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Minimising energy consumption and environmental burden of freight transport using a novel graphical decision-making tool

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  • Fan, Yee Van
  • Klemeš, Jiří Jaromír
  • Walmsley, Timothy Gordon
  • Perry, Simon

Abstract

This study introduces a new graphical decision-making tool to facilitate the rapid selection of transportation modes with minimum energy consumption or emissions, indicating the most sustainable transportation mode. Greenhouse gas (GHG) and air pollutants (NOx, PM and SO2), together with a composite price-weighted total environmental burden (TEB), are considered in the analysis. The graphical tool, which has a similar appearance to a phase diagram, presents a map of energy use (or emission) of different transportation modes based on the values of the ratio of the transportation distances (R) and the absolute load (L). A freight transportation case study (Rotterdam to Antwerp and Genova) demonstrates the construction and application of the graphical tool. For this case study, the electric train is the transport mode that offers the lowest energy consumption and minimum TEB. The graphical decision tool can also indicate the next-best solutions when one or more options (e.g. electric train) are not available. In this scenario, general cargo shipping achieves the lowest GHG emission, but heavy lorry imposes the lowest TEB. The developed tool further demonstrates the impacts of possible future fuels and technology developments on transportation selection, including renewable biodiesel and transport electrification under different grid mixes (e.g. Latvia, Sweden and EU-28). Further criteria, including economics, can be included for future study using the proposed tool as a foundation. The graphical approach transforms the transport selection problem into an easily understandable format from which arises sound solutions.

Suggested Citation

  • Fan, Yee Van & Klemeš, Jiří Jaromír & Walmsley, Timothy Gordon & Perry, Simon, 2019. "Minimising energy consumption and environmental burden of freight transport using a novel graphical decision-making tool," Renewable and Sustainable Energy Reviews, Elsevier, vol. 114(C), pages 1-1.
    • Handle: RePEc:eee:rensus:v:114:y:2019:i:c:34
    DOI: 10.1016/j.rser.2019.109335
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    2. Burić, Melita & Grgurić, Sanja & Mikulčić, Hrvoje & Wang, Xuebin, 2021. "A numerical investigation of tidal current energy resource potential in a sea strait," Energy, Elsevier, vol. 234(C).
    3. Xu, Yifan & Ji, Mengmeng & Klemeš, Jiří Jaromír & Tao, Hengcong & Zhu, Baikang & Varbanov, Petar Sabev & Yuan, Meng & Wang, Bohong, 2023. "Optimal renewable energy export strategies of islands: Hydrogen or electricity?," Energy, Elsevier, vol. 269(C).
    4. Hoo Poh Ying & Cassendra Bong Phun Chien & Fan Yee Van, 2020. "Operational Management Implemented in Biofuel Upstream Supply Chain and Downstream International Trading: Current Issues in Southeast Asia," Energies, MDPI, vol. 13(7), pages 1-26, April.
    5. Hren, Robert & Vujanović, Annamaria & Van Fan, Yee & Klemeš, Jiří Jaromír & Krajnc, Damjan & Čuček, Lidija, 2023. "Hydrogen production, storage and transport for renewable energy and chemicals: An environmental footprint assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 173(C).
    6. Fan, Yee Van & Romanenko, Sergey & Gai, Limei & Kupressova, Ekaterina & Varbanov, Petar Sabev & Klemeš, Jiří Jaromír, 2021. "Biomass integration for energy recovery and efficient use of resources: Tomsk Region," Energy, Elsevier, vol. 235(C).

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