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Emission reduction measures ranking under uncertainty

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  • Yuan, Jun
  • Ng, Szu Hui

Abstract

Shipping is a major contributor to global CO2 emissions. Various operational and technical measures have been proposed to reduce ship emissions. However, these emission reduction measures may not be all economically feasible to implement. Therefore, it is important to rank all these measures and select the most cost-effective measures for emissions reduction. Moreover, there are various uncertainties in evaluating emission reduction measures, such as uncertainties of implementation cost, fuel consumption, abatement potential and fuel price. These uncertainties may significantly influence the ranking of the emission reduction measures, which further result in an inappropriate selection of the measures for implementation. In this paper, a ranking algorithm with a new criterion is proposed to rank all the emission reduction measures by considering the preference between cost and abatement. Furthermore, a ranking under uncertainty method is developed which takes into account various uncertainties of the impact factors. This method can support policy makers in ranking and selecting emission reduction measures more appropriately by better quantifying and reflecting the uncertainties.

Suggested Citation

  • Yuan, Jun & Ng, Szu Hui, 2017. "Emission reduction measures ranking under uncertainty," Applied Energy, Elsevier, vol. 188(C), pages 270-279.
    • Handle: RePEc:eee:appene:v:188:y:2017:i:c:p:270-279
    DOI: 10.1016/j.apenergy.2016.11.109
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