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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Jun Yuan & Jiang Zhu & Victor Nian, 2020. "Neural Network Modeling Based on the Bayesian Method for Evaluating Shipping Mitigation Measures," Sustainability, MDPI, vol. 12(24), pages 1-14, December.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:24:p:10486-:d:462345
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    References listed on IDEAS

    as
    1. Jun Yuan & Haowei Wang & Szu Hui Ng & Victor Nian, 2020. "Ship Emission Mitigation Strategies Choice Under Uncertainty," Energies, MDPI, vol. 13(9), pages 1-20, May.
    2. Kwang-Il Kim & Keon Myung Lee, 2018. "Dynamic Programming-Based Vessel Speed Adjustment for Energy Saving and Emission Reduction," Energies, MDPI, vol. 11(5), pages 1-15, May.
    3. Athanasios Anagnostis & Elpiniki Papageorgiou & Dionysis Bochtis, 2020. "Application of Artificial Neural Networks for Natural Gas Consumption Forecasting," Sustainability, MDPI, vol. 12(16), pages 1-29, August.
    4. Yuan, Jun & Ng, Szu Hui, 2017. "Emission reduction measures ranking under uncertainty," Applied Energy, Elsevier, vol. 188(C), pages 270-279.
    5. Yuan, Jun & Nian, Victor & He, Junliang & Yan, Wei, 2019. "Cost-effectiveness analysis of energy efficiency measures for maritime shipping using a metamodel based approach with different data sources," Energy, Elsevier, vol. 189(C).
    6. Farzin Golzar & David Nilsson & Viktoria Martin, 2020. "Forecasting Wastewater Temperature Based on Artificial Neural Network (ANN) Technique and Monte Carlo Sensitivity Analysis," Sustainability, MDPI, vol. 12(16), pages 1-17, August.
    7. Lago, Jesus & De Ridder, Fjo & De Schutter, Bart, 2018. "Forecasting spot electricity prices: Deep learning approaches and empirical comparison of traditional algorithms," Applied Energy, Elsevier, vol. 221(C), pages 386-405.
    8. Enric Julià & Fabian Tillig & Jonas W. Ringsberg, 2020. "Concept Design and Performance Evaluation of a Fossil-Free Operated Cargo Ship with Unlimited Range," Sustainability, MDPI, vol. 12(16), pages 1-23, August.
    9. Nian, Victor & Yuan, Jun, 2017. "A method for analysis of maritime transportation systems in the life cycle approach – The oil tanker example," Applied Energy, Elsevier, vol. 206(C), pages 1579-1589.
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