Authors: Yuwei Yin; Jasmine Siu Lee Lam; Nguyen Khoi Tran
Addresses: Maritime Institute, Nanyang Technological University, Singapore ' Maritime Energy and Sustainable Development Centre of Excellence, School of Civil and Environmental Engineering, Nanyang Technological University, Singapore ' Maritime Institute, Nanyang Technological University, Singapore
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.
Keywords: emission accounting; big data; bottom-up model; top-down model; ship emission; CO2 emission; greenhouse gases; automatic identification system; AIS.
International Journal of Shipping and Transport Logistics, 2021 Vol.13 No.1/2, pp.156 - 184
Accepted: 18 Sep 2019
Published online: 09 Feb 2021 *