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Uncertainty quantification of CO2 emission reduction for maritime shipping

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  • Yuan, Jun
  • Ng, Szu Hui
  • Sou, Weng Sut

Abstract

The International Maritime Organization (IMO) has recently proposed several operational and technical measures to improve shipping efficiency and reduce the greenhouse gases (GHG) emissions. The abatement potentials estimated for these measures have been further used by many organizations to project future GHG emission reductions and plot Marginal Abatement Cost Curves (MACC). However, the abatement potentials estimated for many of these measures can be highly uncertain as many of these measures are new, with limited sea trial information. Furthermore, the abatements obtained are highly dependent on ocean conditions, trading routes and sailing patterns. When the estimated abatement potentials are used for projections, these ‘input’ uncertainties are often not clearly displayed or accounted for, which can lead to overly optimistic or pessimistic outlooks. In this paper, we propose a methodology to systematically quantify and account for these input uncertainties on the overall abatement potential forecasts. We further propose improvements to MACCs to better reflect the uncertainties in marginal abatement costs and total emissions. This approach provides a fuller and more accurate picture of abatement forecasts and potential reductions achievable, and will be useful to policy makers and decision makers in the shipping industry to better assess the cost effective measures for CO2 emission reduction.

Suggested Citation

  • Yuan, Jun & Ng, Szu Hui & Sou, Weng Sut, 2016. "Uncertainty quantification of CO2 emission reduction for maritime shipping," Energy Policy, Elsevier, vol. 88(C), pages 113-130.
    • Handle: RePEc:eee:enepol:v:88:y:2016:i:c:p:113-130
    DOI: 10.1016/j.enpol.2015.10.020
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    5. Hu, Hongtao & Yuan, Jun & Nian, Victor, 2019. "Development of a multi-objective decision-making method to evaluate correlated decarbonization measures under uncertainty – The example of international shipping," Transport Policy, Elsevier, vol. 82(C), pages 148-157.
    6. 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.
    7. Nguyen, Son & Fu, Xiuju & Ogawa, Daichi & Zheng, Qin, 2023. "An application-oriented testing regime and multi-ship predictive modeling for vessel fuel consumption prediction," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 177(C).
    8. Bühler, Fabian & Guminski, Andrej & Gruber, Anna & Nguyen, Tuong-Van & von Roon, Serafin & Elmegaard, Brian, 2018. "Evaluation of energy saving potentials, costs and uncertainties in the chemical industry in Germany," Applied Energy, Elsevier, vol. 228(C), pages 2037-2049.
    9. Hossam A. Gabbar & Md. Ibrahim Adham & Muhammad R. Abdussami, 2021. "Optimal Planning of Integrated Nuclear-Renewable Energy System for Marine Ships Using Artificial Intelligence Algorithm," Energies, MDPI, vol. 14(11), pages 1-39, May.
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    11. Levihn, Fabian, 2016. "On the problem of optimizing through least cost per unit, when costs are negative: Implications for cost curves and the definition of economic efficiency," Energy, Elsevier, vol. 114(C), pages 1155-1163.

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