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An Assessment of the Efficiency of Canadian Power Generation Companies with Bootstrap DEA

Author

Listed:
  • Mohamed Dia

    (Research Group in Operations, Analytics and Decision Sciences (RGinOADS), School of Business Administration, Faculty of Management, Laurentian University, 935 Ramsey Lake Road, Sudbury, ON P3E 2C6, Canada)

  • Shashi K. Shahi

    (Research Group in Operations, Analytics and Decision Sciences (RGinOADS), School of Business Administration, Faculty of Management, Laurentian University, 935 Ramsey Lake Road, Sudbury, ON P3E 2C6, Canada)

  • Luckny Zéphyr

    (Research Group in Operations, Analytics and Decision Sciences (RGinOADS), School of Business Administration, Faculty of Management, Laurentian University, 935 Ramsey Lake Road, Sudbury, ON P3E 2C6, Canada)

Abstract

Power generation companies play an important role in the Canadian economy, as most of the economic activities in the manufacturing and service sectors are powered by electricity. The significance of the Canadian power generation industry shows that efficiency analysis is essential for efficiently managing power generation and distribution in Canada. However, there have been few attempts to study the relative efficiencies of the Canadian power generation companies. This study fills in this gap by assessing the overall technical, managerial, and scale efficiencies of a sample of Canadian power generation companies via the non-parametric bootstrap DEA methodology, with firm-level annual inputs and outputs data over an 18-year horizon. The results of our investigation indicate low levels of overall technical and managerial efficiencies but relatively high levels of scale efficiencies of the Canadian power generation companies over the entire study period. We also found that the 2007–2009 financial crisis impacted the relative performance of the Canadian power generation companies. Our results also allowed us to identify the benchmark power generation companies for each type of efficiency that the inefficient companies should target toward improving their efficiency.

Suggested Citation

  • Mohamed Dia & Shashi K. Shahi & Luckny Zéphyr, 2021. "An Assessment of the Efficiency of Canadian Power Generation Companies with Bootstrap DEA," JRFM, MDPI, vol. 14(10), pages 1-27, October.
    • Handle: RePEc:gam:jjrfmx:v:14:y:2021:i:10:p:498-:d:658733
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    References listed on IDEAS

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    Cited by:

    1. Zhishuo Zhang & Yao Xiao & Huayong Niu, 2022. "DEA and Machine Learning for Performance Prediction," Mathematics, MDPI, vol. 10(10), pages 1-23, May.
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