Accounting frameworks for tracking energy efficiency trends

Ang, B.W.; Mu, Alexander; Zhou, P.

doi:10.1016/j.eneco.2010.03.011

Cited by 212 publications

(76 citation statements)

References 12 publications

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“…Its main advantage is that it does not have residual terms, which make it difficult to interpret the relative importance of compositional and efficiency effects. Specifically, Ang, Mu, and Zhou (2010) emphasize that perfect decomposition methods should be adopted in the case of crosscountry/region studies. In addition, as mentioned by Ang (2004;, Boyd and Roop (2004), and Ang and Liu (2003), these methods are also preferred in the case of two-factor decomposition due to their theoretical foundation and their adaptability, as well as the ease in interpreting their results.…”

Section: Decomposition Through the Fisher Ideal Indexmentioning

confidence: 99%

Energy intensity: A decomposition and counterfactual exercise for Latin American countries

Jimenez¹,

Mercado²

2014

Energy Economics

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. The opinions expressed in this publication are those of the authors and do not necessarily reflect the views of the Inter-American Development Bank, its Board of Directors, or the countries they represent. Terms of use: Documents inThe unauthorized commercial use of Bank documents is prohibited and may be punishable under the Bank's policies and/or applicable laws. Copyright ©Inter-American Development Bank. This working paper may be reproduced for any non-commercial purpose. It may also be reproduced in any academic journal indexed by the American Economic Association's EconLit, with previous consent by the Inter-American Development Bank (IDB), provided that the IDB is credited and that the author(s) receive no income from the publication. Abstract *This paper investigates trends in energy intensity in Latin American countries over the last 40 years. It applies the Fisher Ideal Index to decompose the energy intensity into the relative contributions of energy efficiency and the activity mix, and then analyzes the determinants of these energy indexes through panel data regression techniques. Finally, the paper compares the performance of Latin American countries to that of a similar set of countries chosen through the synthetic control method. The authors find that the energy intensity in Latin American countries has decreased about 20 percent, closing the gap with respect to its synthetic counterfactual. In both Latin American countries and its synthetic control, efficiency improvements drive these changes, while the activity mix component does not represent a clear source of change. The regression analysis shows that per capita income, petroleum prices, fuel-energy mix, and GDP growth are main determinants of energy intensity and efficiency, while there are no clear correlations with the activity component.2

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Section: Decomposition Through the Fisher Ideal Indexmentioning

confidence: 99%

Energy intensity: A decomposition and counterfactual exercise for Latin American countries

Jimenez¹,

Mercado²

2014

Energy Economics

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“…LMDI was first used in 1998 to study the factor decomposition for the CO 2 emissions of energy consumption from China's industrial sectors [9]. It has since broadened its scope to include analysis of energy supply and demand, energy-related emissions, material flow and dematerialization, monitoring of national energy efficiency trends and making cross-country comparisons of energy performance [10,11].…”

Section: Introductionmentioning

confidence: 99%

Decomposition Analysis of Energy-Related Industrial CO2 Emissions in China

2013

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Based on the logarithmic mean Divisia index (LMDI) approach, this paper presents a decomposition analysis of China's energy-related industrial CO 2 emissions from 1985 to 2007, as well as a comparative analysis of differential influences of various factors on six sectors. Via the decomposition, five categories of influencing factors are included: (1) Per capita GDP (PCG) was the largest positive driving factor for industrial CO 2 emissions growth for all sectors in China, with the largest cumulative contribution value; Population (P), economic structure (YS) and energy structure (ES) also played a positive driving role, but with weak contributions. As the only negative inhibiting factor, energy intensity (EI) significantly reduced the energy-related CO 2 emissions from industrial sectors. Meanwhile, CO 2 emissions reduction based on the efficiency of energy use still held a large space. (2) Various influencing factors imposed differential impacts on CO 2 emissions of six sectors.

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“…The evolution and advances in IDA applied in energy and emissions in particular, were examined in studies such as Ang et al (2010Ang et al ( , 2009Ang et al ( , 2003. Developments in the use of SDA in energy and emissions and comparisons between IDA and SDA are provided and thoroughly discussed in Su and Ang (2012).…”

Section: Related Literature and Discussionmentioning

confidence: 99%

A structural decomposition analysis of the pollution terms of trade

Papadas

Vlassis

2017

Journal of Environmental Economics and Policy

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The Antweiler Pollution Terms of Trade Index (PTTI) measures environmental gains or losses sustained by a country from international trade. Measuring total exports and imports in value terms distorts the results when the index is used for comparisons and analysis. Structural Decomposition Analysis (SDA) can provide an estimate of such distortions, due to price effects. This paper proposes a procedure of SDA of PTTI changes to deal with this problem. It measures impacts of changes in prices of traded outputs, changes in the real trade mix and volume, and changes in technology, but can be extended to account for other factors too. The pollution content refers here to pollution generated directly in the production of outputs, actually traded in the international markets. However, additional induced effects due to international trade can be included, if they are estimated. Pollution intensity is expressed per unit of total exports and imports respectively, and not per unit of value added, an issue discussed. A case study for The Netherlands is provided for the period [2007][2008][2009][2010]. PTTI values and their annual changes are estimated for emission contents in greenhouse CO2 equivalent. Results confirm the significant and distortive role of prices in measurements and comparisons.

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Accounting frameworks for tracking energy efficiency trends

Cited by 212 publications

References 12 publications

Energy intensity: A decomposition and counterfactual exercise for Latin American countries

Energy intensity: A decomposition and counterfactual exercise for Latin American countries

Decomposition Analysis of Energy-Related Industrial CO2 Emissions in China

A structural decomposition analysis of the pollution terms of trade

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