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Estimating Plant Level Energy Efficiency with a Stochastic Frontier

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  • Gale A. Boyd

Abstract

A common distinguishing feature of engineering models is that they explicitly represent best practice technologies, while parametric/statistical models represent average practice. It is more useful to energy management or goal setting to have a measure of energy performance capable of answering the question, “How close is observed performance from the industry best practice?†This paper presents a parametric/statistical approach to measure best practice. The results show how well a plant performs relative to the industry. A stochastic frontier regression analysis is used to model plant level energy use, separating energy into systematic effects, inefficiency, and random error. One advantage is that physical product mix can be included, avoiding the problem of aggregating output to define a single energy/output ratio to measure energy intensity. The paper outlines the methods and gives an example of the analysis conducted for a non-public micro-dataset of wet corn milling plants.

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  • Gale A. Boyd, 2008. "Estimating Plant Level Energy Efficiency with a Stochastic Frontier," The Energy Journal, , vol. 29(2), pages 23-44, April.
    • Handle: RePEc:sae:enejou:v:29:y:2008:i:2:p:23-44
    DOI: 10.5547/ISSN0195-6574-EJ-Vol29-No2-2
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    References listed on IDEAS

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    1. Catherine J. Morrison Paul & Warren E. Johnston & Gerald A. G. Frengley, 2000. "Efficiency in New Zealand Sheep and Beef Farming: The Impacts of Regulatory Reform," The Review of Economics and Statistics, MIT Press, vol. 82(2), pages 325-337, May.
    2. G. Boyd & J. F. McDonald & M. Ross & D. A. Hansont, 1987. "Separating the Changing Composition of U.S. Manufacturing Production from Energy Efficiency Improvements: A Divisia Index Approach," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2), pages 77-96.
    3. Gale Boyd & George Tolley & Joseph Pang, 2002. "Plant Level Productivity, Efficiency, and Environmental Performance of the Container Glass Industry," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 23(1), pages 29-43, September.
    4. Greening, Lorna A. & Davis, William B. & Schipper, Lee & Khrushch, Marta, 1997. "Comparison of six decomposition methods: application to aggregate energy intensity for manufacturing in 10 OECD countries," Energy Economics, Elsevier, vol. 19(3), pages 375-390, July.
    5. Dashti, Imad, 2003. "Inference from concave stochastic frontiers and the covariance of firm efficiency measures across firms," Energy Economics, Elsevier, vol. 25(6), pages 585-601, November.
    6. Luis R. Murillo-Zamorano, 2005. "The Role of Energy in Productivity Growth: A Controversial Issue?," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2), pages 69-88.
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    1. Hongzhou Li & Andrea Appolloni & Yijie Dou & Vincenzo Basile & Maria Kopsakangas-Savolainen, 2024. "A parametric method to estimate environmental energy efficiency with non-radial adjustment: an application to China," Annals of Operations Research, Springer, vol. 342(3), pages 1379-1405, November.

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