Abatement of atmospheric pollutant emissions with autonomous shipping in maritime transportation using Bayesian probabilistic forecasting
This study examines the potential abatement of environmental pollutant emissions with the adoption of autonomous vessels in future maritime transportation using Bayesian probabilistic forecasting algorithm. The emission reductions can be attributed to the related technological advancement, including...
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sg-ntu-dr.10356-1555942022-03-14T08:05:37Z Abatement of atmospheric pollutant emissions with autonomous shipping in maritime transportation using Bayesian probabilistic forecasting Liu, Jiahui Law, Adrian Wing-Keung Duru, Okan School of Civil and Environmental Engineering Engineering::Civil engineering Autonomous Shipping Emission Forecasting This study examines the potential abatement of environmental pollutant emissions with the adoption of autonomous vessels in future maritime transportation using Bayesian probabilistic forecasting algorithm. The emission reductions can be attributed to the related technological advancement, including particularly the improvements in navigational performance and berthing in port, which can achieve better efficiencies and lower fluctuations in sailing speeds. The scenario modeling approach is first established based on the foreseeable development of energy policies and usage as well as ship operations. Subsequently, assessment is performed in five major ports worldwide, namely Shanghai, Singapore, Long Beach, Hamburg, Tokyo from Year 2020 to 2050. The results are compared to the corresponding projections with manned shipping to determine the probabilistic emission abatements with the gradual adoption of autonomous ships into the fleet. Overall, the results provide a better understanding of the future environmental benefits with autonomous shipping to the policymakers, shipowners, and shipping industry. Submitted/Accepted version 2022-03-14T08:05:36Z 2022-03-14T08:05:36Z 2021 Journal Article Liu, J., Law, A. W. & Duru, O. (2021). Abatement of atmospheric pollutant emissions with autonomous shipping in maritime transportation using Bayesian probabilistic forecasting. Atmospheric Environment, 261, 118593-. https://dx.doi.org/10.1016/j.atmosenv.2021.118593 1352-2310 https://hdl.handle.net/10356/155594 10.1016/j.atmosenv.2021.118593 2-s2.0-85109183719 261 118593 en Atmospheric Environment © 2021 Elsevier Ltd. All rights reserved. This paper was published in Atmospheric Environment and is made available with permission of Elsevier Ltd. application/pdf |
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Engineering::Civil engineering Autonomous Shipping Emission Forecasting Liu, Jiahui Law, Adrian Wing-Keung Duru, Okan Abatement of atmospheric pollutant emissions with autonomous shipping in maritime transportation using Bayesian probabilistic forecasting |
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This study examines the potential abatement of environmental pollutant emissions with the adoption of autonomous vessels in future maritime transportation using Bayesian probabilistic forecasting algorithm. The emission reductions can be attributed to the related technological advancement, including particularly the improvements in navigational performance and berthing in port, which can achieve better efficiencies and lower fluctuations in sailing speeds. The scenario modeling approach is first established based on the foreseeable development of energy policies and usage as well as ship operations. Subsequently, assessment is performed in five major ports worldwide, namely Shanghai, Singapore, Long Beach, Hamburg, Tokyo from Year 2020 to 2050. The results are compared to the corresponding projections with manned shipping to determine the probabilistic emission abatements with the gradual adoption of autonomous ships into the fleet. Overall, the results provide a better understanding of the future environmental benefits with autonomous shipping to the policymakers, shipowners, and shipping industry. |
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School of Civil and Environmental Engineering |
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School of Civil and Environmental Engineering Liu, Jiahui Law, Adrian Wing-Keung Duru, Okan |
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Article |
author |
Liu, Jiahui Law, Adrian Wing-Keung Duru, Okan |
author_sort |
Liu, Jiahui |
title |
Abatement of atmospheric pollutant emissions with autonomous shipping in maritime transportation using Bayesian probabilistic forecasting |
title_short |
Abatement of atmospheric pollutant emissions with autonomous shipping in maritime transportation using Bayesian probabilistic forecasting |
title_full |
Abatement of atmospheric pollutant emissions with autonomous shipping in maritime transportation using Bayesian probabilistic forecasting |
title_fullStr |
Abatement of atmospheric pollutant emissions with autonomous shipping in maritime transportation using Bayesian probabilistic forecasting |
title_full_unstemmed |
Abatement of atmospheric pollutant emissions with autonomous shipping in maritime transportation using Bayesian probabilistic forecasting |
title_sort |
abatement of atmospheric pollutant emissions with autonomous shipping in maritime transportation using bayesian probabilistic forecasting |
publishDate |
2022 |
url |
https://hdl.handle.net/10356/155594 |
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1728433418608312320 |