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Towards Cleaner Ports: Predictive Modeling of Sulfur Dioxide Shipping Emissions in Maritime Facilities Using Machine Learning

Author

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  • Carlos D. Paternina-Arboleda

    (Fowler College of Business, Department of Management Information Systems, San Diego State University, San Diego, CA 92182, USA)

  • Dayana Agudelo-Castañeda

    (Department of Civil and Environmental Engineering, Universidad del Norte, Barranquilla 081007, Colombia)

  • Stefan Voß

    (Institute of Information Systems, University of Hamburg, 20146 Hamburg, Germany)

  • Shubhendu Das

    (Computational Science Master Program, San Diego State University, San Diego, CA 92182, USA)

Abstract

Maritime ports play a pivotal role in fostering the growth of domestic and international trade and economies. As ports continue to expand in size and capacity, the impact of their operations on air quality and climate change becomes increasingly significant. While nearby regions may experience economic benefits, there are significant concerns regarding the emission of atmospheric pollutants, which have adverse effects on both human health and climate change. Predictive modeling of port emissions can serve as a valuable tool in identifying areas of concern, evaluating the effectiveness of emission reduction strategies, and promoting sustainable development within ports. The primary objective of this research is to utilize machine learning frameworks to estimate the emissions of SO 2 from ships during various port activities, including hoteling, maneuvering, and cruising. By employing these models, we aim to gain insights into the emission patterns and explore strategies to mitigate their impact. Through our analysis, we have identified the most effective models for estimating SO 2 emissions. The AutoML TPOT framework emerges as the top-performing model, followed by Non-Linear Regression with interaction effects. On the other hand, Linear Regression exhibited the lowest performance among the models evaluated. By employing these advanced machine learning techniques, we aim to contribute to the body of knowledge surrounding port emissions and foster sustainable practices within the maritime industry.

Suggested Citation

  • Carlos D. Paternina-Arboleda & Dayana Agudelo-Castañeda & Stefan Voß & Shubhendu Das, 2023. "Towards Cleaner Ports: Predictive Modeling of Sulfur Dioxide Shipping Emissions in Maritime Facilities Using Machine Learning," Sustainability, MDPI, vol. 15(16), pages 1-18, August.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:16:p:12171-:d:1213539
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    References listed on IDEAS

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    1. Hyangsook Lee & Dongjoo Park & Sangho Choo & Hoang T. Pham, 2020. "Estimation of the Non-Greenhouse Gas Emissions Inventory from Ships in the Port of Incheon," Sustainability, MDPI, vol. 12(19), pages 1-18, October.
    2. Zartarian, V.G. & Schultz, B.D. & Barzyk, T.M. & Smuts, M. & Hammond, D.M. & Medina-Vera, M. & Geller, A.M., 2011. "The environmental protection agency's Community-Focused Exposure and Risk Screening Tool (C-FERST) and its potential use for environmental justice efforts," American Journal of Public Health, American Public Health Association, vol. 101(SUPPL. 1), pages 286-294.
    3. Tichavska, Miluše & Tovar, Beatriz & Gritsenko, Daria & Johansson, Lasse & Jalkanen, Jukka Pekka, 2019. "Air emissions from ships in port: Does regulation make a difference?," Transport Policy, Elsevier, vol. 75(C), pages 128-140.
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    Cited by:

    1. Danilo Abril & Carlos D. Paternina-Arboleda & Jesus Velasquez-Bermudez, 2024. "An Integrated Event-Driven Real-Time Tactical–Operational Optimization Framework for Smart Port Operations Planning," Logistics, MDPI, vol. 8(3), pages 1-37, June.

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