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Tiered prediction models for port vessel emissions inventories

Author

Listed:
  • Philip Cammin

    (University of Hamburg)

  • Jingjing Yu

    (University of Hamburg
    Faculty of Infrastructure Engineering, Dalian University of Technology)

  • Stefan Voß

    (University of Hamburg)

Abstract

Albeit its importance, a large number of port authorities do not provide continuous or publicly available air emissions inventories (EIs) and thereby obscure the emissions contribution of ports. This is caused by, e.g., the economic effort generated by obtaining data. Therefore, the performance of abatement measures is not monitored and projected, which is specifically disadvantageous concerning top contributors such as container ships. To mitigate this issue, in this paper we propose port vessel EI prediction models by exploring the combination of different machine-learning algorithms, data from the one-off application of an activity-based bottom-up methodology and vessel-characteristics data. The results for this specific case show that prediction models enable acceptable trade-offs between the prediction performance and data requirements, promoting the creation of EIs.

Suggested Citation

  • Philip Cammin & Jingjing Yu & Stefan Voß, 2023. "Tiered prediction models for port vessel emissions inventories," Flexible Services and Manufacturing Journal, Springer, vol. 35(1), pages 142-169, March.
    • Handle: RePEc:spr:flsman:v:35:y:2023:i:1:d:10.1007_s10696-022-09468-5
    DOI: 10.1007/s10696-022-09468-5
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    References listed on IDEAS

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    Cited by:

    1. Kjetil Fagerholt & Leonard Heilig & Eduardo Lalla-Ruiz & Frank Meisel & Shuaian Wang, 2023. "Data-driven optimization and analytics for maritime logistics," Flexible Services and Manufacturing Journal, Springer, vol. 35(1), pages 1-4, March.

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