An Overview for Decomposition of Industry Energy Consumption

Liu, Chun-Chu

doi:10.3844/ajassp.2005.1166.1168

Cited by 6 publications

(5 citation statements)

References 12 publications

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“…Boyd et al (1987) propose another arithmetic mean Divisia index (AMDI) decomposition approach and use it to analyse the industrial energy consumption in the USA. Liu et al (1992) further put forward the adaptive weighting Divisia index (AWDI) decomposition approach, which becomes popular in the 1990s, see Greening et al (1998), Fisher‐Vanden et al (2004, 2006), Liu (2006) and Fan et al (2007).…”

Section: Literature Reviewmentioning

confidence: 99%

The Abatement of Carbon Dioxide Intensity in China: Factors Decomposition and Policy Implications

Chen

2011

World Economy

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To deal with the challenges brought about by climate changes, China decided to reduce the carbon dioxide (CO2) intensity by 40–45 per cent from 2005 to 2020. Using the decomposition technique, this study finds that energy intensity is the most important force to drive the decline of CO2 intensity between 1980 and 2008, followed by structural factors such as the adjustment of energy and industrial structure. Therefore, any environmental policy to promote energy efficiency will play the biggest role in achieving the target of abating CO2 intensity in 2020.

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Section: Literature Reviewmentioning

confidence: 99%

The Abatement of Carbon Dioxide Intensity in China: Factors Decomposition and Policy Implications

Chen

2011

World Economy

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“…The broad technique of decomposition analysis undertaken here is often known as IDA, due to the advantage of its simplicity, the availability of statistical data, and the ease of historical comparison. There are a number of different methods available within IDA, and the Log Mean Divisia Index method I (LMDI I) is perfect in decomposition, having no residual term, which is used here in decomposition carbon emissions due to the adaptability and ease of use [4,5]. Using LMDI I method, many studies were carried out on carbon emissions from China in the last decade.…”

Section: Decomposition Carbon Emissionsmentioning

confidence: 99%

Decomposition and Decoupling Analysis of Energy-Related Carbon Emissions from China Manufacturing

Liu

Kong

2015

Mathematical Problems in Engineering

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The energy-related carbon emissions of China's manufacturing increased rapidly, from 36988.97 × 10 4 tC in 1996 to 74923.45 × 10 4 tC in 2012. To explore the factors to the change of the energy-related carbon emissions from manufacturing sector and the decoupling relationship between energy-related carbon emissions and economic growth, the empirical research was carried out based on the LMDI method and Tapio decoupling model. We found that the production scale contributed the most to the increase of the total carbon emissions, while the energy intensity was the most inhibiting factor. And the effects of the intrastructure and fuel mix on the change of carbon emissions were relatively weak. At a disaggregative level within manufacturing sector, EI subsector had a greater impact on the change of the total carbon emissions, with much more potentiality of energy conservation and emission reduction. Weak decoupling of manufacturing sector carbon emissions from GDP could be observed in the manufacturing sector and EI subsector, while strong decoupling state appeared in NEI subsector. Several advices were put forward, such as adjusting the fuel structure and optimizing the intrastructure and continuing to improve the energy intensity to realize the manufacturing sustainable development in low carbon pattern.

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“…The authors also notice that the new formula is more complex than the other commonly adopted IDA approaches. Liu (2006) did a comparison analysis for methodologies related with the decomposition approach based on energy consumption. The author considered the size of residual term as a comparison tool and he concluded the adaptive weighting Divisia index method and simple average Divisia index method represent most robust and smallest residual term.…”

Section: Literature Reviewmentioning

confidence: 99%

Sectoral Decomposition of CO2 Emissions in China and India for the Period 1980-2010

Rüstemoğlu

Uğural

2013

International Conference on Eurasian Economies

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Increasing amount of CO2 emissions and global warming is one of the hottest topics of world’s agenda. At the same time there exists a public awareness about this important chapter. A lot of researcher proved that in order to live in a sustainable world, necessary regulations should be done and CO2 emissions should be reduced immediately. For this study our aim is to decompose the CO2 emissions of the world’s new super powers, China and India, over 1980-2010. In order to see the accelerating and decelerating factors for CO2 emissions, the Refined Laspeyres Index used as a method. Consistent data gathered from the official web sites of the World Bank and United Nations. Five main sectors, agriculture, manufacturing, construction, transportation and other services are used. Four different impacts, scale effect, composition effect, energy intensity effect and carbon intensity effect have been discussed to see the increasing or decreasing factors of CO2 emissions. The results were interesting. The dominant impacts were the scale effect and energy intensity effect. The minor impacts were composition effect and carbon intensity effect. Fuel switching, efficient energy use and increasing usage of renewable resources are efficient tools to reduce the emissions.

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An Overview for Decomposition of Industry Energy Consumption

Cited by 6 publications

References 12 publications

The Abatement of Carbon Dioxide Intensity in China: Factors Decomposition and Policy Implications

The Abatement of Carbon Dioxide Intensity in China: Factors Decomposition and Policy Implications

Decomposition and Decoupling Analysis of Energy-Related Carbon Emissions from China Manufacturing

Sectoral Decomposition of CO2 Emissions in China and India for the Period 1980-2010

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