ENBIS-17 in Naples
9 – 14 September 2017; Naples (Italy)
Abstract submission: 21 November 2016 – 10 May 2017
A Statistical Framework for Monitoring, Reporting and Verification of CO₂ Emissions in the Maritime Transport
12 September 2017, 14:50 – 15:10
- Submitted by
- Luigi Vitiello
- Dario Bocchetti (Grimaldi Group), Christian Capezza (University of Naples Federico II), Fabio Centofanti (University of Naples Federico II), Antonio Lepore (University of Naples Federico II), Rosa Di Matteo (University of Naples Federico II), Biagio Palumbo (University of Naples Federico II), Luigi Vitiello (University of Naples Federico II)
- The shipping industry is facing a new regulatory regime that aims to give public access to CO₂ emissions data. The application of the EU regulation 2015/757, which is mandatory from January 2018, urges shipping companies to set up a system for daily monitoring, reporting and verification (MRV) of emissions for each ship.
Even if manual acquisition of emission data is allowed (e.g. bunker fuel delivery note, bunker fuel tank monitoring), it is affected by a great uncertainty due to human intervention and thus will be eventually unusable for monitoring purposes. On the other hand, the massive amount of navigation data acquired by multi-sensor systems installed on-board of modern ships have a great potential to naturally comply with those regulations but are hampered by the lack of effective methods in the maritime literature.
By means of R programming language, this work implements the statistical framework presented in  through an automatic report that has been just designed to comply with the MRV requirements. The framework has been applied on Grimaldi Group’s Ro-Pax cruise ships and is shown to be also capable of supporting fault detection as well as verifying CO₂ savings after energy efficiency initiatives.
 Lepore A., Palumbo B., Capezza C. (2017). An empirical approach to monitoring CO₂ emissions via Partial Least-Squares regression. In C. Perna, M. Pratesi & A. Ruiz-Gazen (Eds.), International Series Studies in Theoretical and Applied Statistics (to appear)
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