Emission accounting of shipping activities in the era of big data

Yuwei Yin, Jasmine Siu Lee Lam and Nguyen Khoi Tran
Additional contact information
Jasmine Siu Lee Lam: NTU - Nanyang Technological University [Singapour]
Nguyen Khoi Tran: Métis Lab EM Normandie - EM Normandie - École de Management de Normandie = EM Normandie Business School

Post-Print from HAL

Abstract: Maritime transportation has generated a considerable amount of emissions and affected the global atmospheric environment. A key step of effective emission control is to construct reliable models of emission accounting. In recent years, there has been a major innovation in emission accounting, the application of big data, especially the data extracted from automatic identification system (AIS). In this paper, a dynamic and comprehensive analysis is developed to depict how emission accounting models have been evolved in this era of big data. In the perspective-based review, we thoroughly investigate the geographical coverage and pollutant types involved in the existing emission studies. In the process-based review, this paper establishes a solid knowledge framework of the two basic modelling concepts: top-down and bottom-up approaches. Furthermore, updated emission modelling methodologies and high resolute data sources are introduced. But the latest models are still subject to various sources of uncertainties. Hence, this paper identifies such unsolved problems and sets up a future research agenda.

Date: 2021-02-09
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Published in International Journal of Shipping and Transport Logistics, 2021, 13 (1/2), pp.156. ⟨10.1504/IJSTL.2021.112922⟩

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-05044574

DOI: 10.1504/IJSTL.2021.112922

Access Statistics for this paper

More papers in Post-Print from HAL
Bibliographic data for series maintained by CCSD ().