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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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%.
Gale Boyd is an Associate Research Professor in the Social Science Research Institute & Department of Economics. He was the Director of the Triangle Federal Statistical Research Data Center from 2006-2020. Prior to joining Duke University, Gale was an economist at Argonne National Laboratory. His career has been primarily in area of industrial energy/environmental economics.
His recent work includes using the non-public Census micro-data and other non-pubic data from industry and trade associations on energy, environmental, and productivity related issues for industrial energy efficiency and related energy/environmental policy research. His research includes preparing statistical benchmarks of energy performance in manufacturing plants, or Energy Performance Indicators (EPI), is supported by the EPA ENERGY STAR program and is used by industry for energy management and public recognition from ENERGY STAR. Studies of the implications of management practices and environmental policy on industry energy efficiency and total factor productivity are in progress.
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