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Assessment of Greenhouse Gas Emissions from Heavy-Duty Trucking in a Non-Containerized Port through Simulation-Based Methods

Author

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  • Afef Lagha

    (Department of Management, Université du Québec à Trois-Rivières, Trois-Rivières, QC G8Z 4M3, Canada)

  • Bechir Ben Daya

    (Department of Management, Université du Québec à Trois-Rivières, Trois-Rivières, QC G8Z 4M3, Canada)

  • Jean-François Audy

    (Department of Management, Université du Québec à Trois-Rivières, Trois-Rivières, QC G8Z 4M3, Canada
    Interuniversity Research Center on Enterprise Network, Logistics and Transportation (CIRRELT), Montreal, QC H3T IJ4, Canada)

Abstract

Seaports are perceived as multimodal hubs of the logistics supply chain where various transport modes intersect to exchange goods shipped by vessels. Increasing trade and capacity constraints are making this area a major contributor to GHG emissions. National and regional decision-makers perceive port sustainability as a concern while planning GHG mitigation projects. However, to plan and conduct successful GHG management programs, it is critical to first develop an appropriate assessment approach that fits well with the operating and geographical context of the given port. For heavy-duty trucking activities taking place within such ports, several models and methodologies for assessing GHG emissions are available, but their generalization is challenging for many reasons, notably because of the specific features of traffic within the port. Therefore, this paper presents an assessment model for heavy-duty trucking emissions within a non-containerized port based on an in-depth study of the traffic per port zone and on parameters drawn from several real data sources. The GHG model based on road traffic profiles by zone is implemented in a simulation model for emission evaluation and prediction. The output shows the pattern of GHG emissions by zone and provides an outlook on how decision-makers could achieve a GHG reduction plan.

Suggested Citation

  • Afef Lagha & Bechir Ben Daya & Jean-François Audy, 2024. "Assessment of Greenhouse Gas Emissions from Heavy-Duty Trucking in a Non-Containerized Port through Simulation-Based Methods," Sustainability, MDPI, vol. 16(5), pages 1-27, February.
  • Handle: RePEc:gam:jsusta:v:16:y:2024:i:5:p:1904-:d:1346117
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    References listed on IDEAS

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    1. Torkjazi, Mohammad & Huynh, Nathan & Shiri, Samaneh, 2018. "Truck appointment systems considering impact to drayage truck tours," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 116(C), pages 208-228.
    2. Chen, Gang & Govindan, Kannan & Golias, Mihalis M., 2013. "Reducing truck emissions at container terminals in a low carbon economy: Proposal of a queueing-based bi-objective model for optimizing truck arrival pattern," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 55(C), pages 3-22.
    3. Branislav Dragović & Ernestos Tzannatos & Nam Kuy Park, 2017. "Simulation modelling in ports and container terminals: literature overview and analysis by research field, application area and tool," Flexible Services and Manufacturing Journal, Springer, vol. 29(1), pages 4-34, March.
    4. Florin RUSCĂ & Mihaela POPA & Eugen ROȘCA & Mircea ROȘCA & Aura RUSCĂ, 2018. "Simulation Model For Maritime Container Terminal," Transport Problems, Silesian University of Technology, Faculty of Transport, vol. 13(4), pages 47-54, December.
    5. Sanghyuk Yi & Bernd Scholz-Reiter & Taehoon Kim & Kap Hwan Kim, 2019. "Scheduling appointments for container truck arrivals considering their effects on congestion," Flexible Services and Manufacturing Journal, Springer, vol. 31(3), pages 730-762, September.
    6. Leonard Heilig & Stefan Voß, 2017. "Inter-terminal transportation: an annotated bibliography and research agenda," Flexible Services and Manufacturing Journal, Springer, vol. 29(1), pages 35-63, March.
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