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Energies
Article . 2017 . Peer-reviewed
License: CC BY
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Energies
Article
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Energies
Article . 2017
Data sources: DOAJ
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Pathways to Decarbonise the European Car Fleet: A Scenario Analysis Using the Backcasting Approach

Authors: Arne Höltl; Cathy Macharis; Klaas De Brucker;

Pathways to Decarbonise the European Car Fleet: A Scenario Analysis Using the Backcasting Approach

Abstract

This paper analyses decarbonisation scenarios for the European passenger car fleet in 2050. The scenarios have been developed using the backcasting approach and aim to reduce greenhouse gas (GHG) emissions of passenger cars to a level defined in the Transport White paper that is 60% below 1990 levels. Considering the emission levels of 2010, a yearly reduction of 1.7% is required in order to achieve the target. Car emissions were decomposed into the main emission factors of mobility, efficiency and carbon intensity. How these factors change over time depends on various external factors: the pace of technological improvements, the future role of cars in society’s mobility system and the priority given to decarbonising energy demand. The analysis showed that if car mobility and ownership continue to increase as expected in a ‘business as usual’ case, a share of 97% plug-in hybrid or battery electric vehicles might be required by 2050, together with a substantial decrease in greenhouse gas emission from electricity production. A transition to more advanced car technology such as automated driving, advanced batteries or lightweight materials in vehicle production would raise vehicle efficiency. Should car mobility continue at a high level, an early technology transition will be required.

Keywords

Technology, environmental effects, T, CO2 emissions, electric and hybrid vehicles, energy efficiency

  • 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).
    30
    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).
    Top 10%
    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!
30
Top 10%
Top 10%
Top 10%
Green
gold