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Estimating the changes in the distribution of energy efficiency in the U.S. automobile assembly industry

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  • Boyd, Gale A.

Abstract

This paper describes the EPA's voluntary ENERGY STAR program and the results of the automobile manufacturing industry's efforts to advance energy management as measured by the updated ENERGY STAR Energy Performance Indicator (EPI). A stochastic single-factor input frontier estimation using the gamma error distribution is applied to separately estimate the distribution of the electricity and fossil fuel efficiency of assembly plants using data from 2003 to 2005 and then compared to model results from a prior analysis conducted for the 1997–2000 time period. This comparison provides an assessment of how the industry has changed over time. The frontier analysis shows a modest improvement (reduction) in “best practice” for electricity use and a larger one for fossil fuels. This is accompanied by a large reduction in the variance of fossil fuel efficiency distribution. The results provide evidence of a shift in the frontier, in addition to some “catching up” of poor performing plants over time.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:eneeco:v:42:y:2014:i:c:p:81-87
    DOI: 10.1016/j.eneco.2013.11.008
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. 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.
    2. Andrei, Mariana & Thollander, Patrik & Sannö, Anna, 2022. "Knowledge demands for energy management in manufacturing industry - A systematic literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).
    3. Seog-Chan Oh & Alfred J. Hildreth, 2014. "Estimating the Technical Improvement of Energy Efficiency in the Automotive Industry—Stochastic and Deterministic Frontier Benchmarking Approaches," Energies, MDPI, vol. 7(9), pages 1-27, September.
    4. Seog-Chan Oh & Jaemin Shin, 2021. "The Assessment of Car Making Plants with an Integrated Stochastic Frontier Analysis Model," Mathematics, MDPI, vol. 9(11), pages 1-21, June.
    5. Yingbin Zhou & Siqi Lv & Jianlin Wang & Junbo Tong & Zhong Fang, 2022. "The Impact of Green Taxes on the Carbon Emission Efficiency of China’s Construction Industry," Sustainability, MDPI, vol. 14(9), pages 1-18, April.
    6. Ohler, Adrienne M. & Loomis, David G. & Ilves, Kadi, 2020. "A study of electricity savings from energy star appliances using household survey data," Energy Policy, Elsevier, vol. 144(C).
    7. Perroni, Marcos G. & Gouvea da Costa, Sergio E. & Pinheiro de Lima, Edson & Vieira da Silva, Wesley, 2017. "The relationship between enterprise efficiency in resource use and energy efficiency practices adoption," International Journal of Production Economics, Elsevier, vol. 190(C), pages 108-119.
    8. P. Zhou & F. Wu & D. Q. Zhou, 2017. "Total-factor energy efficiency with congestion," Annals of Operations Research, Springer, vol. 255(1), pages 241-256, August.
    9. Gale A. Boyd & Jonathan M. Lee, 2020. "Relative Effectiveness of Energy Efficiency Programs versus Market Based Climate Policies in the Chemical Industry," The Energy Journal, , vol. 41(3), pages 39-62, May.
    10. Giampieri, A. & Ling-Chin, J. & Ma, Z. & Smallbone, A. & Roskilly, A.P., 2020. "A review of the current automotive manufacturing practice from an energy perspective," Applied Energy, Elsevier, vol. 261(C).
    11. Boyd, Gale A. & Curtis, E. Mark, 2014. "Evidence of an “Energy-Management Gap” in U.S. manufacturing: Spillovers from firm management practices to energy efficiency," Journal of Environmental Economics and Management, Elsevier, vol. 68(3), pages 463-479.
    12. Dong, Hanjiang & Wang, Xiuyuan & Cui, Ziyu & Zhu, Jizhong & Li, Shenglin & Yu, Changyuan, 2025. "Machine learning-enhanced Data Envelopment Analysis via multi-objective variable selection for benchmarking combined electricity distribution performance," Energy Economics, Elsevier, vol. 143(C).

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    Keywords

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    JEL classification:

    • Q48 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Government Policy
    • L60 - Industrial Organization - - Industry Studies: Manufacturing - - - General
    • L62 - Industrial Organization - - Industry Studies: Manufacturing - - - Automobiles; Other Transportation Equipment; Related Parts and Equipment
    • O39 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Other
    • Q55 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environmental Economics: Technological Innovation

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