In recent years, the maritime industry’s carbon emissions have garnered increasing attention, leading to the proposal of various policy measures aimed at mitigating emissions and fostering a green and sustainable maritime sector. Among these measures, the book and claim mechanism, which allows shippers to access low or zero-emission bunkering by purchasing such fuels without physically participating in the refueling process, has emerged as a crucial catalyst for fuel conversion within the maritime industry. While book and claim has gained widespread recognition and facilitated the sale of clean fuels by some bunker suppliers, there has been limited research focused on evaluating its practical efficacy. Thus, we construct two distinct Mixed-Integer Linear Programming (MILP) models—one with the inclusion of the book and claim mechanism and one without—and conduct an analytical comparison of optimal decisions made by bunker suppliers and shippers under different model scenarios. Through numerical experiments, we have uncovered a noteworthy insight: with book and claim, bunker suppliers may set higher prices to maximize total profits due to various price sensitivities among shippers towards clean fuels, thus promoting low-price-sensitive shippers to purchase clean fuels while making it challenging for high-price-sensitive shippers to do so. Consequently, when compared to a scenario without book and claim, the total quantity of clean fuels purchased by shippers in the presence of book and claim may decrease, giving rise to a paradox where the implementation of book and claim inadvertently increases societal carbon emissions. This underscores the imperative for policymakers to conduct comprehensive market research, understand different shippers’ price sensitivities towards clean fuels, and make scientifically sound decisions when considering the implementation of the book and claim mechanism.
CITATION STYLE
Wang, S., Liu, Y., Wang, H., & Du, Y. (2023). Paradox of Book and Claim for Carbon Emission Reduction in Maritime Operations Management: Mathematical Models and Numerical Experiments. Mathematics, 11(21). https://doi.org/10.3390/math11214410
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