西北内陆中心城市物流企业空间分异及区位选择——以兰州市为例

doi:10.12118/j.issn.1000-6060.2022.007

Abstract

Abstract:

The spatial distribution of logistics nodes determines the spatial pattern of urban logistics and affects the allocation of urban resources and production spaces. Taking Lanzhou City, located in the inland area of northwest China, as a case study, this paper describes the spatial distribution and type differentiation characteristics of logistics enterprises in Lanzhou City; this work is based on data related to micro logistics enterprises and the use of spatial analysis methods. This work further explores the factors that influence the choice of location via a negative binomial regression model. The results show that: (1) The spatial distribution of logistics enterprises in Lanzhou City is unbalanced. The distribution shows a pattern that can be summarized by the phrase: “the center is dominant, the outer suburbs and the suburbs are supplemented, and gather along both sides of urban main roads”; the observed patterns can further be summarized as five spatial organization patterns: “polycentric agglomeration”, “monocentric agglomeration”, “polycentric dispersion”, “monocentric dispersion”, and coexist of “overall agglomeration” and “local dispersion”. (2) Logistics enterprises show spatial agglomeration characteristics that can be characterized as “having two center areas, two sub-central areas, and four groups”. Integrated and transportation-oriented logistics enterprises show patterns of “regional balance and typological diversification”, whereas freight forwarder, express delivery, and warehousing logistics enterprises show patterns of “regional agglomeration and typological clustering”. (3) The spatial differentiation pattern and type differentiation characteristics of logistics enterprises are influenced by multiple factors, such as transportation conditions, agglomeration factors, government policies, land prices, and urban-rural differences; among these factors, transportation conditions, agglomeration factors, and policy factors have the most significant impact on the location choice of a given industry. (4) The location choice of freight forwarder and integrated logistics enterprises are most significantly affected by agglomeration factors; express delivery and transportation-oriented logistics enterprises are more affected by transportation conditions; finally, warehousing logistics enterprises are more dependent on government policies factors. The results of this research not only enrich the location theory of urban logistics enterprises but also have important reference value for the optimization of spatial layout of logistics enterprises in the inland central cities of northwest China.

Key words: logistics enterprises, spatial differentiation, location choice, Lanzhou City

ZHAO Xuewei,ZHANG Zhibin,FENG Bin,GONG Weimin,MA Xiaomin,GUO Yan. Spatial differentiation and location choice of logistics enterprises in the central cities of inland northwest China: A case of Lanzhou City[J].Arid Land Geography, 2022, 45(5): 1671-1683.

Figures/Tables 7

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Tab. 1

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References 41

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分布类型	总体	综合型	货代型	运输型	仓储型	快递型
样本数/个	2123	868	585	479	124	67
占比/%	100	40.88	27.56	22.56	5.84	3.16

要素	解释变量	定义与解释	预期
交通条件	到最近客运中心的距离（station）/km	到最近客运中心的距离（取对数）	-
	到最近火车站的距离（rail）/km	到最近火车站的距离（取对数）	-
	到兰州新区机场的距离（airport）/km	到兰州新区机场的距离（取对数）	-
	有无高速公路出入口（highway）	各街道或乡镇内有高速公路出入口赋值为1,否则为0	+
	有无货物枢纽中心（hub）	各街道或乡镇内有货物枢纽中心赋值为1,否则为0	+
集聚因素	集聚效益（cluster）	各街道或乡镇内2015年物流企业数量的对数值	+
	有无大型制造业企业（firm）	各街道或乡镇内存在兰州市大型制造业企业赋值为1,否则为0	+
	有无综合批发市场（market）	各街道或乡镇内有综合批发市场赋值为1,否则为0	+
土地价格	商业基准地价（land price）	设定基准地价分七级,其中一级代表最高地价并赋值为7	+/-
城乡差异	是否在中心城区（centre）	各街道位于中心城区赋值为1,否则为0	+
	是否在近郊区（suburb）	各街道位于近郊区赋值为1,否则为0	+
	是否在远郊区（outer suburb）	各街道位于远郊区赋值为1,否则为0	+
政府政策	是否位于物流园区（logistics park）	各街道位于物流园区赋值为1,否则为0	+
	是否位于开发区/保税区（land）	各街道位于开发区/保税区赋值为1,否则为0	+

变量	全体物流企业	货代型物流企业	快递型物流企业	仓储型物流企业	运输型物流企业	综合性物流企业
station	-0.3254^***	-0.1987^**	-0.4158^**	-0.2254^**	-0.4356^***	-0.5968^**
rail	-0.4632^***	-0.2190^**	-0.5425^**	-0.6931^**	-0.4869^***	-0.6129^**
airport	-0.3297^**	-0.4237^**	-0.2203^***	-0.7125^*	-0.3997^**	-0.4751^**
highway	0.3126^**	0.2245^*	0.1736^**	0.3421^*	0.8487^**	0.2117^**
hub	0.4289^**	0.2165^**	0.3259^**	0.6234^**	0.7568^**	0.6251^*
cluster	0.3998^**	0.7536^***	0.2125^**	0.3752^*	0.4195^***	0.2289^**
firm	0.5221^**	0.5126^**	0.2467^*	0.6587^*	0.8813^**	0.5714^**
market	0.2314^**	0.4623^**	0.1421^**	0.2795^*	0.3125^**	0.2657^***
land price	0.4237^**	0.8567^*	-0.4426^*	-1.4326^**	-0.2454^*	0.2374^*
centre	0.6243^**	0.4421^***	0.9187^***	0.3567	0.3564^*	0.6897^**
suburb	0.4924^**	0.3687^**	0.2647^**	1.5321^*	0.9365^**	0.3413^*
outer suburb	0.2945^**	0.2754^**	0.1434^**	0.6215^**	0.3245^*	0.2221^*
logistics park	0.8723^**	0.1288^*	0.2736^*	0.7426^***	1.3765^**	0.9124^**
land	1.1987^**	0.9063^*	0.1611^*	1.5263^***	1.2897^*	0.8906^***
_cons	1.6589^***	1.2153^***	1.3421^***	2.5234^***	2.4102^***	1.6857^***
alpha	1.2359	1.1125	0.8329	1.5759	1.3869	1.4653
LL	-321.2315	-256.3244	-211.4527	-518.3454	-511.0157	-384.2697
LR	42.5632	33.2513	37.6945	72.7554	47.5687	61.5923

Spatial differentiation and location choice of logistics enterprises in the central cities of inland northwest China: A case of Lanzhou City

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