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长江经济带省际贸易网络结构时空特征及其影响因素研究 被引量:16

Research on Spatial-Temporal Feature and Influencing Factors of Inter-Provincial Trade Network in Yangtze River Economic Belt
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摘要 研究长江经济带省际贸易网络结构,有利于揭示其内部经济联系规律,推进区域经济协同发展。基于2000-2015年省际铁路货运交流数据,采用SNA方法刻画长江经济带省际贸易网络时空特征,借助贸易网络引力模型和QAP回归方法实证研究省际贸易网络演变的影响因素。研究表明:(1)2000-2015年长江经济带省际贸易网络结构发生了显著变化,逐步演化成"苏-浙-皖-赣"和"川-渝-滇-贵"两大贸易联系核心圈;(2)网络密度呈现先升后降的弱连结联系态势,圈层特征明显,子圈间联系较弱;(3)中心性水平呈现"下冷中上热"态势,各贸易主体影响力日趋均衡,但点出入度非均衡趋势明显;(4)城镇化、产业结构、基础设施、地方保护是长江经济带省际贸易网络演变的重要因子。建议:着力构建区域联动的新型城镇化格局、适时产业结构调整、加快基础设施互联互通、建立跨区域联合协调机制,以深化地区间经济联系。 Based on the data of inter-provincial railway transportation from 2000 to 2015, the paper uses the social network analysis method to describe the spatial-temporal feature of inter- provincial trade network in Yangtze River Economic Belt based on the trade network gravity model and QAP regression method. The results shows that: (1) The structure of inter-provincial trade network changed significantly, and two different cores of trade relation network formed. (2) The network density rises first then falls within weak links including 6 small groups with the obvious features of regional small group. (3)The level of centrality appear high in east and low in west with more balanced trade body influence and less unbalanced out-degree and in-degree level. (4) Urbanization, industrial structure, infrastructure and the degree of local protectionism are the important factors to the evolution of inter-provincial trade network. In order to strengthen economic ties between regions, it is advisable to construct the new urbanization pattern of regional linkage, adjust the industrial structure in good time, speed up infrastructure construction and establish cross-regional joint coordination mechanism.
作者 曾冰 邱志萍
出处 《上海经济研究》 CSSCI 北大核心 2017年第9期69-77,共9页 Shanghai Journal of Economics
基金 国家自科基金青年项目(编号:71703061)"省际交界区空间结构形成演进与优化整合研究"资助
关键词 长江经济带 铁路货运 省际贸易 社会网络分析 QAP回归方法 Yangtze River Economic Belt Railway Transportation Inter-provincial Trade Spatial-Temporal Feature Trade Network Gravity Model QAP Regression Method
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