{"title":"粤港澳大湾区区域物流空间联系特征及驱动机制","authors":"Xiuli Gao, Peiying Hu, Fei Meng","doi":"10.12783/DTEM/MEBIT2021/35609","DOIUrl":null,"url":null,"abstract":"The paper adopted the entropy weight TOPSIS method and the gravity model to explore the characteristics of logistics spatial connection of Guangdong-Hong Kong-Macao Greater Bay Area that includes nine prefecture-level cities and two special administrative regions, and analyzed the driving factors of the formation of logistics spatial connection pattern by geographical detector model. The results shows that: There are obvious imbalance in the comprehensive capacity of logistics in different regions, and the cities around the Pearl River estuary are generally strong in the logistics quality, like Guangzhou, Shenzhen and Hong Kong. The total amount of logistics links in different cities is significantly various. The logistics connection between cities are mainly weak, and the strong links are concentrated between Hong Kong and Shenzhen. In addition, Hong Kong, Guangzhou and Shenzhen are the three core nodes of the regional logistics network in Guangdong-Hong Kong-Macao Greater Bay Area. Industrial structure, economic scale, population scale, consumption level, the development of post and telecommunications industry are the main factors for the formation of the logistics spatial connection pattern. Moreover, these factors have a prominent driving effect on the cities with large amount of logistics links.","PeriodicalId":406724,"journal":{"name":"2021 International Conference on Management, Economics, Business and Information Technology","volume":"30 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"SPATIAL CONNECTION CHARACTERISTICS AND DRIVING MECHANISM OF REGIONAL LOGISTICS IN GUANGDONG-HONG KONG-MACAO GREATER BAY AREA\",\"authors\":\"Xiuli Gao, Peiying Hu, Fei Meng\",\"doi\":\"10.12783/DTEM/MEBIT2021/35609\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The paper adopted the entropy weight TOPSIS method and the gravity model to explore the characteristics of logistics spatial connection of Guangdong-Hong Kong-Macao Greater Bay Area that includes nine prefecture-level cities and two special administrative regions, and analyzed the driving factors of the formation of logistics spatial connection pattern by geographical detector model. The results shows that: There are obvious imbalance in the comprehensive capacity of logistics in different regions, and the cities around the Pearl River estuary are generally strong in the logistics quality, like Guangzhou, Shenzhen and Hong Kong. The total amount of logistics links in different cities is significantly various. The logistics connection between cities are mainly weak, and the strong links are concentrated between Hong Kong and Shenzhen. In addition, Hong Kong, Guangzhou and Shenzhen are the three core nodes of the regional logistics network in Guangdong-Hong Kong-Macao Greater Bay Area. Industrial structure, economic scale, population scale, consumption level, the development of post and telecommunications industry are the main factors for the formation of the logistics spatial connection pattern. Moreover, these factors have a prominent driving effect on the cities with large amount of logistics links.\",\"PeriodicalId\":406724,\"journal\":{\"name\":\"2021 International Conference on Management, Economics, Business and Information Technology\",\"volume\":\"30 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-06-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 International Conference on Management, Economics, Business and Information Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.12783/DTEM/MEBIT2021/35609\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 International Conference on Management, Economics, Business and Information Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.12783/DTEM/MEBIT2021/35609","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
SPATIAL CONNECTION CHARACTERISTICS AND DRIVING MECHANISM OF REGIONAL LOGISTICS IN GUANGDONG-HONG KONG-MACAO GREATER BAY AREA
The paper adopted the entropy weight TOPSIS method and the gravity model to explore the characteristics of logistics spatial connection of Guangdong-Hong Kong-Macao Greater Bay Area that includes nine prefecture-level cities and two special administrative regions, and analyzed the driving factors of the formation of logistics spatial connection pattern by geographical detector model. The results shows that: There are obvious imbalance in the comprehensive capacity of logistics in different regions, and the cities around the Pearl River estuary are generally strong in the logistics quality, like Guangzhou, Shenzhen and Hong Kong. The total amount of logistics links in different cities is significantly various. The logistics connection between cities are mainly weak, and the strong links are concentrated between Hong Kong and Shenzhen. In addition, Hong Kong, Guangzhou and Shenzhen are the three core nodes of the regional logistics network in Guangdong-Hong Kong-Macao Greater Bay Area. Industrial structure, economic scale, population scale, consumption level, the development of post and telecommunications industry are the main factors for the formation of the logistics spatial connection pattern. Moreover, these factors have a prominent driving effect on the cities with large amount of logistics links.