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Scenario Analysis of Carbon Emissions of Beijing-Tianjin-Hebei

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  • Jianguo Zhou

    () (Department of Economics and Management, North China Electric Power University, Baoding 071003, China)

  • Baoling Jin

    () (Department of Economics and Management, North China Electric Power University, Baoding 071003, China)

  • Shijuan Du

    () (Department of Economics and Management, North China Electric Power University, Baoding 071003, China)

  • Ping Zhang

    () (Department of Economics and Management, North China Electric Power University, Baoding 071003, China)

Abstract

This paper utilizes the generalized Fisher index (GFI) to decompose the factors of carbon emission and exploits improved particle swarm optimization-back propagation (IPSO-BP) neural network modelling to predict the primary energy consumption CO 2 emissions in different scenarios of Beijing-Tianjin-Hebei region. The results show that (1) the main factors that affect the region are economic factors, followed by population size. On the contrary, the factors that mainly inhibit the carbon emissions are energy structure and energy intensity. (2) The peak year of carbon emission changes with the different scenarios. In a low carbon scenario, the carbon emission will have a decline stage between 2015 and 2018, then the carbon emission will be in the ascending phase during 2019–2030. In basic and high carbon scenarios, the carbon emission will peak in 2025 and 2028, respectively.

Suggested Citation

  • Jianguo Zhou & Baoling Jin & Shijuan Du & Ping Zhang, 2018. "Scenario Analysis of Carbon Emissions of Beijing-Tianjin-Hebei," Energies, MDPI, Open Access Journal, vol. 11(6), pages 1-17, June.
  • Handle: RePEc:gam:jeners:v:11:y:2018:i:6:p:1489-:d:151190
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    References listed on IDEAS

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    Cited by:

    1. Jialing Zou & Zhipeng Tang & Shuang Wu, 2019. "Divergent Leading Factors in Energy-Related CO 2 Emissions Change among Subregions of the Beijing–Tianjin–Hebei Area from 2006 to 2016: An Extended LMDI Analysis," Sustainability, MDPI, Open Access Journal, vol. 11(18), pages 1-17, September.

    More about this item

    Keywords

    carbon emissions; generalized fisher index; IPSO-BP neural network model; Beijing-Tianjin-Hebei region;

    JEL classification:

    • Q - Agricultural and Natural Resource Economics; Environmental and Ecological Economics
    • Q0 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General
    • Q4 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy
    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • Q42 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Alternative Energy Sources
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting
    • Q48 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Government Policy
    • Q49 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Other

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