【中英雙語】64“一帶一路”沿線國家貿易關系的結構與演變 畢業(yè)設計外文文獻翻譯成品（最新的2018年）

1、外文標題：The structure and evolution of trade relations between countries along the Belt and Road外文作者：Jung Won SONN，et,al文獻出處:Journal of Geographical Sciences，2018 , 28(9): 1233-1248（最新的哦） 英文3598單詞， 19887字符，中文5198漢字。（字數可根據需

2、要自己進行刪減）此文檔是外文翻譯成品，無需調整復雜的格式哦！下載之后直接可用，方便快捷！價格不貴。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 I

3、nitiative (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.

4、 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 international trade; community core detection; top trade partner1 IntroductionT

5、he 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 constitutio

6、nal 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-2 Methodology2.1 Data and complete international trade network of the Belt

7、 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 cou

8、ntries. 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 export

9、s 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 t

10、his 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

11、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 em

12、erged 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

13、 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

14、 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 incre

15、ase 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 count

16、ries 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 th

17、e 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 t

18、heir 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 softwar

19、e 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 gr

20、aphs. 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, a