Decomposing Influencing Factors of Carbon Emissions in Liaoning Province for 1999–2009 with Logarithmic Mean Divisia Index Method

2013 ◽  
Vol 19 (4) ◽  
pp. 1213-1217 ◽  
Author(s):  
Qianbin Di ◽  
Jialu Wu ◽  
Jie Zhang
Keyword(s):  
Influencing Factors ◽  
Carbon Emissions ◽  
Liaoning Province ◽  
Logarithmic Mean ◽  
Index Method ◽  
Divisia Index

Analysis on the Influence Factors of CO2 Emission in France - Based on LMDI Approach

2012 ◽  
Vol 616-618 ◽  
pp. 1537-1540 ◽  
Author(s):  
Shuo Wang
Keyword(s):  
Energy Use ◽  
Influence Factors ◽  
Energy Structure ◽  
Carbon Density ◽  
Logarithmic Mean ◽  
Index Method ◽  
Divisia Index ◽  
Structure Problem

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.

Influencing Factors Analysis of Carbon Dioxide Emissions of Liaoning Power Consumption Based on Kaya Model

2013 ◽  
Vol 779-780 ◽  
pp. 1476-1481 ◽  
Author(s):  
Chang He Jiang ◽  
Wen Ru Zang ◽  
Lei Lei Hu
Keyword(s):  
Carbon Dioxide ◽  
Power Consumption ◽  
Influencing Factors ◽  
Carbon Emissions ◽  
Carbon Dioxide Emissions ◽  
Liaoning Province ◽  
Low Carbon ◽  
Low Carbon Economy ◽  
Carbon Reduction ◽  
Industry Investment

In order to achieve the carbon reduction targets committed by China, Liaoning Province must take the way of low-carbon economy. Based on the related Statistical Yearbook data, applying the IPCC carbon emissions equation and Kaya model, this paper analyses the influencing factors of the carbon dioxide emissions produced from the power consumption of Liaoning Province. and then put forward some suggestion on reducing carbon emissions. The results show that Liaoning Province can achieve the goal, but unit GDP CO2 emissions reduction is mainly due tothe investment expansion of the second industry and construction industry investment expansion. As a result, it puts forward some suggestions.

Factor decomposition of Chinese GHG emission intensity based on the Logarithmic Mean Divisia Index method

2014 ◽  
Vol 5 (5-6) ◽  
pp. 579-586 ◽  
Author(s):  
Jianyi Lin ◽  
Yuan Liu ◽  
Yuanchao Hu ◽  
Shenghui Cui ◽  
Shengnan Zhao
Keyword(s):  
Emission Intensity ◽  
Ghg Emission ◽  
Logarithmic Mean ◽  
Index Method ◽  
Divisia Index ◽  
Factor Decomposition

Using a new generalized LMDI (logarithmic mean Divisia index) method to analyze China's energy consumption

Energy ◽  
2014 ◽  
Vol 67 ◽  
pp. 617-622 ◽  
Author(s):  
Wenwen Wang ◽  
Xiao Liu ◽  
Ming Zhang ◽  
Xuefeng Song
Keyword(s):  
Energy Consumption ◽  
Logarithmic Mean ◽  
Index Method ◽  
Divisia Index

scholarly journals Characteristics, Decoupling Effect And Driving Factors of Regional Tourism's Carbon Emissions In China

2021 ◽  
Author(s):  
Guobao Xiong ◽  
Junhong Deng ◽  
Baogen Ding
Keyword(s):  
Economic Growth ◽  
Carbon Emissions ◽  
Carbon Emission ◽  
Energy Intensity ◽  
Driving Factors ◽  
Logarithmic Mean ◽  
Emission Data ◽  
Divisia Index ◽  
Its Regions ◽  
Decoupling Effect

Abstract Using the tourism's carbon emission data of 30 provinces (cities) in China from 2007 to 2019, we have established a logarithmic mean Divisia index (LMDI) model to identify the main driving factors of carbon emissions related to tourism and a Tapio decoupling model to analyze the decoupling relationship between tourism's carbon emissions and tourism-driven economic growth. Our analysis suggests that China's regional tourism's carbon emissions are growing significantly with marked differences across its regions. Although there are observed fluctuations in the decoupling relationship between regional tourism's carbon emissions and tourism-driven economic growth in China, the data suggest weak decoupling. Nonetheless, the degree of decoupling is rising to various extents across regions. Three of the five driving factors investigated are also found to affect on emissions. Both tourism scale and tourism consumption lead to the growth of tourism's carbon emissions, while energy intensity has a significant effect on reducing emissions. These effects differ across regions.

What determines China's electricity consumption? New evidence using the logarithmic mean Divisia index method

2018 ◽  
Vol 10 (1) ◽  
pp. 015909 ◽  
Author(s):  
Yu Hao ◽  
Lingou Wang ◽  
Weiyang Fan ◽  
Yaoyao Wei ◽  
Tong Wen ◽  
...  
Keyword(s):  
Electricity Consumption ◽  
Logarithmic Mean ◽  
Index Method ◽  
Divisia Index ◽  
New Evidence
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