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Modeling international trends in energy efficiency

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  • Stern, David I.

Abstract

I use a stochastic production frontier to model energy efficiency trends in 85 countries over a 37-year period. Differences in energy efficiency across countries are modeled as a stochastic function of explanatory variables and I estimate the model using the cross-section of time-averaged data, so that no structure is imposed on technological change over time. Energy efficiency is measured using a new energy distance function approach. The country using the least energy per unit output, given its mix of outputs and inputs, defines the global production frontier. A country's relative energy efficiency is given by its distance from the frontier—the ratio of its actual energy use to the minimum required energy use, ceteris paribus. Energy efficiency is higher in countries with, inter alia, higher total factor productivity, undervalued currencies, and smaller fossil fuel reserves and it converges over time across countries. Globally, technological change was the most important factor counteracting the energy-use and carbon-emissions increasing effects of economic growth.

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Bibliographic Info

Article provided by Elsevier in its journal Energy Economics.

Volume (Year): 34 (2012)
Issue (Month): 6 ()
Pages: 2200-2208

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Handle: RePEc:eee:eneeco:v:34:y:2012:i:6:p:2200-2208

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Web page: http://www.elsevier.com/locate/eneco

Related research

Keywords: Energy; Efficiency; Carbon emissions; Technological change; Between estimator; Stochastic frontier;

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References

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Citations

Blog mentions

As found by EconAcademics.org, the blog aggregator for Economics research:
  1. Blunt Instruments
    by David Stern in Stochastic Trend on 2013-12-02 10:05:00
  2. Australian Energy Intensity Decomposition
    by noreply@blogger.com (David Stern) in Stochastic Trend on 2014-05-28 05:21:00
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
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Cited by:
  1. David I. Stern & Astrid Kander, 2011. "The Role of Energy in the Industrial Revolution and Modern Economic Growth," CAMA Working Papers 2011-01, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
  2. Lin, Boqiang & Yang, Lisha, 2013. "The potential estimation and factor analysis of China′s energy conservation on thermal power industry," Energy Policy, Elsevier, vol. 62(C), pages 354-362.
  3. Honma, Satoshi & Hu, Jin-Li, 2014. "Panel Data Parametric Frontier Technique for Measuring Total-factor Energy Efficiency: Application to Japanese Regions," MPRA Paper 54304, University Library of Munich, Germany.
  4. Anjum, Zeba & Burke, Paul J. & Gerlagh, Reyer & Stern, David I., 2014. "Rethinking the Emissions-Income Relationship in Terms of Growth Rates," 2014 Conference (58th), February 4-7, 2014, Port Maquarie, Australia 165876, Australian Agricultural and Resource Economics Society.
  5. Sadorsky, Perry, 2013. "Do urbanization and industrialization affect energy intensity in developing countries?," Energy Economics, Elsevier, vol. 37(C), pages 52-59.
  6. Zeba Anjum & Paul J. Burke & Reyer Gerlagh & David I. Stern, 2014. "Modeling the Emissions-Income Relationship Using Long-Run Growth Rates," CCEP Working Papers 1403, Centre for Climate Economics & Policy, Crawford School of Public Policy, The Australian National University.
  7. Wang, H. & Zhou, P. & Zhou, D.Q., 2013. "Scenario-based energy efficiency and productivity in China: A non-radial directional distance function analysis," Energy Economics, Elsevier, vol. 40(C), pages 795-803.

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