Assessment of atmospheric pollutant emissions by ships using the Bayesian Markov chain Monte Carlo probabilistic forecasting algorithm

• Main Author: Liu, Jiahui
• Other Authors: Law Wing-Keung, Adrian
• Format: Thesis-Doctor of Philosophy
• Language: English
• Published: Nanyang Technological University 2022
• Subjects: Engineering::Civil engineering::Transportation; Engineering::Maritime studies::Maritime science and technology
• Online Access: https://hdl.handle.net/10356/162168
• Institution: Nanyang Technological University

In recent decades, environmental concerns have been gaining increasing attention. Shipping as a major transportation mode contributes greatly to the overall atmospheric pollutant emissions. Based on the Fourth IMO Greenhouse Gas (GHG) Study 2020, the total GHG emissions from the shipping sector have increased steadily from 977 to 1,076 million tonnes over the 2012-2018 period due to growing shipping demand. Hence, it is vital to accurately measure the future ship emission levels and establish effective control strategies to mitigate the adverse impacts. This thesis aims to provide environmental analyses of the shipping industry from several perspectives. This thesis conducts a systematic review of previous literature on ship emission accounting. The existing methodologies can be classified into two types: (1) the top-down; and (2) the bottom-up method. The bottom-up method is more reliable because it incorporates individual ship-specific information and operational condition into the calculation process, hence producing more accurate emission estimates. This study also reviews relevant literature on ship emission forecasting. According to the systematic review, several literature gaps were found, including the limited research coverage in ship emission prediction and lack of accuracy with the point-forecasting approach because of the underlying uncertainties. Accordingly, this study advances the ship emission accounting methodology by developing a novel Bayesian Markov Chain Monte Carlo (MCMC) probabilistic forecasting algorithm. Specifically, different key drivers of ship emissions ranging from the implications of current and potential future regulations, emission mitigation measures, alternative marine fuels, and ship traffic are simulated. Further, this thesis performs scenario modelling by phasing-in autonomous ships into the future maritime transportation with associated changes in ship operations. Overall, this thesis hopes to provide useful guidance for academic and industry practitioners to better understand the shipping environment.