Browsing by Author "Boyd, GA"

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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
    (2005) Boyd, GA
    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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    A New Benchmark of Energy Performance for Energy Management in U.S. and Canadian Integrated Steel Plants
    (Iron and Steel Technology, 2017-05-01) Boyd, GA; Doollin, M; Dutrow, E; Zhang, S
    ENERGY STAR® supports businesses and consumers by making it easier to save money and protect the climate through superior energy efficiency. A key ENERGY STAR energy management tool for industry is the plant Energy Performance Indicator (EPI), which provides a bird’s-eye view of a plant’s sector-specific plant-level energy use via a functional relationship between the level of energy use and the level and type of various production activities, material input’s quality, and external factors. This paper describes the development of the first EPI for evaluating the energy performance of integrated steel mills in the U.S. and Canada.
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    Creating linked datasets for SME energy-assessment evidence-building: Results from the U.S. Industrial Assessment Center Program
    (Energy Policy, 2017-12-01) Dalzell, NM; Boyd, GA; Reiter, JP
    © 2017 Elsevier Ltd Lack of information is commonly cited as a market failure resulting in an energy-efficiency gap. Government information policies to fill this gap may enable improvements in energy efficiency and social welfare because of the externalities of energy use. The U.S. Department of Energy Industrial Assessment Center (IAC) program is one such policy intervention, providing no-cost assessments to small and medium enterprises (SME). The IAC program has assembled a wealth of data on these assessments, but the database does not include information about participants after the assessment or on non-participants. This study addresses that lack by creating a new linked dataset using the public IAC and non-public data at the Census Bureau. The IAC database excludes detail needed for an exact match, so the study developed a linking methodology to account for uncertainty in the matching process. Based on the linking approach, a difference in difference analysis for SME that received an assessment was done; plants that received an assessment improve their performance over time, relative to industry peers that did not. This new linked dataset is likely to shed even more light on the impact of the IAC and similar programs in advancing energy efficiency.
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    Factor Intensity and Site Geology as Determinants of Returns to Scale in Coal Mining
    (The Review of Economics and Statistics, 1987) Boyd, GA
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    Measuring plant level energy efficiency and technical change in the U.S. metal-based durable manufacturing sector using stochastic frontier analysis
    (Energy Economics, 2019-06-01) Boyd, GA; Lee, JM
    © 2019 This study analyzes the electric and fuel energy efficiency for five different metal-based durable manufacturing industries in the United States over the time period 1987–2012, at the 3 digit North American Industry Classification System (NAICS) level. Using confidential plant-level data on energy use and production from the quinquennial U.S. Economic Census, a stochastic frontier regression analysis (SFA) is applied in six repeated cross sections for each five year census. The SFA controls for energy prices and climate-driven energy demand (heating degree days HDD and cooling degree days CDD) due to differences in plant level locations, as well as 6-digit NAICS industry effects. Own energy price elasticities range from −0.7 to −1.0, with electricity tending to have slightly higher elasticity than fuel. Mean efficiency estimates (100% = best practice level) range from a low of 33% (fuel, NAICS 334 - Computer and Electronic Products) to 86% (electricity, NAICS 332 - Fabricated Metal Products). Electric efficiency is consistently better than fuel efficiency for all NAICS. Assuming that all plants in the least efficient quartile of the efficiency distribution achieve a median level of performance, we compute the decline in total energy use to be 21%. A Malmquist index is used to decompose the aggregate change in energy performance into indices of efficiency and frontier (best practice) change. Modest improvements in aggregate energy performance are mostly change in best practice, but failure to keep up with the frontier retards aggregate improvement. Given that the best practice frontier has shifted, we also find that firms entering the industry are statistically more efficient, i.e. closer to the frontier; about 0.6% for electricity and 1.7% for fuels on average.