[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117765-en":3,"doc-seo-117765-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},117765,1099513958762,"Logic","https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1784791008015729253",8,"Research & Report","Machine Learning for Promoting Environmental Sustainability in Ports","Maritime transportation drives global logistics, while ports act as critical interfaces between sea and hinterland. Climate change, air pollution, and greenhouse-gas emissions increasingly threaten environmental sustainability, motivating green initiatives in port operations. This study conducts a systematic literature review on machine learning methods used to promote environmentally sustainable maritime ports, organizing evidence across machine learning, port operations, and sustainability dimensions. Findings highlight polynomial regression dominance, with RNN and LSTM as newer approaches, and emphasize emissions and energy consumption as leading problem areas. Identified research gaps suggest expanding ML model diversity and enabling more green practical work in port operations.","Hindawi  \nJournal of Advanced Transportation Volume 2023, Article ID 2144733, 17 pages [https://doi.org/10.1155/2023/2144733](https://doi.org/10.1155/2023/2144733)  \nResearch Article  \nMachine Learning for Promoting Environmental Sustainability in Ports  \nMeead Mansoursamaei , 1,2 Mahmoud Moradi , 1 Rosa G. Gonzlez-Ram´ırez  3  \n,  \nand Eduardo Lalla-Ruiz 2  \n1 University of Guilan, Rasht, Iran  \n2 University of Twente, Enschede, Netherlands  \n3Facultad de Ingenier´ı a y Ciencias Aplicadas, Universidad de los Andes, Santiago, Chile  \nCorrespondence should be addressed to Meead Mansoursamaei; [m.mansoursamaei@utwente.nl](m.mansoursamaei@utwente.nl)  \nReceived 19 May 2022; Revised 12 September 2022; Accepted 10 February 2023; Published 3 March 2023  \nAcademic Editor: Dongjoo Park  \nCopyright © 2023 Meead Mansoursamaei et al. Tis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.  \nMaritime transportation is one of the essential drivers of the global economy as it enables both lower transportation costs and intermodal operations across multiple forms of transportation. Maritime ports are essential interfaces that support cargo handling between sea and hinterland transportation. Besides, in this area, environmental protection is becoming extremely important. Global warming, air pollution, and greenhouse gas emissions are all having a detrimental infuence on the environment and will most likely continue to do so for future generations. Hence, there is a growing need to promote environmental sustainability in maritime-based transportation. Te application of machine learning (ML), as one of the main subdomains ofartifcial intelligence (AI), can be considered a component within the process of digital transformation to advance green activities in maritime port logistics. Tus, this article presents the results of a systematic literature review of the recent literature on machine learning for promoting environmentally sustainable maritime ports. It collects and analyses the articles whose contributions lie in the interplay between three main dimensions, i.e., machine learning, port-related operations, and environmental sustainability. Troughout a review protocol, this research is constituted on the major focuses of impact, problems, and techniques to discern the current state of the art as well as research directions. Te research fndings indicate that the articles using polynomial regression models are dominant in the literature, and the recurrent neural network (RNN) and long short-term memory (LSTM) are the most recent approaches. Moreover, in terms of environmental sustainability, emissions and energy consumption are the most studied problems. mAccording to the research gaps observed in the review, two broad directions for future research are identifed: (i) altering attention on a greater diversity of machine learning approaches for promoting environmental sustainability in ports and (ii) leveraging new outlooks to perform more green practical works on port-related operations.  \n1. Introduction  \nMaritime freight transportation is one of the vital drivers of the global economy as it enables both lower transportation costs and faster intermodal operations across multiple forms of transportation [1, 2]. Indeed, maritime ports are the essential interfaces that support cargo handling between sea and hinterland. Besides, environmental sustainability has become oneof the important foundations on the agenda of many maritime ports due to the challenges of climate change as well as the  \ngrowing demands of the logistics and transportation sectors [3]. Expanding maritime transportation activities has enabled urban economies to prosper to some extent; however, they also have caused resource waste and environmental pollution. To achieve the sustainable growth of ports and cities, energysavi","cbCaiestTonbdhD0","https://ap.wps.com/l/cbCaiestTonbdhD0","pdf",554903,1,17,"English","en",105,"# Introduction\n## Maritime freight and port sustainability\n## Role of artificial intelligence and machine learning\n## Scope of the review and research objectives\n## Future research directions","[{\"question\":\"Why is environmental sustainability important for maritime ports?\",\"answer\":\"Environmental sustainability is prioritized because climate change, air pollution, and greenhouse-gas emissions from port activities increasingly harm the environment and create growing operational and logistics pressures.\"},{\"question\":\"How does the article structure its review of machine learning for green ports?\",\"answer\":\"The review analyzes recent literature by focusing on the interplay among three dimensions: machine learning, port-related operations, and environmental sustainability.\"},{\"question\":\"What machine learning approaches and sustainability problems are most common in the reviewed studies?\",\"answer\":\"Polynomial regression models dominate the literature, while RNN and LSTM are among the more recent approaches. Emissions and energy consumption are the most frequently studied sustainability problems.\"}]","Machine Learning for Promoting Environmental Sustainability in Ports | PDF",1785679452,43,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-for-promoting-environmental-sustainability-in-ports","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-for-promoting-environmental-sustainability-in-ports/117765/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is environmental sustainability important for maritime ports?","Question",{"text":75,"@type":76},"Environmental sustainability is prioritized because climate change, air pollution, and greenhouse-gas emissions from port activities increasingly harm the environment and create growing operational and logistics pressures.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the article structure its review of machine learning for green ports?",{"text":80,"@type":76},"The review analyzes recent literature by focusing on the interplay among three dimensions: machine learning, port-related operations, and environmental sustainability.",{"name":82,"@type":73,"acceptedAnswer":83},"What machine learning approaches and sustainability problems are most common in the reviewed studies?",{"text":84,"@type":76},"Polynomial regression models dominate the literature, while RNN and LSTM are among the more recent approaches. 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