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Use of Data Envelopment Analysis for Incentive Regulation of Electric Distribution Firms

Author

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  • Banker, Rajiv
  • Forsund, Finn R.
  • Zhang, Daqun

Abstract

Regulators worldwide increasingly use data envelopment analysis (DEA) for the incentive regulation of electric distribution firms. Although the production/cost frontiers estimated by DEA models provide valuable information for electricity rate setting, the benefit of DEA benchmarking in regulatory practice would be limited due to specification errors of DEA models. In this paper, we summarize and discuss existing issues of using DEA models for efficiency benchmarking from four aspects: 1. Specification of inputs and outputs, 2. Selection of costs for benchmarking, 3. Imposition of structure on benchmarking models, and 4. Treatment of contextual variables. We also give suggestions for improving the use of DEA models.

Suggested Citation

  • Banker, Rajiv & Forsund, Finn R. & Zhang, Daqun, 2017. "Use of Data Envelopment Analysis for Incentive Regulation of Electric Distribution Firms," Data Envelopment Analysis Journal, now publishers, vol. 3(1-2), pages 1-47, November.
    • Handle: RePEc:now:jnldea:103.00000020
    DOI: 10.1561/103.00000020
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    Citations

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    Cited by:

    1. Tsionas, Mike G. & Kumbhakar, Subal C., 2021. "Stochastic frontier models with time-varying conditional variances," European Journal of Operational Research, Elsevier, vol. 292(3), pages 1115-1132.
    2. Atwood, Joseph & Shaik, Saleem, 2020. "Theory and statistical properties of Quantile Data Envelopment Analysis," European Journal of Operational Research, Elsevier, vol. 286(2), pages 649-661.
    3. Andor, Mark A. & Parmeter, Christopher & Sommer, Stephan, 2019. "Combining uncertainty with uncertainty to get certainty? Efficiency analysis for regulation purposes," European Journal of Operational Research, Elsevier, vol. 274(1), pages 240-252.
    4. Tsionas, Mike G., 2021. "Optimal combinations of stochastic frontier and data envelopment analysis models," European Journal of Operational Research, Elsevier, vol. 294(2), pages 790-800.
    5. Heesche, Emil & Asmild, Mette, 2022. "Controlling for environmental conditions in regulatory benchmarking," Utilities Policy, Elsevier, vol. 77(C).
    6. Emil Heesche & Mette Asmild, 2022. "Implications of Aggregation Uncertainty in DEA," IFRO Working Paper 2022/02, University of Copenhagen, Department of Food and Resource Economics.
    7. Emil Heesche & Mette Asmild, 2020. "Controlling for environmental conditions in regulatory benchmarking," IFRO Working Paper 2020/03, University of Copenhagen, Department of Food and Resource Economics.
    8. Jingqi Sun & Nuermaimaiti Ruze & Jianjun Zhang & Haoran Zhao & Boyang Shen, 2019. "Evaluating the Investment Efficiency of China’s Provincial Power Grid Enterprises under New Electricity Market Reform: Empirical Evidence Based on Three-Stage DEA Model," Energies, MDPI, vol. 12(18), pages 1-17, September.
    9. Julio Cesar Mosquera Gutierres & Rafael Coradi Leme & Rodrigo Luiz Mendes Mota & Paulo E. Steele Santos, 2021. "Regulatory efficiency decomposition for utilities’ parallel subsystems," Operational Research, Springer, vol. 21(1), pages 331-347, March.

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