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The Measurement of the Energy Intensity of Manufacturing Industries: A Principal Components Analysis

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  • Bernard, Jean-Thomas
  • Cote, Bruno

Abstract

Energy intensity is the ratio of energy use to output. Most industries deal with several energy sources and outputs. This leads to the usual difficulties of aggregating heterogeneous inputs and outputs. We apply principal components analysis to assess the information derived from six energy intensity indicators. We use two measures of total energy use (thermal and economic) and three measures of industry output (value added, value of production, and value of shipments). The data comes from manufacturing industries in Québec, Ontario, Alberta, and British Columbia from 1976 to 1996. We find that the variation of the six energy intensity indicators that is accounted for by the first principal component is quite large. However, depending on how variables are measured, there may be significant differences in the assessment of the evolution of energy intensity for some industries. There are no particular patterns in this respect. This makes identifying benchmarks that could be used to assess future performance difficult.

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  • Bernard, Jean-Thomas & Cote, Bruno, 2002. "The Measurement of the Energy Intensity of Manufacturing Industries: A Principal Components Analysis," RFF Working Paper Series dp-02-31, Resources for the Future.
    • Handle: RePEc:rff:dpaper:dp-02-31
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    Cited by:

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    2. Muhammad Haseeb & Sebastian Kot & Hafezali Iqbal Hussain & Leonardus WW Mihardjo & Piotr Saługa, 2020. "Modelling the Non-Linear Energy Intensity Effect Based on a Quantile-on-Quantile Approach: The Case of Textiles Manufacturing in Asian Countries," Energies, MDPI, vol. 13(9), pages 1-19, May.
    3. Jin Zhu & Dequn Zhou & Zhengning Pu & Huaping Sun, 2019. "A Study of Regional Power Generation Efficiency in China: Based on a Non-Radial Directional Distance Function Model," Sustainability, MDPI, vol. 11(3), pages 1-18, January.
    4. Azadeh, A. & Amalnick, M.S. & Ghaderi, S.F. & Asadzadeh, S.M., 2007. "An integrated DEA PCA numerical taxonomy approach for energy efficiency assessment and consumption optimization in energy intensive manufacturing sectors," Energy Policy, Elsevier, vol. 35(7), pages 3792-3806, July.
    5. Salta, Myrsine & Polatidis, Heracles & Haralambopoulos, Dias, 2009. "Energy use in the Greek manufacturing sector: A methodological framework based on physical indicators with aggregation and decomposition analysis," Energy, Elsevier, vol. 34(1), pages 90-111.
    6. Cai, Wei & Liu, Fei & Xie, Jun & Liu, Peiji & Tuo, Junbo, 2017. "A tool for assessing the energy demand and efficiency of machining systems: Energy benchmarking," Energy, Elsevier, vol. 138(C), pages 332-347.
    7. Romano, Antonio A. & Scandurra, Giuseppe & Carfora, Alfonso & Pansini, Rosaria V., 2016. "Assessing the determinants of SIDS' pattern toward sustainability: A statistical analysis," Energy Policy, Elsevier, vol. 98(C), pages 688-699.
    8. Kepplinger, D. & Templ, M. & Upadhyaya, S., 2013. "Analysis of energy intensity in manufacturing industry using mixed-effects models," Energy, Elsevier, vol. 59(C), pages 754-763.
    9. Lawrence, Akvile & Karlsson, Magnus & Thollander, Patrik, 2018. "Effects of firm characteristics and energy management for improving energy efficiency in the pulp and paper industry," Energy, Elsevier, vol. 153(C), pages 825-835.
    10. Xu, Bin & Lin, Boqiang, 2016. "Reducing CO2 emissions in China's manufacturing industry: Evidence from nonparametric additive regression models," Energy, Elsevier, vol. 101(C), pages 161-173.
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    More about this item

    Keywords

    Energy intensity; aggregation; principal components analysis;
    All these keywords.

    JEL classification:

    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General
    • C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation
    • L60 - Industrial Organization - - Industry Studies: Manufacturing - - - General

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