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Efficiency and Environmental Factors in the US Electricity Transmission Industry

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  • Llorca, Manuel
  • Orea, Luis
  • Pollitt, Michael
Abstract
The electricity industry in most developed countries has been restructured over recent decades with the aim of improving both service quality and firms’ performance. Regulated segments (e.g. transmission) still provide the infrastructure for the competitive segments and represent a notable amount of the total price paid by final customers. However there is a lack of empirical studies that analyze firms’ performance in the electricity transmission sector. We conduct an empirical analysis of the US electricity transmission companies for the period 2001-2009. We use stochastic frontier models that allow us to identify determinants of firms’ inefficiency and to control for weather conditions, potentially one of the most decisive uncontrollable factors in electricity transportation. Our results suggest that there is room for improvement in the performance of the US electricity transmission system. Regulators should also take into account that more adverse conditions generate higher levels of inefficiency and that achieving long-term efficiency improvements tends to deteriorate firms’ short-term relative performance.

Suggested Citation

  • Llorca, Manuel & Orea, Luis & Pollitt, Michael, 2013. "Efficiency and Environmental Factors in the US Electricity Transmission Industry," Cambridge Working Papers in Economics 1318, Faculty of Economics, University of Cambridge.
    • Handle: RePEc:cam:camdae:1318
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    4. Xie, Bai-Chen & Zhang, Zhen-Jiang & Anaya, Karim L., 2021. "Has the unbundling reform improved the service efficiency of China's power grid firms?," Energy Economics, Elsevier, vol. 95(C).
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    6. Orea, Luis & Álvarez, Inmaculada C., 2019. "A new stochastic frontier model with cross-sectional effects in both noise and inefficiency terms," Journal of Econometrics, Elsevier, vol. 213(2), pages 556-577.
    7. Nie, S. & Li, Y.P. & Liu, J. & Huang, Charley Z., 2017. "Risk management of energy system for identifying optimal power mix with financial-cost minimization and environmental-impact mitigation under uncertainty," Energy Economics, Elsevier, vol. 61(C), pages 313-329.
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    9. Hung-pin Lai & Subal C. Kumbhakar, 2023. "Indirect inference estimation of stochastic production frontier models with skew-normal noise," Empirical Economics, Springer, vol. 64(6), pages 2771-2793, June.
    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. Adwoa Asantewaa & Tooraj Jamasb & Manuel Llorca, 2022. "Electricity Sector Reform Performance in Sub-Saharan Africa: A Parametric Distance Function Approach," Energies, MDPI, vol. 15(6), pages 1-29, March.
    12. Tong, Q. & Swallow, B. & Zhang, L. & Zhang, J., 2018. "Risk Attitude, Technical Efficiency and Adoption: An Integrated Approach to Climate-Smart Rice Production in the Jianghan Plain, China," 2018 Conference, July 28-August 2, 2018, Vancouver, British Columbia 277311, International Association of Agricultural Economists.
    13. Shamsuzzoha & Makoto Tanaka, 2021. "The role of human capital on the performance of manufacturing firms in Bangladesh," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 42(1), pages 21-33, January.
    14. 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.
    15. Tooraj Jamasb & Manuel Llorca & Pavan Khetrapal & Tripta Thakur, 2018. "Institutions and Performance of Regulated Firms: Evidence from Electric Utilities in the Indian States," Working Papers EPRG 1809, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge.
    16. Liu, Xiao-Yan & Pollitt, Michael G. & Xie, Bai-Chen & Liu, Li-Qiu, 2019. "Does environmental heterogeneity affect the productive efficiency of grid utilities in China?," Energy Economics, Elsevier, vol. 83(C), pages 333-344.
    17. Ajayi, V. & Weyman-Jones, T., 2021. "State-Level Electricity Generation Efficiency: Do Restructuring and Regulatory Institutions Matter in the US?," Cambridge Working Papers in Economics 2166, Faculty of Economics, University of Cambridge.
    18. 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.
    19. 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).
    20. Wenche Tobiasson & Manuel Llorca & Tooraj Jamasb, 2021. "Performance Effects of Network Structure and Ownership: The Norwegian Electricity Distribution Sector," Energies, MDPI, vol. 14(21), pages 1-15, November.
    21. 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.
    22. da Silva, Aline Veronese & Costa, Marcelo Azevedo & Ahn, Heinz & Lopes, Ana Lúcia Miranda, 2019. "Performance benchmarking models for electricity transmission regulation: Caveats concerning the Brazilian case," Utilities Policy, Elsevier, vol. 60(C), pages 1-1.
    23. Zhang, Tao & Li, Hong-Zhou & Xie, Bai-Chen, 2022. "Have renewables and market-oriented reforms constrained the technical efficiency improvement of China's electric grid utilities?," Energy Economics, Elsevier, vol. 114(C).

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    More about this item

    Keywords

    electricity transmission; heteroscedastic stochastic cost frontiers; inefficiency determinants;
    All these keywords.

    JEL classification:

    • D22 - Microeconomics - - Production and Organizations - - - Firm Behavior: Empirical Analysis
    • L51 - Industrial Organization - - Regulation and Industrial Policy - - - Economics of Regulation
    • L94 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Electric Utilities

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