IDEAS home Printed from https://ideas.repec.org/a/spr/flsman/v35y2023i1d10.1007_s10696-022-09468-5.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10696-022-09468-5
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10696-022-09468-5?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Zhao, Jiansen & Lu, Jinquan & Chen, Xinqiang & Yan, Zhongwei & Yan, Ying & Sun, Yang, 2022. "High-fidelity data supported ship trajectory prediction via an ensemble machine learning framework," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 586(C).
    2. Chen, Xinqiang & Chen, Huixing & Yang, Yongsheng & Wu, Huafeng & Zhang, Wenhui & Zhao, Jiansen & Xiong, Yong, 2021. "Traffic flow prediction by an ensemble framework with data denoising and deep learning model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).
    3. Bhowmik, Subrata & Paul, Abhishek & Panua, Rajsekhar & Ghosh, Subrata Kumar & Debroy, Durbadal, 2018. "Performance-exhaust emission prediction of diesosenol fueled diesel engine: An ANN coupled MORSM based optimization," Energy, Elsevier, vol. 153(C), pages 212-222.
    4. Brian C Ross, 2014. "Mutual Information between Discrete and Continuous Data Sets," PLOS ONE, Public Library of Science, vol. 9(2), pages 1-5, February.
    5. Luan Thanh Le & Gunwoo Lee & Keun-Sik Park & Hwayoung Kim, 2020. "Neural network-based fuel consumption estimation for container ships in Korea," Maritime Policy & Management, Taylor & Francis Journals, vol. 47(5), pages 615-632, July.
    6. Hyunjin Lee & Taesik Lee, 2021. "Demand modelling for emergency medical service system with multiple casualties cases: k-inflated mixture regression model," Flexible Services and Manufacturing Journal, Springer, vol. 33(4), pages 1090-1115, December.
    7. Du, Yuquan & Meng, Qiang & Wang, Shuaian & Kuang, Haibo, 2019. "Two-phase optimal solutions for ship speed and trim optimization over a voyage using voyage report data," Transportation Research Part B: Methodological, Elsevier, vol. 122(C), pages 88-114.
    8. Yan, Ran & Wang, Shuaian & Du, Yuquan, 2020. "Development of a two-stage ship fuel consumption prediction and reduction model for a dry bulk ship," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 138(C).
    9. Cullinane, Kevin & Wang, Teng-Fei & Song, Dong-Wook & Ji, Ping, 2006. "The technical efficiency of container ports: Comparing data envelopment analysis and stochastic frontier analysis," Transportation Research Part A: Policy and Practice, Elsevier, vol. 40(4), pages 354-374, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jarosław Ziółkowski & Mateusz Oszczypała & Jerzy Małachowski & Joanna Szkutnik-Rogoż, 2021. "Use of Artificial Neural Networks to Predict Fuel Consumption on the Basis of Technical Parameters of Vehicles," Energies, MDPI, vol. 14(9), pages 1-23, May.
    2. Yan, Ran & Yang, Dong & Wang, Tianyu & Mo, Haoyu & Wang, Shuaian, 2024. "Improving ship energy efficiency: Models, methods, and applications," Applied Energy, Elsevier, vol. 368(C).
    3. Yan, Ran & Wang, Shuaian & Psaraftis, Harilaos N., 2021. "Data analytics for fuel consumption management in maritime transportation: Status and perspectives," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 155(C).
    4. Nguyen, Son & Fu, Xiuju & Ogawa, Daichi & Zheng, Qin, 2023. "An application-oriented testing regime and multi-ship predictive modeling for vessel fuel consumption prediction," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 177(C).
    5. Wen Yi & Robyn Phipps & Hans Wang, 2020. "Sustainable Ship Loading Planning for Prefabricated Products in the Construction Industry," Sustainability, MDPI, vol. 12(21), pages 1-12, October.
    6. Wu, Lingxiao & Wang, Shuaian & Laporte, Gilbert, 2021. "The Robust Bulk Ship Routing Problem with Batched Cargo Selection," Transportation Research Part B: Methodological, Elsevier, vol. 143(C), pages 124-159.
    7. Yang, Ying & Liu, Yang & Li, Guorong & Zhang, Zekun & Liu, Yanbin, 2024. "Harnessing the power of Machine learning for AIS Data-Driven maritime Research: A comprehensive review," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 183(C).
    8. Tan, Zhijia & Zeng, Xianyang & Shao, Shuai & Chen, Jihong & Wang, Hua, 2022. "Scrubber installation and green fuel for inland river ships with non-identical streamflow," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 161(C).
    9. Yoon, Junghyun & Lee, Hee Yong & Dinwoodie, John, 2015. "Competitiveness of container terminal operating companies in South Korea and the industry–university–government network," Transportation Research Part A: Policy and Practice, Elsevier, vol. 80(C), pages 1-14.
    10. Schreiner, Lena & Madlener, Reinhard, 2022. "Investing in power grid infrastructure as a flexibility option: A DSGE assessment for Germany," Energy Economics, Elsevier, vol. 107(C).
    11. Figueiredo De Oliveira, Gabriel & Cariou, Pierre, 2015. "The impact of competition on container port (in)efficiency," Transportation Research Part A: Policy and Practice, Elsevier, vol. 78(C), pages 124-133.
    12. Asghari, Mohammad & Jaber, Mohamad Y. & Mirzapour Al-e-hashem, S.M.J., 2023. "Coordinating vessel recovery actions: Analysis of disruption management in a liner shipping service," European Journal of Operational Research, Elsevier, vol. 307(2), pages 627-644.
    13. Peter Fernandes Wanke & Rebecca de Mattos, 2014. "Capacity Issues and Efficiency Drivers in Brazilian Bulk Terminals," Brazilian Business Review, Fucape Business School, vol. 11(5), pages 72-98, October.
    14. Carlos Pestana Barros & Zhongfei Chen & Peter Wanke, 2016. "Efficiency in Chinese seaports: 2002–2012," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 18(3), pages 295-316, September.
    15. George E. Halkos & Nickolaos G. Tzeremes, 2015. "Measuring Seaports' Productivity: A Malmquist Productivity Index Decomposition Approach," Journal of Transport Economics and Policy, University of Bath, vol. 49(2), pages 355-376, April.
    16. Quintano, Claudio & Mazzocchi, Paolo & Rocca, Antonella, 2021. "Evaluation of the eco-efficiency of territorial districts with seaport economic activities," Utilities Policy, Elsevier, vol. 71(C).
    17. Renata Machado de Andrade & Suhyung Lee & Paul Tae-Woo Lee & Oh Kyoung Kwon & Hye Min Chung, 2019. "Port Efficiency Incorporating Service Measurement Variables by the BiO-MCDEA: Brazilian Case," Sustainability, MDPI, vol. 11(16), pages 1-18, August.
    18. Tovar, Beatriz & Wall, Alan, 2015. "Can ports increase traffic while reducing inputs? Technical efficiency of Spanish Port Authorities using a directional distance function approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 71(C), pages 128-140.
    19. María Victoria Uribe‐Bohorquez & Jennifer Martínez‐Ferrero & Isabel‐María García‐Sánchez, 2019. "Women on boards and efficiency in a business‐orientated environment," Corporate Social Responsibility and Environmental Management, John Wiley & Sons, vol. 26(1), pages 82-96, January.
    20. Chiou, Yu-Chiun & Lan, Lawrence W. & Yen, Barbara T.H., 2012. "Route-based data envelopment analysis models," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 48(2), pages 415-425.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:flsman:v:35:y:2023:i:1:d:10.1007_s10696-022-09468-5. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.