Applied Energy, Vol.169, 197-209, 2016
A multi-dimensional well-to-wheels analysis of passenger vehicles in different regions: Primary energy consumption, CO2 emissions, and economic cost
This paper proposes an exergy-based well-to-wheels analysis to compare different passenger vehicles, based on three key indicators: petroleum energy use, CO2 emissions, and economic cost. A set of fuel pathways, including petroleum-based fuels, compressed natural gas, biofuels, and electricity are considered in five representative national energy mixes, namely Brazil, China, France, Italy, and the United States of America. Results show no fundamental difference in the fossil fuel pathways among the five scenarios considered. Compressed natural gas vehicles and electric vehicles can completely displace oil consumption in the personal transportation sector. Compressed natural gas vehicles also reduce CO2 emissions by over 20% compared to gasoline vehicles. Emissions from electric vehicles greatly vary depending on the electricity mix. In low-carbon electricity mixes electric vehicles reach almost-zero CO2 emissions, while the use of biofuels leads to the lowest CO2 emissions in carbon-intensive electricity generation mixes, where vehicles running on E85 could reduce CO2 emission by over 50% compared to gasoline vehicles. Hybrid electric vehicles show the lowest overall economic cost, due to improved efficiency and low cost of petroleum-based fuels. Vehicles running on electricity are characterized by significantly higher capital cost and lower operating costs. Thus, different electricity generation costs impact minimally the overall cost. These results can be used to inform decision-makers regarding the multidimensional impact of passenger vehicles, including environmental impact, economic cost, and depletion of primary energy resources, with particular focus on petroleum. (C) 2016 Elsevier Ltd. All rights reserved.
Keywords:Well-to-wheels;Fuel cycle;Petroleum use;Sustainable mobility;Exergy analysis;Passenger vehicles CO2 emissions