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Analysis of low carbon super credit policy efficiency in European Union greenhouse gas emissions

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  • Álvarez, Roberto
  • Zubelzu, Sergio
  • Díaz, Guzmán
  • López, Alberto

Abstract

In this paper we address the current European Union's support policy for BEV (battery electric vehicles) manufacturing under the Super-credit modality, and its actual relationship with the reduction of carbon emissions derived from the use of battery electric vehicles (BEV). Particularly, we have estimated the BEV associated carbon emissions through the method provided by Intergovernmental Panel on Climate Change (IPCC). In this sense, we have employed a BEV model to investigate the BEV emissions by country in the EU according to the regional electricity mix. We additionally have accounted for the particularities of real-world driving, which further affects the results. We have employed a measure of driving aggressiveness by modifying the standard benchmarking driving cycle fostered by the European Union and of necessary application by vehicle manufacturers—the NEDC—to show that BEV emissions are not negligible when compared with internal combustion vehicles; mainly in urban environments.

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  • Álvarez, Roberto & Zubelzu, Sergio & Díaz, Guzmán & López, Alberto, 2015. "Analysis of low carbon super credit policy efficiency in European Union greenhouse gas emissions," Energy, Elsevier, vol. 82(C), pages 996-1010.
    • Handle: RePEc:eee:energy:v:82:y:2015:i:c:p:996-1010
    DOI: 10.1016/j.energy.2015.01.110
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    Cited by:

    1. Jarosław Brodny & Magdalena Tutak, 2020. "Analyzing Similarities between the European Union Countries in Terms of the Structure and Volume of Energy Production from Renewable Energy Sources," Energies, MDPI, vol. 13(4), pages 1-37, February.
    2. Ren, Fei & Tian, Chenlu & Zhang, Guiqing & Li, Chengdong & Zhai, Yuan, 2022. "A hybrid method for power demand prediction of electric vehicles based on SARIMA and deep learning with integration of periodic features," Energy, Elsevier, vol. 250(C).
    3. Zubelzu, Sergio & Álvarez, Roberto, 2016. "A simplified method to assess the influence of the power generation mix in urban carbon emissions," Energy, Elsevier, vol. 115(P1), pages 875-887.
    4. Junquera, Beatriz & Moreno, Blanca & Álvarez, Roberto, 2016. "Analyzing consumer attitudes towards electric vehicle purchasing intentions in Spain: Technological limitations and vehicle confidence," Technological Forecasting and Social Change, Elsevier, vol. 109(C), pages 6-14.
    5. Yu, Haijun & Dai, Hongliang & Tian, Guangdong & Wu, Benben & Xie, Yinghao & Zhu, Ying & Zhang, Tongzhu & Fathollahi-Fard, Amir Mohammad & He, Qi & Tang, Hong, 2021. "Key technology and application analysis of quick coding for recovery of retired energy vehicle battery," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    6. Zhang, Pan & Wang, Huan, 2022. "Do provincial energy policies and energy intensity targets help reduce CO2 emissions? Evidence from China," Energy, Elsevier, vol. 245(C).
    7. Álvarez Fernández, Roberto & Corbera Caraballo, Sergio & Beltrán Cilleruelo, Fernando & Lozano, J. Antonio, 2018. "Fuel optimization strategy for hydrogen fuel cell range extender vehicles applying genetic algorithms," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 655-668.

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