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Demand Forecasting for Liquified Natural Gas Bunkering by Country and Region Using Meta-Analysis and Artificial Intelligence

Author

Listed:
  • Gi-Young Chae

    (Korea Institute of Ocean Science and Technology, 385, Haeyang-ro, Yeongdo-gu, Busan 49111, Korea)

  • Seung-Hyun An

    (Korea Maritime Institute, 26, Haeyang-ro 301beon-gil, Yeongdo-gu, Busan 49111, Korea)

  • Chul-Yong Lee

    (School of Business, Pusan National University, 2, Busan Daehak-ro 63beon-gil, Geumjeong-gu, Busan 46241, Korea)

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.

Suggested Citation

  • Gi-Young Chae & Seung-Hyun An & Chul-Yong Lee, 2021. "Demand Forecasting for Liquified Natural Gas Bunkering by Country and Region Using Meta-Analysis and Artificial Intelligence," Sustainability, MDPI, vol. 13(16), pages 1-18, August.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:16:p:9058-:d:613544
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    References listed on IDEAS

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    1. Eisuke Watanabe & Ryuichi Shibasaki, 2023. "Extraction of Bunkering Services from Automatic Identification System Data and Their International Comparisons," Sustainability, MDPI, vol. 15(24), pages 1-19, December.

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