IDEAS home Printed from https://ideas.repec.org/p/cam/camdae/1042.html
   My bibliography  Save this paper

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

Author

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

Abstract

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.

Suggested Citation

  • Jamasb, T. & Orea, L. & Pollitt, M.G., 2010. "Weather Factors and Performance of Network Utilities: A Methodology and Application to Electricity Distribution," Cambridge Working Papers in Economics 1042, Faculty of Economics, University of Cambridge.
  • Handle: RePEc:cam:camdae:1042
    as

    Download full text from publisher

    File URL: http://www.econ.cam.ac.uk/research-files/repec/cam/pdf/cwpe1042.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. P. Nillesen & M. Pollitt, 2010. "Using Regulatory Benchmarking Techniques to Set Company Performance Targets: The Case of Us Electricity," Competition and Regulation in Network Industries, Intersentia, vol. 11(1), pages 50-85, March.
    4. Arellano, Manuel & Bover, Olympia, 1995. "Another look at the instrumental variable estimation of error-components models," Journal of Econometrics, Elsevier, vol. 68(1), pages 29-51, July.
    5. Adler, Nicole & Yazhemsky, Ekaterina, 2010. "Improving discrimination in data envelopment analysis: PCA-DEA or variable reduction," European Journal of Operational Research, Elsevier, vol. 202(1), pages 273-284, April.
    6. repec:cup:cbooks:9780521623940 is not listed on IDEAS
    7. 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.
    8. 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.
    9. 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.
    10. 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 325-332.
    11. Lewin, Arie Y & Morey, Richard C & Cook, Thomas J, 1982. "Evaluating the administrative efficiency of courts," Omega, Elsevier, vol. 10(4), pages 401-411.
    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. Denny, Michael & Fuss, Melvyn A, 1977. "The Use of Approximation Analysis to Test for Separability and the Existence of Consistent Aggregates," American Economic Review, American Economic Association, vol. 67(3), pages 404-418, June.
    14. Zhu, Joe, 1998. "Data envelopment analysis vs. principal component analysis: An illustrative study of economic performance of Chinese cities," European Journal of Operational Research, Elsevier, vol. 111(1), pages 50-61, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Rahmatallah Poudineh & Tooraj Jamasb, 2013. "Investment and Efficiency under Incentive Regulation: The Case of the Norwegian Electricity Distribution Networks," Cambridge Working Papers in Economics 1310, Faculty of Economics, University of Cambridge.
    2. Karim L. Anaya & Michael G. Pollitt, 2014. "Does Weather Have an Impact on Electricity Distribution Efficiency? Evidence from South America," Cambridge Working Papers in Economics 1424, Faculty of Economics, University of Cambridge.
    3. Andaluz-Alcazar, Alvaro, 2012. "Choix d'investissement sous incertitude des gestionnaires des réseaux de distribution (GRD) en Europe à l'horizon 2030," Economics Thesis from University Paris Dauphine, Paris Dauphine University, number 123456789/10862 edited by Keppler, Jan Horst, July.
    4. Greene, William & Orea, Luis & Wall, Alan, 2011. "A one-stage random effect counterpart of the fixed-effect vector decomposition model with an application to UK electricity distribution utilities," Efficiency Series Papers 2011/01, University of Oviedo, Department of Economics, Oviedo Efficiency Group (OEG).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Nataraja, Niranjan R. & Johnson, Andrew L., 2011. "Guidelines for using variable selection techniques in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 215(3), pages 662-669, December.
    2. Orea, Luis & Growitsch, Christian & Jamasb, Tooraj, 2012. "Using Supervised Environmental Composites in Production and Efficiency Analyses: An Application to Norwegian Electricity Networks," Efficiency Series Papers 2012/04, University of Oviedo, Department of Economics, Oviedo Efficiency Group (OEG).
    3. Jamal Ouenniche & Skarleth Carrales, 2018. "Assessing efficiency profiles of UK commercial banks: a DEA analysis with regression-based feedback," Annals of Operations Research, Springer, vol. 266(1), pages 551-587, July.
    4. Eskelinen, Juha, 2017. "Comparison of variable selection techniques for data envelopment analysis in a retail bank," European Journal of Operational Research, Elsevier, vol. 259(2), pages 778-788.
    5. Anaya, Karim L. & Pollitt, Michael G., 2017. "Using stochastic frontier analysis to measure the impact of weather on the efficiency of electricity distribution businesses in developing economies," European Journal of Operational Research, Elsevier, vol. 263(3), pages 1078-1094.
    6. Peter Fernandes Wanke & Rebecca de Mattos, 2014. "Capacity Issues and Efficiency Drivers in Brazilian Bulk Terminals," Brazilian Business Review, Fucape Business School, vol. 11(5), pages 72-98, October.
    7. Peyrache, Antonio & Rose, Christiern & Sicilia, Gabriela, 2020. "Variable selection in Data Envelopment Analysis," European Journal of Operational Research, Elsevier, vol. 282(2), pages 644-659.
    8. Karim L. Anaya & Michael G. Pollitt, 2014. "Does Weather Have an Impact on Electricity Distribution Efficiency? Evidence from South America," Working Papers EPRG 1404, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge.
    9. Llorca, Manuel & Orea, Luis & Pollitt, Michael G., 2016. "Efficiency and environmental factors in the US electricity transmission industry," Energy Economics, Elsevier, vol. 55(C), pages 234-246.
    10. Llorca, Manuel & Orea, Luis & Pollitt, Michael G., 2014. "Using the latent class approach to cluster firms in benchmarking: An application to the US electricity transmission industry," Operations Research Perspectives, Elsevier, vol. 1(1), pages 6-17.
    11. Kyuseok Lee & Kyuwan Choi, 2010. "Cross redundancy and sensitivity in DEA models," Journal of Productivity Analysis, Springer, vol. 34(2), pages 151-165, October.
    12. Saastamoinen, Antti & Kuosmanen, Timo, 2016. "Quality frontier of electricity distribution: Supply security, best practices, and underground cabling in Finland," Energy Economics, Elsevier, vol. 53(C), pages 281-292.
    13. Qiwei Xie & Yuanyuan Li & Lizheng Wang & Chao Liu, 2018. "Improving discrimination in data envelopment analysis without losing information based on Renyi’s entropy," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 26(4), pages 1053-1068, December.
    14. Jamasb, Tooraj & Llorca, Manuel & Khetrapal, Pavan & Thakur, Tripta, 2021. "Institutions and performance of regulated firms: Evidence from electricity distribution in India," Economic Analysis and Policy, Elsevier, vol. 70(C), pages 68-82.
    15. Villanueva-Cantillo, Jeyms & Munoz-Marquez, Manuel, 2021. "Methodology for calculating critical values of relevance measures in variable selection methods in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 290(2), pages 657-670.
    16. Charles, Vincent & Aparicio, Juan & Zhu, Joe, 2019. "The curse of dimensionality of decision-making units: A simple approach to increase the discriminatory power of data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 279(3), pages 929-940.
    17. Lenka Šťastn᠍ & Martin Gregor, 2015. "Public sector efficiency in transition and beyond: evidence from Czech local governments," Applied Economics, Taylor & Francis Journals, vol. 47(7), pages 680-699, February.
    18. Toloo, Mehdi & Keshavarz, Esmaeil & Hatami-Marbini, Adel, 2021. "Selecting data envelopment analysis models: A data-driven application to EU countries," Omega, Elsevier, vol. 101(C).
    19. Massimo Finocchiaro Castro & Calogero Guccio, 2014. "Searching for the source of technical inefficiency in Italian judicial districts: an empirical investigation," European Journal of Law and Economics, Springer, vol. 38(3), pages 369-391, December.
    20. Sharma, Mithun J. & Yu, Song Jin, 2015. "Stepwise regression data envelopment analysis for variable reduction," Applied Mathematics and Computation, Elsevier, vol. 253(C), pages 126-134.

    More about this item

    Keywords

    Electricity distribution cost; separability; weather composites; instrumental variable estimator;
    All these keywords.

    JEL classification:

    • L15 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Information and Product Quality
    • L51 - Industrial Organization - - Regulation and Industrial Policy - - - Economics of Regulation
    • L94 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Electric Utilities

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:cam:camdae:1042. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: . General contact details of provider: https://www.econ.cam.ac.uk/ .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Jake Dyer (email available below). General contact details of provider: https://www.econ.cam.ac.uk/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.