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Estimating the Technical Improvement of Energy Efficiency in the Automotive Industry—Stochastic and Deterministic Frontier Benchmarking Approaches

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  • Seog-Chan Oh

    (General Motors Research and Development, 30500 Mound Road, Warren, MI 48090, USA
    These authors contributed equally to this work.)

  • Alfred J. Hildreth

    (General Motors, 30200 Van Dyke, Warren, MI 48090, USA
    These authors contributed equally to this work.)

Abstract

The car manufacturing industry, one of the largest energy consuming industries, has been making a considerable effort to improve its energy intensity by implementing energy efficiency programs, in many cases supported by government research or financial programs. While many car manufacturers claim that they have made substantial progress in energy efficiency improvement over the past years through their energy efficiency programs, the objective measurement of energy efficiency improvement has not been studied due to the lack of suitable quantitative methods. This paper proposes stochastic and deterministic frontier benchmarking models such as the stochastic frontier analysis (SFA) model and the data envelopment analysis (DEA) model to measure the effectiveness of energy saving initiatives in terms of the technical improvement of energy efficiency for the automotive industry, particularly vehicle assembly plants. Illustrative examples of the application of the proposed models are presented and demonstrate the overall benchmarking process to determine best practice frontier lines and to measure technical improvement based on the magnitude of frontier line shifts over time. Log likelihood ratio and Spearman rank-order correlation coefficient tests are conducted to determine the significance of the SFA model and its consistency with the DEA model. ENERGY STAR ® EPI (Energy Performance Index) are also calculated.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:7:y:2014:i:9:p:6196-6222:d:40614
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    2. Yonghan Jeon & Jongoh Nam, 2023. "Estimating Energy Efficiency and Energy Saving Potential in the Republic of Korea’s Offshore Fisheries," Sustainability, MDPI, vol. 15(20), pages 1-17, October.
    3. Mohammed Atris, Amani, 2020. "Assessment of oil refinery performance: Application of data envelopment analysis-discriminant analysis," Resources Policy, Elsevier, vol. 65(C).
    4. Wen, Xuanhao & Cao, Huajun & Li, Hongcheng & Zheng, Jie & Ge, Weiwei & Chen, Erheng & Gao, Xi & Hon, Bernard, 2022. "A dual energy benchmarking methodology for energy-efficient production planning and operation of discrete manufacturing systems using data mining techniques," Energy, Elsevier, vol. 255(C).
    5. María A. Ramón-Jerónimo & Inés Herrero, 2017. "Capturing Firms’ Heterogeneity through Marketing and IT Capabilities in SMEs," Sustainability, MDPI, vol. 9(12), pages 1-25, November.
    6. 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.
    7. Elahi, Ehsan & Zhang, Zhixin & Khalid, Zainab & Xu, Haiyun, 2022. "Application of an artificial neural network to optimise energy inputs: An energy- and cost-saving strategy for commercial poultry farms," Energy, Elsevier, vol. 244(PB).
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    9. Liu, Fengqin & Sim, Jae-yeon & Sun, Huaping & Edziah, Bless Kofi & Adom, Philip Kofi & Song, Shunfeng, 2023. "Assessing the role of economic globalization on energy efficiency: Evidence from a global perspective," China Economic Review, Elsevier, vol. 77(C).
    10. Rogério Diogne de Souza e Silva & Rosana Cavalcante de Oliveira & Maria Emília de Lima Tostes, 2017. "Analysis of the Brazilian Energy Efficiency Program for Electricity Distribution Systems," Energies, MDPI, vol. 10(9), pages 1-19, September.
    11. 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).
    12. Amir Abolhassani & Gale Boyd & Majid Jaridi & Bhaskaran Gopalakrishnan & James Harner, 2023. "“Is Energy That Different from Labor?” Similarity in Determinants of Intensity for Auto Assembly Plants," Energies, MDPI, vol. 16(4), pages 1-35, February.
    13. Jinpeng Liu & Li Wang & Mohan Qiu & Jiang Zhu, 2016. "Promotion Potentiality and Optimal Strategies Analysis of Provincial Energy Efficiency in China," Sustainability, MDPI, vol. 8(8), pages 1-17, August.

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