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Prediction and Analysis of the Relationship between Energy Mix Structure and Electric Vehicles Holdings Based on Carbon Emission Reduction Constraint: A Case in the Beijing-Tianjin-Hebei Region, China

Author

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  • Weijun Wang

    (Department of Economics and Management, North China Electric Power University, 689 Huadian Road, Baoding 071000, China)

  • Dan Zhao

    (Department of Economics and Management, North China Electric Power University, 689 Huadian Road, Baoding 071000, China)

  • Zengqiang Mi

    (Power System and Automation, North China Electric Power University, 689 Huadian Road, Baoding 071000, China)

  • Liguo Fan

    (Department of Economics and Management, North China Electric Power University, 689 Huadian Road, Baoding 071000, China)

Abstract

In response to air pollution problems caused by carbon emissions, electric vehicles are widely promoted in China. Since thermal power generation is the main form of power generation, the large-scale development of electric vehicles is equivalent to replacing oil with coal, which will accordingly result in carbon emissions increasing if the scale of electric vehicles exceeds a certain limit. A relationship model between regional energy mix structure and electric vehicles holdings under the constraint of carbon emission reduction is established to perform a quantitative analysis of the limitation mechanism. In order to measure the scale of the future electric vehicle market under the constraint of carbon emissions reduction, a method called Extreme Learning Machine optimized by Improved Particle Swarm Optimization (IPSO-ELM) with higher precision than Extreme Learning Machine (ELM) is proposed to predict the power structure and the trend of electric vehicle development in the Beijing-Tianjin-Hebei region from 2019–2030. The calculation results show that the maximum number of electric vehicles must not exceed 19,340,000 and 26,867,171 based on emissions reduction aims and also the predicted energy mix structure in the Beijing-Tianjin-Hebei region in 2020 and 2030. At this time, the ratio of electric vehicles to traditional car ownership is 75.6% and 78.3%. The proportion of clean energy generation should reach 0.314 and 0.323 to match a complete replacement of traditional fuel vehicles for electric vehicles. A substantial increase in clean energy generation is needed so that the large-scale promotion of electric vehicles can still achieve the goal of carbon reduction. Therefore, this article will be helpful for policy-making on electric vehicle development scale and energy mix structure in the Beijing-Tianjin-Hebei region.

Suggested Citation

  • Weijun Wang & Dan Zhao & Zengqiang Mi & Liguo Fan, 2019. "Prediction and Analysis of the Relationship between Energy Mix Structure and Electric Vehicles Holdings Based on Carbon Emission Reduction Constraint: A Case in the Beijing-Tianjin-Hebei Region, China," Sustainability, MDPI, vol. 11(10), pages 1-20, May.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:10:p:2928-:d:233571
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    References listed on IDEAS

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    1. Doucette, Reed T. & McCulloch, Malcolm D., 2011. "Modeling the prospects of plug-in hybrid electric vehicles to reduce CO2 emissions," Applied Energy, Elsevier, vol. 88(7), pages 2315-2323, July.
    2. Hackney, Jeremy & de Neufville, Richard, 2001. "Life cycle model of alternative fuel vehicles: emissions, energy, and cost trade-offs," Transportation Research Part A: Policy and Practice, Elsevier, vol. 35(3), pages 243-266, March.
    3. Atul Anand & L Suganthi, 2018. "Hybrid GA-PSO Optimization of Artificial Neural Network for Forecasting Electricity Demand," Energies, MDPI, vol. 11(4), pages 1-15, March.
    4. Yu, Shiwei & Wei, Yi-Ming & Guo, Haixiang & Ding, Liping, 2014. "Carbon emission coefficient measurement of the coal-to-power energy chain in China," Applied Energy, Elsevier, vol. 114(C), pages 290-300.
    5. Ravikiran Vaka & Ritesh Kumar Keshri, 2017. "Review on Contactless Power Transfer for Electric Vehicle Charging," Energies, MDPI, vol. 10(5), pages 1-20, May.
    6. Szoplik, Jolanta, 2015. "Forecasting of natural gas consumption with artificial neural networks," Energy, Elsevier, vol. 85(C), pages 208-220.
    7. Thiel, Christian & Perujo, Adolfo & Mercier, Arnaud, 2010. "Cost and CO2 aspects of future vehicle options in Europe under new energy policy scenarios," Energy Policy, Elsevier, vol. 38(11), pages 7142-7151, November.
    8. Baptista, Patrícia & Ribau, João & Bravo, João & Silva, Carla & Adcock, Paul & Kells, Ashley, 2011. "Fuel cell hybrid taxi life cycle analysis," Energy Policy, Elsevier, vol. 39(9), pages 4683-4691, September.
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    1. Marcin Rabe & Dalia Streimikiene & Yuriy Bilan, 2019. "EU Carbon Emissions Market Development and Its Impact on Penetration of Renewables in the Power Sector," Energies, MDPI, vol. 12(15), pages 1-20, August.

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