相关研究成果：

1.  Are We in a De-Globalization Process? The Evidence from Global Trade During 2007–2017

Through the analysis of statistical data, some scholars believe that the globalization of trade is declining, and de-globalization has become a trend, even since the financial crisis in 2008. However, the superficial decline of global trade volume cannot be taken as a corollary of the de-globalization. It needs go deep into the structural analysis, and “globalization or de-globalization” should be discussed by analyzing whether the global trade structure has changed. This paper finds that during 2007–2017, the global trade resistances are clearly classified, and trade resistance in the global community has increased significantly. Second, an expectation maximization (EM) algorithm is applied to divide global trade relations into two categories: intimate trade relations, whose barriers are mainly related to geographical distance; and unfriendly trade relations with high artificial barriers. Third, the trade purity indicator (TPI) is introduced to describe the trade environment of countries, and its evolution indicates after the financial crisis and for quite a long time, the structure of global trade has not changed much. And it shows some deterioration trend and structural adjustment after 2015, which indicates an opportunity for the emergence of de-globalization in such an international environment full of uncertainty and challenges.

原文链接：https://onlinelibrary.wiley.com/doi/10.1002/gch2.202000096

Figure 1. Relationship of trade resistance and geographic distance between countries/regions in 2017. In category I, ln (1/r i, j ) decreases as the logarithm of geographic distance increases in an approximate linear fashion, as ln (1/r i, j ) = −24.48 − 1.55ln d i, j . But there is no correlation between resistance and geographic distance in category II. Due to the size and shape of the earth, the maximum distance between two countries is about 20 000 (≈e 10 ) km.

Figure 6. Change of trade purity indicator I from 2007 to 2017. The countries/regions below the diagonal line (slope=1) have a reduction in TPI.

2、A Topological Analysis of Trade Distance: Evidence from the Gravity Model and Complex Flow Networks

As a classical trade model, the gravity model plays an important role in the trade policy-making process. However, the effect of physical distance fails to capture the effects of globalization and even ignores the multilateral resistance of trade. Here, we propose a general model describing the effective distance of trade according to multilateral trade paths information and the structure of the trade flow network. Quantifying effective trade distance aims to identify the hidden resistance information from trade networks data, and then describe trade barriers. The results show that flow distance, hybrid by multi-path constraint, and international trade network contribute to the forecasting of trade flows. Meanwhile, we also analyze the role of flow distance in international trade from two perspectives of network science and econometric model. At the econometric model level, flow distance can collapse to the predicting results of geographic distance in the proper time lagging variable, which can also reflect that flow distance contains geographical factors. At the international trade network level, community structure detection by flow distances and flow space embedding instructed that the formation of international trade networks is the tradeoff of international specialization in the trade value chain and geographical aggregation. The methodology and results can be generalized to the study of all kinds of product trade systems.

原文链接：https://www.mdpi.com/2071-1050/12/9/3511/htm

Figure 1. Flow complex network of energy trade. (A) International trade flows network diagram. Black solid lines denote the raw trade data, and dashed lines represent the trade gap between imports and exports. The source node and sink node make the country’s import and export trade in the balanced state, which helps the definition of a Markov matrix during the derivation of flow distances. (B) The plane rectangular coordinate system consisting of flow distance (FD) and geographic distance (GD).  y=x  is the case where the flow distance is equal to the geographic distance. The black area in the picture is the range of standard deviation  ±σ  of a straight line  y=x . Blue and red represent the data points beyond the standard deviation range.

Figure 3. Correlation analysis of flow distances and geographic distances: (A) the correlation coefficient evolution of flow and geographical distance in all products; and (B) the correlation coefficient evolution of flow and geographical distance in more detailed energy products.