Journal ArticleDOI
Factors that influence carbon emissions due to energy consumption in China: Decomposition analysis using LMDI
TLDR
In this article, the authors analyzed factors that influence carbon emissions due to fossil energy consumption in China to identify key factors for policies promoting carbon emission reductions and highlighted the policy implications in terms of industrial structure and energy consumption.About:
This article is published in Applied Energy.The article was published on 2014-08-15. It has received 407 citations till now. The article focuses on the topics: Energy intensity & Energy consumption.read more
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Urbanisation, energy consumption, and carbon dioxide emissions in China: A panel data analysis of China’s provinces
TL;DR: In this article, the authors investigated the relationship between urbanisation, energy consumption, and CO 2 emissions over the period 1995-2011, using a panel data model, based on the data for 30 Chinese provinces.
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Examining the impacts of socioeconomic factors, urban form, and transportation networks on CO2 emissions in China’s megacities
TL;DR: In this article, the authors examined the combined impacts of socioeconomic and spatial planning factors on CO2 emissions in cities that have experienced rapid urbanization, using an econometric model and a comprehensive panel dataset incorporating socioeconomic, urban form, and transportation factors for four Chinese megacities (Beijing, Tianjin, Shanghai and Guangzhou) in the period 1990-2010.
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Impact of urbanization on energy related CO2 emission at different development levels: Regional difference in China based on panel estimation
TL;DR: In this article, the influence of urbanization on CO2 emission at different development levels employing regression on population, affluence and technology (STIRPAT) model on provincial panel data from 1995 to 2013 for China.
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Changing urban forms and carbon dioxide emissions in China: A case study of 30 provincial capital cities
TL;DR: In this article, the effects of urban form on CO 2 emissions were quantified using panel data for China's 30 provincial capital cities from 1990 to 2010, and the results of the study demonstrated a positive correlation between the growth of urban areas and CO 2 emission levels.
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Can a carbon trading system promote the transformation of a low-carbon economy under the framework of the porter hypothesis? —Empirical analysis based on the PSM-DID method
TL;DR: Wang et al. as discussed by the authors used the panel data from 2000 to 2016 of 30 provincial-level administrative regions in China and adopted the Propensity Score Matching-Difference in Differences method to test the impact of China's carbon trading pilot system on the transformation of a low carbon economy.
References
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Decomposition analysis for policymaking in energy:: which is the preferred method?
TL;DR: There is a need to have a common understanding among practitioners and consistency on the choice of decomposition methods in empirical studies, and to address the above-mentioned issues and provide recommendations.
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The LMDI approach to decomposition analysis: a practical guide
TL;DR: In this paper, the authors compared various index decomposition analysis methods and concluded that the logarithmic mean divisia index method is the preferred method for most potential users, providing a practical guide that includes the general formulation process, summary tables for easy reference and examples.
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A survey of index decomposition analysis in energy and environmental studies
B.W. Ang,Fuqiang Zhang +1 more
TL;DR: Index decomposition methodology was a technique first used in the late 1970s to study the impact of changes in product mix on industrial energy demand and has been increasingly used in energy-related environmental analysis.
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Comparing structural decomposition analysis and index
TL;DR: In this paper, two techniques for decomposing indicator changes at the sector level are structural decomposition analysis (SDA) and index decomposition analyses (IDA), which are used to analyze changes in indicators such as energy use, CO2-emissions, labor demand and value added.
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Factorizing changes in energy and environmental indicators through decomposition
TL;DR: A decomposition method for factorizing changes in energy demand or gas emissions over time with the advantage of giving perfect decomposition is introduced and may be generally applied in energy and environmental decomposition studies.