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Scenario Analysis of Carbon Emissions of Beijing-Tianjin-Hebei

Jianguo Zhou (), Baoling Jin (), Shijuan Du () and Ping Zhang ()
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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

Energies, 2018, vol. 11, issue 6, 1-17

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.

Keywords: carbon emissions; generalized fisher index; IPSO-BP neural network model; Beijing-Tianjin-Hebei region (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
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