density (ND)

The network density is used to measure the tightness between all participating countries in a trade network. The network density can be calculated by dividing the actual number of edges in the network by the theoretical maximum value of the number of edges in the network. The calculated value is between 0 and 1. The closer the value is to 1, the more frequent the trade between countries and the higher the prosperity of uranium resource trade; the closer the value is to 0, the sparser the trade relationship between countries, and the lower the degree of trade prosperity [41]. Therefore, if the actual number of edges in the network is M and the actual number of nodes in the network is N, the network density is defined as follows:

$$ND=\frac{2M}{N\left(N-1\right)}$$

(6)

(4)

Average clustering coefficient (ACC)

The clustering coefficient reflects the tightness of the connection between the countries in the uranium resource network, which is defined as:

$$C_i=1/S_i\left(K_i-1\right){\sum }_{ij}\left(W_{ij}\right)/2a_{\mathit{ij}}$$

(7)

where C_i represents the clustering coefficient of economy i. S_i indicates the trade volume of economy i, K_i indicates the partners of economy i. a_ij indicates elements in adjacency matrix of a GURTN, and when there is an edge from economy i to j, a_ij = 1; otherwise it is 0. W_ij indicates the trade volume between economy i and j.

$$\mathit{ACC}=\frac{1}{N}{\sum }_{i=1}^N{C}_i$$

(8)

where N is the number of ports in the network. The average clustering coefficient (ACC) of the network therefore represents the overall tightness of the connection of all the countries in the network [42]. The larger the value, the closer the trade relationship between countries in the trade network is [43].

Community-Level Indicators

(1)

Modularity (M)

Trading communities are defined as a group of countries that trade with community members rather than external countries significantly [36]. The delineation of trade communities helps identify the trade characteristics in a trade network. This paper uses the Fast Unfolding algorithm for community partitioning. The algorithm consists of two main stages: firstly, it divides each node into the communities of its neighboring nodes to make the value of the modularity continuously larger; secondly, it aggregates the communities divided in the first step into a single point, i.e., reconstructing the network according to the community structure generated in the previous step. This process is repeated until the network structure no longer changes. The modularity (M) is a practical benefit function to assess the quality of the partitions, which is defined as:

$$M=\frac{1}{2m}{\sum }_{\mathit{ij}}\left. [W_{ij}-\frac{A_i{A}_j}{2m}\right]\delta \left(c_i-c_j\right)$$

(9)

where M is the modularity and ranges between −1 and 1, W_ij is the weight of the edge between nodes i and j, A_i = ${\sum }_{j=i}^N{W}_{ij}$ is the sum of the weights of the edges associated with the node i, A_j = ${\sum }_{j=i}^N{W}_{ij}$ is the sum of the weights of the edges associated with the node j, $\mathrm{m}=\frac{1}{2}\sum _{i,j}{w}_{i,j}$, $\frac{A_i{A}_j}{2\mathrm{m}}$ represents the expected value of W_ij in a random situation [44], c_i represents the assigned community of the node i, and c_j represents the assigned community of the node j. $\delta \left(c_i-c_j\right)$ is 1 if c_i = c_j and 0 otherwise.

The closer the value is to 0, the freer the trade between countries and the greater the extent of globalization. The closer the value is to 1, the greater the indication that there is a phenomenon of “agglomeration” among countries, forming a relatively independent trade community. Trade is mainly concentrated among the members of the community. The extent of trade regionalization is great.

(2)

Normalized Mutual Information (NMI)

This paper employs normalized mutual information (NMI) to evaluate the extent to which community members in the same community overlap between years and is also often used to quantify the evolutionary stability of communities. NMI values between 0 and 1 are positively correlated with the stability of a community, and higher NMI values indicate a higher rate of overlap of trading countries between communities over two years and a more stable community division. The NMI values for both years were calculated as follows:

$$NM{I}_{\left(y^{\left(a\right)},y^{\left(b\right)}\right)}=\frac{{\sum }_{h=1}^{k^{\left(a\right)}}{\sum }_{l=1}^{k^{\left(b\right)}}{n}_{h,l}\log \left(\frac{n·n_{h,l}}{n_h^{\left(a\right)}{n}_l^{\left(b\right)}}\right)}{\sqrt{\left({\sum }_{h=1}^{k\left(a\right)}{n}_h^{\left(a\right)}\log \frac{n_h^{\left(a\right)}}{n}\right)\left({\sum }_{l=1}^{k\left(b\right)}{n}_l^{\left(b\right)}\log \frac{n_l^{\left(b\right)}}{n}\right)}}$$

(10)

where $y^{\left(a\right)}$ represents year a, $n_h^{\left(a\right)}$ represents the number of countries in trade community h in year a, $y^{\left(b\right)}$ represents year b, and $n_l^{\left(b\right)}$ represents the number of countries in trade community l in year b. $n_{h,l}$ is the same number of countries in trade association h of year a and trade association l of year b, and n is the total number of countries in the trade network. In this paper, two calculation formulas of NMI were designed:

If b = 1, a = t, the trade community h and the trade community l are in the same trade network, then NMI calculates the similarity of the community division between the t year and the first year and indicates the extent to which the network deviates from the initial state during the evolution.

If b = a + 1, the trade community h and the trade community l are in the same trade network, then NMI calculates the similarity of the community division between the t year and the t + 1 year and indicates the extent to which the network stability during the evolution.

Country-Level Indicators

(1)

Closeness centrality (CC)

In directed networks, proximity to the center determines how many steps a given node needs to take to connect to other nodes. The more central a country is, the shorter its total distance from all other nodes, which means the harder it is to be controlled by other countries during trade. Conversely, it means that the country has a longer total distance from other countries, increasing the uncertainty of completing the trade. Therefore, closeness centrality can measure the anti-control ability of a country in trade networks. The closeness centrality can be expressed as follows [45]:

$${\mathit{CC}}_i=\frac{N}{{\sum }_{j=1}^{N}dij}$$

(11)

where N is the number of countries in the network and d_ij is the length of the shortest path between countries i and j in terms of number of intervening countries. Thus, CC displays the given country’s central position in a GURTN.

(2)

Betweenness centrality (BC)

The betweenness centrality of a node is the ratio of the number of shortest paths through the node to the number of all shortest paths in the network, which illustrates the ability of one country to control resources as a media in the network. Since uranium resource trade is a directional flow, the intermediation role of a country mirrors the fact that it assumes the intermediary function of buying and selling flows between other uranium resource importing and exporting countries. The larger the value is, the stronger the mediation of the node. Betweenness centrality is expressed as follows:

$$\mathit{BC}\left(v\right)={\sum }_{i\ne j}\frac{{\delta }_{ij}\left(v\right)}{{\delta }_{ij}}$$

(12)

where ${\delta }_{ij}$ is the topologically shortest path between countries i and j and, ${\delta }_{ij}$(v) is the number of these topologically shortest paths passing through country v.

(3)

Eigencentrality (EC)

The eigencentrality represents that the importance of its connected nodes also determines the importance of a node. The importance of a node can be regarded as the combination of the influence of all its connected nodes. A node with high eigencentrality means that it has trade relations with many countries with a significant position in the network. Therefore, a node with high eigenvector centrality has a strong influence over the entire network, not just a strong influence over its connected neighbors. Eigencentrality is expressed as follows [32]:

$${\mathit{EC}}_i={\lambda }^{-1}{\sum }_{j=1}^N{A}_{ij}{e}_j$$

(13)

where λ and e_j are the largest eigenvalue of the adjacent matrix and its eigenvector. In many circumstances, a node’s importance in a network increase by having connections to other vertices that are themselves important [46], which is the concept behind eigenvector centrality.

2.2. Data

The international uranium resource trade data used in this paper are from the UN Comtrade database (https://comtrade.un.org), accessed on 30 June 2022. The UN Comtrade database is an authoritative, widely used database containing all the export and import flows among countries and regions (hereafter referred to as countries). The trade volumes are measured in kilograms. The trade values are measured in US dollars. Trade prices are calculated by the ratio of total trade value to total trade volume. The product code selected in this study was “HS 284410, 284420”, which includes natural uranium (including its compounds), HS code: 284410; and enriched uranium (containing U235 concentration of less than 5% of low-enriched uranium and its compounds), HS code: 284420. The data are for 2000–2019 and cover 145 countries and regions.

3. Analysis and Discussion

3.1. Characteristics of the GURTN

From 2000 to 2019, the volatility of the average degree of the natural uranium trade network was higher than that of enriched uranium. Since the trade of enriched uranium is mainly concentrated between developed countries such as European and North American countries, Japan and South Korea (nuclear power countries and nuclear-fuel-producing countries), enrichment is highly capital intensive, and because the market segment tends to be far more concentrated, the objects of trade are relatively fixed and less affected by external influences. During the study period, the average degree of the enriched uranium trade network remained at around 2.5, and the network’s connectivity changed little. The average degree of the natural uranium trade network can be divided into two stages of the global financial crisis in 2008. Before the financial crisis, the volatility increased, and then the volatility decreased, similar to the fluctuation trend of uranium prices. The increase in uranium price stimulates the global trade of natural uranium, which increases the network’s average degree and improves the network’s connectivity. When the uranium price gradually decreases, the average degree of the natural uranium network shows a downward trend, indicating that the decline in uranium price has restrained the global natural uranium trading to a certain extent.

The average path length of the GUTRN briefly increased after the Fukushima nuclear accident in 2011 but quickly fell back to its previous level. During the study period, the average path length of enriched uranium fluctuated between 2.0 and 2.5, and the trade efficiency of the network was relatively stable, less affected by the global financial crisis and the Fukushima nuclear accident. Although the countries involved in the enriched uranium trade and the scale of their trade are smaller than those of natural uranium, the prosperity of the enriched uranium trade is higher than that of natural uranium, indicating that the countries involved in the enriched uranium trade are relatively frequent. During the study period, the average clustering coefficient of the GURTN fluctuated between 0.10 and 0.35, which means that the closeness between the countries involved in the trade is low, and the trade relationship is loose (Figure 3).

3.2. Characteristics of Communities in GURTN

3.2.1. Modularity

From 2000 to 2019, the modularity of the GURTN fluctuated wildly. The modularity of the natural uranium trade network showed a trend of rising first, falling, and then gradually leveling off. During 2006–2008, the price of uranium rose rapidly, which gradually increased the modularity of the natural uranium trade network, indicating that when uranium prices rose, trading countries tended to import natural uranium from fixed trading partners to reduce trade costs, resulting in a decline in globalization. Affected by the global financial crisis in 2008, the price of uranium dropped steeply, which led to a decrease in the modularity of the natural uranium trade network, and then was maintained at around 0.4, indicating that globalization had increased. The modularity of the enriched uranium trade network fluctuated considerably during the study period, primarily due to the financial crisis in 2008 and the Fukushima nuclear accident in 2011, indicating that the globalization of the enriched uranium trade network was greatly affected by external shocks (Figure 4). During the study period, the average modularity of natural uranium and enriched uranium were 0.35 and 0.16, which means that the globalization of the enriched uranium trade was higher than that of natural uranium, and cross-regional marketing was more frequent.

3.2.2. Normalized Mutual Information (NMI)

(1)

Network stability

The NMI between year t and year t + 1 was used to quantify the similarity of communities’ members in the trade network between year t and year t + 1, which was used to measure the stability of the trade network. The higher the NMI, the more similar the members in the community, which means the more stable the network. The average NMI of the natural uranium trade network and the enriched uranium trade network were 0.77 and 0.84, and the variances were 0.054 and 0.050. The stability of the GURTN was high, and the stability of the enriched uranium was slightly higher than that of natural uranium (Figure 5A).

(2)

Network deviation

The NMI between year t and the initial year (2000) was used to quantify the similarity of communities’ members in the trade network between years t and 2000, which was used to measure the deviation of the trade network. The higher the NMI, the more similar the members in the community, which means the lower deviation of the network. The average NMI of the natural uranium trade network and the enriched uranium trade network were 0.87 and 0.93, and the variances were 0.045 and 0.058. The deviation of the GURTN was low, and the deviation of the natural uranium was slightly higher than the enriched uranium (Figure 5B).

The evolution of the NMI shows that the community members in the GURTN were highly similar, which is related to the particular consumption field of uranium resources. During the study period, there were fewer newly added nuclear power countries and uranium resource suppliers.

3.2.3. The Impact of Uranium Prices on Trade Network Indicators

With the flourishing development of the nuclear power industry in developing countries, the price of global uranium rose from USD 7.1/lbU₃O₈ in 2000 to USD 99.3/lbU₃O₈ in 2007. Affected by the financial crisis of 2007–2008, the price rapidly declined. After the Fukushima nuclear disaster in 2011, the immediate effect was a decline in the global nuclear power industry, and the price slumped further. In 2019, the price of uranium was only USD 24.9/lbU₃O₈ (Figure 6).

This paper analyzed the Pearson correlation coefficient [47] of uranium price and major TURTN indicators, and furthermore determined the influences of uranium price changes on the characteristics of the GURTN. In the natural uranium trade network, the correlation coefficient of uranium price and average degree was 0.446. A positive correlation existing between uranium price and average degree means the rise of uranium price is correlated to the connectivity of the natural uranium trade network. Meanwhile, uranium price, network density, modularity, average clustering coefficient, and average path length did not pass the significance test (

Table 1. Pearson correlation coefficient of uranium price and major natural uranium trade network indicator * p < 0.05, ** p < 0.01.

		Price	AD	ND	M	ACC	APL
Price	Pearson	1
	p
AD	Pearson	0.446 *	1
	p	0.049
ND	Pearson	0.111	0.632 **	1
	p	0.641	0.003
M	Pearson	0.051	−0.014	−0.351	1
	p	0.832	0.953	0.129
ACC	Pearson	−0.214	0.423	0.478 *	−0.193	1
	p	0.365	0.063	0.033	0.416
APL	Pearson	−0.091	−0.509 *	−0.362	0.149	−0.349	1
	p	0.701	0.022	0.116	0.532	0.132

). In the enriched uranium trade network, the correlation coefficients of uranium price, average degree, network density, modularity, average clustering coefficient and average path length were 0.228, −0.33, −0.423, −0.142 and 0.128, respectively. All are close to zero and the p indicator (

Table 2. Pearson correlation coefficient of uranium price and major enriched uranium network indicators.

		Price	AD	ND	M	ACC	APL
Price	Pearson	1
	p
AD	Pearson	0.228	1
	p	0.335
ND	Pearson	−0.33	0.640 **	1
	p	0.155	0.002
M	Pearson	−0.423	−0.097	0.051	1
	p	0.063	0.684	0.831
ACC	Pearson	−0.142	0.431	0.585 **	0.019	1
	p	0.552	0.058	0.007	0.937
APL	Pearson	0.128	0.295	−0.054	−0.237	−0.287	1
	p	0.59	0.206	0.821	0.315	0.219

** p < 0.01.

). In summary, there is less influence of price on the characteristics evolution of the GURTN.

In order to analyze the influence of uranium price on major TURTN indicators further, this paper tests the Granger causality between the price of uranium and major natural uranium trade network indicators, and the price of uranium and major enriched uranium trade network indicators, respectively. In the natural uranium trade network, price and M passed the 5% significance test, and price and AD passed the 10% significance test, indicating that price changes will affect the connectivity and globalization of trade networks. In the enriched uranium trade network, price and AD passed the 10% significance test, indicating that price changes will affect trade network connectivity (

Table 3. Granger causality test results between uranium price and major TURTN indicators.

284410		F	p	284420		F	p
AD	Price	0.177	0.84	AD	Price	0.408	0.673
Price	AD	3.4	0.065 *	Price	AD	3.328	0.068 *
ND	Price	0.483	0.627	ND	Price	1.358	0.291
Price	ND	0.025	0.975	Price	ND	1.493	0.261
M	Price	1.712	0.219	M	Price	1.639	0.232
Price	M	4.675	0.030 **	Price	M	0.71	0.51
ACC	Price	0.821	0.462	ACC	Price	2.692	0.105
Price	ACC	0.298	0.747	Price	ACC	0.074	0.929
APL	Price	0.2	0.821	APL	Price	0.113	0.894
Price	APL	0.514	0.61	Price	APL	0.886	0.436

**, and * represent the significance levels of, 5%, and 10%.

).

3.2.4. The Evolution of the Global Uranium Trade Community

This paper employs the Gephi software developed by [48] to divide the communities of the natural uranium trade network and the enriched uranium trade network from 2000 to 2019 (Figure 7).

(1)

Natural Uranium

This paper analyzed the changes in the membership of the global natural uranium trade communities from 2000 to 2019 at five-year intervals and discussed the evolution of the natural uranium trade pattern.

In 2000, the global natural uranium trade was divided into three communities: the USA–Germany-dominated trade community, the France–Russia-dominated trade community, and the Japan–Korea-dominated trade community. European and American countries were the prominent leaders in the division of global trade communities. In 2005, with the development of the nuclear power industry, China’s demand for uranium resources increased rapidly, becoming a significant importer of natural uranium resources, and gradually forming a trading community dominated by China and the countries of the former Soviet Union. After the Fukushima nuclear accident in 2011, Japan’s uranium resource demand fell rapidly (up to 8000 tU/y before the Fukushima accident), making it no longer dominant in the trade community. Subsequently, the global natural uranium resources trade pattern formed, and the USA–Germany-dominated trade community and the China–former Soviet Union-dominated trade community were relatively stable. The Korea-dominated trade community and the French–Niger-dominated trade community showed a state of “dividing-combining-dividing” during the study period.

Africa has always been a traditional supplier of natural uranium to Europe, especially uranium resources-rich countries such as Niger, Namibia, and South Africa. In general, almost every trade community has relatively fixed trading partners. For example, in the case of France and Niger, a sizeable French uranium resource company, Orano, controls Niger’s primary uranium mines, making Niger’s natural uranium mainly exported to France. In the case of China and Kazakhstan, Kazakhstan is the world’s largest exporter of natural uranium. Due to the advantage of geographical distance, about 80% of China’s natural uranium is imported from Kazakhstan, forming fixed trade partners.

The analysis concludes that the significant uranium-resource-demanding countries dominated the evolution of the global natural uranium trade community. During the study period, the major global natural uranium trade communities gradually evolved from the USA–Germany-dominated trade community, the France–Russia-dominated trade community and the Japan–Korea-dominated trade community into the USA–Germany-dominated trade community, the French–Niger dominated trade community and the China–former Soviet Union dominated trade community. The United States, France, and China are the world’s largest demanders and importers of uranium resources.

After the Fukushima nuclear accident, Germany and Switzerland announced their abandonment of nuclear power, and the United States and France delayed their development of the nuclear power industry and began to raise the standards for the development of the nuclear power industry. China prudently supports the development of the nuclear power industry [4]. The growth of the nuclear power industry in many emerging countries has stalled. Therefore, the global natural uranium trade pattern will be difficult to change in the short term (Figure 8).

(2)

Enriched Uranium

The production of enriched uranium generally adopts the method of entrusted processing. The demanding country provides raw materials, and the producing country is responsible for processing and then exporting to the demanding country; alternatively, because the nuclear power technology of the demanding country depends on other countries, it needs to import the nuclear fuel components from these countries, and then the trade relationship is formed. The USA, France, Korea, and Spain are the leading importers of enriched uranium, and Russia, the Netherlands, Germany, and the United Kingdom are the leading exporters of enriched uranium. The enriched uranium trade is regulated by the Nuclear Non-Proliferation Treaty and the IAEA (International Atomic Energy Agency). The countries participating in the enriched uranium trade are developed countries in Europe and North America, Japan, and Korea. Therefore, the division of enriched uranium trade communities is dominated by the above countries.

In 1993, the USA and Russian governments signed an agreement, also known as the “Megaton-for-Megawatt Program”, in which the USA purchased redundant high-enriched uranium from Russia and diluted it for use in nuclear power. According to the agreement, some of the diluted high-enriched uranium will be shipped back to Russia. In 2008, the USA and Russia signed an agreement on the trade of uranium resources. From 2011 to 2020, Russia gradually increased the export of enriched uranium to the United States. Therefore, the USA and Russia jointly dominate the same trade community. Due to the developed nuclear power industry, France and the UK have become the world’s major producers and traders of enriched uranium and have formed a France–UK-dominated trade community. After 2015, a Korea-dominated trade community gradually developed. With the increased trade volume of enriched uranium products, China’s position in the trade of enriched uranium products has steadily increased. China’s role in the trade community has been rising due to the increased imports of enriched uranium.

In 2020, with the expiration of the USA–Russia enriched uranium trade agreement, this will become an uncertain factor affecting the evolution of the global enriched uranium trade pattern (Figure 9). If the USA–Russia enriched uranium trade agreement is not renewed after its expiration, this means that both countries will need to purchase corresponding uranium resources from the market to make up for the vacancy, which will lead to an increase in the price of uranium to a certain extent. If the two countries fail to purchase the corresponding uranium resources in the short term, the impact will be transmitted through the industrial chain.

At the same time, in order to end the dependency on Russian fossil fuels, many European countries have proposed to invest in nuclear power. Belgium has decided to postpone its nuclear phase-out scheduled for 2025 by 10 years, worried about the soaring energy prices and the disrupted energy supply due to the Russia–Ukraine conflict. France announced plans to build six new nuclear reactors and to consider building a further eight. The first new reactor can be launched by 2035. The United Kingdom intends to obtain 25% of its electricity from nuclear power by 2050. Even though the conflict does not directly affect the supply and trade of uranium, it has indirect effects through industrial chain transmission. Since the conflict began, nuclear power has been considered again in the EU, and the price of uranium by has increased by 40% to its highest in 11 years. If the price continues to increase in the future, the global uranium trade will be affected to some extent.

3.3. Characteristics of Major Countries in the GURTN

This paper analyzed the evolution characteristics of closeness centrality, betweenness centrality, and eigencentrality of the top ten countries in terms of natural uranium and enriched uranium trade volume at 10-year intervals during the study period. The trade volume of the top ten countries in terms of trade volume accounts for more than 90% of the total global trade volume. Therefore, these countries’ characteristics of closeness centrality, betweenness centrality, and eigencentrality are globally representative. Subsequently, this paper analyzed the changing trends of closeness centrality, betweenness centrality, and eigencentrality of the four major net importers and net exporters in natural uranium trade and enriched uranium trade, respectively.

3.3.1. Closeness Centrality

In 2000 and 2010, the average closeness centrality value of the net importers of natural uranium trade was higher than that of the net exporters, indicating that the net importers paid more attention to their trade security and had strong anti-control capabilities. In 2019, the average closeness centrality value of net exporting countries was higher than that of net importing countries, indicating that the anti-control capabilities of net exporting countries have rapidly improved to safeguard their export interests. For the top ten countries in terms of the trade volume of enriched uranium, except in 2010, the average value of the closeness centrality of net exporters and net importers was similar. In 2000 and 2019, the average closeness centrality of net exporters was higher than that of net importers, indicating that during the study period, the anti-control ability of net exporters was more robust than that of net importers (

Table 4. Top 10 closeness centrality countries in the natural uranium trade network.

	2000	Closeness Centrality	2010	Closeness Centrality	2019	Closeness Centrality
1	USA	0.677	China	1.000	Ukraine	1.000
2	Japan	0.667	USA	0.596	USA	0.632
3	United Kingdom	0.547	France	0.496	Germany	0.533
4	France	0.532	Canada	0.463	France	0.490
5	Canada	0.500	Kazakhstan	0.444	Canada	0.480
6	Russia	0.483	Namibia	0.424	Kazakhstan	0.471
7	Australia	0.477	Uzbekistan	0.422	Namibia	0.381
8	Germany	0.433	Germany	0.412	China	0.369
9	Namibia	0.371	Russia	0.397	Australia	0.361
10	Niger	0.350	Australia	0.368	Russia	0.000
	Average of net importer	0.558	Average of net importer	0.580	Average of net importer	0.506
	Average of net exporter	0.449	Average of net exporter	0.424	Average of net exporter	0.538

Note: Net importer, imports > exports ; Net exporter, imports < exports .

and

Table 5. Top 10 closeness centrality countries in Enriched Uraniumthe enriched uranium trade network.

	2000	Closeness Centrality	2010	Closeness Centrality	2019	Closeness Centrality
1	Japan	1.000	China	1.000	USA	0.681
2	USA	0.643	USA	0.654	Germany	0.582
3	United Kingdom	0.621	France	0.642	France	0.542
4	France	0.563	Germany	0.630	Russia	0.532
5	Russia	0.554	United Kingdom	0.557	Netherlands	0.525
6	Germany	0.529	Netherlands	0.540	United Kingdom	0.500
7	Kazakhstan	0.463	Russia	0.529	China	0.416
8	Spain	0.414	Japan	0.453	Spain	0.410
9	Belgium	0.371	Spain	0.395	Sweden	0.364
10	Indonesia	0.000	Sweden	0.291	Korea	0.000
	Average of net importer	0.486	Average of net importer	0.570	Average of net importer	0.392
	Average of net exporter	0.546	Average of net exporter	0.567	Average of net exporter	0.518

Note: Net importer, imports > exports ; Net exporter, imports < exports .

).

China, the world’s largest importer of natural uranium, showed relatively large fluctuations in its closeness centrality during the study period. In the past, China’s natural uranium imports mainly depended on former Soviet Union countries such as Kazakhstan and Uzbekistan, and the import distance was short, making China maintain a closeness centrality value in the natural uranium trade network. However, with the increase in China’s imports of natural uranium from countries such as Namibia and Australia, the import distance of China’s natural uranium trade has increased. At the same time, the closeness centrality value has decreased, which means that the anti-control ability of China’s natural uranium trade has declined. As the world’s largest consumer of uranium resources, the United States maintained its closeness centrality value at around 0.6, and its natural uranium trade has strong anti-control capabilities. As the world’s third-largest consumer of uranium resources, France maintained a relatively high value of closeness centrality during the study period. The three leading countries of the natural uranium trading community-supported strong anti-control capabilities in their trade networks. The closeness centrality value of other countries fluctuated in the range of 0.3–0.5 (Figure 10A).

In the enriched uranium trade network, the closeness centrality value of China and Japan fluctuated considerably during the study period. The United States maintained a high closeness centrality value. The closeness centrality value for other countries fluctuated in the range of 0.5–0.7. The anti-control ability of the trade network of European and American countries was higher than that of Asian countries (Figure 10B).

3.3.2. Betweenness Centrality

In 2000, 2010, and 2019, the average betweenness centrality value of natural uranium net importers was 221.373, 294.539, and 294.141, so significantly higher than that of the net exporters that net importers mainly controlled the global natural uranium trade. The United States, France, Germany, Canada, and other European and American countries have absolute control over the international natural uranium trade. In 2000 and 2010, the average betweenness centrality value of net exporters in enriched uranium trade was higher than that of net importers, implying that net exporters had higher control over the trade network than net importers. In 2019, the average betweenness centrality value of net importers rose rapidly. It was slightly higher than that of net exporters, indicating that net importing countries gradually improved their control ability in the enriched uranium trade. During the study period, net exporters had more control ability than net importers in the enriched uranium trade network (

Table 6. Top 10 betweenness centrality countries in the natural uranium trade network.

	2000	Betweenness Centrality	2010	Betweenness Centrality	2019	Betweenness Centrality
1	USA	505.933	USA	617.717	Germany	450.750
2	France	374.282	France	476.094	USA	415.167
3	Germany	130.169	Germany	186.061	France	246.783
4	Canada	119.524	Canada	149.183	Canada	234.883
5	Russia	93.683	Namibia	148.967	China	63.867
6	Australia	33.971	Russia	122.922	Namibia	46.317
7	Japan	2.800	China	69.900	Kazakhstan	30.500
8	Niger	1.833	Kazakhstan	52.667	Australia	4.500
9	United Kingdom	0.000	Australia	5.300	Russia	0.000
10	Namibia	0.000	Uzbekistan	0.000	Ukraine	0.000
	Average of net importer	221.373	Average of net importer	294.539	Average of net importer	294.141
	Average of net exporter	31.066	Average of net exporter	71.223	Average of net exporter	79.050

Note: Net importer, imports > exports ; Net exporter, imports < exports .

and

Table 7. Top 10 betweenness centrality countries in the enriched uranium trade network.

	2000	Betweenness Centrality	2010	Betweenness Centrality	2019	Betweenness Centrality
1	USA	247.633	USA	165.092	USA	325.114
2	United Kingdom	125.067	France	106.383	Netherlands	305.326
3	France	119.717	Germany	89.242	France	145.748
4	Germany	59.133	United Kingdom	29.975	Germany	86.340
5	Japan	17.750	Netherlands	21.442	United Kingdom	50.940
6	Russia	11.550	China	14.083	China	38.069
7	Spain	6.683	Japan	3.067	Spain	3.819
8	Belgium	5.333	Sweden	1.583	Sweden	2.350
9	Indonesia	0.000	Spain	1.000	Russia	0.000
10	Kazakhstan	0.000	Russia	0.000	Korea	0.000
	Average of net importer	69.349	Average of net importer	45.678	Average of net importer	138.041
	Average of net exporter	78.867	Average of net exporter	52.600	Average of net exporter	135.308

Note: Net importer, imports > exports ; Net exporter, imports < exports .

).

In the natural uranium trade network, the betweenness centrality value of the United States, France, Germany, Canada, and Russia is relatively high. Except for Canada, all these countries are major uranium resource consumers, which indicated that the uranium resource consumers have robust control over the trade network. Affected by the Fukushima nuclear accident, the betweenness centrality values of the United States and France have gradually decreased, indicating that the two countries have less control over the natural uranium trade network. Affected by the Fukushima nuclear accident, the betweenness centrality values of the United States and France have gradually declined, indicating that the two countries’ ability to control the natural uranium trade network has steadily weakened. As the world’s major natural uranium exporters, Kazakhstan, Australia, and Namibia have low betweenness centrality values and have little control over the natural uranium trade. Although China has become the world’s second-largest consumer of uranium resources (in 2019, China’s uranium resource consumption was 9834 t, second only to the United States’ 19,746 t) and the largest importer of natural uranium (over 60% of imports come from Kazakhstan), it does not participate in the global natural uranium trade at a deeper level due to its relatively concentrated import sources. This results in its lack of control ability in the natural uranium trade network (Figure 10C).

Developed countries almost monopolize the enriched uranium trade market with their technological advantages and barriers. The United States and France have maintained high betweenness centrality values during the study period. The Netherlands, as the world’s most significant concentration of enriched uranium product processing and re-exports, plays a prominent role as a ‘media’, which further enhances its betweenness centrality. As major trading countries, China and Japan have low betweenness centrality values, meaning they have little control over the trade network (Figure 10D).

3.3.3. Eigencentrality

In 2000, 2010, and 2019, the average eigencentrality value of net importers was higher than that of net exporters in the natural uranium trade network. The net importers are more inclined to trade with essential countries in the natural uranium trade network, ensuring the safety of natural uranium imports to a certain extent. During the study period, the average eigencentrality value of net exporters showed an upward trend, indicating that net exporters were also working hard to increase their importance in the network to ensure the stability of their export earnings. In the enriched uranium trade, both net importers and net exporters maintained high eigencentrality values, especially in the United States, which has always supported the status of the most critical country in the network (

Table 8. Top 10 eigencentrality countries in the natural uranium trade network.

	2000	Eigen Centrality	2010	Eigen Centrality	2019	Eigen Centrality
1	France	0.616	France	0.970	France	0.718815
2	USA	0.505	USA	0.606	Germany	0.672207
3	Canada	0.497	Canada	0.583	Canada	0.624305
4	Japan	0.430	Germany	0.553	USA	0.621839
5	Germany	0.348	China	0.407	Russia	0.488229
6	Niger	0.258	Russia	0.350	Australia	0.357737
7	Russia	0.142	Kazakhstan	0.251	Kazakhstan	0.202398
8	Australia	0.034	Namibia	0.142	China	0.084802
9	Namibia	0.008	Australia	0.132	Namibia	0.08074
10	United Kingdom	0.000	Uzbekistan	0.000	Ukraine	0
	Average of net importer	0.408	Average of net importer	0.577	Average of net importer	0.517
	Average of net exporter	0.199	Average of net exporter	0.277	Average of net exporter	0.316

Note: Net importer, imports > exports ; Net exporter, imports < exports .

and

Table 9. Top 10 eigencentrality countries in the enriched uranium trade network.

	2000	Eigen Centrality	2010	Eigen Centrality	2019	Eigen Centrality
1	USA	1.000	USA	1.000	USA	1.000
2	France	0.978	Sweden	0.893	Netherlands	0.837
3	United Kingdom	0.744	Japan	0.814	France	0.752
4	Japan	0.675	Germany	0.785	United Kingdom	0.744
5	Germany	0.564	France	0.949	Sweden	0.673
6	Belgium	0.482	United Kingdom	0.744	Germany	0.595
7	Indonesia	0.185	Netherlands	0.622	Korea	0.575
8	Spain	0.168	Spain	0.490	China	0.515
9	Russia	0.131	China	0.328	Spain	0.392
10	Kazakhstan	0.000	Russia	0.000	Russia	0.000
	Average of net importer	0.502	Average of net importer	0.718	Average of net importer	0.702
	Average of net exporter	0.604	Average of net exporter	0.772	Average of net exporter	0.644

Note: Net importer, imports > exports ; Net exporter, imports < exports .

).

In the natural uranium trade network, France, the United States, Germany, Canada, and other European and American countries have higher eigencentrality value, which means that these countries tend to trade with essential countries in the network to ensure their trade security and stability. However, the eigencentrality value of major natural uranium exporters such as Australia, Kazakhstan, and Namibia are lower, which is not conducive to ensuring the safety and stability of resource exports. Due to the excessive dependence on Kazakhstan for natural uranium imports, the eigencentrality value of China shows a downward trend, which means there are security risks in China’s natural uranium imports (Figure 10E).

In the trade network of enriched uranium, the United States has always maintained a higher eigencentrality value. After the Fukushima nuclear accident, the eigencentrality value of the United Kingdom, Germany, and France showed a downward trend, and the eigencentrality value of the Netherlands showed a fluctuating upward trend. In general, European and American countries maintained a higher eigencentrality value, which ensures the stability and security of European and American countries in the enriched uranium trade. As China’s imports of enriched uranium increase, its eigencentrality value shows an upward trend, indicating the increasing importance of its trading partners, indicating that China tended to trade with essential countries in the network during the study period (Figure 10F).

Both in natural uranium trade and enriched uranium trade, the United States and France maintained a higher value of closeness centrality, betweenness centrality and eigencentrality, indicating that these two countries have higher abilities of anti-control and control in the GURTN, which is inseparable from the uranium resources trade strategy of the two countries. The United States and France are deeply involved in the global uranium resources trade. According to the UN Comtrade, about 40 countries trade uranium resources with the United States each year. The United States is the world’s major importer and exporter of uranium resources. The United States mainly relies on Canada for natural uranium imports, and its natural uranium is mainly exported to Russia, the Netherlands, and Germany. Russia, the Netherlands, and the UK account for most of America’s enriched uranium imports, while Japan accounts for most of its exports. These countries not only have a short trade distance from the United States but also play an essential role in the trade network (Figure 11).

4. Conclusions and Suggestion

4.1. Conclusions

Based on the perspective of the industry chain, this paper constructed the upstream natural uranium trade complex network (upstream) and the enriched uranium trade complex network (downstream) to analyze the characteristics and evolution trend of the GURTN from the global, community, and national levels. The conclusions are as follows:

(1) The trade of enriched uranium is mainly concentrated between developed countries such as European and North American countries, Japan and South Korea (nuclear power countries and nuclear-fuel-producing countries). The objects of trade are relatively fixed and less affected by external influences. Natural uranium is used as a raw material, and its trade characteristics are greatly affected by uranium price fluctuations. During the study period, the average clustering coefficient of the GURTN fluctuated between 0.10 and 0.35, indicating that the trade relationship was relatively loose. Natural uranium trade was regionalized, while enriched uranium trade was highly globalized. During the study period, fewer newly added nuclear power countries and resource suppliers made the GURTN more stable.

(2) The evolution of the global natural uranium trade community is dominated by the significant uranium-resource-demanding countries. The USA–Germany-dominated trade community, the French–Niger-dominated trade community, and the China–former Soviet Union-dominated trade community have formed around the world. After the Fukushima nuclear accident, the development of the nuclear power industry in many emerging countries has stalled. Therefore, the global natural uranium trade pattern will be difficult to change in the short term. The countries participating in the enriched uranium trade are developed countries in European and North America, Japan, and Korea. Therefore, the division of enriched uranium trade communities is dominated by the above countries. In 2020, with the expiration of the USA–Russia enriched uranium trade agreement, it will become an uncertain factor affecting the evolution of the global enriched uranium trade pattern.

(3) The leading countries of the three major trade communities maintain strong anti-control capabilities over the natural uranium trade. The anti-control ability of the trade network of European and American countries was higher than that of Asian countries in the enriched uranium trade. The net importing country mainly controls the global natural uranium trade, and the primary export has almost no power over the natural uranium trade. Although China has become the world’s second-largest consumer of uranium resources and the largest importer of natural uranium, it does not participate in the global natural uranium trade at a deeper level due to its relatively concentrated import sources. This results in its lack of control ability and discourse power in the natural uranium trade network. Net exporters have more control over trade than net importers in the enriched uranium trade.

(4) Since the United States and France are deeply involved in the global uranium resources trade, both maintained a higher value of closeness centrality, betweenness centrality, and eigencentrality. This indicates that these two countries have higher abilities of anti-control and control in the GURTN, which is inseparable from the uranium resource trade strategy of the two countries.

4.2. Suggestions

(1) The structure of the global uranium resource trade network is loose, and the close connection between trading countries needs to be improved. Natural uranium exporters need to expand export sources and trade with important countries to enhance their control ability and discourse power in marketing. Asian countries need to participate in the enriched uranium trade actively and strive to break the monopoly of European and American countries.

(2) Trading countries should take positive measures to deal with the impact on the market with the expiration of the USA–Russia enriched uranium trade agreement. In particular, natural uranium producers can increase supplies appropriately to stabilize the market.

(3) China needs to actively participate in the trade of uranium resources, particularly increase trade with important countries, gradually increase its influence on the market of uranium resources, and ensure the trade security of uranium resources.