Neural Network Modeling Based on the Bayesian Method for Evaluating Shipping Mitigation Measures

Jun Yuan, Jiang Zhu and Victor Nian
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Jun Yuan: China Institute of FTZ Supply Chain, Shanghai Maritime University, Shanghai 201306, China
Jiang Zhu: Institute of Logistics Science and Engineering, Shanghai Maritime University, Shanghai 201306, China
Victor Nian: Energy Studies Institute, National University of Singapore, 29 Heng Mui Keng Terrace, Singapore 119620, Singapore

Sustainability, 2020, vol. 12, issue 24, 1-14

Abstract: Climate change caused by greenhouse gas emissions is of critical concern to international shipping. A large portfolio of mitigation measures has been developed to mitigate ship gas emissions by reducing ship energy consumption but is constrained by practical considerations, especially cost. There are difficulties in ranking the priority of mitigation measures, due to the uncertainty of ship information and data gathered from onboard instruments and other sources. In response, a neural network model is proposed to evaluate the cost-effectiveness of mitigation measures based on decarbonization. The neural network is further enhanced with a Bayesian method to consider the uncertainties of model parameters. Three of the key advantages of the proposed approach are (i) its ability to simultaneously consider a wide range of sources of information and data that can help improve the robustness of the modeling results; (ii) the ability to take into account the input uncertainties in ranking and selection; (iii) the ability to include marginal costs in evaluating the cost-effectiveness of mitigation measures to facilitate decision making. In brief, a negative “marginal cost-effectiveness” would indicate a priority consideration for a given mitigation measure. In the case study, it was found that weather routing and draft optimization could have negative marginal cost-effectiveness, signaling the importance of prioritizing these measures.

Keywords: gas emission; mitigation measures; cost-effectiveness; uncertainty; neural network; Bayesian method (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
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