Scenario Analysis of Carbon Emissions of Beijing-Tianjin-Hebei
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 CO2 emissions in different scenarios of Beijing-Tianjin-H...
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2018-06-01
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Online Access: | http://www.mdpi.com/1996-1073/11/6/1489 |
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doaj-71c9bedfd323430da6c20ff534c1e0fa2020-11-24T23:49:14ZengMDPI AGEnergies1996-10732018-06-01116148910.3390/en11061489en11061489Scenario Analysis of Carbon Emissions of Beijing-Tianjin-HebeiJianguo Zhou0Baoling Jin1Shijuan Du2Ping Zhang3Department of Economics and Management, North China Electric Power University, Baoding 071003, ChinaDepartment of Economics and Management, North China Electric Power University, Baoding 071003, ChinaDepartment of Economics and Management, North China Electric Power University, Baoding 071003, ChinaDepartment of Economics and Management, North China Electric Power University, Baoding 071003, ChinaThis 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 CO2 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.http://www.mdpi.com/1996-1073/11/6/1489carbon emissionsgeneralized fisher indexIPSO-BP neural network modelBeijing-Tianjin-Hebei region |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Jianguo Zhou Baoling Jin Shijuan Du Ping Zhang |
spellingShingle |
Jianguo Zhou Baoling Jin Shijuan Du Ping Zhang Scenario Analysis of Carbon Emissions of Beijing-Tianjin-Hebei Energies carbon emissions generalized fisher index IPSO-BP neural network model Beijing-Tianjin-Hebei region |
author_facet |
Jianguo Zhou Baoling Jin Shijuan Du Ping Zhang |
author_sort |
Jianguo Zhou |
title |
Scenario Analysis of Carbon Emissions of Beijing-Tianjin-Hebei |
title_short |
Scenario Analysis of Carbon Emissions of Beijing-Tianjin-Hebei |
title_full |
Scenario Analysis of Carbon Emissions of Beijing-Tianjin-Hebei |
title_fullStr |
Scenario Analysis of Carbon Emissions of Beijing-Tianjin-Hebei |
title_full_unstemmed |
Scenario Analysis of Carbon Emissions of Beijing-Tianjin-Hebei |
title_sort |
scenario analysis of carbon emissions of beijing-tianjin-hebei |
publisher |
MDPI AG |
series |
Energies |
issn |
1996-1073 |
publishDate |
2018-06-01 |
description |
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 CO2 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. |
topic |
carbon emissions generalized fisher index IPSO-BP neural network model Beijing-Tianjin-Hebei region |
url |
http://www.mdpi.com/1996-1073/11/6/1489 |
work_keys_str_mv |
AT jianguozhou scenarioanalysisofcarbonemissionsofbeijingtianjinhebei AT baolingjin scenarioanalysisofcarbonemissionsofbeijingtianjinhebei AT shijuandu scenarioanalysisofcarbonemissionsofbeijingtianjinhebei AT pingzhang scenarioanalysisofcarbonemissionsofbeijingtianjinhebei |
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1725483295632785408 |