Properties and linkages of some index decomposition analysis methods

Ang, B.W.; Huang, Hongye; Mu, Alexander

doi:10.1016/j.enpol.2009.06.017

Cited by 148 publications

(78 citation statements)

References 26 publications

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“…Ang [23] and Xu and Ang [24] summarized the key preliminaries for IDA. From the methodological viewpoint, there are two main strands of the IDA: techniques based on the Divisia index (type I) and techniques based on the Laspeyres index (type II) [25]. Originally, IDA has been extensively applied in the field of energy economics [26][27][28][29].…”

Section: Preliminaries For Index Decomposition Analysismentioning

confidence: 99%

Are the Changes in China’s Grain Production Sustainable: Extensive and Intensive Development by the LMDI Approach

Baležentis

Cao

et al. 2016

Sustainability

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Abstract:China has experienced an uninterrupted growth of grain output during the past decade. However, a long-term analysis indicates fluctuations in productivity and output levels, as well as dramatic shifts in grain crop mix and regional distribution. This paper, therefore, re-examines the major factors behind the dynamics in China's grain production over the period of 1978-2013. The Index Decomposition Analysis technique, facilitated by means of Logarithmic Mean Divisia Index, is employed to factorize the changes in China's grain output into four effects, i.e., yield effect, area effect, crop-mix effect and spatial distribution effect. The results show that yield effect, having been the major driver behind the growth, is experiencing a declining trend over time, with crop-mix effect gaining increasing importance. The results also indicate that changes in crop-mix caused an increase in the total grain output during 2003-2013, however this was due to abandonment of soybean farming, which is not sustainable in terms of self-sufficiency. The effect of spatial distribution has been diminishing ever since 1984. Therefore, re-allocation of areas sown is not likely to damper the sustainability of grain farming.

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Section: Preliminaries For Index Decomposition Analysismentioning

confidence: 99%

Are the Changes in China’s Grain Production Sustainable: Extensive and Intensive Development by the LMDI Approach

Baležentis

Cao

et al. 2016

Sustainability

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“…Ang et al [15] found that the Logarithmic Mean Divisia Index (LMDI) decomposition method has advantages of handling null, independent data paths compared with the Laspeyres Index Decomposition Method, and it was more applicable to empirical research. Therefore, the application of the LMDI method in the field of carbon emission measurement and decomposition was more extensive in recent years [16,17]. Many scholars also put forward many corresponding improved forms of LMDI on the basis of specific study objects [18][19][20].…”

Section: Literature Reviewmentioning

confidence: 99%

Effect of Population Structure Change on Carbon Emission in China

2016

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This paper expanded the Logarithmic Mean Divisia Index (LMDI) model through the introduction of urbanization, residents' consumption, and other factors, and decomposed carbon emission changes in China into carbon emission factor effect, energy intensity effect, consumption inhibitory factor effect, urbanization effect, residents' consumption effect, and population scale effect, and then explored contribution rates and action mechanisms of the above six factors on change in carbon emissions in China. Then, the effect of population structure change on carbon emission was analyzed by taking 2003-2012 as a sample period, and combining this with the panel data of 30 provinces in China. Results showed that in 2003-2012, total carbon emission increased by 4.2117 billion tons in China. The consumption inhibitory factor effect, urbanization effect, residents' consumption effect, and population scale effect promoted the increase in carbon emissions, and their contribution ratios were 27.44%, 12.700%, 74.96%, and 5.90%, respectively. However, the influence of carbon emission factor effect (´2.54%) and energy intensity effect (´18.46%) on carbon emissions were negative. Population urbanization has become the main population factor which affects carbon emission in China. The "Eastern aggregation" phenomenon caused the population scale effect in the eastern area to be significantly higher than in the central and western regions, but the contribution rate of its energy intensity effect (´11.10 million tons) was significantly smaller than in the central (´21.61 million tons) and western regions (´13.29 million tons), and the carbon emission factor effect in the central area (´3.33 million tons) was significantly higher than that in the eastern (´2.00 million tons) and western regions (´1.08 million tons). During the sample period, the change in population age structure, population education structure, and population occupation structure relieved growth of carbon emissions in China, but the effects of change of population, urban and rural structure, regional economic level, and population size generated increases in carbon emissions. Finally, the change of population sex structure had no significant influence on changes in carbon emissions.

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“…The Shapley value (Shapley, 1953;Ang, 2009) is defined as a marginal contribution of a factor of interest to the overall change in milk revenue:…”

Section: Methodsmentioning

confidence: 99%

“…To overcome the drawback, the Shapley decomposition was proposed (Albrecht, 2002;Ang, 2009). Indeed, it is due to P. De Boer (2009) that the Shapley index number can be traced back to Siegel index number.…”

Section: Introductionmentioning

confidence: 99%

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The factors of milk revenue change in Lithuania: index decomposition analysis based on the Shapley value

Baležentis

Kriščiukaitienė

2015

Management Theory and Studies for Rural Business and Infrastructure Development

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The paper aims to analyse the main driving forces behind the changes in milk revenue in Lithuania during 1993-2013 by the virtue of the index decomposition analysis. Specifically, an index decomposition model based upon Shapley value is introduced to account for dynamics in endogenous and exogenous factors underlying dynamics of the revenue. Particularly, the number of cows, average milk yield, market integration, milk composition, and milk price are considered as the main factors causing variation in milk revenue. The country level data are used for the analysis.Results show that such practices as animal recording and application of improved fodder have increased milk revenue by 67 million EUR and 75 million EUR during 1993-2004 and 2004-2013, respectively, due to increase in milk yield and fat.

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Properties and linkages of some index decomposition analysis methods

Cited by 148 publications

References 26 publications

Are the Changes in China’s Grain Production Sustainable: Extensive and Intensive Development by the LMDI Approach

Are the Changes in China’s Grain Production Sustainable: Extensive and Intensive Development by the LMDI Approach

Effect of Population Structure Change on Carbon Emission in China

The factors of milk revenue change in Lithuania: index decomposition analysis based on the Shapley value

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