Article(id=1263533681487036734, tenantId=1146029695717560320, journalId=1240618002186551303, issueId=1263533678655881533, articleNumber=null, orderNo=null, doi=10.3969/j.issn.1000-4653.2026.02.005, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1740672000000, receivedDateStr=2025-02-28, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1779179504987, onlineDateStr=2026-05-19, pubDate=1777046400000, pubDateStr=2026-04-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1779179504987, onlineIssueDateStr=2026-05-19, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1779179504987, creator=13701087609, updateTime=1779179504987, updator=13701087609, issue=Issue{id=1263533678655881533, tenantId=1146029695717560320, journalId=1240618002186551303, year='2026', volume='49', issue='2', pageStart='1', pageEnd='163', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1779179504312, creator=13701087609, updateTime=1779179563167, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1263533925629047616, tenantId=1146029695717560320, journalId=1240618002186551303, issueId=1263533678655881533, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1263533925629047617, tenantId=1146029695717560320, journalId=1240618002186551303, issueId=1263533678655881533, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=42, endPage=50, ext={EN=ArticleExt(id=1263533681902272834, articleId=1263533681487036734, tenantId=1146029695717560320, journalId=1240618002186551303, language=EN, title=Research on the spatiotemporal evolution of port-industry-city integration in China's port cities based on PCA-CCDM, columnId=1240655021705065434, journalTitle=Navigation of China, columnName=Port and Waterway Engineering, runingTitle=null, highlight=null, articleAbstract=

The port, industry and city are significantly related, and their integration degree reflects the coordinated evolution relationship among the three in the spiral development. Against the backdrop of deepening reforms in China's port management system, there is a growing need to scientifically assess the state of port-industry-city integration and analyze its underlying mechanisms. To address the issues of multidimensional indicator overlap and the difficulty in quantifying systemic synergy in existing research, this study constructs a coupling coordination degree model based on principal component analysis. Based on the panel data of 75 port cities in China from 2004 to 2023, the model applies principal component analysis to reduce the dimensionality of high-dimensional indicators across the port, industry, and city subsystems, thereby addressing multicollinearity issues among the indicators. Subsequently, a coupling coordination degree model is employed to quantify the level of synergy among the three subsystems, while the criteria importance through intercriteria correlation weighting method and panel entropy weight method are integrated for comprehensive weighting and robustness testing. The research shows that the overall integration level of Chinese port cities showed an upward trend during the study period, with its evolution exhibiting phased fluctuations influenced by the port management system. Significant disparities in integration were observed both across and within regions, with a maximum range of 4.65. Institutional changes in port management, path dependence in industrial development, and differences in regional institutional flexibility were identified as the core drivers of this spatial-temporal differentiation. Accordingly, policy recommendations such as establishing a cross-regional collaborative governance system and implementing differentiated industrial development strategies are proposed to advance the coordinated development of the port-industry-city system and provide a decision-making reference.

, correspAuthors=Nan Zhao, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, authorCompany=null, fund=null, authors=null, authorsList=Nan Zhao, Hongwei Yu), CN=ArticleExt(id=1263533682787270982, articleId=1263533681487036734, tenantId=1146029695717560320, journalId=1240618002186551303, language=CN, title=基于PCA-CCDM的我国港口城市港产城融合度时空演变研究, columnId=1240655021902197734, journalTitle=中国航海, columnName=水运工程, runingTitle=null, highlight=null, articleAbstract=

港口、产业与城市具有显著的关联性，港产城融合体现了三者在螺旋式发展中的协同演进关系。随着我国港口管理体制改革的深化，对港产城融合状态进行科学测度与机制解析的需求日益突出。针对既有研究中多指标信息重叠与系统协同关系难以量化的问题，构建一种基于主成分分析的耦合协调度模型（PCA-CCDM）。该方法以2004—2023年我国75个港口城市的面板数据为基础，通过主成分分析对港口、产业与城市子系统的高维指标进行降维处理，克服了指标间的多重共线性问题；继而运用耦合协调度模型量化三系统间的协同水平，并结合通过指标间相关性确定指标重要性（CRITIC）赋权法与面板熵权法进行综合赋权与稳健性检验。研究表明：研究期内我国港口城市融合度整体呈上升趋势，其演进过程受港口管理体制影响呈现阶段性波动；区域间与区域内融合度差异显著，极差达4.65，管理体制变迁、产业发展路径依赖与区域制度弹性差异是导致该分异格局的核心因素。据此提出构建跨域协同治理体系、实施差异化产业发展策略等政策建议，为推进港产城系统协同发展提供决策依据。

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赵楠（1986—），女，正高级研究员，博士，研究方向为港口规划、港口物流。

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赵楠（1986—），女，正高级研究员，博士，研究方向为港口规划、港口物流。

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