Changes in energy-related carbon dioxide emissions of the agricultural sector in China from 2005 to 2013. (October 2018)

• Record Type: Journal Article
• Authors: Chen, Jiandong; Cheng, Shulei; Song, Malin
• Is Part Of: Renewable & sustainable energy reviews. Volume 94(2018)
• Page Start: 748
• Page End: 761
• Publication Date: 2018-10
• Subjects: Agricultural sector -- Energy-related CO2 emissions -- Extended LMDI decomposition -- Rural-urban Gini coefficient -- Chain decomposition; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.rser.2018.06.050
• Languages: English
• ISSNs: 1364-0321

Abstract

Abstract: This study analyses the changes in energy-related carbon dioxide (CO2 ) emissions of the agricultural sector in China from 2005 to 2013. Using the logarithmic mean Divisia index (LMDI) decomposition method, this study attributes the changes in agricultural CO2 emissions to agricultural CO2 emissions intensity, agricultural productive income intensity, rural residents' income structure, the distribution pattern of residential income, the distribution pattern of national income, economic development, provincial population distribution, and population scale, and treats these factors as technology, distribution, and population effects. Based on this, the nested decomposition problem, which has not been mentioned in related studies, is solved. To emphasize the importance of the logarithmic mean weight functions, two different chain LMDI decomposition methods are developed that are based on differences in the logarithmic mean weight functions. The results show that the distribution pattern of national income and rural residents' income structure are two key factors that separately stimulate and suppress the changes in China's agricultural energy-related CO2 emissions. After nested decomposition of the distribution pattern of residential income, the suppressing influence from the rural population proportion is stronger than the stimulating influence from rural-urban income inequity. Although the results of the two chain LMDI decomposition methods are similar, only the distribution pattern of national income and rural residents' income structure maintain positive impacts on the changes in China's agricultural CO2 emissions by year, while the rural residents' income structure, distribution pattern of residential income, and rural population proportion continue to have negative impacts on changes in China's agricultural CO2 emissions by year. Furthermore, the technology, distribution, and population effects could not suppress China's agricultural CO2 emissions simultaneously in most years. Highlights: We develop two chain logarithmic mean Divisia index decomposition methods. We introduce the rural–urban income Gini coefficient into these methods. We decompose the changes of CO2 emissions from China's agricultural sector. We examine the changes in agricultural CO2 emissions based on eight factors. We assess the impact of technology-, distribution-, and population-related effects.