Demand forecasting for liquified natural gas bunkering by country and region using meta-analysis and artificial intelligence SCIE SSCI SCOPUS

Cited 1 time in WEB OF SCIENCE Cited 1 time in Scopus
Title
Demand forecasting for liquified natural gas bunkering by country and region using meta-analysis and artificial intelligence
Author(s)
Chae, Gi Young; An, Seung-Hyun; Lee, Chul-Yong
KIOST Author(s)
Chae, Gi Young(채기영)
Alternative Author(s)
채기영
Publication Year
2021-08
Abstract
Ship exhaust emission is the main cause of coastal air pollution, leading to premature death from cardiovascular cancer and lung cancer. In light of public health and climate change concerns, the International Maritime Organization (IMO) and several governments are reinforcing policies to use clean ship fuels. In January 2020, the IMO reduced the acceptable sulfur content in ship fuel to 0.5% m/m (mass/mass) for sustainability. The use of liquified natural gas (LNG) as a ship fuel is currently the most likely measure to meet this regulation, and LNG bunkering infrastructure investment and network planning are underway worldwide. Therefore, the aim of this study is to predict the LNG bunkering demand for investment and planning. So far, however, there has been little quantitative analysis of LNG bunkering demand prediction. In this study, first, the global LNG bunkering demand was predicted using meta-regression analysis. Global demand for LNG bunkering is forecast to increase from 16.6 million tons in 2025 to 53.2 million tons in 2040. Second, LNG bunkering prediction by country and region was performed through analogy and artificial intelligence methods. The information and insights gained from this study may facilitate policy implementation and investments. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
ISSN
2071-1050
URI
https://sciwatch.kiost.ac.kr/handle/2020.kiost/42181
DOI
10.3390/su13169058
Bibliographic Citation
SUSTAINABILITY, v.13, no.16, 2021
Publisher
MDPI AG
Subject
BOTTOM-UP; TOP-DOWN; MARITIME TRANSPORT; LNG; EMISSIONS; MODELS; FUEL
Keywords
LNG bunkering; demand forecasting; shipping industry for sustainability; climate change; IMO regulation
Type
Article
Language
English
Document Type
Article
Publisher
MDPI AG
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