全球高端航运服务业的时空演化及影响因素分析

doi:10.11821/dlyj020191031

摘要/Abstract

摘要：

发展高端航运服务业是港口城市产业转型的重要着力点。基于劳合社海事企业名录,利用连锁网络、回归模型等,研究1982—2018年全球高端航运服务业节点城市和城际联系的空间演变及影响因素。结果表明：① 全球高端航运服务业骨架由“T”字型向“大三角形”转变,网络重心不断东移,欧洲、地中海和亚太地区构成的东西向市场是其发展主线。② 节点城市的网络地位提升与该城市和前序城市建立的联系强度有密切关系,与覆盖的市场范围联系不大。③ 高端航运服务业的区位选择主要受资本、技术和市场的共同影响,其布局临近客户和市场,倾向于知识密集型的产业集聚。④ 集装箱吞吐量和地区宏观经济对高端航运服务业布局的显著性增强,人口数量、港口和行政中心的显著性消失。

关键词: 全球高端航运服务业, 时空演化, 连锁网络, 影响机制

Abstract:

The maritime service industry has become a key driving force for promoting the spatial structure adjustment and economic development of port cities and is also a core element for enhancing the global competitiveness of hub port cities. Based on the chain network model, the regression model and the list of British Lloyd's Maritime Enterprises, this paper studies the spatial shaping process of the node cities and inter-city links in the global advanced maritime service industry in 1982-2018. The study analyzes the relevance between the connectivity or the number of advanced maritime service producers in the node cities and the shipping, economic, political and cultural location factors. The analysis concludes as follows: (1) Advanced maritime service producers mainly gather in Europe, the Mediterranean and Asia-Pacific region. The east-west market formed by these three regions is the main line of the global advanced maritime service industry. The main spatial pattern changes from "T" shape to triangle. The barycenter of the network is constantly moving eastward and has moved from Europe to the Asia-Pacific region. (2) The network status of the node city is closely related to the strength of the connection between this node city and the top cities and has nothing to do with the extensiveness of the city connection. Node cities can quickly improve their strategic position by establishing strong links with international shipping service centers. (3) The location of the advanced maritime service industry is mainly influenced by capital, technology and market. Its layout is close to customers and markets. These three factors also affect the strength of the aggregation effect of high-end shipping service industry activities. The advanced maritime service industry tends to be knowledge-intensive. (4) With the influence of economic globalization and technological progress, the significance of the relevance between container throughput or regional economy and the layout of the advanced maritime service industry has increased. The significance of the population, ports and administrative centers disappear. The research results reveal the enlightenment and experience of the space-time evolution of the global advanced maritime service industry and provide useful references for the efficient development of international shipping service centers.

Key words: global advanced maritime service industry, spatio-temporal variations, chain network, factors

王列辉, 张楠翌, 林羽珊. 全球高端航运服务业的时空演化及影响因素分析[J]. 地理研究, 2021, 40(3): 708-724.

WANG Liehui, ZHANG Nanyi, LIN Yushan. Spatial evolution and factors of global advanced maritime service industry[J]. GEOGRAPHICAL RESEARCH, 2021, 40(3): 708-724.

图/表 11

表1

表2

表3

图1

图2

图3

图4

表4

表5

AMPS和GNCa的负二项回归结果"

	AMPS		$GN C a$
	系数	p值	系数	p值
港口	0.243	(0.209)	0.494	(0.018)**
集装箱吞吐量（万TEU）	0.001	(0.000)***	0.000	(0.000)***
船东（家）	0.000	(0.006)**	0.000	(0.070)*
人均资本投资金额（美元）	0.000	(0.003)**	0.000	(0.185)
人均国内生产总值（美元）	0.000	(0.003)**	0.000	(0.000)***
航空联系（个）	0.003	(0.009)**	0.001	(0.090)*
人口数量（人）	0.000	(0.338)	0.000	(0.137)
行政中心	0.096	(0.448)	0.059	(0.610)
政府平均治理水平	0.068	(0.280)	0.053	(0.432)
人才（所）	0.152	(0.040)**	0.022	(0.664)
常数	2.738	(0.000)***	7.804	(0.000)***
Log pseudolikelihood	-765.788		-1709.380
Wald chi2	164.500		101.530
LR chi2	167.190		19.660

表5

表6

港口的负二项回归结果"

	AMPS					$GN C a$
	1982年	1996年	2006年	2018年		1982年	1996年	2006年	2018年
系数	1.122	0.004	0.365	0.365	-0.314		-0.890	0.620	0.569
P值	0.065*	0.992	0.135	0.154	0.676		0.194	0.027**	0.031**
LR chi2	2.700	0.000	2.070	1.850	0.190		2.160	4.080	3.930
Log likelihood	-364.717	-566.638	-647.047	-847.726	-255.726		-970.884	-1621.650	-1682.040

表6

表7

行政中心的零膨胀负二项回归结果"

	AMPS				$GN C a$
	1982年	1996年	2006年	2018年	1982年	1996年	2006年	2018年
系数	1.240	0.925	0.452	0.464	1.172	1.157	0.198	0.119
P value	0.047**	0.029**	0.094*	0.114	0.016**	0.000**	0.100*	0.317
Wald chi2	3.950	4.780	2.800	2.500	5.770	40.820	2.710	1.000
Log pseudolikelihood	-359.003	-558.712	-643.252	-844.172	-268.379	-993.530	-1679.580	-1715.450

表7

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	1982年	1996年	2006年	2018年
企业数（家）	43	258	352	454
城市数（个）	81	319	759	838
航运金融占比（%）	74^* 74^*	18	14	12
航运保险占比（%）	74^* 74^*	44	30	25
航运咨询占比（%）	26	38	56	63
服务价值数据（条）	3483	82302	267168	380452

变量（单位）	平均值	标准差	最小值	最大值
高端航运服务企业数（家）	35.108	58.561	9.000	464.000
全球网络联系度	3837.511	2656.680	0.000	10093.000
港口	0.919	0.272	0.000	1.000
集装箱吞吐量（万TEU）	205.613	532.363	0.000	4023.000
船东（家）	1480.586	1674.923	0.000	5794.000
人均资本投资金额（美元）	1927.433	6756.613	0.000	72819.000
人均国内生产总值（美元）	37357.846	124532.189	1256.661	1705885.000
航空联系（个）	72.823	81.636	0.000	411.000
人口数量（人）	3723229.048	6323642.864	2957.000	46400000.000
行政中心	0.242	0.428	0.000	1.000
政府平均治理水平	0.541	0.868	-1.978	1.861
人才（所）	0.387	0.968	0.000	7.000

	1982年	1996年	2006年	2018年
首都	伦敦	伦敦	新加坡	新加坡
	马尼拉	巴黎	迪拜	迪拜
	巴黎	新加坡	布宜诺斯艾利斯	布宜诺斯艾利斯
	安曼	马德里	伦敦	伦敦
	雅典	曼谷	马尼拉	卡塔赫纳
	贝鲁特	马尼拉	哥本哈根	奥斯陆
	布隆方丹	吉隆坡	瓦莱塔	雅加达
	开普敦	首尔	开普敦	悉尼
	卢森堡	迪拜		巴黎
	茨瓦内	纽约		赫尔辛基
	罗马	东京
	温得和克	墨西哥城
	东京
港口城市	吉达	香港	比雷埃夫斯	香港
	香港	台北	伊斯坦布尔	汉堡
	德班	汉堡	亚历山大	比雷埃夫斯
	伊丽莎白港	安特卫普	汉堡	亚历山大
	沃尔维斯湾	奥克兰	鹿特丹	热那亚
	比雷埃夫斯	伊斯坦布尔	香港	孟买
			里约热内卢	鹿特丹
			神户	安特卫普
			休斯敦	里约热内卢
			热那亚	上海
			上海
			安特卫普
内陆城市	约翰内斯堡	日内瓦
内陆城市		圣保罗