ORIGINAL RESEARCH article
Front. Mar. Sci.
Sec. Marine Affairs and Policy
Volume 12 - 2025 | doi: 10.3389/fmars.2025.1637660
A Study on Influencing Factors of Port Cargo Throughput Based on Multi-scale Geographically Weighted Regression
Provisionally accepted
- 1Shanghai Maritime University, pudong, China
- 2Shanghai Maritime University School of Economics and Management, Shanghai, China
- 3Zhejiang Sci-Tech University, Hangzhou, China
- 4School of Civil Engineering and Architecture, Zhejiang Sci-Tech University, Hangzhou 310018, China, Hangzhou 310018, China, China
- 5School of Economics & Management, Weifang University, Weifang, China
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Port cargo throughput plays a pivotal role in driving national economic growth, facilitating trade activities, and promoting urban development. This study employs a Multi-scale Geographically Weighted Regression (MGWR) model to analyse the influencing factors of port cargo throughput, with regional Gross Domestic Product (GDP), highway construction investment, waterway construction investment, total import and export volume of goods, total retail sales of consumer goods, number of port berths, and urban residents' consumption expenditure as independent variables. Based on data collected from 43 ports across China, the research reveals the magnitude and spatial distribution characteristics of these variables' impacts on port cargo throughput. By comparing the fitting results of the global regression model with those of local regression models, the study demonstrates that the MGWR model achieves superior local regression fitting compared to the fixed-bandwidth Geographically Weighted Regression (GWR) model. This research provides theoretical support for understanding the spatial heterogeneity of factors influencing port cargo throughput and offers actionable insights for policy formulation and port planning.
Keywords: Port, heterogeneity, throughput, Influencing factors, Multi-scale geographically weighted regression
Received: 29 May 2025; Accepted: 15 Jul 2025.
Copyright: © 2025 Guo, Xiao, Zhang and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Ruitong Guo, Shanghai Maritime University, pudong, China
Qingjun Li, School of Economics & Management, Weifang University, Weifang, China
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