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US residential energy demand and energy efficiency: A stochastic demand frontier approach

  • Filippini, Massimo
  • Hunt, Lester C.

This paper estimates a US frontier residential aggregate energy demand function using panel data for 48 ‘states’ over the period 1995 to 2007 using stochastic frontier analysis (SFA). Utilizing an econometric energy demand model, the (in)efficiency of each state is modeled and it is argued that this represents a measure of the inefficient use of residential energy in each state (i.e. ‘waste energy’). This underlying efficiency for the US is therefore observed for each state as well as the relative efficiency across the states. Moreover, the analysis suggests that energy intensity is not necessarily a good indicator of energy efficiency, whereas by controlling for a range of economic and other factors, the measure of energy efficiency obtained via this approach is. This is a novel approach to model residential energy demand and efficiency and it is arguably particularly relevant given current US energy policy discussions related to energy efficiency.

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Article provided by Elsevier in its journal Energy Economics.

Volume (Year): 34 (2012)
Issue (Month): 5 ()
Pages: 1484-1491

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Handle: RePEc:eee:eneeco:v:34:y:2012:i:5:p:1484-1491
Contact details of provider: Web page: http://www.elsevier.com/locate/eneco

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  1. 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.
  2. Jondrow, James & Knox Lovell, C. A. & Materov, Ivan S. & Schmidt, Peter, 1982. "On the estimation of technical inefficiency in the stochastic frontier production function model," Journal of Econometrics, Elsevier, vol. 19(2-3), pages 233-238, August.
  3. Mehdi Farsi & Massimo Filippini & Michael Kuenzle, 2005. "Unobserved heterogeneity in stochastic cost frontier models: an application to Swiss nursing homes," Applied Economics, Taylor & Francis Journals, vol. 37(18), pages 2127-2141.
  4. Baltagi, Badi H., 2006. "An Alternative Derivation Of Mundlak'S Fixed Effects Results Using System Estimation," Econometric Theory, Cambridge University Press, vol. 22(06), pages 1191-1194, December.
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  9. Lester C Hunt & Guy Judge & Yasushi Ninomiya, 2003. "Modelling Underlying Energy Demand Trends," Surrey Energy Economics Centre (SEEC), School of Economics Discussion Papers (SEEDS) 105, Surrey Energy Economics Centre (SEEC), School of Economics, University of Surrey.
  10. Hunt, L.C. & Judge, G. & Ninomiya, Y., 2000. "Underlying Trends and Seasonality in UK Energy Demands: A Sectorial Analysis," Papers 134, Portsmouth University - Department of Economics.
  11. Mundlak, Yair, 1978. "On the Pooling of Time Series and Cross Section Data," Econometrica, Econometric Society, vol. 46(1), pages 69-85, January.
  12. Massimo Filippini & Lester Hunt, 2009. "Energy demand and energy efficiency in the OECD countries: a stochastic demand frontier approach," CEPE Working paper series 09-68, CEPE Center for Energy Policy and Economics, ETH Zurich.
  13. Greene, William, 2005. "Reconsidering heterogeneity in panel data estimators of the stochastic frontier model," Journal of Econometrics, Elsevier, vol. 126(2), pages 269-303, June.
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  16. Mehdi Farsi & Massimo Filippini & Michael Kuenzle, 2006. "Cost Efficiency in Regional Bus Companies: An Application of Alternative Stochastic Frontier Models," Journal of Transport Economics and Policy, London School of Economics and University of Bath, vol. 40(1), pages 95-118, January.
  17. Adeyemi, Olutomi I. & Hunt, Lester C., 2007. "Modelling OECD industrial energy demand: Asymmetric price responses and energy-saving technical change," Energy Economics, Elsevier, vol. 29(4), pages 693-709, July.
  18. Banfi, Silvia & Filippini, Massimo & Hunt, Lester C., 2005. "Fuel tourism in border regions: The case of Switzerland," Energy Economics, Elsevier, vol. 27(5), pages 689-707, September.
  19. James M. Griffin & Craig T. Schulman, 2005. "Price Asymmetry in Energy Demand Models: A Proxy for Energy-Saving Technical Change?," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2), pages 1-22.
  20. Schmidt, Peter & Sickles, Robin C, 1984. "Production Frontiers and Panel Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 2(4), pages 367-74, October.
  21. Pitt, Mark M. & Lee, Lung-Fei, 1981. "The measurement and sources of technical inefficiency in the Indonesian weaving industry," Journal of Development Economics, Elsevier, vol. 9(1), pages 43-64, August.
  22. Buck, J. & Young, D., 2007. "The potential for energy efficiency gains in the Canadian commercial building sector: A stochastic frontier study," Energy, Elsevier, vol. 32(9), pages 1769-1780.
  23. Huntington, Hillard G., 1994. "Been top down so long it looks like bottom up to me," Energy Policy, Elsevier, vol. 22(10), pages 833-839, October.
  24. Zhou, P. & Ang, B.W., 2008. "Linear programming models for measuring economy-wide energy efficiency performance," Energy Policy, Elsevier, vol. 36(8), pages 2901-2906, August.
  25. Silvia Banfi & Massimo Filippini & Lester C. Hunt, 2003. "Fuel tourism in border regions," CEPE Working paper series 03-23, CEPE Center for Energy Policy and Economics, ETH Zurich.
  26. Joanne Evans & Lester C. Hunt (ed.), 2009. "International Handbook on the Economics of Energy," Books, Edward Elgar, number 12764.
  27. 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.
  28. Aigner, Dennis & Lovell, C. A. Knox & Schmidt, Peter, 1977. "Formulation and estimation of stochastic frontier production function models," Journal of Econometrics, Elsevier, vol. 6(1), pages 21-37, July.
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