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Planning vessel air emission regulations compliance under uncertainty

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Abstract

In this paper we consider the reduction of air emissions from vessels when uncertainty is taken into account. Uncertainty in the reduction effects of the different existing air emission controls is currently high and makes their selection for vessel emission regulations compliance a challenging process. We develop a two-stage stochastic optimization model that addresses this uncertainty. The model’s objective is to plan the installation of air emission controls over a specified time horizon for a vessel to comply in the most cost-efficient way with the air emission regulations. The uncertain reduction effects of the controls are modelled by a set of scenarios. The approach is applied to a case study with real data. The solution exposes the important impact of uncertainty on this problem, especially on the SO X reduction, while the CO2 reduction plan seems in this case not affected by uncertainty.

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Acknowledgments

This research was part of the Ship4C project ‘Sustainable design of ships for the future’, funded by the Norwegian Research Council and industry partners. The authors would like to thank Michal Kaut for his help in the scenario generation. The authors are also grateful to the three anonymous reviewers for their insightful comments and suggestions that improved the paper.

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Authors and Affiliations

  1. Department of Marine Technology, Norwegian University of Science and Technology, Trondheim, Norway

    Océane Balland & Stein Ove Erikstad

  2. Department of Industrial Economics and Technology, Norwegian University of Science and Technology, Trondheim, Norway

    Kjetil Fagerholt

  3. Department of Business and Management Science, Norwegian School of Economics, Bergen, Norway

    Stein W. Wallace

Authors
  1. Océane Balland
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  2. Stein Ove Erikstad
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  3. Kjetil Fagerholt
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  4. Stein W. Wallace
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Corresponding author

Correspondence to Océane Balland.

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Balland, O., Erikstad, S.O., Fagerholt, K. et al. Planning vessel air emission regulations compliance under uncertainty. J Mar Sci Technol 18, 349–357 (2013). https://doi.org/10.1007/s00773-013-0212-7

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  • DOI: https://doi.org/10.1007/s00773-013-0212-7

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