Neural Network Modeling Based on the Bayesian Method for Evaluating Shipping Mitigation Measures
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 ar...
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doaj-60d8f9db052243bd8552ec2f8b3e980a2020-12-16T00:02:12ZengMDPI AGSustainability2071-10502020-12-0112104861048610.3390/su122410486Neural Network Modeling Based on the Bayesian Method for Evaluating Shipping Mitigation MeasuresJun Yuan0Jiang Zhu1Victor Nian2China Institute of FTZ Supply Chain, Shanghai Maritime University, Shanghai 201306, ChinaInstitute of Logistics Science and Engineering, Shanghai Maritime University, Shanghai 201306, ChinaEnergy Studies Institute, National University of Singapore, 29 Heng Mui Keng Terrace, Singapore 119620, SingaporeClimate 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.https://www.mdpi.com/2071-1050/12/24/10486gas emissionmitigation measurescost-effectivenessuncertaintyneural networkBayesian method |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Jun Yuan Jiang Zhu Victor Nian |
spellingShingle |
Jun Yuan Jiang Zhu Victor Nian Neural Network Modeling Based on the Bayesian Method for Evaluating Shipping Mitigation Measures Sustainability gas emission mitigation measures cost-effectiveness uncertainty neural network Bayesian method |
author_facet |
Jun Yuan Jiang Zhu Victor Nian |
author_sort |
Jun Yuan |
title |
Neural Network Modeling Based on the Bayesian Method for Evaluating Shipping Mitigation Measures |
title_short |
Neural Network Modeling Based on the Bayesian Method for Evaluating Shipping Mitigation Measures |
title_full |
Neural Network Modeling Based on the Bayesian Method for Evaluating Shipping Mitigation Measures |
title_fullStr |
Neural Network Modeling Based on the Bayesian Method for Evaluating Shipping Mitigation Measures |
title_full_unstemmed |
Neural Network Modeling Based on the Bayesian Method for Evaluating Shipping Mitigation Measures |
title_sort |
neural network modeling based on the bayesian method for evaluating shipping mitigation measures |
publisher |
MDPI AG |
series |
Sustainability |
issn |
2071-1050 |
publishDate |
2020-12-01 |
description |
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. |
topic |
gas emission mitigation measures cost-effectiveness uncertainty neural network Bayesian method |
url |
https://www.mdpi.com/2071-1050/12/24/10486 |
work_keys_str_mv |
AT junyuan neuralnetworkmodelingbasedonthebayesianmethodforevaluatingshippingmitigationmeasures AT jiangzhu neuralnetworkmodelingbasedonthebayesianmethodforevaluatingshippingmitigationmeasures AT victornian neuralnetworkmodelingbasedonthebayesianmethodforevaluatingshippingmitigationmeasures |
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1724381860517117952 |