Carbon And Energy Footprints Of Electric Delivery Trucks: A Hybrid Multi-Regional Input-Output Life Cycle Assessment

Keywords

Battery electric trucks; Commercial delivery truck; Multi-regional input-output analysis; Regional carbon footprint analysis Hybrid life cycle assessment

Abstract

Due to frequent stop-and-go operation and long idling periods when driving in congested urban areas, the electrification of commercial delivery trucks has become an interesting topic nationwide. In this study, environmental impacts of various alternative delivery trucks including battery electric, diesel, diesel-electric hybrid, and compressed natural gas trucks are analyzed. A novel life cycle assessment method, an environmentally-extended multi-region input-output analysis, is utilized to calculate energy and carbon footprints throughout the supply chain of alternative delivery trucks. The uncertainties due to fuel consumption or other key parameter variations in real life, data ranges are taken into consideration using a Monte Carlo simulation. Furthermore, variations in regional electricity mix greenhouse gas emission are also considered to present a region-specific assessment for each vehicle type. According to the analysis results, although the battery electric delivery trucks have zero tailpipe emission, electric trucks are not expected to have lower environmental impacts compared to other alternatives. On average, the electric trucks have slightly more greenhouse emissions and energy consumption than those of other trucks. The regional analysis also indicates that the percentage of cleaner power sources in the electricity mix plays an important role in the life cycle greenhouse gas emission impacts of electric trucks.

Publication Date

8-1-2016

Publication Title

Transportation Research Part D: Transport and Environment

Volume

47

Number of Pages

195-207

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1016/j.trd.2016.05.014

Socpus ID

84973879907 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84973879907

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