Measuring plant level energy efficiency and technical change in the U.S. metal-based durable manufacturing sector using stochastic frontier analysis
Abstract
© 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.
Type
Journal articlePermalink
https://hdl.handle.net/10161/21599Published Version (Please cite this version)
10.1016/j.eneco.2019.03.021Publication Info
Boyd, GA; & Lee, JM (2019). Measuring plant level energy efficiency and technical change in the U.S. metal-based
durable manufacturing sector using stochastic frontier analysis. Energy Economics, 81. pp. 159-174. 10.1016/j.eneco.2019.03.021. Retrieved from https://hdl.handle.net/10161/21599.This is constructed from limited available data and may be imprecise. To cite this
article, please review & use the official citation provided by the journal.
Collections
More Info
Show full item recordScholars@Duke
Gale Allen Boyd
Associate Research Professor in the Social Science Research Institute
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 dat

Articles written by Duke faculty are made available through the campus open access policy. For more information see: Duke Open Access Policy
Rights for Collection: Scholarly Articles
Works are deposited here by their authors, and represent their research and opinions, not that of Duke University. Some materials and descriptions may include offensive content. More info