贸易协定对全球贸易流的影响研究：基于复杂网络视角

doi:10.3969/j.issn.1004-9479.2024.01.20220258

摘要/Abstract

摘要：

基于复杂网络理论，从共生演化视角出发建立2002—2019年全球136个国家/地区的进出口贸易流网络矩阵，运用社会网络分析法对区域贸易协定对全球贸易流的影响进行了实证研究。研究发现：①由于择优依附机制的存在，全球贸易格局服从经典的幂律分布，由“平头”走向“陡尾”，产生小世界网络的群聚现象；②全球贸易网络结构从2002年以美、德为首的两大阵营向中、美、德三大国际级社团演化。同配效应推动着边缘国家和地区走向“抱团取暖”，继而通过“择优依附”逐步融入核心国家，推动全球贸易向更加具有开放性的局部世界演化；③政府间贸易协定作为政府“看得见的手”可以解释全球贸易网络结构变动的19.2%，说明国家间协同合作对于推动世界贸易体系发展具有积极影响；④由于网络外部性的影响，多边贸易协定对于贸易的促进作用大于双边贸易协定。鉴于区域化贸易在疫情后各国加强制造业回流时会更加明显，各国政府之间签订多边贸易协定更符合经济复苏和人类命运共同体的大势。

关键词: 共生演化, 网络分析, QAP回归, 择优依附, 同配效应, 网络效应

Abstract:

This research explores the impacts of regional trade agreements on global trade flows from the perspective of symbiotic evolution. By using network analysis method on 18 years of two-ways (including import and export) of trade flow network matrixes of 136 countries/regions of the world from 2002 to 2019, the study finds that: 1) Due to the mechanism of the preferential attachment, the global trade pattern obeys the classic law of power distribution, moving from "flat head" to "steep tail" from 2002 to 2019, which results in the clustering phenomenon of small world networks. 2) The structure of the global trade network has evolved from the dual pillars led respectively by the U.S. and Germany in 2002 to the tripartite forces of China, the U.S., and Germany. The matching effect drives many peripheral countries and regions to "unite together" and then gradually integrate into the three forces through "preferential attachment", promoting the global trade flow entering a more open world. 3) Intergovernmental trade agreements, as the "visible hand" of governments, can explain 19.2% of changes in the global trade, which indicates that collaborative cooperation between countries has positive impacts on the evolution of the world trading system. 4) Under the influence of network externalities, multilateral trade agreements demonstrate greater impetus than bilateral trade agreements to global trade. Countries initiating into the multilateral trade agreements conform not only with the general trend of economic recovery after the epidemic, but it is also beneficial to the bright prospect of a community with a shared future for mankind.

Key words: co-evolution, network analysis, QAP regression, preferential attachment, assortative effect, network effects

左冰, 武刚, 张晨, 沙甫拉·努尔别克, 林润基, 尹俊尧, 戴昊炜, 伍姣. 贸易协定对全球贸易流的影响研究：基于复杂网络视角[J]. 世界地理研究, 2024, 33(1): 1-17.

Bing ZUO, Gang WU, Chen ZHANG, Shafarla NURBEKE, Runji LIN, Junyao YIN, Haowei DAI, Jiao WU. Research on the impacts of trade agreements on global trade flows: Based on complex network perspective[J]. World Regional Studies, 2024, 33(1): 1-17.

图/表 9

图1 全球136个国家/地区RTA数量与全球贸易进口总额与整体网络特征变化数据来源：1）跨区域协定：世界贸易组织官网，http：//rtais.wto.org/UI/PublicAllRTAList.aspx；2）全球贸易进口总额：世界银行官网，https：//data.worldbank.org/indicator/BM.GSR.GNFS.CD。

Fig.1 Changes in RTA number， global trade import volume and overall network characteristics of 136 countries

图2 2019年RTAs在全球不同地区的分布状况注：1）数据来源：世界贸易组织官网（http：//rtais.wto.org/UI/PublicAllRTAList.aspx）；2）此图基于自然资源部标准地图服务网站审图号为GS（2016）1663号的标准地图制作，底图无修改。以下地图同。

Fig.2 Distribution of RTAs in different regions of the world in 2019

图3 2019年全球签署RTAs的国家/地区社群划分图注：国家/地区名称代码采用GB/T 2659-2000 世界各国和地区名称三位字母代码。

Fig.3 Group analysis of countries and regions signed RTAs in the world in 2019

图4 全球136个国家/地区2002年（a）和2019年（b）进口贸易幂律分布

Fig.4 Power law distribution of import trade of 136 countries in 2002 （a） and 2019 （b）

图5 全球贸易网络TOP1国家/地区数量（左轴）与中心度（右轴）变化趋势

Fig.5 Changes in TOP1 country/ region numbers （left） and the trend of centrality （right） of global trade network

图6 2002—2019年全球贸易网络核心度Top10等级钟

Fig.6 Global trade network core top 10 clock in 2002-2019

图7 2002—2019年全球进口贸易网络关系（TOP1）演化图

Fig.7 The evolution of global import networks （TOP1） from 2002-2019

图8 2002—2019年全球出口贸易网络关系（TOP1）演化图

Fig.8 The evolution of global export networks （TOP1） from 2002-2019

表1 贸易协定与贸易流矩阵QAP回归分析

Tab.1 QAP regression analysis of trade agreement and trade flow matrix

年度	整体QAP回归		多边-出口		多边-进口		双边-出口		双边—进口
年度	相关系数（1）	R²	相关系数（2）	R²	相关系数（3）	R²	相关系数（4）	R²	相关系数（5）	R²
2002	0.186	0.061	0.180	0.057	0.185	0.059	0.068	0.029	0.070	0.030
2003	0.190	0.062	0.160	0.050	0.185	0.059	0.069	0.030	0.081	0.032
2004	0.172	0.054	0.163	0.050	0.166	0.050	0.092	0.032	0.080	0.029
2005	0.183	0.055	0.168	0.049	0.174	0.052	0.092	0.030	0.089	0.030
2006	0.200	0.061	0.182	0.052	0.184	0.053	0.102	0.030	0.106	0.031
2007	0.210	0.065	0.194	0.055	0.189	0.055	0.115	0.031	0.115	0.033
2008	0.212	0.067	0.198	0.059	0.190	0.057	0.123	0.035	0.117	0.035
2009	0.203	0.063	0.189	0.056	0.188	0.054	0.084	0.029	0.093	0.028
2010	0.203	0.062	0.187	0.055	0.187	0.055	0.116	0.034	0.116	0.034
2011	0.203	0.067	0.226	0.066	0.187	0.058	0.126	0.032	0.109	0.035
2012	0.201	0.072	0.190	0.061	0.187	0.059	0.103	0.036	0.102	0.035
2013	0.201	0.067	0.191	0.061	0.182	0.055	0.108	0.036	0.104	0.033
2014	0.192	0.065	0.187	0.059	0.179	0.055	0.102	0.035	0.105	0.034
2015	0.191	0.065	0.178	0.056	0.175	0.055	0.100	0.035	0.107	0.036
2016	0.186	0.063	0.167	0.051	0.173	0.054	0.095	0.033	0.110	0.037
2017	0.178	0.061	0.163	0.051	0.165	0.052	0.090	0.032	0.105	0.036
2018	0.174	0.061	0.157	0.052	0.162	0.048	0.104	0.039	0.082	0.030
2019	0.171	0.057	0.150	0.052	0.147	0.046	0.102	0.039	0.084	0.031
平均值	0.192	—	0.179	—	0.178	—	0.100	—	0.099	—

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