CAN AI ADDRESS THE SHORTCOMINGS OF THE CURRENT PORT EMISSIONS INVENTORY PRACTICES?

Flóra Zs. Gulyás

Abstract


The creation of air emission inventories (EIs) in seaports has proven to be a valuable method providing a quantitative basis to meet increasingly stringent regulations and ambitious emission reduction targets. These inventories are essential for implementing and monitoring necessary measures. However, despite their significance, EIs are often unpublished or not regularly updated, obscuring the ports' contributions to emissions. Key reasons for this include data security issues, accuracy problems, and the substantial effort required to obtain and analyze the necessary emission data. Recent advancements in artificial intelligence (AI) offer promising solutions to reduce this effort and enhance the accuracy of these inventories. Based on a systematic literature review of 13 relevant articles, this study examines the potentials and barriers of AI and machine learning (ML) techniques. The findings reveal the AI/ML techniques considered in the context of EIs, pinpoint the barriers that can be overcome using AI/ML, and highlight the improvements needed for the further application of these technologies. The proposed future research agenda aims to incorporate practical evidence from port authorities, providing a comprehensive understanding of how AI can be effectively leveraged for improving the accuracy and relevance of emission inventories. 

Keywords


Artificial Intelligence, Machine Learning, Air Emission Inventories, Emission Reduction, Seaports.

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DOI: https://dx.doi.org/10.21622/MARLOG.2025.14.1.68

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