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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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Suggested Citation

  • Bernard, Jean-Thomas & Cote, Bruno, 2005. "The measurement of the energy intensity of manufacturing industries: a principal components analysis," Energy Policy, Elsevier, vol. 33(2), pages 221-233, January.
  • Handle: RePEc:eee:enepol:v:33:y:2005:i:2:p:221-233
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    Cited by:

    1. Liao, Hua & Wei, Yi-Ming, 2010. "China's energy consumption: A perspective from Divisia aggregation approach," Energy, Elsevier, vol. 35(1), pages 28-34.
    2. 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.
    3. Li, Ming-Jia & Song, Chen-Xi & Tao, Wen-Quan, 2016. "A hybrid model for explaining the short-term dynamics of energy efficiency of China’s thermal power plants," Applied Energy, Elsevier, pages 738-747.
    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. Li, Ming-Jia & Tao, Wen-Quan, 2017. "Review of methodologies and polices for evaluation of energy efficiency in high energy-consuming industry," Applied Energy, Elsevier, pages 203-215.
    7. repec:eee:energy:v:138:y:2017:i:c:p:332-347 is not listed on IDEAS
    8. 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.
    9. 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.

    More about this item

    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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