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Accounting frameworks for tracking energy efficiency trends

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  • Ang, B.W.
  • Mu, A.R.
  • Zhou, P.

Abstract

Many differences can be found among the existing accounting systems for tracking economy-wide energy efficiency trends. There is a need for greater uniformity in the design and application of such systems but a formal study does not exist. This paper seeks to fill some of the gaps. It begins by introducing the basic concepts, indicators and terminology in this study area. This is followed by a review of the existing economy-wide energy efficiency accounting systems with a focus on the analytical framework. The merit of having a precise and meaningful relationship between two basic energy indicators, the energy efficiency index and the energy savings due to efficiency improvement, is elaborated. An accounting framework based on the LMDI decomposition technique which possesses a number of desirable properties is proposed. Numerical examples are presented to highlight these properties and show the differences among the various accounting frameworks. Several methodological and application issues are discussed, and the study concludes with key findings and recommendations.

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Bibliographic Info

Article provided by Elsevier in its journal Energy Economics.

Volume (Year): 32 (2010)
Issue (Month): 5 (September)
Pages: 1209-1219

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Handle: RePEc:eee:eneeco:v:32:y:2010:i:5:p:1209-1219

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Web page: http://www.elsevier.com/locate/eneco

Related research

Keywords: Energy performance index Energy efficiency Energy intensity Index decomposition analysis LMDI;

References

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  1. Ang, B. W., 2005. "The LMDI approach to decomposition analysis: a practical guide," Energy Policy, Elsevier, vol. 33(7), pages 867-871, May.
  2. Ang, B.W. & Huang, H.C. & Mu, A.R., 2009. "Properties and linkages of some index decomposition analysis methods," Energy Policy, Elsevier, vol. 37(11), pages 4624-4632, November.
  3. Ang, B.W., 2006. "Monitoring changes in economy-wide energy efficiency: From energy-GDP ratio to composite efficiency index," Energy Policy, Elsevier, vol. 34(5), pages 574-582, March.
  4. Ang, B.W. & Liu, F.L. & Chung, Hyun-Sik, 2004. "A generalized Fisher index approach to energy decomposition analysis," Energy Economics, Elsevier, vol. 26(5), pages 757-763, September.
  5. Ang, B. W., 2004. "Decomposition analysis for policymaking in energy:: which is the preferred method?," Energy Policy, Elsevier, vol. 32(9), pages 1131-1139, June.
  6. Gale A. Boyd and Joseph M. Roop, 2004. "A Note on the Fisher Ideal Index Decomposition for Structural Change in Energy Intensity," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1), pages 87-102.
  7. Liu, F. L. & Ang, B. W., 2003. "Eight methods for decomposing the aggregate energy-intensity of industry," Applied Energy, Elsevier, vol. 76(1-3), pages 15-23, September.
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Citations

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Cited by:
  1. Ang, B.W. & Xu, X.Y., 2013. "Tracking industrial energy efficiency trends using index decomposition analysis," Energy Economics, Elsevier, vol. 40(C), pages 1014-1021.
  2. Ebrahim Hosseini Nasab & Robab Aalami & Shermineh Foroughi Dahr & Mohammad Amin Sadeghzadeh, 2012. "An Analysis of Energy Consumption in Transportation and Industrial Sectors- a Multiplicative LMDI Approach with Application to Iran," Iranian Economic Review, Economics faculty of Tehran university, vol. 17(2), pages 1-17, spring.
  3. Chung, William & Kam, M.S. & Ip, C.Y., 2011. "A study of residential energy use in Hong Kong by decomposition analysis, 1990–2007," Applied Energy, Elsevier, vol. 88(12), pages 5180-5187.
  4. Wang, H. & Zhou, P. & Zhou, D.Q., 2013. "Scenario-based energy efficiency and productivity in China: A non-radial directional distance function analysis," Energy Economics, Elsevier, vol. 40(C), pages 795-803.
  5. Wang, H. & Zhou, P. & Zhou, D.Q., 2012. "An empirical study of direct rebound effect for passenger transport in urban China," Energy Economics, Elsevier, vol. 34(2), pages 452-460.
  6. Hasanbeigi, Ali & Price, Lynn & Fino-Chen, Cecilia & Lu, Hongyou & Ke, Jing, 2013. "Retrospective and prospective decomposition analysis of Chinese manufacturing energy use and policy implications," Energy Policy, Elsevier, vol. 63(C), pages 562-574.
  7. Bian, Yiwen & He, Ping & Xu, Hao, 2013. "Estimation of potential energy saving and carbon dioxide emission reduction in China based on an extended non-radial DEA approach," Energy Policy, Elsevier, vol. 63(C), pages 962-971.
  8. Su, Bin & Ang, B.W., 2012. "Structural decomposition analysis applied to energy and emissions: Some methodological developments," Energy Economics, Elsevier, vol. 34(1), pages 177-188.
  9. Baležentis, Alvydas & Baležentis, Tomas & Streimikiene, Dalia, 2011. "The energy intensity in Lithuania during 1995–2009: A LMDI approach," Energy Policy, Elsevier, vol. 39(11), pages 7322-7334.
  10. Tan, Zhongfu & Li, Li & Wang, Jianjun & Wang, Jianhui, 2011. "Examining the driving forces for improving China’s CO2 emission intensity using the decomposing method," Applied Energy, Elsevier, vol. 88(12), pages 4496-4504.
  11. Choi, Ki-Hong & Ang, B.W., 2012. "Attribution of changes in Divisia real energy intensity index — An extension to index decomposition analysis," Energy Economics, Elsevier, vol. 34(1), pages 171-176.
  12. Choi, Ki-Hong & Oh, Wankeun, 2014. "Extended Divisia index decomposition of changes in energy intensity: A case of Korean manufacturing industry," Energy Policy, Elsevier, vol. 65(C), pages 275-283.
  13. Lin, Boqiang & Du, Kerui, 2013. "Technology gap and China's regional energy efficiency: A parametric metafrontier approach," Energy Economics, Elsevier, vol. 40(C), pages 529-536.

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