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Decomposition analysis on influence factors of direct household energy‐related carbon emission in G uangdong province—Based on extended K aya identity
Author(s) -
Wang Wenxiu,
Zhao Daiqing,
Kuang Yaoqiu
Publication year - 2016
Publication title -
environmental progress and sustainable energy
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.495
H-Index - 66
eISSN - 1944-7450
pISSN - 1944-7442
DOI - 10.1002/ep.12219
Subject(s) - divisia index , energy consumption , carbon fibers , energy intensity , population , environmental economics , environmental science , environmental engineering , economics , engineering , mathematics , demography , electrical engineering , algorithm , sociology , composite number
The decomposition quantitative model of household energy‐related carbon emission in Guangdong is established based on the extended Kaya identity with the Logarithmic Mean Divisia Index (LMDI) method; influence factors of household energy‐related carbon emission are decomposed into eight factors, energy price, average consumption propensity, and urban–rural structure of population included in the model. Results show that total direct household energy‐related carbon emission in Guangdong province show increasing trend from 1995 to 2012. Oil and electric power consumption are two main source of household carbon emission. Results of decomposition show that resident's living standard has the largest contribution to the increase of carbon emission, which is the first promoting factor to household energy‐related carbon emission, followed by energy use level. Energy price has the largest contribution to the reduction of carbon emission, which is the first inhibiting factor, followed by average consumption propensity. Guangdong can realize household energy‐related carbon mitigation effectively by the following four measures, namely (1) Adjust energy structure, especially energy structure of power generation, enhances the proportion of nuclear power, (2) Improve energy price mechanism, (3) Optimize urban–rural structure of population, (4) Drive application of home intelligent energy management technology, to realize automatic home energy‐saving control. © 2015 American Institute of Chemical Engineers Environ Prog, 35: 298–307, 2016