Exploring decarbonization priorities for sustainable shipping: A natural language processing-based experiment
The shipping industry is currently the sixth largest contributor to global emissions, responsible for one billion tons of greenhouse gas emissions. Urgent action is needed to achieve carbon neutrality in the shipping industry for sustainability. In this paper, we use natural language processing tech...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Sustainable Futures |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666188824002077 |
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| author | Enna Hirata Kevin X. Li Daisuke Watanabe |
| author_facet | Enna Hirata Kevin X. Li Daisuke Watanabe |
| author_sort | Enna Hirata |
| collection | DOAJ |
| description | The shipping industry is currently the sixth largest contributor to global emissions, responsible for one billion tons of greenhouse gas emissions. Urgent action is needed to achieve carbon neutrality in the shipping industry for sustainability. In this paper, we use natural language processing techniques to analyze policies, announcements, and position papers from national and international organizations related to the decarbonization of shipping. In particular, we perform the analysis using a novel matrix-based corpus and a fine-tuned machine learning model, BERTopic. Our research suggests that the top four priorities for decarbonizing shipping are preventing emissions from methane leaks, promoting non-carbon-based hydrogen, implementing reusable modular containers to reduce packaging waste in container shipping, and protecting Arctic biodiversity while promoting the Arctic shipping route to reduce costs. Our study highlights the validity of NLP techniques in quantitatively extracting critical information related to the decarbonization of the shipping industry. |
| format | Article |
| id | doaj-art-7835d847db064923b262cb44f8605dba |
| institution | OA Journals |
| issn | 2666-1888 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Sustainable Futures |
| spelling | doaj-art-7835d847db064923b262cb44f8605dba2025-08-20T01:57:59ZengElsevierSustainable Futures2666-18882024-12-01810035810.1016/j.sftr.2024.100358Exploring decarbonization priorities for sustainable shipping: A natural language processing-based experimentEnna Hirata0Kevin X. Li1Daisuke Watanabe2Graduate School of Maritime Sciences, Center for Mathematical and Data Sciences, Kobe University, Fukaeminami-machi 5-1-1, Higashinada-ku, Kobe 658-0022, Japan; Corresponding author.Faculty of Commerce, Maritime Logistics and Free Trade Islands Research Center, Zhejiang University, Zhejiang 316021, ChinaDepartment of Logistics and Information Engineering, Tokyo University of Marine Science and Technology, Tokyo 108-8477, JapanThe shipping industry is currently the sixth largest contributor to global emissions, responsible for one billion tons of greenhouse gas emissions. Urgent action is needed to achieve carbon neutrality in the shipping industry for sustainability. In this paper, we use natural language processing techniques to analyze policies, announcements, and position papers from national and international organizations related to the decarbonization of shipping. In particular, we perform the analysis using a novel matrix-based corpus and a fine-tuned machine learning model, BERTopic. Our research suggests that the top four priorities for decarbonizing shipping are preventing emissions from methane leaks, promoting non-carbon-based hydrogen, implementing reusable modular containers to reduce packaging waste in container shipping, and protecting Arctic biodiversity while promoting the Arctic shipping route to reduce costs. Our study highlights the validity of NLP techniques in quantitatively extracting critical information related to the decarbonization of the shipping industry.http://www.sciencedirect.com/science/article/pii/S2666188824002077ShippingDecarbonizationSustainabilityNatural language processingBertopicMachine learning |
| spellingShingle | Enna Hirata Kevin X. Li Daisuke Watanabe Exploring decarbonization priorities for sustainable shipping: A natural language processing-based experiment Sustainable Futures Shipping Decarbonization Sustainability Natural language processing Bertopic Machine learning |
| title | Exploring decarbonization priorities for sustainable shipping: A natural language processing-based experiment |
| title_full | Exploring decarbonization priorities for sustainable shipping: A natural language processing-based experiment |
| title_fullStr | Exploring decarbonization priorities for sustainable shipping: A natural language processing-based experiment |
| title_full_unstemmed | Exploring decarbonization priorities for sustainable shipping: A natural language processing-based experiment |
| title_short | Exploring decarbonization priorities for sustainable shipping: A natural language processing-based experiment |
| title_sort | exploring decarbonization priorities for sustainable shipping a natural language processing based experiment |
| topic | Shipping Decarbonization Sustainability Natural language processing Bertopic Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S2666188824002077 |
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