Three potential benefits of the EU and IMO’s landmark efforts to monitor carbon dioxide emissions from shipping

Shuaian WANG, Lu ZHEN, Harilaos N. PSARAFTIS

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Front. Eng ›› 2021, Vol. 8 ›› Issue (2) : 310-311. DOI: 10.1007/s42524-020-0096-2
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Three potential benefits of the EU and IMO’s landmark efforts to monitor carbon dioxide emissions from shipping

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Shuaian WANG, Lu ZHEN, Harilaos N. PSARAFTIS. Three potential benefits of the EU and IMO’s landmark efforts to monitor carbon dioxide emissions from shipping. Front. Eng, 2021, 8(2): 310‒311 https://doi.org/10.1007/s42524-020-0096-2

References

[1]
Adland R, Alger H, Banyte J, Jia H (2017). Does fuel efficiency pay? Empirical evidence from the drybulk timecharter market revisited. Transportation Research Part A: Policy and Practice, 95: 1–12
CrossRef Google scholar
[2]
Agnolucci P, Smith T, Rehmatulla N (2014). Energy efficiency and time charter rates: Energy efficiency savings recovered by ship owners in the Panamax market. Transportation Research Part A: Policy and Practice, 66: 173–184
CrossRef Google scholar
[3]
Du Y, Meng Q, Wang S, Kuang H (2019). Two-phase optimal solutions for ship speed and trim optimization over a voyage using voyage report data. Transportation Research Part B: Methodological, 122: 88–114
CrossRef Google scholar
[4]
ECMWF (2019). ERA interim, daily. Available at: apps.ecmwf.int/datasets/data/interim-full-daily/levtype = sfc/
[5]
EU (2015). Regulations on the monitoring, reporting and verification of carbon dioxide emissions from maritime transport. EU2015/757
[6]
IMO (2016). Resolution MEPC.278(70): Amendments to MARPOL Annex VI on data collection system for fuel oil consumption of ships. London: IMO
[7]
IMO (2018). Resolution MEPC.304(72): Initial IMO strategy on reduction of GHG emissions from ships. London: IMO
[8]
Meng Q, Du Y, Wang Y (2016). Shipping log data based container ship fuel efficiency modeling. Transportation Research Part B: Methodological, 83: 207–229
CrossRef Google scholar
[9]
Smith T W P, Jalkanen J P, Anderson B A, Corbett J J, Faber J, Hanayama S, O’Keeffe E, Parker S, Johansson L, Aldous L, Raucci C, Traut M, Ettinger S, Nelissen D, Lee D S, Ng S, Agrawal A, Winebrake J J, Hoen M, Chesworth S, Pandey A (2014). Third IMO GHG Study 2014. London: International Maritime Organization
[10]
Yang D, Wu L, Wang S, Jia H, Li K X (2019). How big data enriches maritime research—A critical review of Automatic Identification System (AIS) data applications. Transport Reviews, 39(6): 755–773
CrossRef Google scholar

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