Analysis and prediction of ship energy efficiency based on the MRV system

Ran Yan, Haoyu Mo, Shuaian Wang, Dong Yang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

7 Citations (Scopus)


To reduce CO2 emissions from shipping activities to, from, and within the European Union (EU) area, a system of monitoring, reporting, and verification (MRV) of CO2 emissions from ships are implemented in 2015 by the EU. Although the MRV records in 2018 and 2019 have been published, there are scarce studies on the MRV system especially from a quantitative perspective, which restrains the potential of the MRV. To bridge this gap, this paper first analyzes and compares MRV records in 2018 and 2019, and then develops machine learning models for annual average fuel consumption prediction for each ship type combining ship features from an external database. The performance of the prediction models is accurate, with the mean absolute percentage error (MAPE) on the test set no more than 12% and the average R-squared of all the models at 0.78. Based on the analysis and prediction results, model meanings, implications, and extensions are thoroughly discussed. This study is a pioneer to analyze the emission reports in the MRV system from a quantitative perspective. It also develops the first fuel consumption prediction models from a macro perspective using the MRV data. It can contribute to the promotion of green shipping strategies.

Original languageEnglish
Pages (from-to)117–139
Number of pages23
JournalMaritime Policy and Management
Issue number1
Publication statusPublished - Jan 2023


  • CO emissions from shipping
  • GBRT for vessel fuel consumption prediction
  • Monitoring
  • reporting
  • ship energy efficiency
  • verification (MRV) regulation
  • vessel fuel consumption

ASJC Scopus subject areas

  • Geography, Planning and Development
  • Transportation
  • Ocean Engineering
  • Management, Monitoring, Policy and Law


Dive into the research topics of 'Analysis and prediction of ship energy efficiency based on the MRV system'. Together they form a unique fingerprint.

Cite this