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Static versus dynamic DEA in electricity regulation: the case of US transmission system operators

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  • Philipp Geymueller

Abstract

In this paper a dynamic DEA-model (data envelopment analysis) is developed that takes up Nemoto and Goto’s (Econ Lett 64(1):51–56, 1999) concept of quasi-fixed inputs but can dispense with price-information as it solely deals with technical efficiencies that are derived in an additive-model type of setting. This model is applied to the data of 50 of the largest US electric transmission system operators in the period 2000–2006 in order to show that the static DEA-models that are usually employed by electricity regulators can lead to wrong conclusions about the enterprises’ efficiency since they ignore the short-run fixity of essential inputs like transmission lines. Copyright Springer-Verlag 2009

Suggested Citation

  • Philipp Geymueller, 2009. "Static versus dynamic DEA in electricity regulation: the case of US transmission system operators," 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. 17(4), pages 397-413, December.
    • Handle: RePEc:spr:cejnor:v:17:y:2009:i:4:p:397-413
    DOI: 10.1007/s10100-009-0099-x
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    6. Toshiyuki Sueyoshi & Mika Goto, 2017. "World trend in energy: an extension to DEA applied to energy and environment," Journal of Economic Structures, Springer;Pan-Pacific Association of Input-Output Studies (PAPAIOS), vol. 6(1), pages 1-23, December.
    7. 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.
    8. Magdalena Kapelko, 2019. "Measuring productivity change accounting for adjustment costs: evidence from the food industry in the European Union," Annals of Operations Research, Springer, vol. 278(1), pages 215-234, July.
    9. Pointon, Charlotte & Matthews, Kent, 2016. "Reprint of: Dynamic efficiency in the English and Welsh water and sewerage industry," Omega, Elsevier, vol. 60(C), pages 98-108.
    10. Llorca, Manuel & Orea, Luis & Pollit, Michael G., 2013. "Using in the latent class approach as a supervised method to cluster firms in DEA: An application to the US electricity transmission industry," Efficiency Series Papers 2013/03, University of Oviedo, Department of Economics, Oviedo Efficiency Group (OEG).
    11. Haney, Aoife Brophy & Pollitt, Michael G., 2013. "International benchmarking of electricity transmission by regulators: A contrast between theory and practice?," Energy Policy, Elsevier, vol. 62(C), pages 267-281.
    12. Magdalena Kapelko, 2017. "Dynamic versus static inefficiency assessment of the Polish meat‐processing industry in the aftermath of the European Union integration and financial crisis," Agribusiness, John Wiley & Sons, Ltd., vol. 33(4), pages 505-521, September.
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    14. Wei-Kang Wang & Irene Wei Kiong Ting & Kuo-Cheng Kuo & Qian Long Kweh & Yan-Heng Lin, 2018. "Corporate diversification and efficiency: evidence from Taiwanese top 100 manufacturing firms," Operational Research, Springer, vol. 18(1), pages 187-203, April.
    15. 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.
    16. Mohammad Nemati & Reza Kazemi Matin & Mehdi Toloo, 2020. "A two-stage DEA model with partial impacts between inputs and outputs: application in refinery industries," Annals of Operations Research, Springer, vol. 295(1), pages 285-312, December.
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