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Weather Factors and Performance of Network Utilities: A Methodology and Application to Electricity Distribution

  • Jamasb, T.
  • Orea, L.
  • Pollitt, M.G.

Incentive regulation and efficiency analysis of network utilities often need to take the effect of important external factors, such as the weather conditions, into account. This paper presents a method for estimating the effect of weather conditions on the costs of electricity distribution networks using parametric techniques. It examines whether the use of popular statistical variable reduction techniques is conceptually and econometrically sound for analyzing the effect of weather on the network costs. In this paper we estimate cost functions with the whole set of weather variables, identifying, when necessary, a subset of variables that can accurately reflect the effects of weather conditions. We show that weather conditions significantly affect distribution costs and the absence of weather variables has a downward biased impact on the effect of quality on costs. Also, the performance of statistical weather composites to capture this effect is poor. Finally, we show that there is a distinction between the effects of persistent and time varying weather conditions.

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File URL: http://www.econ.cam.ac.uk/research/repec/cam/pdf/cwpe1042.pdf
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Paper provided by Faculty of Economics, University of Cambridge in its series Cambridge Working Papers in Economics with number 1042.

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Date of creation: 22 Sep 2010
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Handle: RePEc:cam:camdae:1042
Contact details of provider: Web page: http://www.econ.cam.ac.uk/index.htm

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  1. Battese, G E & Coelli, T J, 1995. "A Model for Technical Inefficiency Effects in a Stochastic Frontier Production Function for Panel Data," Empirical Economics, Springer, vol. 20(2), pages 325-32.
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  5. Lewin, Arie Y & Morey, Richard C & Cook, Thomas J, 1982. "Evaluating the administrative efficiency of courts," Omega, Elsevier, vol. 10(4), pages 401-411.
  6. M Arellano & O Bover, 1990. "Another Look at the Instrumental Variable Estimation of Error-Components Models," CEP Discussion Papers dp0007, Centre for Economic Performance, LSE.
  7. Kim, Moshe, 1986. "Banking technology and the existence of a consistent output aggregate," Journal of Monetary Economics, Elsevier, vol. 18(2), pages 181-195, September.
  8. Hung-jen Wang & Peter Schmidt, 2002. "One-Step and Two-Step Estimation of the Effects of Exogenous Variables on Technical Efficiency Levels," Journal of Productivity Analysis, Springer, vol. 18(2), pages 129-144, September.
  9. Jenkins, Larry & Anderson, Murray, 2003. "A multivariate statistical approach to reducing the number of variables in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 147(1), pages 51-61, May.
  10. Yu, William & Jamasb, Tooraj & Pollitt, Michael, 2009. "Does weather explain cost and quality performance? An analysis of UK electricity distribution companies," Energy Policy, Elsevier, vol. 37(11), pages 4177-4188, November.
  11. William Yu & Tooraj Jamasb & Michael Pollitt, 2009. "Willingness-to-Pay for Quality of Service: An Application to Efficiency Analysis of the UK Electricity Distribution Utilities," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4), pages 1-48.
  12. Wagner, Janet M. & Shimshak, Daniel G., 2007. "Stepwise selection of variables in data envelopment analysis: Procedures and managerial perspectives," European Journal of Operational Research, Elsevier, vol. 180(1), pages 57-67, July.
  13. repec:cup:cbooks:9780521623940 is not listed on IDEAS
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