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In the context of green shipping, this paper analyzes factors influencing carbon emissions during vessel navigation, including wind speed, wind direction, wave conditions, and ocean currents. Through qualitative analysis and data collection from various sources, the study identifies key factors with operational significance that impact vessel navigation speed. A predictive model for shaft power is proposed, outputting Energy Efficiency Existing Ship Index (EEDI) predictions. The research aligns with the trend of low-carbon green shipping, providing insights for reducing operational costs and emission expenses. The study holds significance in the field of artificial intelligence, particularly in machine learning and deep learning.

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Study on Factors Affecting Carbon Emissions in Ship Navigation and Prediction of EEDI

  • 会议论文

  • 发表日期：

    2025年

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  • Jingwen Zhang,
  • Zitong Peng,
  • Jiaying Liu,
  • Boxin Yang

摘要

In the context of green shipping, this paper analyzes factors influencing carbon emissions during vessel navigation, including wind speed, wind direction, wave conditions, and ocean currents. Through qualitative analysis and data collection from various sources, the study identifies key factors with operational significance that impact vessel navigation speed. A predictive model for shaft power is proposed, outputting Energy Efficiency Existing Ship Index (EEDI) predictions. The research aligns with the trend of low-carbon green shipping, providing insights for reducing operational costs and emission expenses. The study holds significance in the field of artificial intelligence, particularly in machine learning and deep learning.