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Regional Sustainability >

2024 , Vol. 5 >Issue 4: 100176

DOI: https://doi.org/10.1016/j.regsus.2024.100176

Full Length Article

Evolutionary characteristics of export trade network in the Arctic region

  • MA Xing a ,
  • QIANG Wenli , a, * ,
  • WANG Shijin b ,
  • LIU Jiayi a ,
  • Arunima MALIK c, d ,
  • LI Mengyu c ,
  • WANG Xiang e
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  • aCollege of Earth and Environmental Sciences, Lanzhou University, Lanzhou, 730000, China
  • bYulong Snow Mountain Cryosphere and Sustainable Development Field Science Observation and Research Station, State Key Laboratory of Cryospheric Sciences and Frozen Soil Engineering, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, 730000, China
  • cSchool of Physics, The University of Sydney, Sydney, 2006, Australia
  • dIntegrated Sustainability Analysis, School of Physics, The University of Sydney, Sydney, 2006, Australia
  • eCollege of Geography and Environmental Sciences, Northwest Normal University, Lanzhou, 730000, China
*E-mail address: (QIANG Wenli).

Received date: 2023-12-30

  Revised date: 2024-07-16

  Accepted date: 2024-11-25

  Online published: 2025-08-13

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Abstract

The economic potential induced by environmental changes in the Arctic region garnered substantial interest, which positions Arctic trade as a crucial indicator in forecasting the impacts of climate change on the global economy. Nevertheless, attention devoted to the evolving dynamics of trade in the Arctic region remains scarce. In this study, we constructed export trade network in the Arctic region (including Denmark, Finland, Sweden, Norway, Iceland, the Canadian Arctic, the Russian Arctic, Alaska State of the USA, and Greenland) from 1990 to 2019 and analyzed its topology and evolutionary characteristics through complex network theory. We used a structural entropy index based on the distribution of the number of trading partners and the degree of trade concentration to assess export diversity, while we also utilized a revealed comparative advantage index to evaluate product export competitiveness using the share of trade volume of each type of product. The results indicate that the total export trade in the Arctic region increased by 53.4% during 1990-2019, with the most significant growth observed in the exports of chemical products and mineral fuels. The increasing complexity of trade network in the Arctic region resulted in the region’s export destinations no longer being concentrated on a few major countries and regions. The proportion of exports from the Arctic region to Europe decreased by 13.5%, while the proportion of exports from the Arctic region to Asia and North America increased by 6.8% and 3.1%, respectively. The Arctic region exhibited clear distinctions in the range of flows of different products, and its export trade was becoming increasingly diversified. Although differences in comparative advantages between products within individual countries or regions have narrowed, substantial gaps persist. The findings of this study can enhance the comprehensive understanding of the significance and function of Arctic trade activities within the global economy, providing a scientific basis for addressing the associated challenges and opportunities in the context of climate change.

Key words： Trade network; Structural entropy index; Revealed comparative advantage (RCA); Global economy; Climate change; Arctic region

Cite this article

MA Xing , QIANG Wenli , WANG Shijin , LIU Jiayi , Arunima MALIK , LI Mengyu , WANG Xiang . Evolutionary characteristics of export trade network in the Arctic region[J]. Regional Sustainability, 2024 , 5(4) : 100176 . DOI: 10.1016/j.regsus.2024.100176

1. Introduction

The impacts of global warming are becoming increasingly evident in the Arctic region (Rantanen et al., 2022; Smith et al., 2022), which draws significant attention to the economic prospects that emerge from environmental changes in the Arctic region (ACIA, 2004; Glomsrød et al., 2021). Climate warming, sea ice melting, and permafrost degradation have facilitated increased access to resources in the Arctic region, triggering a series of chain reactions that affect food supply, resource consumption, and economic system (National Research Council, 2015). Emerging economic opportunities in oil, gas, mineral extraction, agriculture, fisheries, commercial shipping, and tourism in the Arctic region hold the potential to generate billions of dollars in annual revenue, positioning the region as an engine for global economic development (McCauley et al., 2016; Guo et al., 2022).
The future economic potential in the Arctic region is expected to increase significantly. Increased resource extraction and commodity supply from the Arctic region are expected to alter the global economic structure and accelerate the integration of the Arctic region into the global trade network (Hanaček et al., 2022). Currently, the Arctic region accounts for approximately 10.0% of the global oil market and 25.0% of the natural gas market (Loe and Kelman, 2016). The technically recoverable reserves of oil, natural gas, and liquefied natural gas in the Arctic region constitute 13.0%, 30.0%, and 20.0% of the proven reserves of the world, respectively (Bird et al., 2008; Gautier et al., 2009). The melting of the ice cap and degradation of permafrost will facilitate the extraction of these resources, which would lead to increased global investment in the Arctic region. By 2030, the total investment in mining, oil, gas, and infrastructure is projected to reach 1.0×1012 USD (Hanaček et al., 2022). Climate change also impacted the agricultural sector in the Arctic region, which resulted in increased fishing income and expansion of arable land. The continuous rise in Arctic Ocean temperature and primary productivity has led to an increase in fisheries yield (Mueter, 2022). By 2050, the total fishery revenue in the Arctic region may increase by 39.0% compared with the levels in 2000 (Lam et al., 2016). Climate change also created favorable conditions for the expansion of agriculture in the Arctic region, with 10.0%-20.0% of the region projected to become suitable for agricultural development by 2100 (King et al., 2018), which will potentially support an additional 0.25×109 to 1.00×109 people (Unc et al., 2021).
Climate change and the melting of sea ice also make trans-Arctic navigation feasible (Zhang et al., 2019; Ye et al., 2021). The competitiveness of shipping is driving the evolution of trade patterns, and the close integration of energy resources and shipping routes is becoming a key factor in the transformation of the global trade landscape (Melia et al., 2016; Xia and Hu, 2017). On the one hand, the number of vessels in the Arctic region continues to increase, with a 37.0% increase in ships entering the area from 2013 to 2023. In 2022, 44.0%, 24.0%, and 18.0% of the ships that entered the Arctic region were fishing vessels, cargo ships, and those for resource extraction, respectively (PAME, 2024). On the other hand, the distance of trans-Arctic voyages is rapidly increasing. Over the past decade, the total sailing distance of ships in the Arctic region increased by 111.0%, while the number of vessels in the Arctic region has increased by 37.0% from 2013 to 2023 (Farré et al., 2014; Deggim, 2018). The opening of Arctic routes has reduced the transportation time and distance among East Asia, Europe, and the Americas (Zhu et al., 2018; Huang et al., 2021) and strengthened the trade links of the Arctic region with the rest of the world (Li et al., 2017). Compared with traditional trade routes through the Panama and Suez Canals, Arctic routes can save 33.3% and 40.0% of transportation time and distance, respectively, which could result in a 10.0% increase in trade volume between northern Europe and East Asia (Bekkers et al., 2018). From 2013 to 2023, the freight volume of Russia through the northern sea route increased 13 times. The transport potential of the Arctic shipping route is projected to continue expanding, which could drive global economic growth. By 2030, the global freight volume on the northern sea route is expected to reach 1.2×108-2.4×108 t. Moreover, year-round navigability on the northern sea route could increase trade between the European Union (EU) and Asia by up to 6.0% (Bekkers et al., 2018; Alvarez et al., 2020). Under unobstructed navigation conditions, 5.0% of the global cargo trade could be transported via Arctic routes, which could lead to an increase of 0.3% in global trade (Yumashev et al., 2017).
Arctic trade serves as a critical medium for predicting the impacts of climate change on global economy (AMAP, 2021). Previous studies of the Arctic region have primarily focused on natural environmental changes and their consequences (Biresselioglu et al., 2020). This aspect encompasses the opportunities and challenges presented by Arctic shipping routes (Beveridge et al., 2016; Guo et al., 2022), the exploration and development of energy resources, such as oil, natural gas, and minerals (Ermida, 2016; Petrick et al., 2017), and shifts in geopolitical relations (Brutschin and Schubert, 2016). However, attention to trade dynamics across the entire Arctic region has remained relatively limited in which quantitative analyses are particularly scarce (Li et al., 2017). This gap represents an emerging research domain. The existing studies typically concentrate on the role of Arctic regions in global trade (Zou, 2014; Larsen and Huskey, 2015) or conduct separate analyses based on individual countries (Andreassen, 2016; Crépin et al., 2017; Nong et al., 2018). Given that the Arctic region constitutes a naturally cohesive geographical entity that is especially sensitive to climate change (Gibson et al., 2021), examining it as a unified natural region for study can more effectively elucidate the intricate relationship between climate change and economic dynamics. This methodology enables the determination of the specific impacts of environmental changes on export trade in the Arctic region and reveals differences and particularities across regions. Furthermore, a systematic analysis of changes in trade patterns within the Arctic region can offer an in-depth understanding of the significance and role of trade activities in global economy.
This study first utilized complex network theory to analyze the topological changes in export trade network from 1990 to 2019 to reveal the spatiotemporal differences and evolutionary characteristics of export trade network in the Arctic region. Subsequently, we employed the structural entropy index to quantify the diversity of export trade in the Arctic region. Finally, through analysis of the identified comparative advantages of various export products, we further evaluated the export competitiveness of the Arctic region. This study offers valuable insights into the significance of trade activities in the context of climate change, which drives regional and global economic transformation.

2. Data and methodology

2.1. Data sources

Trade data were obtained from the United Nations Commodity Trade Statistics Database (https://comtradeplus.un.org/). We selected 1990, 2000, 2010, and 2019 as reference points for the analysis of the evolution of export trade volume and trade products. In this study, the Arctic region includes Denmark, Finland, Sweden, Norway, Iceland, the Canadian Arctic, the Russian Arctic, Alaska State of the USA, and Greenland (McCauley et al., 2016). We calculated the regional exports of the Canadian Arctic, the Russian Arctic, and Alaska of the USA based on their respective industry shares in the entire country. Industry share data were from the statistical offices of the corresponding countries or regions (https://www.statcan.gc.ca/; https://rosstat.gov.ru/; https://www.bea.gov/). According to the Standard International Trade Classification Revision 3 in the United Nations Commodity database (UNCTADstat Data Center, 2022), we classified the trade categories into: foods, crude oil, mineral fuels, chemical products, manufactured goods, machinery equipment, bulk commodities, and miscellaneous manufactured goods. To eliminate the effect of changes in currency purchasing power, we converted export trade values to constant USD data based on 2019 values.

2.2. Complex network analysis

Complex network analysis can be used to elucidate the topological structure and dynamic features of the entire international trade system and specific regional trade network (He et al., 2019). Complex network theory unveils the relevant characteristics and evolutionary processes of trade network, highlighting the positions and roles of various economies within the network (Wang et al., 2018). Utilizing complex network theory, we selected node degree, node strength, and network density as the primary indices of network characteristics to examine the structures and dynamic changes of trade network in the Arctic region. In complex networks, the set of nodes and edges describes the trade relationship between regions (Yang et al., 2015). Trade network between nodes can be expressed as follows:
$V=\left\{ \begin{matrix} {{v}_{11}} & \cdots & {{v}_{1j}} \\ \vdots & \ddots & \vdots \\ {{v}_{i1}} & \cdots & {{v}_{ij}} \\\end{matrix} \right\}$,
where V is an undirected unweighted trade network; and vij denotes the trade relationship between node i and node j.
Networks in complex network theory can be undirected and unweighted as well as directed and weighted. By considering the trade volume between regions as the weights of edges, a directionally weighted trade network can be constructed as follows:
$W=\left\{ \begin{matrix} {{a}_{11}} & \cdots & {{a}_{1j}} \\ \vdots & \ddots & \vdots \\ {{a}_{i1}} & \cdots & {{a}_{ij}} \\\end{matrix} \right\}$,
where W denotes a directionally weighted trade network; and aij is the trade value between node i and node j.
Node degree is the number of nodes in a trade network that are directly connected to a particular node. The greater the node degree, the higher the level of diversification of trading partners. In a directed network, node degree is divided into export degree and import degree. Relevant equations are as follows:
$K_{i}^{\text{out}}=\sum\limits_{j=1}^{n}{{{a}_{ij}}}$,
$K_{i}^{\text{in}}=\sum\limits_{i=1}^{n}{{{a}_{ji}}}$,
${{K}_{i}}=K_{i}^{\text{in}}+K_{i}^{\text{out}}$,
where Kout i is the export degree; Kin i is the import degree; Ki is the node degree; n is the number of nodes in trade network; and aji is the import value from node j to node i.
Node degree reflects the existence of trade links between national nodes and the number of regions with which they engage in trade but fail to incorporate the magnitude of trading activities. Typically, the difference in trade volume between nodes in a trade network is significant. We introduced node strength to study the export trade strength of each region. Node strength is defined as the sum of all link weights of a given node. Node strength can be calculated as follows:
$S_{i}^{\text{out}}=\sum\nolimits_{j=1}^{n}{{{a}_{ij}}}$,
$S_{i}^{\text{in}}=\sum\nolimits_{i=1}^{n}{{{a}_{ji}}}$,
where Sout i and Sin i indicate export strength and import strength, respectively.
Network density can be used to measure the closeness of all regions in trade network; the larger the value, the closer the ties between regions and the more frequent the trade activities. That is, it reflects the tendency of trade network decentralization or concentration. An increasing value of network density indicates more trade relations and higher network density. The density of the directed network can be expressed as follows:
$D=\frac{m}{n(n-1)}$,
where D is the density of trade network; and m represents the actual number of trade relations that exist in trade network.

2.3. Structural entropy index

The structural entropy index is a common measure of externality or diversification whose results demonstrate the diversity and geospatial aggregation of export destination countries (Qi et al., 2021). The structural entropy index can be calculated as follows:
$\text{Entrop}{{\text{y}}_{c}}=\sum\limits_{c=1}^{R}{\frac{{{P}_{c}}}{P}}\log \left( \frac{1}{{{P}_{c}}/P} \right)$,
where Entropyc denotes the structural entropy index; R is the number of regions involved in trade; and Pc/P stands for the share of the trade of a country with the cth trading partner in the total export of the country. The structural entropy index is influenced by the number of trading partners and the degree of trade concentration of the country. High values indicate that the country exhibits a decentralized distribution of trading partners, a homogeneous export volume among trading partners, more options to face the trade market, and a strong ability to counter export market perturbations (Yang et al., 2013; Hartmann et al., 2017).

2.4. Revealed comparative advantage (RCA) index

RCA is grounded in Ricardian trade theory (French, 2017), which asserts that the trade patterns between regions are determined by their relative productivity discrepancies. While these productivity discrepancies may be challenging to directly observe, we can calculate the RCA using trade data to effectively reveal these discrepancies (He and Wu, 2021). When the ratio of the exports of product z from country A to its total exports of all products exceeds the same ratio for the world or region, country A is considered to have a significant comparative advantage in a given product z. The equation for calculating RCA is as follows:
${{\operatorname{RCA}}_{{{A}_{z}}}}=\frac{{{x}_{{{A}_{z}}}}}{\sum\nolimits_{y\in {P}'}{{{x}_{{{A}_{y}}}}}}/\frac{{{x}_{{{w}_{z}}}}}{\sum\nolimits_{y\in {P}'}{{{x}_{{{w}_{y}}}}}}$,
where RCAAz is the revealed comparative advantage of exports of product z from country A; xAz is the exports of product z from country A; xwz is the exports of product z from the world; P' is the set of all products (z∈P'); Σy∈P'xAy is the total exports from country A; and Σy∈P'xwy is the total exports from the world. When a region features a dominant comparative advantage (RCA>1) for a given product, it is inferred as a competitive producer and exporter of such a product for the region that produces and exports it or for the region that is below the average in the Arctic region. The region with a comparative advantage in product z is considered to have export strength in such a product. The higher the RCA value of a region for product z, the greater its export strength for product z.

3. Results

3.1. Characteristics of export trade network in the Arctic region

3.1.1. Changes in export strength and export structure

The Arctic region experienced a fluctuating but generally upward trend in its export strength, with a 53.4% increase from 3.1×1011 to 4.8×1011 USD during 1990-2019 (Fig. 1a). Denmark, Finland, Sweden, and Norway were predominant exporters, which contributed more than 90.0% of the total exports in the Arctic region over the past three decades. In 2019, Sweden alone accounted for 33.3% of the total exports in the Arctic region, while Greenland, the Canadian Arctic, Iceland, Alaska State of the USA, and the Russian Arctic collectively accounted for less than 10.0%. Nevertheless, the Russian Arctic exhibited the highest growth rate of exports, with a 6.2-fold increase from 2.4×109 USD in 1996 to 1.7×1010 USD in 2019.
Fig. 1. Changes in export strength (a) and export structure (b) in the Arctic region from 1990 to 2019.
In 1990, the top five export categories in the Arctic region were machinery equipment, manufactured goods, mineral fuels, foods, and miscellaneous manufactured goods, collectively accounting for 84.3% of the total exports. By 2019, this composition shifted to machinery equipment (26.9%), mineral fuels (19.9%), manufactured goods (14.2%), chemical products (11.8%), and foods (10.1%), with a cumulative share of 82.9% (Fig. 1b). During 1990-2019, all product categories experienced an increase in export volume with the exception of manufactured goods, which underwent a decline of 5.5%. Notably, the exports of chemical products, mineral fuels, and machinery equipment grew significantly, which expanded by 1.5, 1.4, and 0.4 times, respectively. In terms of export proportion changes, the shares of mineral fuels, chemical products, bulk commodities, and foods in total exports increased by 7.3%, 4.6%, 2.7%, and 0.3%, respectively. Conversely, the export proportions of manufactured goods, machinery equipment, crude oil, and miscellaneous manufactured goods decreased by 8.8%, 3.6%, 1.9%, and 0.8%, respectively.
Export trade in the Arctic region is shown in Figure 2. Iceland and Greenland were the leading regions in the exports of foods. During 1990-2019, foods accounted for 59.3% and 90.1% of the total export value in Iceland and Greenland, respectively. The total export volume of Iceland increased by 2.3 times during this period. In Iceland, the proportion of exports of manufactured goods increased from 15.2% to 37.4% during 1990-2019, while that of machinery equipment increased from 2.1% to 8.5%. Conversely, the proportion of exports of foods decreased from 79.8% to 46.8%.
Fig. 2. Trends in product categories exported from the Arctic region by country or region from 1990 to 2019. (a), Iceland; (b), Greenland; (c), Norway; (d), the Russian Arctic; (e), Alaska State of the USA; (f), Sweden; (g), Denmark; (h), Finland; (i), the Canadian Arctic.
The primary exporters of mineral fuels were Norway, the Russian Arctic, and Alaska State of the USA. Norway’s trade was predominantly dependent on oil and gas exports, which constituted 45.2% of its total trade in 1998 and peaked at 71.2% in 2012. In Alaska State of the USA, crude oil accounted for 84.8% of the total exports in 2019 compared with 56.4% in 1990.
Denmark, Finland, Sweden, and the Canadian Arctic were significant contributors to the exports of machinery equipment in the Arctic region. During 1990-2019, Denmark’s exports comprised 26.2% machinery equipment and 20.4% foods. The export of its chemical products increased from 8.5% to 23.6%, while the export of foods decreased from 26.5% to 17.6%. Finland’s exports included 36.0% machinery equipment and 32.4% manufactured goods. Specifically, the export of manufactured goods reduced from 41.2% to 26.0%. Meanwhile, Sweden’s exports included 36.0% machinery equipment and 16.5% manufactured goods. Between 1990 and 2019, the export of machinery equipment decreased from 43.4% to 38.9% in Sweden, and the export of manufactured goods declined from 25.5% to 16.5%. In the Canadian Arctic, the export of machinery equipment decreased from 37.2% in 1990 to 26.8% in 2019.

3.1.2. Analysis of network characteristics

From 1990 to 2019, the size of export trade network in the Arctic region continued to expand, and the structure of network connections became denser and more complex (Table 1). The number of regions involved in export trade in the Arctic region increased from 200 to 235, and node degree increased from 144.5 in 1990 to 183.6 in 2019.
Table 1 Evolution of export trade network in the Arctic region.
Year Overall network parameter Node degree Node strength
Node Link Density Total flow (×109 USD) Mean K Kout Mean Sout (×109 USD) Mean Sin (×109 USD)
1990 200 1138 0.78 314.6 144.5 192.0 35.0 1.6
2000 232 1546 0.80 377.5 172.7 223.0 42.0 1.7
2010 235 1622 0.84 560.8 179.9 235.0 62.3 2.4
2019 235 1637 0.85 482.4 183.6 235.0 53.6 2.1

Note: Node represents the number of regions involved in the network. Link represents the number of trade links between regions. Density is the ratio of the number of links in the network to the maximum number of potential links. Total flow is the sum of trade volumes of all links in the network. Mean K represents the node degree of average export (the number of export destinations). Kout represents the node degree of the total export (the number of total export destinations). Mean Sout represents the average outbound strength of nodes. Mean Sin represents the average inbound strength of nodes.

During 1990-2019, node degree has been consistently high in Sweden, Denmark, Finland, and Norway, averaging 204.3, and also higher in the Canadian Arctic (208.6) and Alaska State of the USA (218.3), followed by the Russian Arctic (182.2) and Iceland (112.6); the lowest node degree was found in Greenland, with a node degree of 13.6. All countries and regions in the Arctic region exhibited increasing trends in the node degree of export trade, with Iceland and Sweden showing the most significant increases. The number of network edges increased from 1138 in 1990 to 1637 in 2019. The density of export networks in the Arctic region depicted an upward trend, with network density values increasing from 0.78 to 0.85.
Export trade network in the Arctic region exhibited marked heterogeneity, as reflected in the unequal distribution of node strength and significant variations in link weights between nodes (Fig. 3). Specifically, the majority of nodes in export trade network displayed low levels of strengths annually, whereas a small group of regions demonstrated high levels of export trade flows and strengths. The entire network was dominated by key nodes with high connectivity. In 1990, the top 15 importing regions accounted for 80.8% of the total exports in the Arctic region, and by 2019, the top 15 importing countries accounted for 72.9% of the total exports in the Arctic region.
Fig. 3. Changes in the node degree of export trade in the Arctic region from 1990 to 2019.

3.2. Characteristics of export trade flows in the Arctic region

3.2.1. Characteristics of interregional trade flow

In 1990, export trade network in the Arctic region exhibited a relatively unidirectional export pattern (Fig. 4). Long-distance trade was minimal, with 72.3% of exports from the Arctic region going to Europe. Exports to European countries, specifically Denmark, Norway, Sweden, and Finland, constituted 96.3% of the total. Exports to North America and Asian countries accounted for only 14.3% and 8.4%, respectively. In the Canadian Arctic, 75.8% of exports were directed to North America. The top five import strengths were Germany, the UK, Sweden, Alaska State of the USA, and France, which accounted for 13.6%, 13.6%, 7.6%, 6.7%, and 6.0% of the exports in the Arctic region, respectively. The export values of 4 countries exceeded 1.0×1010 USD, while those of 21 countries exceeded 5.0×109 USD. Among the trade linkages, the Norway-the UK connection exhibited the highest weight, with a trading volume of 1.8×1010 USD, followed by the Sweden-Germany, Denmark-Germany, Sweden-the UK, and Sweden-Alaska State of the USA connections, with trading volumes of 1.6×1010, 1.3×1010, 1.2×1010, and 9.9×109 USD, respectively.
Fig. 4. Strength and structure of export trade in the Arctic region in 1990 (a), 2000 (b), 2010 (c), and 2019 (d). Yellow nodes indicate export regions, while green nodes denote import regions. Node size indicates the trade intensity; the larger the point, the greater the trade intensity. The width of connecting lines indicates the intensity of trade flow between nodes; the thicker the connecting line, the stronger the intensity of trade flow.
Trade between the Arctic region and European countries decreased to 58.8% from 1990 to 2019, while economic ties with more distant Asian countries grew rapidly (Fig. 4d). The share of exports to the Asian region increased the most (6.8%). The primary export destinations for the Arctic region were Germany, the UK, the USA, the Netherlands, and Sweden, accounting for 12.1%, 8.2%, 7.6%, 6.9%, and 5.4%, respectively. The significance of major trade routes increased, with 12 connections exceeding 1.0×1010 USD and 27 connections surpassing 5.0×109 USD. Moreover, the export trade of the Arctic region to Asia increased by 2.2 times. China, Japan, South Korea, Turkey, and other Asian countries emerged as primary export targets following European countries. The exports from the Arctic region to China rose from the 30th to the 7th, accounting for 4.8% of the total exports of the Arctic region. Similarly, the exports from the Arctic region to Japan increased from 7.0×109 to 7.6×109 USD during 1990-2019, which represented 1.6% of the total exports. This overall increase in the exports of the Arctic region was accompanied by a decline in trade concentration in which a few export destination countries experienced a decreasing trend in their share of exports. From 1990 to 2019, the shares of Germany and the UK in the trade of the Arctic region decreased from 13.6% to 12.1% and from 13.6% to 8.2%, respectively.
The Arctic region experienced an increase in trade network density and an expansion of export destinations (Fig. 5). Europe remained the primary trade partner of the Arctic region, with North America and Asia increasingly viewed as secondary markets for the exports of the Arctic region. Prior to 2000, trade was concentrated on short transportation routes, such as neighboring European countries, including Germany, the UK, France, and the Netherlands. In addition to traditional European powers and their neighboring countries, other leading global trading nations were becoming major export targets for the Arctic region. Despite strengthening its trade connections with Asia and North America after 2000, the Arctic region continued to mainly rely on European trade (Fig. 5). In terms of mobility patterns, Denmark, Norway, Sweden, and Finland witnessed their share of exports decrease by only 4.0% during 1990-2019. The percentage of exports from the Arctic region to Europe declined from 72.3% to 58.8% during 1990-2019, while the proportion of exports from the Arctic region to North America (primarily the USA) and Asian countries increased from 14.3% to 17.4% and from 8.4% to 15.2%, respectively. Thus, Europe and major trading nations worldwide constituted the core of the export trade of the Arctic region. Concurrently, the Arctic region was integrating into the global trade network by strengthening trade connections with major trading nations globally.
Fig. 5. Export trade network of the Arctic region to continents and major countries in 1990 (a) and 2019 (b).

3.2.2. Trade flow characteristics of major products

Crude oil, mineral fuels, machinery equipment, and foods accounted for 57.3%-66.6% of the total exports of the Arctic region, playing a pivotal role in economic development and energy security. However, these product categories exhibited significant differences in supply and demand dynamics. We analyzed their trade networks in 2019 and depicted the networks of the top 100 links by weight (Fig. 6). The findings revealed divergent network patterns based on the characteristics of these products.
Fig. 6. Export trade network of primary products from the Arctic region in 2019. (a), crude oil; (b), mineral fuels; (c), machinery equipment; (d), foods. The size of each node indicates the trade intensity. Large nodes represent high trade intensity. The width of the connecting line indicates the intensity of trade flow between the nodes. The thicker the connecting line, the greater the intensity of trade flow between two nodes.
In 2019, Sweden, Finland, Denmark, and Norway were the top four countries that exported crude oil in the Arctic region, which accounted for 94.0% of the total crude oil exports of the Arctic region. Sweden, Finland, and Norway contributed to the major share of the top 100 trade links, with 30.0%, 30.0%, and 11.0%, respectively. Sweden’s crude oil exports constituted 42.7% of the Arctic region’s total crude oil exports, with 68.1% exported to Europe, primarily to Germany, the UK, and the Netherlands. Asia accounted for 22.8% of the crude oil exports of Sweden. Finland’s crude oil exports accounted for 27.5% of the Arctic region’s total crude oil exports, while Norway’s crude oil exports accounted for 15.5%. In 2019, the largest crude oil trade link in the Arctic region is Finland-China (Fig. 6a).
Norway was responsible for 60.6% of the total exports of mineral fuels in the Arctic region in 2019, followed by the Russian Arctic (14.1%) and Sweden (10.6%). Among the 100 most heavily weighted connections, Norway, the Russian Arctic, and Sweden accounted for 27, 31, and 18, respectively. The top six export links for mineral fuels were Norway-the UK, Norway-Germany, Norway-the Netherlands, Norway-France, Norway-Sweden, and Norway-Belgium, accounting for 17.9%, 11.7%, 7.8%, 4.5%, 4.0%, and 3.6% of the total exports of mineral fuels in the Arctic region in 2019, respectively (Fig. 6b). Additionally, the Russian Arctic-China and the Canadian Arctic-the USA connections constituted 2.6% and 2.5% of the total exports of mineral fuels in the Arctic region in 2019. The exports of the Russian Arctic were relatively evenly distributed among countries and regions, with 59.3% and 37.3% going to Europe and Asia, respectively. The mineral fuels of Sweden were primarily exported to Norway, Finland, and the UK, representing 18.3%, 18.1%, and 10.6% of the total exports of mineral fuels in Sweden, respectively. Furthermore, there were significant bilateral relations in the Arctic region in terms of the trade of mineral fuels, with Arctic inter-state trade accounting for 23.7% of the exports of mineral fuels in the Arctic region in 2019.
In machinery equipment export trade network, Sweden held the largest share at 51.7%, followed by Denmark (22.1%), Finland (17.3%), Norway (6.7%), and the Canadian Arctic (2.3%). Trade among Arctic countries represented 33.8% of the total exports of machinery equipment in the region. Meanwhile, the exports of machinery equipment from the Arctic region were primarily concentrated in developed countries and emerging market economies in Europe (70.9%), North America (13.3%), and Asia (12.4%; Fig. 6c). In Europe, the exports of machinery equipment mainly went to Germany (14.1%), Norway (8.2%), the Netherlands (5.7%), and the UK (5.5%). In Asia, the top three importing countries were China (5.4%), South Korea (1.7%), and Japan (1.4%). The USA received 12.1% of the total exports of machinery equipment from the Arctic region, with 82.5% of these exports coming from the Canadian Arctic. The largest trade link was between Sweden and Norway, which accounted for 4.9% of the total exports of machinery equipment in the Arctic region in 2019.
Regarding the export of foods, Denmark, Norway, Finland, and Iceland were the major exporters in the Arctic region, which composed 39.5%, 26.4%, 20.6%, and 5.0% of the total food exports in 2019, respectively. Exports from other countries each accounted for less than 3.8% of the total food exports. Among the top 100 trade connections, 79.6% of food flows were directed towards Europe. The top four largest connections in terms of weight originated from Denmark, with exports flowing to Germany, Sweden, China, and the UK. These four trade connections accounted for 19.9% of the top 100 trade connections and 17.2% of the total food exports in the Arctic region. Bilateral food exports between Arctic regions accounted for 26.8% of the total food exports. In addition to major European countries, food exports of the Arctic region targeted significant food-consuming countries, such as China (5.5%), the USA (5.4%), Japan (2.8%), and South Korea (1.2%), as well as neighboring countries, such as Poland (6.8%) and Lithuania (1.8%; Fig. 6d).

3.3. Export advantages in the Arctic region

3.3.1. Evolution of export trade diversification in the Arctic region

The parameters that determine the nodal position of a particular region in trade network included the total trade volume and its distribution. We used the structural entropy index to comprehensively evaluate the distribution of export destinations and the proportional distribution of total export destinations in different regions. Since the exports from Greenland and Alaska State of the USA accounted for a small share of the total trades in the Arctic region, these two regions were not taken into account in the detailed analysis. The diversification of exports in the Arctic region undergone phases of increase, slowdown, fluctuation, and adjustment (Fig. 7). The diversification of export trade in the Arctic region exhibited an upward trend. Between 1990 and 2019, Sweden, Denmark, and Finland maintained their positions in the top three in terms of the number of export destinations and structural entropy index. Denmark, Finland, Norway, Sweden, and the Russian Arctic, which are positioned in the first quadrant, obtained more than 169 export destinations, with a balanced distribution of export shares among all destinations, thus maintaining a structural entropy index of more than 1.2. The export destinations of Iceland increased from 67 in 1990 to 119 in 2019. Although Iceland had fewer export destinations, its structural entropy index increased from 1.1 to 1.2. Between 1990 and 2019, the export destinations of the Canadian Arctic increased from 178 to 219, but the structural entropy index of export trade ranged only from 0.4 (in 2000) to 0.6 (in 2019), which was significantly lower compared with those of other regions and indicated a largely uneven distribution of exports among its trading partners. This study showed that the exports from the Canadian Arctic to the USA accounted for 71.4%-80.2% of its total exports between 1990 and 2019.
Fig. 7. Nodal degree and structural entropy distribution of export trade in the Arctic region from 1990, 2000, 2010 to 2019. The connecting line indicates the change in the position of the point, and the black dot represents the position in 2019.

3.3.2. Explicit comparative advantage analysis of the export products in the Arctic region

We constructed the RCA distribution matrix by calculating the RCA index for product exports across regions to characterize comparative advantage. Figure 8 illustrated significant differences in comparative advantage across products and regions in the Arctic region. The Canadian Arctic, Denmark, Finland, and Sweden exhibited higher RCA values, whereas Norway, the Russian Arctic, and Alaska State of the USA demonstrated a strong competitive advantage in mineral fuels. Iceland and Greenland featured a high comparative advantage in food exports. Comparison of RCA heatmaps from 1990 to 2019 revealed a gradual lightening of matrix colors. While the variation in comparative advantages for different product exports within regions was diminishing, the disparities in comparative advantage across regional product exports remained pronounced.
Fig. 8. Matrix of comparative advantage of the exports of the Arctic region. (a), 1990; (b), 2000; (c), 2010; (d), 2019. Red and blue colors indicate relatively high and low comparative advantage, respectively. The shaded area represents the year for which no trade data were recorded.
In the Canadian Arctic, the RCA index for bulk commodities was initially the highest at 4.0 in 1990 but decreased significantly to 2.0 by 2019. The RCA index of Denmark for chemical products increased from 1.2 to 2.0, whereas that for bulk commodities and foods decreased from 3.2 to 0.3 and from 2.7 to 1.8, respectively. The RCA index of Sweden increased for all product categories, with increments ranging from 0.0 (crude oil) to 0.4 (bulk commodities). In 1990, Finland obtained the lowest RCA index for bulk commodities (0.1), which increased to 1.7 in 2019 and second only to manufactured goods (1.8) and crude oil (1.8). From 1990 to 2019, Norway, the Russian Arctic, and Alaska State of the USA maintained RCA index values ranging from 2.3 to 4.5 for mineral fuels. The RCA index values of Norway for bulk commodities and foods increased by 1.0 and 0.5, respectively, while the RCA index values for other types of products decreased, ranging from 0.0 (miscellaneous manufactured goods) to 1.0 (mineral fuels). In 2019, the RCA index values of the Russian Arctic for all product categories, except for mineral fuels, were less than 1.0, while those of Alaska State of the USA displayed decreases that ranged from 0.1 (manufactured goods) to 0.8 (foods); specifically, the RCA index for foods decreased from 1.0 to 0.3.
In 1990, the RCA index values of Greenland and Iceland for foods were 9.9 and 8.2, respectively, while the RCA index value for miscellaneous manufactured goods was less than 1.0. By 2019, the RCA index of Iceland for foods decreased to 4.6, while that for manufactured goods increased to 2.6. During 1990-2019, the RCA index values for foods and manufacture goods decreased by 3.6 and increased by 2.0, respectively.

4. Discussion

The evolving trade dynamics in the Arctic region underscore the complex interplay among environmental change, economic opportunity, and global market integration. Multiple factors drive the trend in the export volume and structure of the Arctic region. First, globalization has accelerated trade links worldwide, which positions Arctic countries, particularly the USA, Canada, and Russia, as pivotal players in the global trade network and as ranking among the largest exporters (Qiang et al., 2020). Additionally, regional trade agreements played a crucial role in trade linkages. For example, the stronger trade connections of Sweden, Finland, and Denmark compared with those of other Arctic countries and regions can be attributed to their membership in the EU (Valková, 2017). In 2017, China and Russia proposed a vision of jointly building the “Polar Silk Road”, which could catalyze the commercial use and regular operation of Arctic shipping routes (Guo et al., 2022). Second, climate change has unlocked the trade potential of the Arctic region, because natural resource production and transportation have increased due to melting ice and permafrost degradation. Consequently, exports of mineral fuels, crude oil, and foods (fisheries) have emerged in recent years. Significant increases in mineral oil exports have been observed in the Russian Arctic, the Canadian Arctic, Alaska State of the USA, and Norway, while notable growth in fishery exports has occurred in Denmark and the Canadian Arctic. The expanding export trade in the Arctic region has positively influenced economic activities and altered the position of the region in global trade.
The economy of the Arctic region exerts a global impact through trade ripple effects as evidenced by changes in export structure and the diversification and expansion of trade patterns. An increase has been observed in trade partners and distances. Trade network is becoming increasingly diversified and extensive, with a decreased proportion of exports to Europe and increased proportions to Asia and North America. This conclusion aligns with those of previous studies (Bensassi, 2016; Bekkers, 2018; Guo et al., 2022), that is, the Arctic region is more tightly integrating into the global trade network, in which nearly all regions strengthen their positions through increased export partners and network density. This transformation has altered the position of the Arctic region in global trade dynamics. The findings indicate that environmental changes in the Arctic region have generated spillover effects through the trade market (Crépin et al., 2017). Increased connectivity between the Arctic region and the global economy results in its economic development more fragile and susceptible to external shocks. This vulnerability can be mitigated through the diversification of trade partners and establishment of stable trade relationships.
Attention should also be given to the influences that underlie the increase in exports of the Arctic region. First, the existing maritime infrastructure in the Arctic region is insufficient to meet current and future operational demands (Bensassi et al., 2016). Consequently, environmental changes may necessitate significant investments in infrastructure to support resource extraction and shipping activities (Glomsrød et al., 2021). Second, the sensitivity and fragility of environment in the Arctic region render it susceptible to human activities, which warrants attention to the environmental impacts of Arctic region trade. Studies have recommended that although shorter Arctic shipping routes may reduce pollution, the growth of fossil fuel trade and the opening of shipping lanes could result in additional emissions (Lindstad et al., 2016; Yang et al., 2022). By 2100, Arctic shipping routes may contribute to the global average increase in temperature of 0.05% (Yumashev et al., 2017). Thus, Arctic countries and the global community must strike a balance between economic opportunities and the environmental challenges posed by climate change. Third, resource extraction in the Arctic region has sparked political competitions among countries. In light of these uncertainties and challenges, a comprehensive assessment of Arctic shipping routes is required (Melia et al., 2016; Cao et al., 2021). This assessment should consider not only the potential economic benefits but also the associated costs, environmental pollution, and geopolitical factors.
This study investigated the connectivity of export trade network between the Arctic region and the rest of the global economies using Arctic region’s export trade data. This analysis provides new insights into the implications of climate change on the global economic landscape. However, the study has its limitations. The study evaluated the export value of the Arctic region based on the proportions of industry contribution, particularly for Alaska State of the USA, the Canadian Arctic, and the Russian Arctic, due to limitations in data accuracy, which may not reflect the true export situation. Furthermore, a comprehensive evaluation, including production, import, and export data, is required to assess the impact of climate change on economic development in the Arctic region. Future studies should include a quantitative analysis of the factors that drive trade changes, because various factors beyond climate change, such as resource endowment, economic development, geographical location, and trade agreement, influence trade network.

5. Conclusions

We conducted a complex network analysis to examine changes in trade network of the Arctic region by utilizing the structural entropy and comparative advantage indices to reveal changes in export diversity and product export competitiveness in the Arctic region. From 1990 to 2019, the Arctic region’s exports increased by 53.4%, with Denmark, Finland, Sweden, and Norway contributed more than 90.0% of the total exports. Notably, the Russian Arctic exhibited the highest growth rate for exports. Mechanical equipment, manufactured goods, and mineral fuels constituted the primary exports from the Arctic region, which collectively represented more than 60.0% of the total exports. Trade network of the Arctic region expanded in terms of scale and complexity in which Europe remaining as its core trading partner, while engagement with Asian markets, particularly China, Japan, and South Korea, increased. During 1990-2019, the proportion of exports from the Arctic region to Asia increased from 8.4% to 15.2%. China becomes the largest export destination for crude oil and food exports within the Arctic region in 2019.
Different products exhibited significant differences in flow patterns. Crude oil, mineral fuel, machinery equipment, and food were the Arctic region’s major exports. Machinery equipment was mainly exported to Europe, North America, and Asia. Foods mainly flowed to Europe and other large food-consuming countries. The number of export destinations from the Arctic region increased, leading to a balanced distribution of exports. Sweden, Denmark, and Finland consistently ranked in the top three export destinations in terms of structural entropy index. While export destinations have expanded in the Canadian Arctic, the structural entropy index of trade was low, and the Canadian Arctic exports mainly flow to the USA. Disparities in comparative advantage between exports of various products have diminished over time, but the exports of products between regions are still significant.

Authorship contribution statement

MA Xing: data curation, formal analysis, methodology, visualization, writing - original draft, and writing - review & editing; QIANG Wenli: conceptualization, funding acquisition, methodology, project administration, writing - original draft, and writing - review & editing; WANG Shijin: funding acquisition and writing - review & editing; LIU Jiayi: visualization and writing - original draft; Arunima MALIK: writing - review & editing; LI Mengyu: writing - review & editing; and WANG Xiang: writing - original draft. All authors approved the manuscript.

Declaration of conflict of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

This work is supported by the National Natural Science Foundation of China (42471309) and the National Key Research and Development Program of China (2020YFA0608504).

References

Publishing order | Descend order by publishing year | Descend order by cited within
[1]
ACIA (Arctic Climate Impact Assessment), 2004. Impacts of a Warming Arctic:Arctic Climate Impact Assessment. London: Cambridge University Press.

[2]
Alvarez, J., Yumashev, D., Whiteman, G., 2020. A framework for assessing the economic impacts of Arctic change. Ambio. 49(2), 407-418.

DOI PMID

[3]
AMAP (Arctic Monitoring and Assessment Programme), 2021. Arctic Climate Change Update 2021:Key Trends and Impacts. Summary for Policy-makers. In: Arctic Council Repository: Arctic Monitoring and Assessment Programme. Tromsø, Norway.

[4]
Andreassen, N., 2016. Arctic energy development in Russia - How “sustainability” can fit? Energy Res. Soc. Sci. 16, 78-88.

[5]
Bekkers, E., Francois, J.F., Rojas-Romagosa, H., 2018. Melting ice caps and the economic impact of opening the Northern Sea Route. Econ. J. 128(610), 1095-1127.

[6]
Bensassi, S., Stroeve, J.C., Martínez-Zarzoso, I., et al., 2016. Melting ice, growing trade? Elementa-Sci. Anthrop. 4, 1-11.

[7]
Beveridge, L., Fournier, M., Lasserre, F., et al., 2016. Interest of Asian shipping companies in navigating the Arctic. Polar Sci. 10(3), 404-414.

[8]
Bird, K.J., Charpentier, R.R., Gautier, D.L., et al., 2008. Circum-Arctic resource appraisal:Estimates of undiscovered oil and gas north of the Arctic Circle. In: In: US Geological Survey. California, USA.

[9]
Biresselioglu, M.E., Demir, M.H., Solak, B., et al., 2020. Investigating the trends in arctic research: The increasing role of social sciences and humanities. Sci. Total Environ. 729, 139027, doi: 10.1016/j.scitotenv.2020.139027.

[10]
Brutschin, E., Schubert, S.R., 2016. Icy waters, hot tempers, and high stakes: Geopolitics and Geoeconomics of the Arctic. Energy Res. Soc. Sci. 16, 147-159.

[11]
Cao, Y.F., Yu, M., Hui, F.M., et al., 2021. Review of navigability changes in trans-Arctic routes. Chin. Sci. Bull. 66(1), 21-33 (in Chinese).

[12]
Crépin, A.S., Karcher, M., Gascard, J.C., 2017. Arctic climate change, economy and society (ACCESS): Integrated perspectives. Ambio. 46(Suppl. 3), 341-354.

[13]
Deggim, H., 2018. The international code for ships operating in polar waters (polar code). In: Hildebrand, L., Brigham, L., Johansson, T., (eds.). Sustainable Shipping in a Changing Arctic. Berlin: Springer, 15-35.

[14]
Ermida, G., 2016. Energy outlook for the Arctic: 2020 and beyond. POLAR REC. 52(2), 170-175.

[15]
Farré, A.B., Stephenson, S.R., Chen, L.L., et al., 2014. Commercial Arctic shipping through the Northeast Passage: Routes, resources, governance, technology, and infrastructure. Polar Geogr. 37(4), 298-324.

[16]
French, S., 2017. Revealed comparative advantage: What is it good for? J. Int. Econ. 106, 83-103.

[17]
Gautier, D.L., Bird, K.J., Charpentier, R.R., et al., 2009. Assessment of undiscovered oil and gas in the Arctic. Science. 324(5931), 1175-1179.

DOI PMID

[18]
Gibson, C.M., Brinkman, T., Cold, H., et al., 2021. Identifying increasing risks of hazards for northern land-users caused by permafrost thaw: Integrating scientific and community-based research approaches. Environ. Res. Lett. 16(6), 064047, doi: 10.1088/1748-9326/abfc79.

[19]
Glomsrød, S., Duhaime, G., Aslaksen, I., 2021. The Economy of the North - ECONOR 2020. [2023-12-10]. https://www.ssb.no/en/natur-og-miljo/artikler-og-publikasjoner/_attachment/454081?_ts=17bc94cfc98.

[20]
Guo, J.K., Guo, S., Lv, J., 2022. Potential spatial effects of opening Arctic shipping routes on the shipping network of ports between China and Europe. Mar. Pol. 136, 104885, doi: 10.1016/j.marpol.2021.104885.

[21]
Hanaček, K., Kröger, M., Scheidel, A., et al., 2022. On thin ice - The Arctic commodity extraction frontier and environmental conflicts. Ecol. Econ. 191, 107247, doi: 10.1016/j.ecolecon.2021.107247.

[22]
Hartmann, D., Guevara, M.R., Jara-Figueroa, C., et al., 2017. Linking economic complexity, institutions, and income inequality. World Dev. 93, 75-93.

[23]
He, C.F., Wu, W.J., 2021. China’s food export network and its evolution. Human Geography. 36(3), 24-36 (in Chinese).

[24]
He, Z., Yang, Y., Liu, Y., et al., 2019. Characteristics of evolution of global energy trading network and relationships between major countries. Progress in Geography. 38(10), 1621-1632 (in Chinese).

DOI

[25]
Huang, J.X., Zhang, T.Y., Cao, Y.F., et al., 2021. The evolution of navigation performance of Northeast Passage under the scenario of Arctic sea ice melting. Acta Geographica Sinica. 76(5), 1051-1064 (in Chinese).

DOI

[26]
King, M., Altdorff, D., Li, P.F., et al., 2018. Northward shift of the agricultural climate zone under 21st-century global climate change. Sci Rep. 8, 7904, doi: 10.1038/s41598-018-26321-8.

[27]
Lam, V.W.Y., Cheung, W.W.L., Sumaila, U.R., 2016. Marine capture fisheries in the Arctic: Winners or losers under climate change and ocean acidification? Fish. Fish. 17(2), 335-357.

[28]
Larsen, J.N., Huskey, L., 2015. The Arctic economy in a global context. In: Evengård, B., Nymand Larsen, J., Paasche, Ø., (eds.). The New Arctic. Cham: Springer, 159-174.

[29]
Li, Z.F., Miao, Y., Chen, J., 2017. Analysis on the economic circle trade work of Arctic shipping routes based on structural holes theory. Journal of Central China Normal University (Natural Sciences). 51(1), 100-107, 114 (in Chinese).

[30]
Lindstad, H., Bright, R.M., Strømman, A.H., 2016. Economic savings linked to future Arctic shipping trade are at odds with climate change mitigation. Transp. Policy. 45, 24-30.

[31]
Loe, J.S.P., Kelman, I., 2016. Arctic petroleum’s community impacts: Local perceptions from Hammerfest, Norway. Energy Res. Soc. Sci. 16, 25-34.

[32]
McCauley, D., Heffron, R., Pavlenko, M., et al., 2016. Energy justice in the Arctic: Implications for energy infrastructural development in the Arctic. Energy Res. Soc. Sci. 16, 141-146.

[33]
Melia, N., Haines, K., Hawkins, E., 2016. Sea ice decline and 21st century trans-Arctic shipping routes. Geophys. Res. Lett. 43(18), 9720-9728.

[34]
Mueter, F.J., 2022. Arctic fisheries in a changing climate. In: Finger, M., Rekvig, G., (eds.). Global Arctic. Cham: Springer, 279-295.

[35]
National Research Council, 2015. Arctic Matters:The Global Connection to Changes in the Arctic. Washington: The National Academies Press.

[36]
Nong, D., Countryman, A.M., Warziniack, T., 2018. Potential impacts of expanded Arctic Alaska energy resource extraction on US energy sectors. Energy Policy. 119, 574-584.

[37]
PAME (Protection of the Arctic Marine Environment), , 2024. The Increase in Arctic Shipping 2013-2023. In: Arctic Council Repository: Arctic Shipping Status Reports. Tromsø, Norway.

[38]
Petrick, S., Riemann-Campe, K., Hoog, S., et al., 2017. Climate change, future arctic sea ice, and the competitiveness of European arctic offshore oil and gas production on world markets. Ambio. 46(Suppl. 3), 410-422.

[39]
Qi, F., He, C.F., Zhang, W., 2021. Evolution of geographical patterns of export trade and the expansion of trade network at the provincial level. Economic Geography. 41(2), 35-46 (in Chinese).

[40]
Qiang, W.L., Niu, S.W., Liu, A.M., et al., 2020. Trends in global virtual land trade in relation to agricultural products. Land Use Pol. 92, 104439, doi: 10.1016/j.landusepol.2019.104439.

[41]
Rantanen, M., Karpechko, A.Y., Lipponen, A., et al., 2022. The Arctic has warmed nearly four times faster than the globe since 1979.. Commun. Earth Environ. 3(1), 168, doi: 10.1038/s43247-022-00498-3.

[42]
Smith, S.L., O’Neill, H.B., Isaksen, K., et al., 2022. The changing thermal state of permafrost. Nat. Rev. Earth Environ. 3(1), 10-23.

[43]
Unc, A., Altdorff, D., Abakumov, E., et al., 2021. Expansion of agriculture in northern cold-climate regions: A cross-sectoral perspective on opportunities and challenges. Front. Sustain. Food Syst. 5, 663448, doi: 10.3389/fsufs.2021.663448.

[44]
UNCTADstat, Data Centre, 2022. Standard International Trade Classification (SITC) Revision 3. [2023-12-05]. https://unctadstat.unctad.org/EN/Classifications/DimSitcRev3Products_Official_Hierarchy.pdf.

[45]
Valková, I., 2017. Centrality in the network of regional trade agreements: Effects on the strategies of the arctic claimant states. Int. Area Stud. Rev. 20(2), 122-143.

[46]
Wang, X., Qiang, W.L., Niu, S.W., et al., 2018. Analysis on global agricultural trade network and its evolution. Journal of Natural Resources. 33(6), 940-953 (in Chinese).

DOI

[47]
Xia, Y.P., Hu, M.X., 2017. Comparative analysis on the geographical location advantage of the Arctic routes and the traditional routes. World Regional Studies. 26(2), 20-32 (in Chinese).

[48]
Yang, W.Q., Yang, J., Wang, X.B., 2013. The temporal-spatial distribution of agricultural trade between China and Africa. Geogr. Res. 32(7), 1316-1324 (in Chinese).

[49]
Yang, W.X., Yang, Y.P., Chen, H., 2022. How to stimulate Chinese energy companies to comply with emission regulations? Evidence from four-party evolutionary game analysis. Energy. 258, 124867, doi: 10.1016/j.energy.2022.124867.

[50]
Yang, Y., Poon, J.P.H., Liu, Y., 2015. Small and flat worlds: A complex network analysis of international trade in crude oil. Energy. 93, 534-543.

[51]
Ye, B.H., Cheng, Y., Wang, L., et al., 2021. The geo-economics relationship in the Arctic Region. World Regional Studies. 30(2), 234-244 (in Chinese).

DOI

[52]
Yumashev, D., van Hussen, K., Gille, J., et al., 2017. Towards a balanced view of Arctic shipping: Estimating economic impacts of emissions from increased traffic on the Northern Sea Route. Clim. Change. 143(1-2), 143-155.

[53]
Zhang, Z.H., Huisingh, D., Song, M.L., 2019. Exploitation of trans-Arctic maritime transportation. J. Clean Prod. 212, 960-973.

[54]
Zhu, S.D., Fu, X.W., Ng, A.K.Y., et al., 2018. The environmental costs and economic implications of container shipping on the Northern Sea Route. Marit. Policy Manag. 45(4), 456-477.

[55]
Zou, Z.Q., 2014. The impact of the Arctic passage on the global energy trading pattern. Journal of PLA Nanjing Institute of Politics. 30(1), 75-80 (in Chinese).

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