An estimation of heavy-duty vehicle fleet CO2 emissions based on sampled data. (May 2021)

• Record Type: Journal Article
• Authors: Zacharof, Nikiforos; Fontaras, Georgios; Ciuffo, Biagio; Tansini, Alessandro; Prado-Rujas, Iker
• Is Part Of: Transportation research. Volume 94(2021)
• Publication Date: 2021-05
• Subjects: CO2 emissions -- Heavy-duty -- Fleet -- Simulation -- Truck -- VECTO; Transportation -- Research -- Periodicals; Transportation -- Environmental aspects -- Periodicals; 354.76
• DOI: 10.1016/j.trd.2021.102784
• Languages: English
• ISSNs: 1361-9209

Abstract

Graphical abstract: Highlights: Three sampling approaches were investigated for estimating HDV fleet CO2 emissions. Mean CO2 emissions deviate less than 2% in the best cases and less than 5% overall. The JDE sampling preserves well the distribution characteristics. MEDP and MSD are suitable to investigate fleet emissions when limited data are available. The alternative methodologies can be used to predict CO2 emissions in future fleet scenarios. Abstract: Certification and monitoring of heavy vehicle CO2 emissions in several countries are based on individual vehicle simulation. Smaller fleet subsets can be used for accurate fleet-level results while preserving the characteristics of the underlying fleet-emissions distributions. The paper focuses on three approaches to capture fleet CO2 emissions: a) sampling directly from the fleet-data, b) sampling from data of individual vehicle components and c) using key statistics regarding the fleet composition that are available. The first and second approach deliver marginal divergences of the mean, between 1.1 and 2.1% and below 2.7 respectively, preserving the characteristics of the distribution. The third deviated by up to 5%, but lacked the detailed characteristics of the underlying statistical distribution. All three are useful when setting up fleet-wide monitoring schemes where detailed data are not available and to investigate the potential CO2 savings of various future fleet compositions, and scenarios regarding the diffusion of different types of technologies.