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Measuring residential energy efficiency improvements with DEA

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  • Peter Grösche

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Abstract

This paper measures energy efficiency improvements of US single-family homes between 1997 and 2001 using a two-stage procedure. In the first stage, an indicator of energy efficiency is derived by means of Data Envelopment Analysis (DEA),and the analogy between theDEAestimator and traditional measures of energy efficiency is demonstrated. The second stage employs a bootstrapped truncated regression technique to decompose the variation in the obtained efficiency estimates into a climatic component and factors attributed to efficiency improvements. Results indicate a small but significant improvement of energy efficiency over the studied time interval, mainly accounted for by fuel oil and natural gas users.

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File URL: http://hdl.handle.net/10.1007/s11123-008-0121-7
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Bibliographic Info

Article provided by Springer in its journal Journal of Productivity Analysis.

Volume (Year): 31 (2009)
Issue (Month): 2 (April)
Pages: 87-94

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Handle: RePEc:kap:jproda:v:31:y:2009:i:2:p:87-94

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Web page: http://www.springerlink.com/link.asp?id=100296

Related research

Keywords: Energy efficiency; Household production; Data envelopment analysis; Bootstrap; C14; C61; D13; Q4;

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  1. Phylipsen, G. J. M. & Blok, K. & Worrell, E., 1997. "International comparisons of energy efficiency-Methodologies for the manufacturing industry," Energy Policy, Elsevier, vol. 25(7-9), pages 715-725.
  2. Tulkens, H. & Vanden Eeckaut, P., . "Non-parametric efficiency, progress and regress measures for panel data: Methodological aspects," CORE Discussion Papers RP -1132, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  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. Mukherjee, Kankana, 2008. "Energy use efficiency in U.S. manufacturing: A nonparametric analysis," Energy Economics, Elsevier, vol. 30(1), pages 76-96, January.
  5. G.D. Ferrier & J. G. Hirschberg, 1992. "Climate Control Efficiency," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1), pages 37-54.
  6. Seiford, Lawrence M. & Thrall, Robert M., 1990. "Recent developments in DEA : The mathematical programming approach to frontier analysis," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 7-38.
  7. Manuel Frondel & Christoph M. Schmidt, 2008. "Measuring Energy Security – A Conceptual Note," Ruhr Economic Papers 0052, Rheinisch-Westfälisches Institut für Wirtschaftsforschung, Ruhr-Universität Bochum, Universität Dortmund, Universität Duisburg-Essen.
  8. Simar, Leopold & Wilson, Paul W., 2007. "Estimation and inference in two-stage, semi-parametric models of production processes," Journal of Econometrics, Elsevier, vol. 136(1), pages 31-64, January.
  9. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
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Cited by:
  1. Peter Grösche, 2009. "Housing, Energy Cost, and the Poor – Counteracting Effects in Germany’s Housing Allowance Program," Ruhr Economic Papers 0110, Rheinisch-Westfälisches Institut für Wirtschaftsforschung, Ruhr-Universität Bochum, Universität Dortmund, Universität Duisburg-Essen.
  2. Darold Barnum & John Gleason, 2011. "Measuring efficiency under fixed proportion technologies," Journal of Productivity Analysis, Springer, vol. 35(3), pages 243-262, June.
  3. Sabuj Kumar Mandal & S Madheswaran, 2009. "Measuring Energy Use Efficiency in Presence of Undesirable Output: An Application of Data Envelopment Analysis (DEA) to Indian Cement Industry," Working Papers 235, Institute for Social and Economic Change, Bangalore.
  4. Zhou, Xianbo & Li, Kui-Wai & Li, Qin, 2011. "An analysis on technical efficiency in post-reform China," China Economic Review, Elsevier, vol. 22(3), pages 357-372, September.

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