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【精品文档】64中英文双语毕业设计外文文献翻译成品:“一带一路”沿线国家贸易关系的结构与演变 (最新的2018年)

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精品文档 【精品文档】64中英文双语毕业设计外文文献翻译成品“一带一路”沿线国家贸易关系的结构与演变 最新的2018年 精品 文档 64 中英文 双语 毕业设计 外文 文献 翻译 成品 一带 一路
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外文标题:The structure and evolution of trade relations between countries along the Belt and Road外文作者:Jung Won SONN,et,al文献出处:Journal of Geographical Sciences,2018 , 28(9): 1233-1248(如觉得年份太老,可改为近2年,毕竟很多毕业生都这样做)英文3598单词, 19887字符(字符就是印刷符),中文5198汉字。(字数可根据需要自己进行删减)此文档是毕业设计外文翻译成品( 含英文原文+中文翻译),无需调整复杂的格式!下载之后直接可用,方便快捷!本文价格不贵,也就几十块钱!一辈子也就一次的事!The structure and evolution of trade relations between countries along the Belt and RoadAbstract: Trade facilitation is one of the five main agendas of the Belt and Road Initiative (BRI). Social network analysis has helped understand the complexity of trade networks, but existing studies tend to overlook the fact that not all bilateral trade relations are equally im- portant to a country. To fill this gap in the literature, this paper focuses on the top 2 trade relations networks to illuminate the structure and evolution of B&R trade relations, the relative positions of different countries, and changes in the composition of trade communities (e.g., the community leaders) and the changing patterns of trade between them. We find rich dy- namics over time both inter- and intra-communities. The overall international trade networks of B&R countries experienced a leadership change from Russia to China on one hand, some temporary communities experienced emergence, disappearance (e.g. the Kuwait- and Thai- land-led communities) or reemergence (e.g. Poland-led community), and a community membership was generally consistent on the other hand. Since the future impacts of China’s BRI will depend on the degree of integration of the connected regions, some countries with stable and high centrality indices (e.g. Russia, Singapore, Serbia, Greece, Turkey, Iran, Po- land, Hungary and Romania) could be selected by China as strategic regional partners, and countries with a strategically important geographical position but weak trade links (e.g. My- anmar, Pakistan, and Belarus) should be prioritized.Keywords: The Belt and Road Initiative; international trade; community core detection; top trade partner1 IntroductionThe Belt and Road Initiative (‘BRI’, hereafter) proposed by Chinese President Xi Jinping in 2013 found its way into the new revised Charter of the Chinese Communist Party in October 2017, giving the BRI a firm constitutional status as part of China’s new thinking about open development. The BRI refers to the overland Silk Road Economic Belt (the Belt) and the 21st-Century Maritime Silk Road (the Road) that were announced in September and October 2013 respectively. Since then, particularly after March 2015 when the “Vision and Actions on Jointly Building the Silk Road Economic Belt and the 21st Century Maritime Silk Road” (NDRC, MFA and MC, 2015, “Vision and Actions” hereafter) were announced, the BRI has received widespread international attention (Toops, 2016; Bennett, 2016; Vinokurov and Tsukarev, 2017). Not only journalists but also academics were quick to take part in the de- bate. Academic journals published special issues (e.g., East Asia, 2015; China & World Economy, 2017; Geopolitics, 2017), as well as individual papers on the BRI from various perspectives (e.g., Liu and Dunford, 2016; James and Chih, 2017; Cinar et al., 2016; Huang 2016; Summers, 2016; Ravi, 2016).Considering the increasing influence of China as a global power in a turbulent world and the positive and widespread worldwide responses to China’s BRI, the initiative is considered to be a platform for an increasing number of countries to explore new international econom- ic governance mechanisms (Liu et al., 2018). According to the Vision and Actions and Pres- ident Xi’s speeches at the Belt and Road Forum for International Cooperation, the intention of this initiative was not to re-establish the ancient networks of silk trade routes between Asia and Europe, but to use the metaphor of the ancient Silk Roads as a soft basis to create a promising platform for international cooperation (Liu, 2015; 2017a; 2017b). The ancient Silk Road is a transcontinental network of routes, connecting China to other parts of the Eurasian continent and facilitating economic, scientific, technological, religious, and cultur- al exchanges (Liu, 2010). Consequently, the Silk Road is an important, if not the only, sym- bol of a common historical and cultural heritage of most countries in Asia, Europe, and northern and eastern Africa. China’s BRI uses this historical metaphor to denote “peace and cooperation, openness and inclusiveness, mutual learning and mutual benefit,” which are referred to as the “Silk Road Spirit” in the Vision and Actions, and as a way to promote in- clusive globalization (Liu and Dunford, 2016).These studies have proved fruitful and shed new light on China’s trade with countries along the Belt and Road. However, they ignored explicitly or implicitly the fact that trade relationships are trilateral as well as bilateral. To better understand China’s trade with coun- tries along the Belt and Road, a social network approach is therefore needed. From a social network analysis point of view, the trade relations between countries along the Belt and Road constitute a complex and interdependent network. International trade networks have been studied through the lens of network analysis for a long time by sociologists, economists, mathematicians, and even physicists (Breiger, 1981; Fagiolo et al., 2008; Beckfield, 2009; Kali and Reyes, 2010; Kim and Shin, 2002), and have also been used to investigate the trade relations of the BRI countries (Zou and Liu, 2016; Song et al., 2017a). These studies re- vealed that the BRI area trade relations possesses typical properties of complex networks, including a three-tiered structure (core, semi-periphery, and periphery), and a high clustering coefficient. However, these studies have paid insufficient attention to the varying importance of a country’s trade relations. Not all bilateral trade relations are of equal importance to a country because, in a country’s trade value, a few top partners account for a dominant share.2 Methodology2.1 Data and complete international trade network of the Belt and Road countriesFrom a social network analysis point of view, international trade comprises a network in which the nodes are countries and connections between nodes or edges are the trade rela- tions between those countries. Data from 2000 to 2016 from the IMF Direction of Trade Sta- tistics (DOTS) was used. As one of the most frequently used trade databases, it provides data on the international distribution of each country’s exports and imports. Because most states report imports in CIF values (i.e. including cost, insurance and freight) and exports in FOB values (i.e. free on board), the recorded total global imports exceeds that of exports. In this study, import data was used, as states tend to monitor imports more closely than exports (Barbieri et al., 2009) so that import data is considered to be more accurate than export data (Smith and White, 1992; Kim and Shin, 2002). Despite being affected by the 2008 global economic crisis, the complete trade network of the B&R countries from 2009 to 2016 developed very slowly, even becoming unstable. Fif- ty-eight new trade ties emerged in this seven-year period, but there was also a noticeable fluctuation in the number of ties and the density and degree of centralization during the cri- sis and post-crisis periods. The number of trade ties stayed relatively steady between 2164 and 2188 in the period from 2009 to 2012, suddenly increasing to 2216 in 2014, decreasing to 2192 one year later, and then increasing to 2222 in 2016, with an overall increase of 58 new trade relations. This may indicate that the B&R countries were still able to find new trade partners, despite the short period of stagnation immediately after the 2008 economic crisis. On the whole, the small increase in both the density and the degree centralization of the B&R trade network during this turbulent period implies that B&R countries might have started to recover from the economic slowdown and that some countries continued to strengthen their ties with key trade partners while shedding nonessential ties. Therefore, further focus on a country’s top trade partners might be more useful in understanding the real picture of the international trade network of the B&R countries (Zhou et al., 2016).2.2 Community detection approachAlong with descriptive statistics, community detection illuminates the features of the top networks, especially their structural characteristics. There are a number of publicly available tools for exploring complex networks. Gephi, for example, is an open source platform with analytical and data visualization functions. The software runs on Windows. Gephi provides many common metrics for social network analysis (SNA) and scale-free networks, measur- ing the centrality, density, clustering coefficients, path lengths, community detection, etc. of graphs. Many social network analysts choose Gephi because it is extremely powerful in vis- ualization and community detection. It allows users to interact with the representation and to manipulate the structures, shapes, and colours to reveal hidden patterns. And users can cus- tomize the colour, size and labels for readability and overall aesthetics. Moreover, Gephi uses a modularity optimization method—the fast unfolding algorithm for community detec- tion—to decompose a gigantic network into several relatively independent modules (also called groups, clusters, or communities), which are sets of highly connected nodes (Blondel et al., 2008). For these reasons, Gephi was used to detect and visualize the community structure in the top trade networks of the B&R countries from 2000 and 2016. This study split the overall top 2 trade network into several relatively independent trade communities. Each country is densely connected internally within the communities, but there are sparser connections between communities.3 Results and analysis3.1 Countries’ positions in the top 2 networkIdentifying influential nodes in dynamic processes is crucial to understanding network structure and evolution. Centrality concepts were developed in social network analysis to quantify the importance of nodes in a network. Various centrality measures have been pro- posed to identify the hierarchical structures of a network. In order to identify countries’ po- sitions in the top 2 trade network of the B&R region, three classic quantitative indicators of centrality were used: degree centrality (DC), closeness centrality (CC), and betweenness centrality (BC). Each centrality measure has particular structural properties (De Benedictis, 2014).Degree centrality records the number of connections of a node. In international trade, be- cause degree centrality is the number of countries a particular country exports to or imports from, it can be used as the measure of a country’s influence on the entire international trade network. Closeness centrality is a measure of the geodesic distance from a node to other nodes (i.e. a measure of how topologically close a node is with respect to others) and is re- lated to the ability to reach other nodes. In trade network, degree centrality captures how much a country is influenced by and how much it influences other countries. Betweenness centrality is a measure of the share of all of the shortest paths between each pair of nodes going through a particular node and quantifies the ability of a node to act as a bridge among other nodes (Benedictis and Tajoli, 2011). Betweenness centrality measures how much a country acts as an intermediary or gatekeeper in the trade network. Both degree centrality and closeness centrality are based on the idea that the centrality of a node in a network is related to its distance to the other nodes, while betweenness centrality is based on the idea that central nodes stand between others. Ucinet6 software was used again to measure these centralities.3.2 Structural evolution of the top 2 networkGephi and the ForceAtlas2 algorithm was used to detect communities and visualize the structures and communities of the B&R trade network (Jacomy et al., 2014). In the visuali- zation, the nodes indicate countries, the links represent trade relationships between two countries, countries with the same colour belong to the same community, and the size of a node is proportional to the number of trade relations of the country.Table 4 shows the size of identified communities in the top 2 trade network of the B&R countries. While the number of communities remained stable at six or seven, the patterns and commodity composition of trade across different communities did not. First, the overall international trade networks of the B&R countries experienced a leadership change from Russia to China, owing to the economic rise of the latter. Second, some communities expe- rienced substantial membership reorganization, especially in the cases of the Russia- and Iran-led communities. Third, temporary communities emerged and disappeared quickly. For instance, two communities, each led by Kuwait and Thailand appeared in 2000, but did not last long. A Poland-led community disappeared in 2004 but reappeared in 2016. It is worth nothing here that a community may comprise smaller sub-communities of counties. Finally, the number of trade communities declined from seven in 2000 to five in 2016, which means that the level of concentration has increased over time.As Figure 3 shows, seven trade communities were identified in 2000, centred on Russia, China, Kuwait, Hungary, Poland, and Thailand. The Russia-led community was the largest with 18 member countries, most of which were former Soviet Union countries (Azerbaijan, Latvia, Tajikistan, Kyrgyzstan, Georgia, Armenia, Moldova, and Estonia) and the Balkan states (Greece, Romania, Macedonia, and Albania). The second largest community was the one around China and included sixteen countries, most of which were South and Southeast Asian countries (Singapore, Sri Lanka, Malaysia, Indonesia, Pakistan, Bangladesh, Brunei, and the Philippines), and the Middle East (Turkey, Saudi Arabia, Qatar, Israel, Syria, Jordan, and Egypt). In this community, Singapore was the second largest after China. The Hungary and Kuwait communities both covered eight countries, while the other two communities were oriented towards Poland and Thailand, and were smaller in terms of the number of countries as both consisted of only six countries each. The Kuwait, Poland and Thailand-led communities were geographically decentered and disappeared in 2004, 2008, and 2013.4 Conclusions and policy implicationsThe facilitation of international trade is one of the key aims of the Belt and Road Initiative. Since the Belt and Road Initiative was proposed in 2013, an increasing number of studies have examined China’s trade wi
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