Organization network of Chinese automobile logistics and its influencing factors from the perspective of enterprises data

doi:10.3969/j.issn.1004-9479.2020.01.2018425

Abstract

Abstract:

Spatial organization of logistics activities has always been the common research topic of economic geography and transportation geography. Based on the data of automobile logistics companies, this paper selected 137 automobile logistics companies as research samples and used urban network methods to analyze the hierarchical structure, spatial differentiation, and spatial connection characteristics of organization network of automobile logistics. The regression model was further applied to explore the influencing factors of the spatial networks and locations of automobile logistics companies. The research results show that: the automobile logistics organization network in China shows pyramidal hierarchical structure, which can be divided into five levels: national, regional, sub-regional, local hub cities, and ordinary cities. The organization capacity of each node is uneven. It forms a “3+2” spatial clusters of automobile logistics consisting of three core regions in the eastern coast area and two high-value regions in the central and western parts of China. A dense network of links has been established with Shanghai as the core and links between cities in Guangzhou, Beijing, Chongqing and Wuhan. The core network structure is in line with the "T"-shaped spatial structure strategy formed by the coastal economic zone and the Yangtze River Economic Belt. The overall network connection shows a trend of decreasing from the eastern coast to the central and western regions and expanding from the urban clusters to the surrounding areas. Through regression analysis, it was found that the level of economic development, automobile logistics market demand, and transportation infrastructure are the main factors affecting the layout of organization network of automobile logistics in China.

Key words: enterprise network, automotive logistics, organization network, spatial structure, spatial layout

摘要：

物流活动及其空间组织是经济地理和交通地理共同的研究话题。本文基于汽车物流企业数据，选取137家典型汽车物流企业为研究样本，运用城市网络研究方法，分析了中国汽车物流组织网络的等级结构、空间分异和空间联系特征，并进一步应用回归模型，探讨汽车物流企业在区域尺度的空间分布及区位选址的影响因素。研究结果表明：区域尺度的中国城市汽车物流组织网络存在明显的金字塔型等级结构，可将其划分为全国性、区域性、次区域性、地方性枢纽城市和普通型城市五个等级；各节点城市组织能力空间分布不均，非均衡化发展特征明显，形成由东部沿海3个核心区和中西部2个高值区所构成的“3+2”空间分布格局；节点城市间基本形成以上海为核心，广州、北京、重庆、武汉城市间联系为骨干链条的密集联系网络，其核心网络结构与中国沿海经济带和长江经济带构成的“T”字型空间结构相吻合，网络联系呈现出从东部沿海向中西部地区递减、由城市群向周边地区递减的趋势。通过回归分析发现，城市经济发展水平、汽车物流市场需求和交通基础设施是影响区域尺度的中国汽车物流组织网络布局的重要因素。

关键词: 企业网络, 汽车物流, 组织网络, 空间结构, 空间布局

Huiming ZONG, Ruihui LV. Organization network of Chinese automobile logistics and its influencing factors from the perspective of enterprises data[J]. World Regional Studies, 2020, 29(1): 130-139.

宗会明, 吕瑞辉. 基于企业数据的中国汽车物流网络组织特征及影响因素分析[J]. 世界地理研究, 2020, 29(1): 130-139.

Figures/Tables 5

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类型*	城市	组织系数（LOI）	均值	比重 /%
全国性枢纽城市(1)	上海	4.81	4.81	0.6
区域性枢纽城市(5)	广州、北京、重庆、武汉、天津	3.15-3.96	3.6	2.98
次区域性枢纽城市(13)	成都、南京、沈阳、大连、苏州、青岛、深圳、西安、长春、昆明、长沙、柳州、宁波	1.60-2.44	1.96	7.74
地方性枢纽城市(46)	哈尔滨、杭州、济南、郑州、烟台、厦门、无锡、福州、石家庄、芜湖、东莞、兰州、保定、合肥、南宁、贵阳、扬州、海口、南昌、营口、乌鲁木齐、襄阳、太原、潍坊、连云港、镇江、南通、临沂、惠州、嘉兴、淄博、湛江、吉林、锦州、威海、江门、廊坊、绍兴、晋中、秦皇岛、丹东、徐州、泉州、东营、洛阳、汕头	0.65-1.58	0.97	27.37
普通型城市(103)	佛山、拉萨、呼和浩特、西宁、银川、盐城、金华、九江、龙岩、乌海、株洲、绵阳、岳阳、珠海、常州、包头、宜昌、唐山、沧州、钦州、防城港、台州、呼伦贝尔、德阳、运城、济宁、宝鸡、梧州、三亚、咸阳、十堰、鄂尔多斯、湘潭、阜阳、景德镇等	0.01-0.59	0.15	61.31

类型*	城市	组织系数（LOI）	均值	比重 /%
全国性枢纽城市(1)	上海	4.81	4.81	0.6
区域性枢纽城市(5)	广州、北京、重庆、武汉、天津	3.15-3.96	3.6	2.98
次区域性枢纽城市(13)	成都、南京、沈阳、大连、苏州、青岛、深圳、西安、长春、昆明、长沙、柳州、宁波	1.60-2.44	1.96	7.74
地方性枢纽城市(46)	哈尔滨、杭州、济南、郑州、烟台、厦门、无锡、福州、石家庄、芜湖、东莞、兰州、保定、合肥、南宁、贵阳、扬州、海口、南昌、营口、乌鲁木齐、襄阳、太原、潍坊、连云港、镇江、南通、临沂、惠州、嘉兴、淄博、湛江、吉林、锦州、威海、江门、廊坊、绍兴、晋中、秦皇岛、丹东、徐州、泉州、东营、洛阳、汕头	0.65-1.58	0.97	27.37
普通型城市(103)	佛山、拉萨、呼和浩特、西宁、银川、盐城、金华、九江、龙岩、乌海、株洲、绵阳、岳阳、珠海、常州、包头、宜昌、唐山、沧州、钦州、防城港、台州、呼伦贝尔、德阳、运城、济宁、宝鸡、梧州、三亚、咸阳、十堰、鄂尔多斯、湘潭、阜阳、景德镇等	0.01-0.59	0.15	61.31

变量名称	相关系数	显著水平
地区生产总值	0.873	0.000
人均地区生产总值	0.414	0.055
城镇居民人均可支配收入	0.606	0.006
社会消费品零售总额	0.923	0.000
年末常住人口	0.837	0.000
汽车年产量	0.810	0.000
工业生产总值	0.644	0.004
汽车制造业总产值	0.855	0.000
货运量	0.496	0.025
各种运输方式线路总里程	0.549	0.014
规模以上汽车制造业单位个数	0.661	0.003
交通运输、仓储和邮政业就业人口	0.813	0.000
交通运输、仓储和邮政业法人单位数	0.370	0.079

变量名称	相关系数	显著水平
地区生产总值	0.873	0.000
人均地区生产总值	0.414	0.055
城镇居民人均可支配收入	0.606	0.006
社会消费品零售总额	0.923	0.000
年末常住人口	0.837	0.000
汽车年产量	0.810	0.000
工业生产总值	0.644	0.004
汽车制造业总产值	0.855	0.000
货运量	0.496	0.025
各种运输方式线路总里程	0.549	0.014
规模以上汽车制造业单位个数	0.661	0.003
交通运输、仓储和邮政业就业人口	0.813	0.000
交通运输、仓储和邮政业法人单位数	0.370	0.079

模型	非标准化系数		标准系数	t	Sig.
模型	B	标准误差	试用版	t	Sig.
常量	1.071	0.60		17.928	0.000^***
社会消费品零售总额	0.734	0.214	0.830	3.430	0.002^***
汽车制造业总产值	0.337	0.080	0.335	4.232	0.000^***
各种运输方式线路总里程	0.302	0.105	0.309	2.876	0.008^***
城镇居民人均可支配收入	0.225	0.083	0.255	2.714	0.012^**
地区生产总值	-0.698	0.324	-0.781	-2.156	0.041^**
工业生产总值	0.250	0.126	0.275	1.975	0.059^*