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Comparative Analysis of Carbon Intensity Indicators Applicable to Harbor Tugboats

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  • Janmanuel Jaramillo

    (School of Mechanical Engineering, Pontificia Universidad Católica de Valparaíso, Quilpué 2430000, Chile
    Research and Development Department, SAAM Towage Corporate, Las Condes 7550000, Chile)

  • Joaquín Gutiérrez

    (Research and Development Department, SAAM Towage Corporate, Las Condes 7550000, Chile)

  • Yunesky Masip Macia

    (School of Mechanical Engineering, Pontificia Universidad Católica de Valparaíso, Quilpué 2430000, Chile)

Abstract

This study compares four carbon intensity indicators applicable to harbor tugboats to identify the most representative greenhouse gas emissions management. Using operational data from SAAM Towage’s fleet, the indicators evaluated traveled distance, operating time, energy consumption, and average engine load demand. Statistical analyses revealed that the energy consumption-based indicator exhibited lower variability and greater capacity to reflect the operational particularities of tugboats. In contrast, indicators based on average load presented high dispersion, limiting their applicability. These conclusions highlight the importance of considering vessel-specific characteristics when selecting indicators. This work provides tools to improve environmental monitoring and facilitates the implementation of sustainability strategies aligned with the maritime industry’s emission reduction objectives.

Suggested Citation

  • Janmanuel Jaramillo & Joaquín Gutiérrez & Yunesky Masip Macia, 2025. "Comparative Analysis of Carbon Intensity Indicators Applicable to Harbor Tugboats," Sustainability, MDPI, vol. 17(4), pages 1-20, February.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:4:p:1706-:d:1594121
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    References listed on IDEAS

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    1. Alam Md Moshiul & Roslina Mohammad & Fariha Anjum Hira & Nurazean Maarop, 2022. "Alternative Marine Fuel Research Advances and Future Trends: A Bibliometric Knowledge Mapping Approach," Sustainability, MDPI, vol. 14(9), pages 1-27, April.
    2. Mohammed H. Alshareef & Ayman F. Alghanmi, 2024. "Optimizing Maritime Energy Efficiency: A Machine Learning Approach Using Deep Reinforcement Learning for EEXI and CII Compliance," Sustainability, MDPI, vol. 16(23), pages 1-28, November.
    3. Elizabeth Lindstad & Gunnar S. Eskeland & Agathe Rialland & Anders Valland, 2020. "Decarbonizing Maritime Transport: The Importance of Engine Technology and Regulations for LNG to Serve as a Transition Fuel," Sustainability, MDPI, vol. 12(21), pages 1-21, October.
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