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Abatement of atmospheric pollutant emissions with autonomous shipping in maritime transportation using Bayesian probabilistic forecasting
https://orcid.org/0000-0002-2593-6361
https://orcid.org/0000-0001-7966-0025
Abstract 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.
Graphical abstract Display Omitted
Highlights Gradual phase-in of autonomous shipping in maritime transportation A MCMC probabilistic algorithm to forecast emissions with autonomous shipping Emission forecasts in the Ports of Shanghai, Singapore, Tokyo, Long Beach, Hamburg Multiple scenarios with regulatory, market and operational uncertainties Policymakers can evaluate environmental progress towards meeting emission goals
Abatement of atmospheric pollutant emissions with autonomous shipping in maritime transportation using Bayesian probabilistic forecasting
https://orcid.org/0000-0002-2593-6361
https://orcid.org/0000-0001-7966-0025
Abstract 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.
Graphical abstract Display Omitted
Highlights Gradual phase-in of autonomous shipping in maritime transportation A MCMC probabilistic algorithm to forecast emissions with autonomous shipping Emission forecasts in the Ports of Shanghai, Singapore, Tokyo, Long Beach, Hamburg Multiple scenarios with regulatory, market and operational uncertainties Policymakers can evaluate environmental progress towards meeting emission goals
Abatement of atmospheric pollutant emissions with autonomous shipping in maritime transportation using Bayesian probabilistic forecasting
https://orcid.org/0000-0002-2593-6361
https://orcid.org/0000-0001-7966-0025
Abstract 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.
Graphical abstract Display Omitted
Highlights Gradual phase-in of autonomous shipping in maritime transportation A MCMC probabilistic algorithm to forecast emissions with autonomous shipping Emission forecasts in the Ports of Shanghai, Singapore, Tokyo, Long Beach, Hamburg Multiple scenarios with regulatory, market and operational uncertainties Policymakers can evaluate environmental progress towards meeting emission goals
Abatement of atmospheric pollutant emissions with autonomous shipping in maritime transportation using Bayesian probabilistic forecasting
Liu, Jiahui (author) / PhD Law, Adrian Wing-Keung (author) / Duru, Okan (author)
Atmospheric Environment ; 261
2021-06-28
Article (Journal)
Electronic Resource
English
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