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粤港澳大湾区港口群参与全球航运网络特征
引用本文:王琪,陈炜,韦春竹.粤港澳大湾区港口群参与全球航运网络特征[J].热带地理,2022,42(2):236-246.
作者姓名:王琪  陈炜  韦春竹
作者单位:1.中山大学 地理科学与规划学院,广州 510275;2.南方海洋科学与工程广东省实验室(珠海),珠海 519080
基金项目:国家自然科学基金项目(42001178);国家自然科学基金项目(41930646);南方海洋科学与工程广东省实验室(珠海)创新团队建设项目(311021018)。
摘    要:以粤港澳大湾区港口群为研究对象,结合区域港口集装箱吞吐量数据、全球航运网络航线数据,运用社会网络分析方法相关指标,对粤港澳大湾区港口群内部港口吞吐量占比变化、港口群内部结构变化、湾区港口群参与全球八大区域航运网络格局等进行分析。结果表明:1)2007—2020年粤港澳大湾区港口总吞吐量增长率达到32.3%,并且在2020年总吞吐量达到7 294万TEU,占全球的9.2%,占中国的28.3%。其中,深圳港、广州港增长速度较快;2)在参与全球航运网络方面,粤港澳大湾区出现以深圳港、香港港为核心参与全球八大航运区域的层级结构,湾区整体与东亚、东南亚区域联系最为紧密,与欧洲、美洲、非洲等区域的航线联系则局限于区域核心港口间的直接联系;3)粤港澳大湾区港口群内部深圳港、香港港、广州港三港鼎立,主要业务错位化发展,国际集装箱中转枢纽港香港港、外贸港口深圳港与内贸综合大港广州港以及区域其他支线港珠海港、东莞港等港口共同发展。粤港澳大湾区的港口亟待以港口群为整体,积极参与到全球航运网络的建设中,以应对全球化进程中未知的机遇和挑战。

关 键 词:粤港澳大湾区  港口群  航运网络  港口吞吐量  社会网络分析  
收稿时间:2021-05-06

Participation of the Global Shipping Network in the Guangdong- Hong Kong-Macao Greater Bay Area
Wang Qi,Chen Wei,Wei Chunzhu.Participation of the Global Shipping Network in the Guangdong- Hong Kong-Macao Greater Bay Area[J].Tropical Geography,2022,42(2):236-246.
Authors:Wang Qi  Chen Wei  Wei Chunzhu
Institution:1.School of Geography and Urban Planning, Sun Yat-sen University, Guangzhou 510275, China;2.Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519080, China
Abstract:This paper focuses on the internal hierarchical structure of the port group in the Guangdong-Hong Kong-Macao Greater Bay Area and the external connection patterns of the region. This article considers the Greater Bay Area port cluster as its research object, uses regional container port throughput data , global shipping network route data, and social network analysis methods to study the relative indicators of the cluster's internal port throughput. The results show that: (1) the total port throughput of the Greater Bay Area increased by 32.3% from 2007 to 2020, and reached 72.94 million TEU(Twenty-foot Equivalent Unit) in 2020, accounting for 9.2% worldwide and 28.3% for China. Among them, Shenzhen and Guangzhou ports are experiencing rapid growth. The port group of the Greater Bay Area occupies an important position in national and global maritime markets. (2) In terms of participation in the global shipping network, the Greater Bay Area has emerged as a hierarchical structure with Shenzhen and Hong Kong ports as the core in the eight global shipping regions. The Greater Bay Area has the closest connection with East and Southeast Asia, while its routes to Europe, America, Africa, and other regions are limited to the direct connection between regional core ports. (3) Shenzhen, Hong Kong, and Guangzhou within the Greater Bay Area port group will be firmly established, and their major businesses will be developed simultaneously. Shenzhen port, an international container transport hub in Hong Kong, a foreign trade port and a comprehensive domestic trade port in Guangzhou, and other regional ports, such as Zhuhai and Dongguan, will be jointly developed. The rapid development of the port group in the Greater Bay Area has provided important support to the country's construction of the 21st-century Maritime Silk Road Economic Belt. It has formed a multi-level structure with Shenzhen, Hong Kong, and Guangzhou as the core, and the leading ports play an important role in the internal market and external shipping links. In the future, the positioning and regional coordinated development among Hong Kong, Shenzhen, and Guangzhou ports will become the key to the overall competitiveness of the port group in the Greater Bay Area. In future development, the regional integration of port groups will surpass the competitive pattern among them. Instead of competing, Shenzhen, Guangzhou, and Hong Kong should break down barriers and integrate port resources to combine the advantages and allocate them efficiently. The port group of the Greater Bay Area urgently needs to adjust its internal development pattern to meet the unknown opportunities and challenges of globalization.
Keywords:Guangdong-Hong Kong-Macao Greater Bay Area  port group  shipping network  port throughput  social network analysis
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