딥러닝을 이용한 주요항만별 LNG 벙커링 수요예측 연구 KCI

DC Field Value Language
dc.contributor.author Chae, Gi Young -
dc.contributor.author Lee, Chul-Yong -
dc.date.accessioned 2022-11-14T00:30:29Z -
dc.date.available 2022-11-14T00:30:29Z -
dc.date.created 2022-11-10 -
dc.date.issued 2022-10 -
dc.identifier.issn 2093-5919 -
dc.identifier.uri https://sciwatch.kiost.ac.kr/handle/2020.kiost/43395 -
dc.description.abstract Environmental regulations on ship exhaust emissions are being strengthened by the International Maritime Organization (IMO) and the Korean government. Stakeholders consider liquified natural gas (LNG) to be the most realistic alternative to existing fuels. This study predicted the LNG bunkering demand of five major domestic ports: Busan Port, Ulsan Port, Incheon Port, Pyeongtaek and Dangjin Port, and Gwangyang Port. Forecasting using recent performance data and deep learning techniques found that the LNG bunkering demand at Busan Port will increase from 220,000 tons in 2025 to 580,000 tons in 2040. The demand for LNG bunkering at Busan Port was the greatest, followed by Gwangyang Port, Ulsan Port, Pyeongtaek and Dangjin Port, and Incheon Port. The results of this study can be used as important data for the establishment of government carbon-neutral policies and, in terms of industry, it can be used as key data for investment decisions regarding LNG bunkering facilities and LNG-powered ship construction. -
dc.description.uri 2 -
dc.language Korean -
dc.publisher 한국기후변화학회 -
dc.title 딥러닝을 이용한 주요항만별 LNG 벙커링 수요예측 연구 -
dc.title.alternative Demand Forecasting for Liquified Natural Gas Bunkering at Major Ports in South Korea -
dc.type Article -
dc.citation.endPage 688 -
dc.citation.startPage 679 -
dc.citation.title 한국기후변화학회지 -
dc.citation.volume 13 -
dc.citation.number 5 -
dc.contributor.alternativeName 채기영 -
dc.identifier.bibliographicCitation 한국기후변화학회지, v.13, no.5, pp.679 - 688 -
dc.identifier.kciid ART002893998 -
dc.description.journalClass 2 -
dc.description.isOpenAccess N -
dc.subject.keywordAuthor LNG Bunkering -
dc.subject.keywordAuthor Demand Forecasting -
dc.subject.keywordAuthor Port -
dc.subject.keywordAuthor Regulations for Ship Exhaust Emissions -
dc.subject.keywordAuthor Deep Learning -
dc.description.journalRegisteredClass kci -
Appears in Collections:
Ocean Law and Policy Institute > Ocean Policy Research Center > 1. Journal Articles
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