The Energy Journal · 1997 · 564 citations · 13 references
Energy ConsumptionEconomicsEngineeringIndustrial EngineeringEnergy EfficiencyEco-efficiencyEmission ControlAggregate EnergyDivisia Index MethodEconomic AnalysisBusinessIndustrial EmissionAir PollutionGas Emission IntensitiesEmission ReductionSustainable ProductionKorean IndustryStatistics
Several methods for decomposing energy consumption or energy‑induced gas emissions in industry have been proposed, but common issues include residuals after decomposition and handling zero values in datasets. The study aims to modify the Divisia index decomposition method by replacing its arithmetic mean weight function with a logarithmic one to eliminate residuals and address zero‑value data. The refined Divisia index, using a logarithmic weight function, is applied to Korean industry data to illustrate its properties. The refined method achieves perfect decomposition with no residual and yields converging results when zero values are replaced by a small number.
Several methods for decomposing energy consumption or energy-induced gas emissions in industry have been proposed by various analysts. Two commonly encountered problems in the application of these methods are the existence of a residual after decomposition and the handling of the value zero In the data set. To overcome these two problems, we modify the often used Divisia index decomposition method by replacing the arithmetic mean weight function by a logarithmic one. This refined Divisia index method can be shown to give perfect decomposition with no residual. It also gives converging decomposition results when the zero values in the data set are replaced by a sufficiently small number. The properties of the method are highlighted using the data of the Korean industry.
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The Ideal Log-Change Index Number
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