Hou, F., Chen, X., Chen, X., Yang, F., Ma, Z., Zhang, S., Liu, C., Zhao, Y., & Guo, F. ORCID: https://orcid.org/0000-0001-6415-8083 (2021). Comprehensive analysis method of determining global long-term GHG mitigation potential of Passenger Battery Electric Vehicles. Journal of Cleaner Production 289 e125137. 10.1016/j.jclepro.2020.125137.
Full text not available from this repository.Abstract
The development of electric vehicles can help reduce the use of fossil fuels and further mitigate greenhouse gas (GHG) emissions. A comprehensive method is proposed for estimating the GHG mitigation potential of passenger battery electric vehicles (PBEVs) continentally and globally, considering the electricity consumption, energy transition, and other main influencing factors. The future energy transition is considered to change the electricity generation structures, improve the share of clean power, and further increase the GHG mitigation potential of electric vehicles based on a life-cycle analysis. An uncertainty analysis is performed to investigate the main influencing factors, such as energy intensity, battery size, autonomous vehicles, charging infrastructure, carpooling and ridesharing, and the development of other competitive vehicles on the electricity consumption and mitigation potential. Results show that the global stock of PBEVs will reach 1.2 × 108 and 1.0 × 109 vehicles in 2030 and 2050, respectively, and the global electricity consumption will be 250–480 TWh in 2030 and 1140–2840 TWh in 2050, equivalent of saving 3.4-6.5 × 108 barrel of gasoline in 2030 and 1.6-3.9 × 109 barrel of gasoline in 2050. The calculated global GHG mitigation potentials of PBEVs are 40–215 Mt CO2e and 340–1380 Mt CO2e in 2030 and 2050, respectively, which will accelerate the decarbonization transition in the transport sector and help achieve the global temperature control goals in the Paris Agreement and sustainable development goals.
Item Type: | Article |
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Uncontrolled Keywords: | Passenger battery electric vehicle; GHG mitigation potential; energy transition; uncertainty analysis |
Research Programs: | Energy (ENE) |
Depositing User: | Luke Kirwan |
Date Deposited: | 23 Nov 2020 09:36 |
Last Modified: | 15 Mar 2022 10:07 |
URI: | https://pure.iiasa.ac.at/16854 |
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