Article(id=1263557427023196355, tenantId=1146029695717560320, journalId=1240618002186551303, issueId=1263557421033767765, articleNumber=null, orderNo=null, doi=10.3969/j.issn.1000-4653.2026.01.016, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1740585600000, receivedDateStr=2025-02-27, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1779185166364, onlineDateStr=2026-05-19, pubDate=1771948800000, pubDateStr=2026-02-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1779185166364, onlineIssueDateStr=2026-05-19, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1779185166364, creator=13701087609, updateTime=1779185166364, updator=13701087609, issue=Issue{id=1263557421033767765, tenantId=1146029695717560320, journalId=1240618002186551303, year='2026', volume='49', issue='1', pageStart='1', pageEnd='197', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1779185164936, creator=13701087609, updateTime=1779185226134, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1263557677813252309, tenantId=1146029695717560320, journalId=1240618002186551303, issueId=1263557421033767765, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1263557677813252310, tenantId=1146029695717560320, journalId=1240618002186551303, issueId=1263557421033767765, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=155, endPage=164, ext={EN=ArticleExt(id=1263557427761393871, articleId=1263557427023196355, tenantId=1146029695717560320, journalId=1240618002186551303, language=EN, title=Multi-objective optimization of low-carbon transition strategies for container ship fleets, columnId=1240655020656489358, journalTitle=Navigation of China, columnName=Green Shipping, runingTitle=null, highlight=null, articleAbstract=

With the continued advancement of China "dual-carbon" goals and the increasingly stringent emission reduction regulations of the International Maritime Organization (IMO), the shipping industry is facing more severe emission reduction challenges and urgently needs to clarify the green transition pathways. Most of the existing research focuses on the selection of alternative fuels, while there is relatively little research on emission reduction strategies from the perspective of the fleet. To address the shortcomings of the existing research, this study identifies the key factors influencing fleet green transition decisions. Based on this, a bi-objective linear programming model jointly considering economic and environmental objectives is established, and a genetic algorithm combined with the ε-constraint method is constructed to solve the model. Finally, taking the 10, 000-11, 000 TEU container fleet of COSCO Shipping Group as a case study, this research derives the optimal green transition strategy for the fleet during the planning horizon, encompassing fuel choices and operational configurations for individual vessels. Sensitivity analysis reveals that fluctuations in fuel prices significantly affect the selection of engine types during vessel retrofitting and renewal decisions, while the stringency of emission reduction targets directly influences fleet transition costs, thereby affecting corporate proactiveness in pursuing decarbonization initiatives. Consequently, policymakers should establish appropriately calibrated emission reduction targets and incentive mechanisms to accelerate the advancement of low-carbon technologies, reduce fleet transition costs, and expedite the achievement of decarbonization objectives in the shipping industry.

, correspAuthors=Mingjun JI, 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=Mingjun JI, Yuxin WANG, Zhenglin KUAN, Wanwei FANG), CN=ArticleExt(id=1263557430064066794, articleId=1263557427023196355, tenantId=1146029695717560320, journalId=1240618002186551303, language=CN, title=集装箱运输船队低碳转型策略多目标优化研究, columnId=1240655020820067227, journalTitle=中国航海, columnName=绿色航运, runingTitle=null, highlight=null, articleAbstract=

随着国家“双碳”目标的不断推进和国际海事组织（IMO）减排规定的日益趋严，航运业正面临愈发严苛的减排考验，迫切需要明确绿色转型路径。当前研究大多聚焦于替代燃料的选择，较少从船队角度进行减排策略的研究。为弥补当前研究不足，针对船队绿色转型策略选择这一问题，梳理转型过程中的核心影响因素，基于此建立考虑经济与环境的双目标线性规划模型，并结合ε-约束法构建遗传算法对模型进行求解。以中远海运集团10 000 TEU~11 000TEU集装箱船队为例，求解规划期内该船队的绿色转型策略，包括各船舶的燃料使用方案和船舶操作方案。通过灵敏度分析，发现燃料价格的变动会对船舶更新改造时发动机的类型选择产生影响，减排目标的强度会影响船队转型成本进而影响企业对船队进行低碳转型的积极性。因此，政府应制定合理的减排目标和激励政策，促进相关低碳技术发展，降低船队转型成本，尽早实现航运业减排目标。

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计明军（1973—），男，教授，博士，研究方向为物流工程与管理。

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计明军（1973—），男，教授，博士，研究方向为物流工程与管理。

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