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Traffic Simulation-Based Approach for A Cradle-to-Grave Greenhouse Gases Emission Model

Authors: Sergio Maria Patella; Flavio Scrucca; Francesco Asdrubali; Stefano Carrese;

Traffic Simulation-Based Approach for A Cradle-to-Grave Greenhouse Gases Emission Model

Abstract

This paper presents a model to evaluate the life cycle greenhouse gases (GHG) emissions, expressed in terms of carbon dioxide equivalent (CO2eq), of a generic fleet composition as a function of the traffic simulation results. First we evaluated the complete life cycle of each category of the vehicles currently circulating; next, by defining a general linear equation, the traffic environmental performances of a real road network (city of Rome) were evaluated using a traffic simulation approach. Finally, the proposed methodology was applied to evaluate the GHG emission of a 100% penetration of battery electric vehicles (BEVs) and various electric and conventional vehicles composition scenarios. In terms of life cycle impacts, BEVs are the vehicles with the highest GHG emissions at the vehicle level (construction + maintenance + end-of-life processes) that are, on average, 20% higher than internal combustion engine vehicles, and 6.5% higher than hybrid electric vehicles (HEVs). Nevertheless, a 100% BEVs penetration scenario generates a reduction of the environmental impact at the mobility system level of about 65%.

Country
Italy
Keywords

traffic emissions, traffic simulation, Environmental effects of industries and plants, greenhouse gases emissions, TJ807-830, TD194-195, Renewable energy sources, Environmental sciences, life cycle assessment, GE1-350, electric vehicles

  • BIP!
    Impact byBIP!
    citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    9
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
9
Top 10%
Average
Top 10%
gold