中国陆港型物流枢纽时空演化及其驱动因素

doi:10.13249/j.cnki.sgs.2022.11.001

Abstract

Abstract:

This article analyzes the development mechanism of dry port logistics hub from multiple dimensions, measures the comprehensive development strength of China’s dry port logistics hubs by using the panel data from 2001 to 2018, reveals the spatial pattern and evolution characteristics of the hubs by using the methods of spatial analysis and Gini coefficient, and explores the main driving factors for the development of China's dry port logistics hubs. The results show that: 1) The development of hubs in the southeast is generally more mature and stable than that in the western, and shows the differentiation characteristics on the side of the Hu Huanyong Line; 2) The spatial differences in the development of dry port logistics hubs are more obvious, and the degree of difference is increasing; 3) Infrastructure and logistics operations have a greater contribution to the hub's Gini coefficient, but the impact of information technology has improved more significantly; 4) Population size, financial development and external environmental factors promote the development of hubs. Economic strength, education level and government investment have different impacts on hubs at different levels. The article is expected to provide scientific basis for the country to construct logistics system and optimize the logistics network giving full play to the supporting role of the logistics hub in the regional economy.

Key words: dry port logistics hub, port geography, logistics level, panel quantile regression

CLC Number:

F061.5

Jiang Ziran, Lei Liping, Jin Huanhuan, Zhang Jianzhen. Spatio-temporal Evolution and Driving Factors of Chinese Dry Port Logistics Hubs[J].SCIENTIA GEOGRAPHICA SINICA, 2022, 42(11): 1857-1866.

Figures/Tables 6

Fig. 1

Table 1

Evaluation index system of comprehensive development of dry port logistics hub

评价维度	二级指标	指标选取及含义	权重
数据主要来源于《中国城市统计年鉴》^[28]、《中国港口年鉴》^[29]、《从统计看民航》^[30]等。其中，物流业从业人口由交通运输、仓储和邮政业从业人口替代；铁路站点等级根据《全国铁路车站等级核定办法》（ https://baike.baidu.com/item/%E9%93%81%E8%B7%AF%E8%BD%A6%E7%AB%99%E7%AD%89%E7%BA%A7%E6%A0%B8%E5%AE%9A%E5%8A%9E%E6%B3%95/23346477）获取，并根据站点等级进行分级赋分，特等站赋7分，一等站赋5分，二等站赋3分，其他站点均赋1分；类似地，机场等级、口岸等级以及物流企业等级均作相同赋分处理。
基础设施	公路网密度/（km/100 km²）	100 km²拥有公路总里程数	0.014
	铁路站点等级	按铁路站点等级进行隶属度处理	0.007
	码头长度/m	码头结构自身的长度	0.059
	泊位个数/个	码头泊位数	0.062
	等级公路里程/km	根据公路的使用任务、功能和流量进行划分	0.017
	机场等级	按机场等级进行隶属度处理	0.006
物流运营	公路货运量/万t	公路在1 a内实际运送货物的数量	0.019
	水路货运量/万t	水路在1 a内实际运送货物的数量	0.066
	民航货邮量/万t	民航在1 a内实际运送货物的数量	0.053
	公路客运量/万人	公路在1 a内实际运送旅客的数量	0.108
	民航客运量/万人	民航在1 a内实际运送旅客的数量	0.035
	物流从业人口/万人	交通运输、仓储和邮政业从业人口	0.019
	口岸等级	按港口等级进行隶属度处理	0.016
	A类物流企业/个	A类物流企业个数	0.020
信息技术	互联网宽带接入用户数/户	国际互联网用户总数	0.224
	移动电话年末用户数/户	移动电话使用总用户数量	0.199
	邮政业务收入/亿元	邮政业务收入/GDP	0.052
	电信业务收入/亿元	电信业务收入/GDP	0.025

Table 1

Fig. 2

Fig. 3

Table 2

Fig. 4

References 36

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	模型1（0.1）	模型 2（0.3）	模型 3（0.5）	模型 4（0.7）	模型5（0.9）
注：^、^、^** 分别为 P<0.05、P<0.01、P<0.001；括号内数值为标准误；模型n后面的括号数值为分位点；peo为人口规模；gdp为国内生产总值；edu为教育水平；gov为财政投资；fin为金融发展水平；ser为服务化指数；global为全球化指数；不含西藏、港澳台数据。
截距项（β₀）	−0.3710^***	−0.3670^***	−0.3530^***	−0.1670^***	−0.0471
截距项（β₀）	（0.0242）	（0.0234）	（0.0216）	（0.0586）	（0.0934）
peo	0.0482^***	0.0561^***	0.0884^***	0.1980^***	0.3000^***
peo	（0.0198）	（0.0191）	（0.0176）	（0.0478）	（0.0763）
gdp	−0.0014	0.0363	0.0616^**	0.4430^***	0.9410^***
gdp	（0.0446）	（0.0430）	（0.0397）	（0.1079）	（0.1721）
edu	0.3350^***	0.2970^***	0.1800^***	−0.0259	−0.2970^***
edu	（0.0335）	（0.3229）	（0.0298）	（0.0810）	（0.1292）
gov	−0.0026	0.0176	0.0983^***	−0.1670^**	−0.4010^***
gov	（0.0446）	（0.4298）	（0.0397）	（0.1078）	（0.1720）
fin	0.1500^***	0.1020^***	0.1270^***	0.0826^*	0.2340^***
fin	（0.0263）	（0.0253）	（0.0234）	（0.0635）	（0.101）
ser	0.0323^***	0.0402^***	0.0537^***	0.0557^**	0.0427
ser	（0.0135）	（0.0130）	（0.0120）	（0.0326）	（0.0520）
global	0.1300^***	0.2030^***	0.1900^***	0.1680^***	0.1730^**
global	（0.0242）	（0.0233）	（0.2157）	（0.5856）	（0.0934）
Pseudo R²	0.3880	0.4531	0.3892	0.3297	0.3613
样本数	684	684	684	684	684

Spatio-temporal Evolution and Driving Factors of Chinese Dry Port Logistics Hubs

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