Article(id=1240655515735347861, tenantId=1146029695717560320, journalId=1240618002186551303, issueId=1240655508231746242, articleNumber=null, orderNo=null, doi=10.3969/j.issn.1000-4653.2025.02.016, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1704816000000, receivedDateStr=2024-01-10, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1773724925216, onlineDateStr=2026-03-17, pubDate=1750780800000, pubDateStr=2025-06-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773724925216, onlineIssueDateStr=2026-03-17, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773724925215, creator=13701087609, updateTime=1773724925215, updator=13701087609, issue=Issue{id=1240655508231746242, tenantId=1146029695717560320, journalId=1240618002186551303, year='2025', volume='48', issue='2', pageStart='1', pageEnd='180', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1773724923427, creator=13701087609, updateTime=1773725151164, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1240656463488667953, tenantId=1146029695717560320, journalId=1240618002186551303, issueId=1240655508231746242, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1240656463492862258, tenantId=1146029695717560320, journalId=1240618002186551303, issueId=1240655508231746242, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=127, endPage=135, ext={EN=ArticleExt(id=1240655515966034582, articleId=1240655515735347861, tenantId=1146029695717560320, journalId=1240618002186551303, language=EN, title=Research on prediction method of carbon emission intensity for inland waterway vessel operations, columnId=1240655020656489358, journalTitle=Navigation of China, columnName=Green Shipping, runingTitle=null, highlight=null, articleAbstract=

"The road to water" is one of the emission reduction pathways for inland transportation. The low carbon emission reduction of the inland waterway shipping industry has received widespread attention. The carbon emission intensity index is the core issue in the process of greening the inland waterway shipping industry. Developing a prediction model for the carbon emission intensity of inland vessels is the basis for formulating new energy policies for inland shipping. This paper discusses the green transformation of the inland waterway shipping industry, drawing on cutting-edge domestic and international research. First, it analyses the factors influencing inland waterway shipping carbon emissions and their interrelationships. Based on this analysis, a system dynamics model was used to construct a prediction method for carbon emission intensity. This method was then used to analyse the changing trend of carbon emission intensity for container vessels, using the Yangtze River as a case study under various scenarios. The results show that: i. The system dynamics model for predicting carbon emission intensity in inland river operations is valid and reflects the real system more accurately. ii. Under the vessel energy efficiency improvement scenario, the carbon emission intensity decreases to 94.04% and 64.40% in 2030 and 2060 respectively, compared with the baseline scenario. iii. The carbon emission intensity in 2030 and 2060 decreases to 87.47% and 29.35% of the baseline scenario for the same year respectively when multiple measures are implemented to improve ship energy efficiency, optimise energy structure, accelerate the elimination of vessels, and levy a carbon tax.

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“公转水”是内陆运输减排路径之一，内河航运业的低碳减排受到广泛关注，内河船舶碳排放强度指标既能反映内河航运绿色化进程，也能一定程度上反映“公转水”效果，开展内河船舶碳排放强度预测也是制定内河航运新能源政策的基础。文章针对国内外前沿研究中探讨的内河航运业绿色转型问题，在分析内河船舶营运碳排放影响因素及其相互关系的基础上，运用系统动力学（SD）模型提出了内河船舶营运碳排放强度预测方法，并以长江集装箱船舶为案例验证对象，分析各情景下长江集装箱船舶碳排放强度变化趋势。论文结论显示：1) 文章构建的内河船舶营运碳排放强度预测模型通过了有效性检验，能够较为真实地反映现实系统。2) 提高船舶能效情景下，2030年和2060年碳排放强度相较于基准情景分别下降到94.04%、64.40%；优化能源结构情景下，与基准情景相比，2030年和2060年碳排放强度分别下降到92.62%、44.59%。3) 在提高船舶能效措施、优化能源结构措施、加速淘汰船舶措施和征收碳税措施的多重作用下，2030年和2060年碳排放强度分别下降到同年基准情景的87.47%、29.35%。

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刘清（1966—），女，教授，博士，研究方向为交通运输规划与管理。E-mail:673633005@qq.com

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刘清（1966—），女，教授，博士，研究方向为交通运输规划与管理。E-mail:673633005@qq.com

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