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<article xsi:noNamespaceSchemaLocation="http://jats.nlm.nih.gov/publishing/1.1/xsd/JATS-journalpublishing1-mathml3.xsd" dtd-version="1.1" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"><front><journal-meta><journal-id journal-id-type="publisher-id">PBES</journal-id><journal-title-group><journal-title>Proceedings of Business and Economic Studies</journal-title></journal-title-group><issn>2209-2641</issn><eissn>2209-265X</eissn><publisher><publisher-name>Bio-Byword Scientific Publishing Pty. Ltd.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.26689/pbes.v8i7.13134</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title>Logistics Measurement and Influencing Factors of Guangdong-Hong Kong-Macao Greater Bay Area Urban Agglomeration</title><url>https://artdesignp.com/journal/PBES/8/7/10.26689/pbes.v8i7.13134</url><author>LiuRong,ZhaoWeibin,ChenHuiting</author><pub-date pub-type="publication-year"><year>2025</year></pub-date><volume>8</volume><issue>7</issue><history><date date-type="pub"><published-time>2025-12-15</published-time></date></history><abstract>This study examines 11 cities in the Guangdong-Hong Kong-Macao Greater Bay Area, focusing on regional logistics efficiency disparities and their driving mechanisms. Using 2023 statistical data, the DEA-BCC model was applied to measure logistics efficiency across three dimensions: technical efficiency, scale efficiency, and comprehensive efficiency. Empirical analysis through the Tobit regression model revealed the influence of key factors, including openness and economic development levels. These conclusions provide a scientific basis for optimizing logistics resource allocation and enhancing efficiency in the Greater Bay Area.</abstract><keywords/></article-meta></front><body/><back><ref-list><ref id="B1" content-type="article"><label>1</label><element-citation publication-type="journal"><p>Li M, 2024, Measurement and Influencing Factors of High-Quality Development in China’s Logistics Industry. Logistics Research, 2024(3): 46–53.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B2" content-type="article"><label>2</label><element-citation publication-type="journal"><p>Deng Z, Liu L, Li Y, et al., 2024, Measurement of Logistics Efficiency and Influencing Factors in China’s Coastal Ports: An Empirical Analysis Based on the Super Efficiency SBM-Tobit Model. Resource Development and Market, 40(9): 1342–1349.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B3" content-type="article"><label>3</label><element-citation publication-type="journal"><p>Ge Y, 2024, Research on Logistics Efficiency and Spatial Correlation in the Yangtze River Delta Region, thesis, Anhui University of Science and Technology.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B4" content-type="article"><label>4</label><element-citation publication-type="journal"><p>Wang Z, Xiao Y, 2024, Measurement of Logistics Efficiency and Influencing Factors in the Yangtze River Economic Belt: A Study Using DEA-BCC and Tobit Models. Journal of Hubei University of Science and Technology, 44(04): 46–52.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B5" content-type="article"><label>5</label><element-citation publication-type="journal"><p>Liu X, 2024, Measurement of Green Logistics Efficiency and Influencing Factors in China’s Belt and Road Port Cities, thesis, Shandong University of Finance and Economics.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B6" content-type="article"><label>6</label><element-citation publication-type="journal"><p>Chen S, Wang H, Fu Y, 2024, Measurement of Resilience in the Logistics Industry and Its Influencing Factors. Journal of Business Economics Research, 2024(5): 84–90.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B7" content-type="article"><label>7</label><element-citation publication-type="journal"><p>He M, Yang M, Wu X, et al., 2024, Evaluating and Analyzing the Efficiency and Influencing Factors of Cold Chain Logistics in China’s Major Urban Agglomerations under Carbon Constraints. Sustainability, 16(5): 1–15.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B8" content-type="article"><label>8</label><element-citation publication-type="journal"><p>Duan J, Wang Y, 2021, Measurement of the Chinese Logistics Substitution Elasticity and the Influencing Factors: Base on the VES Model. Academic Journal of Business &amp; Management, 3(8): 45–58.</p><pub-id pub-id-type="doi"/></element-citation></ref></ref-list></back></article>
