Coupling the driving forces of urban CO2 emission in Shanghai with logarithmic mean Divisia index method and Granger causality inference

2021 ◽  
Vol 298 ◽  
pp. 126843
Author(s):  
Yulong Luo ◽  
Weiliang Zeng ◽  
Xianbiao Hu ◽  
Hong Yang ◽  
Lin Shao
2012 ◽  
Vol 616-618 ◽  
pp. 1537-1540 ◽  
Author(s):  
Shuo Wang

This article uses Logarithmic Mean Divisia Index Method (LMDI) to analyze influence factors of emission in France during last 50 years, including energy use, GDP, carbon density, energy structure and population. Energy structure problem is proposed at the end of the article.


2015 ◽  
Vol 26 (1) ◽  
pp. 67-73 ◽  
Author(s):  
Ming Zhang ◽  
Shuang Dai ◽  
Yan Song

South Africa has become one of the most developing countries in the world, and its economic growth has occurred along with rising energy-related CO2 emission levels. A deeper understanding of the driving forces governing energy-related CO2 emissions is very important in formulating future policies. The LMDI (Log Mean Divisia Index) method is used to analyse the contribution of the factors which influence energy-related CO2 emissions in South Africa over the period 1993-2011. The main conclusions drawn from the present study may be summarized as follows: the energy intensity effect plays the dominant role in decreasing of CO2 emission, followed by fossil energy structure effect and renewable energy structure effect; the economic activity is a critical factor in the growth of energy-related CO2 emission in South Africa.


2019 ◽  
Vol 11 (18) ◽  
pp. 4929 ◽  
Author(s):  
Zou ◽  
Tang ◽  
Wu

In recent decades, the Beijing–Tianjin–Hebei (BTH) region has experienced rapid economic growth accompanied by increasing energy demands and CO2 emissions. Understanding the driving forces of CO2 emissions is necessary to develop effective policies for low-carbon economic development. However, because of differences in the socioeconomic systems within the BTH region, it is important to investigate the differences in the driving factors of CO2 emissions between Beijing, Tianjin, and Hebei. In this paper, we calculated the energy-related industrial CO2 emissions (EICE) in Beijing, Tianjin, and Hebei from 2006 to 2016. We then applied an extended LMDI (logarithmic mean Divisia index) method to determine the driving forces of EICE during different time periods and in different subregions within the BTH region. The results show that EICE increased and then decreased from 2006 to 2016 in the BTH region. In all subregions, energy intensity, industrial structure, and research and development (R&D) efficiency effect negatively affected EICE, whereas gross domestic product per capita effect and population had positive effects on EICE. However, R&D intensity and investment intensity had opposite effects in some parts of the BTH region; the effect of R&D intensity on EICE was positive in Beijing and Tianjin but negative in Hebei, while the effect of investment intensity was negative in Beijing but positive in Tianjin and Hebei. The findings of this study can contribute to the development of policies to reduce EICE in the BTH region.


2014 ◽  
Vol 5 (5-6) ◽  
pp. 579-586 ◽  
Author(s):  
Jianyi Lin ◽  
Yuan Liu ◽  
Yuanchao Hu ◽  
Shenghui Cui ◽  
Shengnan Zhao

Energy ◽  
2014 ◽  
Vol 67 ◽  
pp. 617-622 ◽  
Author(s):  
Wenwen Wang ◽  
Xiao Liu ◽  
Ming Zhang ◽  
Xuefeng Song

2018 ◽  
Vol 10 (1) ◽  
pp. 015909 ◽  
Author(s):  
Yu Hao ◽  
Lingou Wang ◽  
Weiyang Fan ◽  
Yaoyao Wei ◽  
Tong Wen ◽  
...  

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