Abstract
The electric power sector is the largest contributor of CO2 emissions in China. With an increasing concern about environment problems, it is essential to identify key factors that affect CO2 emissions from China's electric power industry so as to help the fossil fuel-based country reduce carbon emissions. For this purpose, the two-phase Logarithmic Mean Division Index (LMDI) decomposition method is presented in this paper. Covering the whole industry chain including power generation, transmission, and consumption, the two-phase LMDI decomposition model is constructed. Then, the influencing aspects are decomposed into ten driving factors, namely, (1) fossil energy power generation structure, (2) fossil energy consumption coefficient, (3) thermal power proportion, (4) power generation and consumption ratio, (5) transmission and distribution loss, (6) industrial power consumption intensity, (7) industrial structure, (8) per capita gross domestic product (GDP), (9) total population, and (10) resident power consumption intensity. Based on data from China statistical yearbook, China energy statistics yearbook, and China power statistics yearbook (2005–2017 edition), the decomposition calculation results show that the power generation and consumption ratio, industrial structure, resident power consumption intensity, per capita GDP, population size, and transmission and distribution loss factors are positive driving factors with contributions of 1.2%, 2.47%, 1.5%, 94.29%, 5.43%, and 4.64%, respectively. However, the fossil energy power generation structure, fossil energy consumption coefficient, thermal power proportion, and industrial power consumption intensity are negative driving factors with contribution rates of −0.34%, −21.72%, −9.85%, and −8.44%, respectively. According to the main effect factors identified, some corresponding measures are proposed to reduce carbon emissions from China's power industry.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
