Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/106817
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Logistics and Maritime Studies | - |
dc.creator | Yan, R | - |
dc.creator | Mo, H | - |
dc.creator | Wang, S | - |
dc.creator | Yang, D | - |
dc.date.accessioned | 2024-06-04T07:39:56Z | - |
dc.date.available | 2024-06-04T07:39:56Z | - |
dc.identifier.issn | 0308-8839 | - |
dc.identifier.uri | http://hdl.handle.net/10397/106817 | - |
dc.language.iso | en | en_US |
dc.publisher | Routledge | en_US |
dc.subject | CO2 emissions fromshipping | en_US |
dc.subject | GBRT for vesselfuel consumption prediction | en_US |
dc.subject | Monitoring | en_US |
dc.subject | Reporting | en_US |
dc.subject | Ship energy efficiency | en_US |
dc.subject | Verification (MRV) regulation | en_US |
dc.subject | Vessel fuel consumption | en_US |
dc.title | Analysis and prediction of ship energy efficiency based on the MRV system | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 117 | - |
dc.identifier.epage | 139 | - |
dc.identifier.volume | 50 | - |
dc.identifier.issue | 1 | - |
dc.identifier.doi | 10.1080/03088839.2021.1968059 | - |
dcterms.abstract | 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. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Maritime policy and management, 2023, v. 50, no. 1, p. 117-139 | - |
dcterms.isPartOf | Maritime policy and management | - |
dcterms.issued | 2023 | - |
dc.identifier.scopus | 2-s2.0-85113956272 | - |
dc.identifier.eissn | 1464-5254 | - |
dc.description.validate | 202406 bcch | - |
dc.description.oa | Accepted Manuscript | en_US |
dc.identifier.FolderNumber | a2751 | en_US |
dc.identifier.SubFormID | 48233 | en_US |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | National Natural Science Foundation of China | en_US |
dc.description.pubStatus | Published | en_US |
dc.description.oaCategory | Green (AAM) | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Yan_Analysis_Prediction_Ship.pdf | Pre-Published version | 982.53 kB | Adobe PDF | View/Open |
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