低碳视角下的宁波舟山港港口物流效率评价及影响因素研究

随着全球气候变化问题日益严峻，交通领域作为碳排放的主要来源之一，受到越来越多的关注。本文以宁波市为研究对象，通过对交通碳排放的测算与分析，探讨了宁波市交通部门的碳排放现状。本文基于宁波市2021~2023的相关统计数据，分别利用行驶里程法、周转量法、机场起降法三种碳排放测算方法计算了宁波市2021~2023年道路交通、铁路交通、城市轨道交通、水路交通和航空交通的碳排放量。结果显示，道路交通仍是交通碳排放的主要来源，通过对不同交通方式的碳排放强度进行对比分析，发现公共交通工具(如地铁)的单位碳排放量明显低于私家车和货车。最后，结合中国整体碳排放目标与政策，提出了降低宁波市交通碳排放的若干建议。

交通强国是未来的发展方向，但交通强国如何指导交通运输规划需要进一步研究。本论文从梳理交通强国的省域层面的特征出发，分析交通强国在省域层面的内涵，提出科技含量高、支撑引领作用强、先行示范性强及公众获得感强的4大内涵特点。最后，通过浙江省交通运输规划方案进行了案例分析，提出发展原则、发展目标、“五纵四横”综合交通通道格局、全国性综合交通枢纽及绿色美丽交通建设。该研究从而为未来交通科学发展提供依据，为综合交通系统规划结构的调整提供理论支撑。

本文以港口企业为核心的服务供应链为主要研究对象，分析其组成节点、形态特点和作业绩效，同时，强调港口服务供应链运作应遵循可持续发展观原则，并提出港口服务供应链新内涵，即须重视经济效益、社会效益、港口基础设施建设与自然环境和资源承载力间协调有序发展。本文利用因果关系图深入剖析港口服务供应链发展机理，建立可持续发展评价指标体系，并结合层次分析法和模糊综合评价法给出港口服务供应链综合评价模型，并以厦门港为例进行实证分析，弥补现有评价方法忽视环境可持续发展性评价的不足。

我国在船舶工业、港口以及各种船舶的拥有数量方面居于世界前列，但目前船舶尾气排放实测研究严重缺乏。目前，海事部门在现场执法过程中主要对燃油质量、记录台账等要求的内容进行检查，并根据实际情况确定是否进行燃油抽样测试的方式实现对船舶燃油硫含量的监管，该方法存在检查目标比较随机、针对性不强的缺点，而且登船检查成本较高，检查数量较少、效率相对较低。因此，设计了基于无人机的船舶尾气监测系统。监测系统由旋翼无人机自主飞行系统、机载气体采样系统、地面通讯控制系统和无线传输系统组成。地面通讯控制系统使用LORA模块与PC端进行数据传输，地面通讯控制系统基于建立的排放数学模型，通过Labview软件编程进行数据处理。对两种型号的船用发动机进行排放监测，监测数据结果显示，该系统能够快速监测船舶尾气中的SO2浓度，并通过数学模型计算得出燃油中的含硫量和NOx比排放，与原有的燃油抽取检测方式相比，更具有针对性和实时性。

近年来，随着港口绿色发展的要求和大气环境保护的需要，船舶岸电技术正在越来越多的应用于各省市集装箱、干散货、邮轮等码头。石化码头是我国重要码头类型之一，石化码头岸电对响应打赢蓝天保卫战具有一定的积极意义。针对石化码头用电安全要求高、码头空间有限等特点，本文开展了石化码头船舶岸电系统应用技术研究，重点从岸电系统静电防护、岸电电源并网切换控制、岸电设备布置与选择等方面，提出了石化码头岸电建设中技术关键点，最后介绍了当前国内石化码头岸电建设及应用情况。

本文选取“港口城市”“发展”为主题，搜集并获取1980~2018年间的中国学术期刊网络出版总库的期刊论文609篇，运用CiteSpace软件，对中国港口城市发展进程研究文献进行综合分析，绘制出论文核心作者群、研究机构以及研究热点的知识结构图谱。结果显示：自中国改革开放以来，国内有关港口城市发展进程的研究中，核心发文作者少，尚未形成紧密的学术团队；大连海事大学、辽宁师范大学、宁波大学等为发文较多的机构；主要围绕“港口”、“港口城市”、“港口经济”、“港城关系”等展开，经济视角是研究主线。此外，除了区域腹地经济差异、港城产业结构与布局优化、临港产业竞争力、临港产业集群、港城协调发展、邮轮旅游等研究外，在新的国内外形势下，对港城发展相关模式探究、绿色港口经济的发展研究、“一带一路”战略对港口城市发展的相关研究、新时代背景下港城联动发展等问题及促进多样化发展对策的探索等方面的研究将成为重点。本文选取“港口城市”“发展”为主题，搜集并获取1980~2018年间的中国学术期刊网络出版总库的期刊论文609篇，运用CiteSpace软件，对中国港口城市发展进程研究文献进行综合分析，绘制出论文核心作者群、研究机构以及研究热点的知识结构图谱。结果显示：自中国改革开放以来，国内有关港口城市发展进程的研究中，核心发文作者少，尚未形成紧密的学术团队；大连海事大学、辽宁师范大学、宁波大学等为发文较多的机构；主要围绕“港口”、“港口城市”、“港口经济”、“港城关系”等展开，经济视角是研究主线。此外，除了区域腹地经济差异、港城产业结构与布局优化、临港产业竞争力、临港产业集群、港城协调发展、邮轮旅游等研究外，在新的国内外形势下，对港城发展相关模式探究、绿色港口经济的发展研究、“一带一路”战略对港口城市发展的相关研究、新时代背景下港城联动发展等问题及促进多样化发展对策的探索等方面的研究将成为重点。

基于华东六省一市2010~2022年的面板数据，本研究测度了考虑能源消耗与碳排放约束下的区域绿色物流效率。超效率SBM模型用于评估静态效率水平及其时空分异特征，ML指数则用于分解全要素生产率变动，探究效率动态演进的内在动力。结果表明：静态效率呈现显著梯度分化，上海长期处于领先地位，江苏、安徽构成第二梯队，福建、浙江、山东、江西效率相对较低，且驱动模式各异。动态演进呈现三阶段特征：2010~2015年波动调整期、2016~2019年技术转型期、2020~2022年韧性重构期。技术进步(TC)是推动华东地区绿色物流效率增长的核心驱动力，普遍高于技术效率(EC)的贡献。疫情冲击下虽导致技术效率显著下滑，但技术进步逆势增长，成为支撑效率快速恢复的关键韧性因素。

物流行业作为国家经济增长的核心驱动力，其高效性在推动区域经济的高品质进展和物流配置的优化等领域起到了至关重要的作用。因此，对物流产业效率进行精准评价显得尤为关键。本研究采用物流业固定资产投资总额、从业人数以及交通运输网络里程作为投入指标，同时以货运量、货物周转量和物流业生产总值为产出指标，并将电信业务总量、地区生产总值和R&D内部经费支出纳入环境因素的考量，借助三阶段DEA模型，对2022年中国31个省、自治区、直辖市的物流产业效率进行了深入分析和评价。实证结果表明：从总体上看，在剔除环境因素和随机误差影响因素后，中国各省际物流产业效率的差异比较明显，呈现大多数东部沿海地区物流产业效率达到DEA有效状态，而物流产业效率偏低的地区集中在西北地区和东北地区的特征；同时研究也得出提高中国物流产业综合效率的关键在于提高纯技术效率的结论。

西部陆海新通道作为我国西部重要的物流通道，其物流低碳高效运行具有重要意义。本文以2011~2022年西部陆海新通道沿线14个省市为研究对象，运用超效率SBM模型和GML指数法评价新通道低碳物流效率，选用fsQCA分析法探索效率提升路径。结果表明：1) 效率方面，新通道整体低碳物流效率提升空间较大但区域发展不均衡，广东、内蒙古、宁夏等省域起引领作用；GML指数波动上升，绿色技术进步是核心因素。2) 路径方面，新通道低碳物流效率受经济发展水平、环保重视程度等多种影响因素组合作用，形成效率提升路径：经济发展与对外开放协同驱动路径、资源约束型路径、政府制约型路径、科技低端型路径和政产研协同驱动路径。

在全球化和区域一体化以及“双碳”目标的推动下，长江经济带作为我国关键经济区域，其绿色物流效率的评价对资源优化和区域发展至关重要。本文聚焦2012~2022年长江经济带11个省市，构建包含能源和二氧化碳排放的投入产出指标体系，运用非期望产出超效率SBM模型与Malmquist-Luenberger指数，从静态和动态视角评估绿色物流效率。研究发现，长江经济带的绿色物流效率整体呈现波动上升趋势，存在下游领先、中游居中、上游滞后的明显区域差异；全要素生产率也在增长，主要由技术进步推动，但技术效率提升有限。基于此，提出构建梯度协同的区域发展体系、深化技术与管理双轮驱动机制等建议，以期为推动该区域物流业高质量可持续发展提供科学决策支持。

河北、山东和河南均是工业、农业大省，在经济，政治，文化等方面都有相近之处。冀鲁豫物流协同发展，对于拉动中国经济增长具有重要作用。本文选取2014年至2022年的面板数据，构建投入产出指标，通过超效率SBM模型，测算出冀鲁豫整体物流超效率值和子系统超效率值，并进行物流协同评价。研究结果表明，山东、河南、河北三省的物流超效率值均小于1，说明三省的物流资源并没有得到充分利用，没有实现以最少的投入，实现最大的产出；山东河南省的物流实现较低程度协同，说明没有实现1 + 1 > 2的效果。山东河北、河南河北以及山东河南河北物流实现较高程度协同，有利于物流资源的协调可持续发展，推动物流业高质量发展。

物流运输优化是现代供应链管理中的一个重要组成部分，它涉及对运输网络、运输方式、运输成本、运输效率和运输服务等方面的持续改进和创新。随着全球化和市场竞争的加剧，物流运输优化对于企业降低成本、提高效率、增强竞争力具有重要意义。物流运输优化主要关注如何更好的管理物流运输链，提高运输效率和降低运营成本。基于此，本文研究了影响运输效率的因素以及建立模型比较不同路径所花费的成本。首先，本文基于某港口物流公司实际运输数据，分析影响货物是否准点到达的影响因素，运用回归分析确定影响货物准点到达的几个重要的影响因素。其次本文基于回归结果建立了一个数学模型，确定最小化物流运输成本条件下的最佳路径。通过收集了运输过程中的各个环节的时间数据，本文分析了这些时间数据与货物准点到达之间的关联性，并对影响因素进行分析。本文将数据代入模型来计算出每条路径的成本，比较了不同路径之间的成本差异，包括物流运输过程中的时间成本、人工成本以及其他相关费用。最后，本文再采用节约里程算法对所研究企业的配送路径优化问题进行探究剖析，通过分析这些数据和计算结果来确定最佳成本路径。

随着全球贸易往来频率及数量的发展，港口及其发展水平逐渐成为衡量一国或地区社会经济发展水平的重要因素。港口发展水平与港口运作效率密切相关，高效的港口运作效率是港口市场竞争力的直接体现。就江苏省五大国家主要港口而言，其运行效率的高低直接影响江苏港口群，乃至于全省经济发展水平与高质量步伐。其中，连云港作为国家一带一路战略支点，其港口运行效率将与国家一带一路战略贯彻息息相关。基于此，本文运用DEA和超效率DEA模型，建立港口运行效率评价指标体系，结合2011~2020年指标数据对江苏省国家主要港口的运行效率进行实证研究。

黄骅港口位于环渤海经济圈中部，在北方港口中具有重要战略地位。深入探讨影响黄骅港货物处理能力的各种因素，并以此为基础进行预测，对于优化黄骅港口的未来发展具有重要意义。此外，研究货物吞吐量的预测有利于确定港口的未来走向、投资规模、基础设施建设等，对黄骅港口的合理布局、发展策略、泊位选址等有着深远意义。本文结合了黄骅港的现状和存在的一些问题，构建了黄骅港吞吐量影响因素指标体系，得出了黄骅港吞吐量的重要影响因素。基于这些重要影响因素，建立了多元线性回归模型，对黄骅港货物吞吐量进行预测研究。

在国家政策的大力支持下，近年来广西北部湾港口建设、货物吞吐创历史新高，2020年底排入了全国十大港口。为进一步探讨港口物流与区域经济互动发展过程中的相互作用机理，本文以2008年~2019年北部湾港口物流与区域经济为研究对象，使用熵值法以及耦合协调模型，分析了北部湾港口物流与区域经济的协同发展状况，发现北部湾港口物流与区域经济之间存在相互依赖关系。从2015年开始北部湾港口物流与区域经济达到了高度协调状态，形成了一种比较理想的发展模式：北部湾港口物流促进北部湾经济特区区域经济增长，北部湾经济特区区域经济又发展推动北部湾港口物流的发展，两者协同发展，共同推动港口区域周边城市经济增长。

为把握全国港口货物吞吐量的变化趋势，为相关决策提供支撑，本文以2019年1月~2024年12月的全国港口货物吞吐量月度数据为基础，运用时间序列分析方法构建ARIMA模型进行拟合与预测。经数据预处理、平稳性检验、模型构建与优化后，确定ARIMA(0,(1,12),1)为最优模型，该模型拟合效果良好且通过相关检验。预测结果显示，2025年全国港口货物吞吐量将呈稳步增长态势，具有显著的季节性特征。

针对目前上海洋山港的竞争优势及建设现状，论文系统分析了洋山港未来开发过程中存在的超负荷运转、进出口口岸环境需要进一步优化、物流标准化水平低下等问题，结合国家和上海市地方的规划及政策，并提出了提高信息化、智能化建设、加强区域协同和制度创新等相应的策略，这对上海港物流中心的建设以及增强上海国际航运中心综合竞争力具有一定的借鉴意义。

本文以宁波港为研究对象，研究以港口大数据为驱动要素，整合港口物流链、信息链与价值链，促进物流链高效协同，降低物流与交易成本，构建智慧港口大物流体系。本文以“港口 + 互联网”的模式为基准，通过干系人需求分析拟定应用场景和模块功能，从业务系统需求及数据信息流动两方面开展研究，提出构建基于混合云平台的智慧港口物流电子商务系统架构，对系统架构中的数据层、接口层、平台支撑层、应用展现层进行详细设计，通过数据中台实现关检、港航、物流的多领域数据互联互通和多式联运一站式服务，实现港口物流单证无纸化、业务模式协同创新。本云服务平台的上线实现了物流全生命周期的透明化管理，使参与各方效率大幅度提升，全面提高宁波港在港口生态圈的影响力和竞争优势。

本文通过复杂网络理论与社区结构分析相结合的方法，以港口为节点构建东北亚货运航运网络，通过研究网络整体拓扑特征、节点中心性指标及社区划分结果，揭示东北亚海上贸易网络的空间格局及其沿线港口分布特征。该网络整体呈现“核心–支线”层级结构，度分布呈现显著长尾特征，尾部更符合受容量约束的重尾过程而非严格幂律分布，具有无标度倾向与典型“小世界”特性；同时，从节点中心性角度分析，东北亚沿线港口呈现明显的区域集聚与功能分化，其中上海港、宁波舟山港、釜山港、大连港及天津新港中心性值最高，构成网络的多功能核心枢纽；社区划分显示清晰的地理–功能模块化格局，主要分为中国东部沿海群、中国珠三角–南海群、日本港口群、韩国港口群，跨社区连接高度依赖少数桥梁节点。基于上述结构事实，东北亚海上贸易网络的优化与韧性提升应重点关注以中国东部沿海核心港群(上海–宁波舟山)为主导、联动韩国釜山及日本关西–关东港群的多核心协同格局，同时将重心置于跨板块通道冗余与替代连接能力的增强、以及区域内中小港口集疏运与服务能力的补齐，从而在维持高效联通的同时降低对少数关键枢纽与桥梁节点的结构性依赖并提升系统抗风险能力。

多式联运可以提高运输统一性，提升效率，降低成本，减轻环境影响。通过提高联运衔接、协同运作，物流供应链可以将不同的运输方式有效融合，使得供应链综合效益最大化。中国企业的物流供应链管理面临着运营环境、管理效率、生态协同、绿色转型等方面的挑战。随着产业规模的扩张，物流供应链生态协同难度增大，信息透明性、链内协同性、运营成本控制等问题亟待解决。由于中国国有物流企业在物流供应链产业中占据主体地位，这类国有大型物流企业拥有资源优势，这种资源优势不仅体现在资金、规模效应上，更体现在产业数字化革新基础上。核心物流企业通过构建完善的闭环物流环境凸出企业信用，借助数字化手段进一步协调多式联运物流关系以实现规模效益。这类核心企业已具备良好的数字化管理基础，借助管理会计的一些方法可以实现低碳化多式联运物流供应链产品开发。这为多式联运物流供应链产业发展提供了可行性方案。

面向大宗货物的散杂货港口在向资源节约型、环境友好型的绿色港口转型升级过程中，对港口绿色发展的生态性与智能性要求提升到新高度，所以需要以生态系统论的观点解决散杂货港口的整体生态型运营优化问题。针对影响港口运营管理的关键问题，综合考虑经济性、生态性、社会性等影响因素，以现代散杂货港口生态型运营管理为基础，重点研究能源、环境、设备、全场资源管理智能化监测与运营优化，构建创新的驱动型绿色港口运营管理模式，运用相应智能化策略，实现现代港口运营各环节智能化、生态化的最优解决途径。

Under the “Dual Carbon” strategy, collaborative governance of port atmospheric pollutants and carbon emissions is critical for low-carbon transformation. Focusing on Ningbo-Zhoushan Port (48% regional ship emissions), this study examines government, port enterprises, and public interactions. A tripartite evolutionary game model with numerical simulation reveals dynamic patterns and key factors. The results show the following: (1) A substitution effect exists between government incentive costs and penalty intensity—increased environmental governance budgets reduce the probability of government incentives, whereas higher public reporting rewards accelerate corporate emission reduction convergence. (2) Public supervision exhibits cyclical fluctuations due to conflicts between individual rationality and collective interests, with excessive reporting rewards potentially triggering free-rider behavior. (3) The system exhibits two stable equilibria: a low-efficiency equilibrium (0,0,0) and a high-efficiency equilibrium (1,1,1). The latter requires policy cost compensation, corporate emission reduction gains exceeding investments, and a supervision benefit–cost ratio greater than 1. Accordingly, the study proposes a three-dimensional “Incentive–Constraint–Collaboration” governance strategy, recommending floating penalty mechanisms, green financial instrument innovation, and community supervision network optimization to balance environmental benefits with fiscal sustainability. This research provides a dynamic decision-making framework for multi-agent collaborative emission reduction in ports, offering both methodological innovation and practical guidance value.

Green development is a primary path for ports and cities to achieve a low-carbon transition under the Sustainable Development Goals and a powerful driving force to elevate regional port–city relations to a high level of coordination. In this paper, twenty port cities in the Yangtze River Delta (YRD) were selected and port environmental efficiency (PEE) was calculated through the window SBM model, while the EW-TOPSIS model was used to evaluate high-quality urban economic development (HED). The coupling coordination degree (CCD) model, the kernel density model, GIS spatial analysis, and the grey prediction model were used to further explore the spatial–temporal dynamic evolution and prediction of the CCD between PEE and HED. The results suggested that: (1) PEE fluctuation in the YRD is increasing, with a trend of seaports achieving higher PEE than river ports; (2) HED in the YRD shows upward trends, and the polarization of individual cities is obvious; (3) Temporally, the CCD in the YRD has risen from 0.438 to 0.518. Shanghai consistently maintains intermediate coordination, and Jiangsu has experienced the most significant increase in CCD. Spatially, CCD is led by Lianyungang, Suzhou, Shanghai, and Ningbo-Zhoushan, displaying a decreasing distribution pattern from east to west. The projection for 2026 suggests that all port cities within the YRD will have transitioned to a phase of orderly development. To enhance the coordination level in the YRD, policymakers should consider the YRD as a whole to position the ports functionally and manage them hierarchically, utilize the ports to break down resource boundaries to promote the synergistic division of labor among cities, and then tilt the resources towards Anhui.

At present, the economy of China has changed from a high-speed growth stage to a high-quality development stage. Integrate the existing logistics infrastructure resources, give better play to the scale economy effect of logistics hub, promote the reform of logistics organization mode, and improve the overall operation efficiency and modernization level of logistics. It is conducive to making up the shortcomings of logistics infrastructure, expanding the supply of high-quality logistics services, building a low-cost and efficient national logistics service network, and improving the vitality and competitiveness of the real economy. Select the 32 key cities in China that the Warehousing in Cloud focuses on, and the 32 key cities carry different types and functional positioning of the national logistics hub. In order to compare the input of transportation logistics land and the performance and output of different types of logistics hubs based on DEA, the data of 32 cities are used as the sample of decision-making units. Firstly, the selection and analysis of input and output indicators are carried out. Secondly, using the data of 32 cities as the sample of decision-making unit, the normality test and correlation analysis of the above input and output indicators are carried out. From the perspective of correlation, the land for logistics storage is related to the land for road traffic facilities. The land for road traffic facilities is related to the added value of the secondary industry, the total retail sales of consumer goods, the import and export volume of goods, and the cargo and mail throughput of the airport. The land for logistics and storage is related to the total retail sales of consumer goods and the import and export volume of goods. Based on the analysis of BCC model of DEA, it is found that from the comparison of the sample cities of different cities’ transportation and logistics land input and the performance output of the production-oriented national logistics hub, Foshan City and Shijiazhuang city are strongly effective in DEA, while other cities are non DEA effective. From the comparison of the sample cities of different cities’ transportation and logistics land input and the performance output of commercial and trade national logistics hub, it can be seen that Shanghai, Hefei and Shijiazhuang are the most efficient DEA cities, while the rest are non DEA cities. From the comparison of the sample cities of transportation and logistics land input in different cities and the performance output of the airport type national logistics hub, it can be seen that Shenzhen is the most efficient city in DEA, while other cities are non DEA efficient. From the comparison of the sample cities of transportation and logistics land input in different cities and the performance output of the inland port type national logistics hub, Shijiazhuang is the only city where DEA is strong and effective, while other cities are non DEA effective. From the comparison of the sample cities of transportation and logistics land input in different cities and the performance output of the port type national logistics hub, it can be seen that Shenzhen, Suzhou and Ningbo are highly effective in DEA, while the rest of the cities are non DEA effective. The corresponding return to scale analysis also gives the direction and trend that the return to scale will change under different production scales of different logistics hub carrying cities.

With the increasing competition between ports, how to improve port efficiency and promote the sustainable development of ports becomes the focus of attention. The paper applies the three-stage data envelopment analysis (T-S DEA) model to measure the efficiency of top 10 Chinese coastal ports, including the scale efficiency (SE) and technical efficiency (TE). Besides, the principle component analysis approach is adopted to reduce the influence of redundant information of evaluation indicators on results. The stochastic frontier analysis regression model is applied to eliminate the effects of environmental factors and stochastic errors under similar exterior conditions. The results show: (1) The ports efficiency (SE and TE) in different regions is quite different; the efficiency of Chinese coastal ports is generally low, which is mainly caused by scale inefficiency; (2) The SE and TE are improved and the input and output redundancy ratio are decreased significantly by comparing the results of traditional DEA model. (3) Shanghai, Ningbo-Zhoushan, Shenzhen, Hong Kong and Kaohsiung have relatively high comprehensive efficiency. This study provides valuable information for estimating port efficiency and establishing competitive strategies in the future.

No abstract available

This study presents a comprehensive analysis of carbon emission trends and their driving factors at Shanghai Port, with a particular focus on the decoupling relationship between port economic development and carbon emissions, as well as forecasting the timeline for achieving the port’s carbon peak. The findings reveal distinct temporal patterns in emission growth: from 2009 to 2012, Shanghai Port experienced steady increases in carbon emissions, while from 2020 to 2023, it witnessed accelerated growth, primarily driven by fuel oil consumption. Using the Logarithmic Mean Divisia Index (LMDI) decomposition model, the study identifies operational revenue as the most significant contributor to carbon emission growth, while economic intensity emerges as the strongest inhibiting factor. Notably, the carbon-promoting effects of energy structure and efficiency improvements substantially outweigh the emission reductions achieved through enhanced economic intensity. The Tapio decoupling analysis indicates that during 2010–2023, neither operational revenue nor port cargo throughput capacity achieved stable decoupling from carbon emissions at Shanghai Port. Operational revenue exhibited alternating patterns of strong and weak decoupling, while cargo throughput showed more pronounced fluctuations, cycling through phases of decoupling and negative decoupling. Scenario-based predictions using the GRU-LSTM hybrid model provide critical insights: under the baseline scenario, Shanghai Port is projected to fail to achieve a carbon peak by 2035. However, both the low-carbon and enhanced mitigation scenarios project a carbon peak around 2026, with the enhanced scenario enabling earlier attainment of the target. These findings offer valuable theoretical foundations for formulating Shanghai Port’s carbon peak strategy and provide practical guidance for emission management and policy development at ports. The methodological framework and empirical results presented in this study may serve as a reference for other major ports pursuing similar decarbonization goals.

In the context of a carbon trading mechanism, this study examines the distribution of emission reduction responsibilities between ports and shipping companies. By constructing Stackelberg game models in four scenarios, equilibrium strategies are found from both economic and environmental dimensions, and the impacts of carbon pricing and port green subsidies on the economy and environment are analyzed. The research indicates that, considering both economic and environmental benefits, it is optimal for members of the port and shipping supply chain to undertake emission reduction investments. Furthermore, under the carbon trading mechanism, the profits of ports and shipping companies vary with carbon prices, with the highest profits for ports in the YY scenario. The YN and YY scenarios enhance emission reduction efficiency, while the environmental damage in the NY scenario may be close to that in the NN scenario. Green subsidies improve the profits and environmental performance of port and shipping enterprises, highlighting the critical role of green subsidies in environmental benefits.

Against the backdrop of the coordinated advancement of the “Dual Carbon” goals and the Digital China strategy, this study focuses on the mechanism through which the digital economy influences port emission reduction. Based on panel data from 16 coastal ports in China from 2014 to 2023, this study systematically examines the impact of the digital economy on port carbon emission reduction. Using two-way fixed effects and mediation effect models, this study empirically tests the direct and indirect pathways through which digital economy development affects port carbon emissions. The results indicate that the digital economy significantly reduces port carbon emissions, a finding robust to endogeneity and sensitivity tests. Mechanism analysis reveals that the digital economy not only directly enhances operational efficiency but also indirectly promotes emission reduction through green technology innovation, whereas the mediating role of industrial structure upgrading appears limited. This research provides theoretical foundations and policy insights for promoting sustainable development and deep integration of port green transformation and digital technology.

In 2024, the EU intends to include the global shipping industry in the European Union Emission Trading Scheme (EU ETS). Shipping companies will have to pay for the carbon emissions of ships over 5,000 GT on routes between EU and non-EU ports. This paper selects typical shipping companies in the world. Based on the principle of fairness, historical method, baseline method and mixed method are adopted to explore their carbon emission quota allocation. The ZSG-DEA efficiency model is used to evaluate the distribution results and verify the optimal efficiency. The research results show that the mixed method has a high efficiency of allocation. The method predicts that the carbon quota of typical shipping companies in the world will reach the Pareto optimal allocation in 2024 and Maersk has the highest carbon emission quota among the eight typical shipping companies, reaching 32,431,800 tons, followed by MSC and EMC, reaching 8,542,400 tons and 6,809,500 tons, respectively. Based on the results, we can obtain a reasonable allocation of carbon allowances in the EU carbon market according to the proportion of business of shipping companies involved in EU routes. The research is still applicable to the allocation of carbon emmissions in future years. Therefore, this paper provides suggestions for the orderly allocation of carbon quota and carbon trading in the global shipping market.

No abstract available

Electrifying port horizontal transportation is constrained by downtime and deadheading from fixed charging/swapping systems, large battery sizes, and the lack of integrated decision tools for life-cycle emissions. This study develops a carbon-efficiency-centered bi-objective optimization framework benchmarking Mobile Charging Stations (MCSs) against Fixed Charging Stations (FCSs) and Battery Swapping Stations (BSWSs). The framework integrates operational parameters such as charging power, range, dispatch, and non-operational mileage, along with grid carbon intensity, battery embodied emissions, and carbon-market factors. It generates Pareto fronts using the NSGA-II algorithm with real port data. Port horizontal transportation refers to the movement of goods within the port area, typically involving the use of specialized vehicles to transport containers short distances across the terminal. Results show that MCSs can reuse idle windows to reduce deadheading and infrastructure demand, yielding significant economic improvements. The trade-off between emissions and profitability is context-dependent: at low-to-moderate reuse levels, low-carbon and profitable solutions coexist; beyond a threshold of approximately 0.5–0.75, the Pareto fronts shift to high emissions and high profits, highlighting the context-specific advantages of MCSs for port-infrastructure planning. MCSs thus provide context-dependent advantages over FCSs and BSWSs, offering practical guidance for port infrastructure planning and carbon-informed policy design.

This paper, using the ultra-efficiency DEA-Tobit model, takes 16 listed port enterprises as an example to measure the port efficiency from 2015 to 2021. Based on the unexpected output, the measured port enterprise operation efficiency is more objective, and provides reasonable suggestions for the improvement of the port operation efficiency. The research results show that: (1) the operating efficiency of port enterprises is increasing, but the growth rate is slow; (2) the operating efficiency is limited by the growth of technical efficiency; (3) the fixed assets, management expenses and R & D expenses promote the port operation efficiency, while the paid-in capital, operating cost and carbon emission inhibit the port operation efficiency.

The collection and distribution network of ports is the main cause of carbon emissions. The carbon peak is a basic policy in China, and the subsidy policy is one of the common measures used by the government to incentivize carbon reduction. We analyzed the transportation methods and the flow direction of a port and proposed a carbon emission calculation method based on emission factors. Based on the transportation time and the cost, a generalized transportation utility function was constructed, and the logit model was used to analyze the impacts of subsidy policies on transportation, thus calculating the effects of the subsidies on carbon reduction. We used Guangzhou Port as a case study, and calculated the carbon reduction effects in six different subsidy policy scenarios and concluded that the absolute carbon reduction value was proportional to the subsidy intensity. In addition, we constructed a subsidy carbon reduction efficiency index and found that the Guangzhou Port collection and distribution network had higher subsidy carbon reduction efficiency in low-subsidy scenarios. Finally, a sensitivity analysis was conducted on the subsidy parameters, and scenario 8 was found to have the highest subsidy carbon reduction efficiency. This achievement can provide decision support for the carbon emission strategy of the port collection and distribution network.

Blockchain technology is very useful. This paper considers the application of blockchain technology to smart contracts, green certification, and market information disclosure, and introduces the carbon trading market price as a parameter to solve the dynamic incentive problem of the government for port enterprises to reduce emissions under the carbon trading policy. Based on the state change of port carbon emission reduction, this paper uses principal–agent theory to construct the dynamic incentive contract model of government without blockchain, with blockchain, and when carbon trading is considered under blockchain, respectively, and uses the optimal control method to solve and analyze the model. This paper finds that only when the opportunity cost of port enterprises is greater than a certain critical point and the fixed cost of blockchain is less than a certain critical point, the implementation of blockchain will help improve government efficiency. However, only when the critical value of carbon emission reduction of port enterprises and the unit operating cost of blockchain are small, the government should start the carbon trading market under blockchain technology. Through numerical simulation, this paper also finds that it is usually beneficial for the government to regulate and appropriately increase the carbon trading market price.

No abstract available

This paper investigates the relationship between port productivity and carbon dioxide (CO2) emissions in port cities. The study initially employs the global Malmquist productivity index (MPI) to measure productivity growth in 16 major inland ports along the Yangtze River, obtaining data on the ports’ total factor productivity (TFP). Through an analysis using the panel data model with two-way fixed effects, we find a positive correlation between the improvement of port TFP and the increase in CO2 emissions in port cities. Further panel quantile regression analysis reveals the heterogeneity of this impact, especially in cities with medium and higher CO2 emissions, where the positive effects of TFP on carbon emissions are particularly significant. The study also indicates a threshold effect of port size in the relationship between TFP and CO2 emissions: in smaller ports, the impact of TFP improvement on CO2 emissions is less significant; however, once the port size exceeds a certain threshold, the growth in TFP significantly promotes an increase in CO2 emissions. These findings provide theoretical justification and decision-making references for policymakers to adopt effective measures to mitigate the growth of CO2 emissions while promoting the efficiency of port production.

No abstract available

The green transformation of ports along the Belt and Road Initiative faces dual challenges of the absence of a standardized system and insufficient methods for benefit assessment, which seriously hinders the progress of cross-border port low-carbon cooperation. To address this, this study innovatively constructs a four-dimensional standardized indicator system covering environmental performance, energy structure, technological innovation, and policy coordination by integrating the international green port evaluation framework (EcoPorts/GPAS) with China's "Green Port Grade Evaluation Guidelines" (JTST105-4-2020), and proposes a three-in-one port transformation path of "standardized system - policy coordination - digital twin". To achieve the above goals, a three-stage DEA model is adopted to eliminate environmental interference factors such as regional development level differences, combined with a non-radial directional distance function to measure port carbon emission efficiency, and blockchain technology is introduced to achieve immutable traceability of environmental data throughout the entire chain. Empirical analysis based on Shanghai Port, Piraeus Port, and Djibouti Port from 2015 to 2024 shows that after the implementation of the standardized system, the comprehensive efficiency of ports along the route has increased by 15% to 30%, and carbon emission intensity has decreased by more than 20%. Among them, digital twin technology has reduced Shanghai Port's ineffective carbon emissions by 14%, and blockchain smart contracts have achieved an automatic execution rate of environmental compensation of 92% in Djibouti Port. The research confirms that this system has significantly improved the scale efficiency of ports in developing countries (such as Djibouti Port's efficiency value increasing from 0.68 to 0.83), and established a standard mutual recognition mechanism for cross-border green port alliances. The research results provide a solution that is both academically rigorous and operationally feasible for the construction of a low-carbon corridor along the "21st Century Maritime Silk Road", and have strategic support value for the realization of the carbon reduction goals of the International Maritime Organization (IMO).

To accomplish IMO’s emission reduction targets, the Chinese government has established emission control areas and implemented strict sulfur limitation policies. Faced with a downturn in the shipping industry and the challenge of an insufficient supply of compliant fuel, Hong Kong and Shenzhen in China have implemented different low-sulfur fuel oil subsidy policies. It is particularly important to study non-cooperative games between two ports considering low-sulfur fuel oil subsidies. In this paper, first, non-cooperative game models considering low-sulfur fuel oil subsidies are constructed. Second, the mechanisms of various factors affecting port pricing, throughput and profit are analyzed. Then, a case study is conducted using AIS data of container ships in Shanghai and Ningbo-Zhoushan ports. The study reveals that in both sequential and simultaneous games, the gross tonnage of a ship has an impact on the optimal service price, throughput and profit of the port. The subsidy rate has a positive impact on the profitability of the port itself, to the detriment of competitor ports. In conclusion, a low-sulfur fuel oil subsidy policy has a significant positive impact on the step-by-step implementation of more stringent air pollution reduction policies in port waters.

This study proposes an optimal scheduling model for Port Integrated Energy Systems (PIES) based on Stackelberg game theory. In this hierarchical framework, the Port Energy Management Center (PEMC) acts as the leader, while Renewable Energy Operator (REO) and port Load Aggregator (LA) serve as followers. Price signals issued by the PEMC guide the followers in optimizing decisions regarding the power and thermal outputs of Combined Heat and Power (CHP) units, as well as the dynamic adjustment of user-side lighting, residential, and marine loads. Each participant independently maximizes its economic benefit. Adopt a tiered carbon penalty measure to control the carbon emissions generated during the production process of REO energy. A bi-level optimization method is employed to solve the model: the upper level applies a differential evolution algorithm to optimize the PEMC's pricing strategy, while the lower level uses quadratic programming to determine the supply and demand strategies of REO and LA. Through iterative information exchanges between the leader and followers, the system reaches a Stackelberg equilibrium. Simulation results verify that the proposed model effectively enhances the economic performance of port energy management, increases stakeholder profits, reduces carbon emissions, and improves the overall operational efficiency of the port energy system.

Despite the benefits of smart ports development for productivity, energy saving, and environmental improvement, an intelligent investment strategy should consider potential adverse effects on marine ecosystems during the construction and operation processes. To address this issue, this study aims to examine the integration of green finance instruments with artificial intelligence (AI)-driven intelligent decision-making (IDM), utilizing data on 15 major Chinese ports.Employing machine learning (ML) models, alongside SHapley Additive exPlanations (SHAP) analysis, the research quantifies the impact of green finance on critical environmental metrics, including total organic carbon (TOC), carbon fluxes, carbon burial rate, pollution load index (PLI), flow velocity, and erosion/deposition rate (E/DR). First, ML models are employed to estimate these indicators based on historical data. Subsequently, SHAP is utilized to interpret the impact of financial instruments on ecological indicators. This enables the identification of financial instruments that positively influence ecological indicators in specific marine regions, thereby supporting IDM to prioritize those instruments in the corresponding areas.Findings highlight green bonds as the most influential, with SHAP values of 0.24-0.30 for carbon burial rate and 0.17-0.20 for PLI, particularly in advanced ports like Shanghai and Ningbo-Zhoushan, while eXtreme gradient boosting (XGBoost) achieves superior predictive accuracy.This study suggests that green bonds, green leasing, and green credit should be prioritized. Policymakers should establish a dedicated framework for green bonds and green leasing, specifically targeting ports with advanced smart infrastructure (L3-L4). Green credit schemes should be promoted to support infrastructure enhancement and renewable energy projects in L1-L2 ports.

In this study, we develop a game-theoretic optimization framework to analyze competing vessels’ technology choices between shore power (SP) and low-sulfur fuel oil (LSFO) within a maritime supply chain which is regulated by a cap-and-trade mechanism. Using a Stackelberg game approach, we construct two models—one port-led and the other vessel-led—to derive closed-form equilibrium for pricing, service quantities, profits, emissions, and social welfare. The results reveal three key findings. First, the leader in either Stackelberg structure always achieves higher profits, while total supply chain profits remain identical across power structures. Second, at low carbon prices, LSFO-equipped vessels provide more services and earn higher profits due to cost advantages. As the carbon price rises—which directly incentivizes emission reduction and accelerates maritime decarbonization—SP becomes more attractive and eventually dominates in profitability despite higher initial investment. Notably, although SP has lower unit emissions, its total emissions may surpass those of LSFO at certain carbon-price thresholds because the SP-equipped vessel optimally expands output. Third, intensified competition reduces service quantities, profits, and emissions, with a more substantial reduction effect on LSFO vessels. Overall, our results provide mathematically grounded insights for optimizing low-carbon technology adoption in maritime transport and offer actionable policy implications for carbon pricing that balance environmental objectives and supply chain efficiency. This research contributes specifically to the United Nations’ Sustainable Development Goals (SDGs), specifically SDG 13 (Climate Action) and SDG 9 (Industry, Innovation and Infrastructure).

In order to implement China's carbon emissions peak strategy and accelerate the promotion of reaching carbon emissions peak in container terminals, this study uses container terminals as the object, accounts current direct carbon emissions in Chinese container terminals through investigating typical terminals; compares and analyzes the main carbon emission reduction measures from the perspectives of technical level, investment cost, and others; on this basis, construct the predictive model of direct carbon emissions, and uses scenario analysis to carry out carbon emission predictions under each scenario and analyzes the effect of policy intervention, technological development and other factors on carbon emission peak. According to the predictive results, this study holds that in order to achieve direct carbon emission peak in container terminals as soon as possible, container terminals should strongly promote the application of clean energy to port machinery instead of fuel on the basis of the industrial development and cost reduction of high-power and large-capacity power batteries, actively adopt high-efficiency and low-carbon fuel equipment, and vigorously promote the production efficiency of fuel machinery; at the same time, strengthen policy encouragement and guidance are needed.

Improving the digital economy and environmental governance efficiency are important methods for current high-quality economic development. Based on the panel data of 11 cities in Zhejiang, on the eastern coast of China, fine particulate matter smaller than a 2.5 μm (PM2.5) environmental efficiency (PMEE) was measured by the undesirable output Slack-Based Measure-Data Envelopment Analysis (SBM-DEA) model. The fixed effect regression model, the divergences in the difference model and other empirical methods were obtained to test the driving mechanism of social-economic factors on the PMEE. The results showed that: (1) the concentration of PM2.5 was continually decreasing, and environmental quality experienced a continuous improvement in Zhejiang province in the observation period, although cities such as Hangzhou, Jiaxing and Shaoxing have relatively severe PM2.5 pollution. (2) The total average value of PMEE in Zhejiang was 0.6430 over the observation period, while there was still a lot of room for improvement when compared to the production frontier. Additionally, PMEE in each city showed a fluctuating growth trend. Cities with a higher PMEE were mainly Zhoushan, Hangzhou and Ningbo. (3) The level of the digital economy had a positive role in promoting the PMEE, which was statistically significant. The level of pollution control and technological innovation also had a significantly positive effect. However, the ratio of the industrial output value to the gross domestic product (GDP) presented a negative effect on the PMEE. In the future, it is suggested that the development of the urban digital economy should be accelerated in an all-around way to improve the efficiency of government pollution control and to improve the technical efficiency of PM2.5 via innovative technological progress.

The port logistics service innovation (PLSI) is closely associated with cross-border e-commerce (CBEC) enterprise performance, given that the port, as the spatial carrier and the joint point of goods, information, customs house affairs, etc., is essentially a key node of the CBEC logistics chain. However, the influence mechanism of PLSI on CBEC enterprise performance has still not yet been elaborated by consensus. To fill this gap, this study aims to figure out the effect mechanism integrating the probe into two variables (i.e., information interaction and environmental upgrade) in a moderated mediation model. Specifically, this study collects questionnaire survey data of logistics enterprises and CBEC enterprises in the Ningbo-Zhoushan Port of China by the Bootstrap method in the software SPSS 26.0. The results show the following: (1) PLSI can positively affect the CBEC enterprise performance; (2) information interaction plays an intermediary role between PLSI and CBEC enterprise performance; and (3) environmental upgrade can not only positively regulate the relationship between information interaction and CBEC enterprise performance, but also enhance the mediating role of information interaction with a moderated intermediary effect.

With the “Belt and Road” initiative, the relationship between various regions and countries has become increasingly close, and container transportation has gradually developed into one of the important modes of cargo transportation. Among them, Sea-rail Combined Transport (SCT) is the most promising logistics transportation method. With the globalization of the world economy and the rapid development, Sea-rail Combined Transport is about to gradually develop into a universal international cargo transportation method. In recent years, in order to ensure that China can become an emerging maritime power, the Ministry of Transport has successively issued many policies to support it, expressly requiring the strengthening of the development of the container Sea-rail Combined Transport port station. Although Zhoushan Port has become one of Chinese important foreign trade ports, today with huge competition pressure, there is still a gap with the predetermined target. How to find the development focus and breakthrough point of Zhoushan Port is to make Ningbo Zhoushan Port’s sea rail inter-modal is an important task for building the strongest brand. In this paper, we discussed the development status of container Sea-rail Combined Transport in Zhoushan Port of Ningbo, and the modified index curve method is used to predict its future traffic demand. At the same time, the necessity and innovation of developing Sea-rail Combined Transport in Zhoushan Port are explored, and corresponding suggestions are put forward to promote the prosperity and development of Zhoushan Port.