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European Industrial Energy Intensity: The Role of Innovation 1995-2009

Author

Listed:
  • Victor Ajayi

    (EPRG, Judge Business School, University of Cambridge)

  • David Reiner

    (EPRG, Judge Business School, University of Cambridge)

Abstract

We investigate the direct role of technological innovation and other influencing factors on industry-level energy intensity based on a sample of 12 industries across 17 EU countries over 1995–2009. We develop an innovative industry-level patent dataset and find compelling evidence that patent stock negatively influences industrial energy intensity. Using a fixed effects estimator, we find a much stronger effect on energy-intensive industries with an estimated coefficient of -0.138 almost double that of less energy-intensive industries (estimated at -0.085). While our results show energy price remains the major determinant of energy intensity, the chemicals industry appears to be more susceptible to energy prices relative to other energy-intensive industries that are covered by the EU Emissions Trading Scheme (ETS). Our study reveals that asymmetric response of energy intensity to energy prices in which price rises between 2004 and 2008 accounts for more change in efficiency than when prices fall. We also explore regional differences, notably that carbon tax policy in Northern European countries, which began in the early 1990s, is responsible for a significant fraction of the decline in energy intensity in Northern Europe.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Victor Ajayi & David Reiner, 2018. "European Industrial Energy Intensity: The Role of Innovation 1995-2009," Working Papers EPRG 1818, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge.
    • Handle: RePEc:enp:wpaper:eprg1818
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    Cited by:

    1. Hui, Wang & Xin-gang, Zhao & Ling-zhi, Ren & Ji-cheng, Fan & Fan, Lu, 2021. "The impact of technological progress on energy intensity in China (2005–2016): Evidence from a geographically and temporally weighted regression model," Energy, Elsevier, vol. 226(C).

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    JEL classification:

    • O13 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Agriculture; Natural Resources; Environment; Other Primary Products
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • Q55 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environmental Economics: Technological Innovation

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