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

Energies

Seog-Chan Oh¹ 

[1] General Motors Research and Development, 30500 Mound Road, Warren, MI 48090, USA

关键词: stochastic frontier analysis;    data envelopment analysis;    energy efficiency;    technical change in energy efficiency;   

DOI  :  10.3390/en7096196

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2014 by the authors; licensee MDPI, Basel, Switzerland.