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A Method for Measuring the Efficiency Gap between Average and Best Practice Energy Use: The ENERGY STAR Industrial Energy Performance Indicator

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  • Gale A. Boyd

Abstract

A common feature distinguishing between parametric/statistical models and engineering economics models is that engineering models explicitly represent best practice technologies, whereas parametric/statistical models are typically based on average practice. Measures of energy intensity based on average practice are of little use in corporate management of energy use or for public policy goal setting. In the context of companyor plant‐level indicators, it is more useful to have a measure of energy intensity that is capable of indicating where a company or plant lies within a distribution of performance. In other words, is the performance close to (or far from) the industry best practice? This article presents a parametric/statistical approach that can be used to measure best practice, thereby providing a measure of the difference, or “efficiency gap,” at a plant, company, or overall industry level. The approach requires plant‐level data and applies a stochastic frontier regression analysis used by the ENERGY STARTM industrial energy performance indicator (EPI) to energy intensity. Stochastic frontier regression analysis separates energy intensity into three components: systematic effects, inefficiency, and statistical (random) error. The article outlines the method and gives examples of EPI analysis conducted for two industries, breweries and motor vehicle assembly. In the EPI developed with the stochastic frontier regression for the auto industry, the industry median “efficiency gap” was around 27%.

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  • Gale A. Boyd, 2005. "A Method for Measuring the Efficiency Gap between Average and Best Practice Energy Use: The ENERGY STAR Industrial Energy Performance Indicator," Journal of Industrial Ecology, Yale University, vol. 9(3), pages 51-65, July.
    • Handle: RePEc:bla:inecol:v:9:y:2005:i:3:p:51-65
    DOI: 10.1162/1088198054821609
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    Cited by:

    1. Fernández González, P. & Landajo, M. & Presno, M.J., 2014. "Tracking European Union CO2 emissions through LMDI (logarithmic-mean Divisia index) decomposition. The activity revaluation approach," Energy, Elsevier, vol. 73(C), pages 741-750.
    2. Wei, Chu & Löschel, Andreas & Liu, Bing, 2015. "Energy-saving and emission-abatement potential of Chinese coal-fired power enterprise: A non-parametric analysis," Energy Economics, Elsevier, vol. 49(C), pages 33-43.
    3. Lundgren, Tommy & Marklund, Per-Olov & Zhang, Shanshan, 2016. "Industrial energy demand and energy efficiency – Evidence from Sweden," Resource and Energy Economics, Elsevier, vol. 43(C), pages 130-152.
    4. Song, Chenxi & Li, Mingjia & Wen, Zhexi & He, Ya-Ling & Tao, Wen-Quan & Li, Yangzhe & Wei, Xiangyang & Yin, Xiaolan & Huang, Xing, 2014. "Research on energy efficiency evaluation based on indicators for industry sectors in China," Applied Energy, Elsevier, vol. 134(C), pages 550-562.
    5. Boyd, Gale A. & Lee, Jonathan M., 2019. "Measuring plant level energy efficiency and technical change in the U.S. metal-based durable manufacturing sector using stochastic frontier analysis," Energy Economics, Elsevier, vol. 81(C), pages 159-174.
    6. Ang, B.W., 2006. "Monitoring changes in economy-wide energy efficiency: From energy-GDP ratio to composite efficiency index," Energy Policy, Elsevier, vol. 34(5), pages 574-582, March.
    7. Wei, Yi-Ming & Liao, Hua & Fan, Ying, 2007. "An empirical analysis of energy efficiency in China's iron and steel sector," Energy, Elsevier, vol. 32(12), pages 2262-2270.
    8. Li, Ming-Jia & Tao, Wen-Quan, 2017. "Review of methodologies and polices for evaluation of energy efficiency in high energy-consuming industry," Applied Energy, Elsevier, vol. 187(C), pages 203-215.
    9. Oleg Badunenko & Subal C. Kumbhakar, 2020. "Energy Intensity and Long- and Short-Term Efficiency in US Manufacturing Industry," Energies, MDPI, vol. 13(15), pages 1-21, August.
    10. Cahill, Caiman J. & Ó Gallachóir, Brian P., 2012. "Combining physical and economic output data to analyse energy and CO2 emissions trends in industry," Energy Policy, Elsevier, vol. 49(C), pages 422-429.
    11. Amjadi, Golnaz & Lundgren, Tommy, 2022. "Is industrial energy inefficiency transient or persistent? Evidence from Swedish manufacturing," Applied Energy, Elsevier, vol. 309(C).
    12. Mkwananzi, Thobeka & Mandegari, Mohsen & Görgens, Johann F., 2019. "Disturbance modelling through steady-state value deviations: The determination of suitable energy indicators and parameters for energy consumption monitoring in a typical sugar mill," Energy, Elsevier, vol. 176(C), pages 211-223.
    13. Boyd, Gale A., 2014. "Estimating the changes in the distribution of energy efficiency in the U.S. automobile assembly industry," Energy Economics, Elsevier, vol. 42(C), pages 81-87.
    14. Jie Wu & Beibei Xiong & Qingxian An & Jiasen Sun & Huaqing Wu, 2017. "Total-factor energy efficiency evaluation of Chinese industry by using two-stage DEA model with shared inputs," Annals of Operations Research, Springer, vol. 255(1), pages 257-276, August.

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