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Energy Efficiency in Transition Economies: A Stochastic Frontier Approach

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  • Antonio Carvalho

    () (Centre for Energy Economics Research and Policy, Heriot-Watt University)

Abstract

The paper outlines and estimates a measure of underlying efficiency in electricity consumption for an unbalanced panel of 28 transition economies and 5 Western European OECD countries in the period 1994-2007, by estimating a Bayesian Generalized True Random Effects (GTRE) stochastic frontier model that estimates both persistent and transient inefficiency. The properties of alternative GTRE estimation methods in small samples are explored to guide the estimation strategy. The paper analyses the behaviour of underlying efficiency in electricity consumption in these economies after accounting for time-invariant technological differences. After outlining the specific characteristics of the transition economies and their heterogeneous structural economic changes, an aggregate electricity demand function is estimated to obtain efficiency scores that give new insights for transition economies than a simple analysis of energy intensity. There is some evidence of convergence between the CIS countries and a block of Eastern European and selected OECD countries, although other country groups do not follow this tendency, such as the Balkans.

Suggested Citation

  • Antonio Carvalho, 2016. "Energy Efficiency in Transition Economies: A Stochastic Frontier Approach," CEERP Working Paper Series 004, Centre for Energy Economics Research and Policy, Heriot-Watt University.
  • Handle: RePEc:hwc:wpaper:004
    as

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    File URL: http://ceerp.hw.ac.uk/RePEc/hwc/wpaper/004.pdf
    File Function: First version, 2016
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    References listed on IDEAS

    as
    1. Filippini, Massimo & Hunt, Lester C., 2012. "US residential energy demand and energy efficiency: A stochastic demand frontier approach," Energy Economics, Elsevier, vol. 34(5), pages 1484-1491.
    2. Cooper, R.Caron & Schipper, Lee, 1992. "The efficiency of energy use in the USSR —an international perspective," Energy, Elsevier, vol. 17(1), pages 1-24.
    3. Aleh Tsyvinski & Martin Petri & Günther Taube, 2002. "Energy Sector Quasi-Fiscal Activities in the Countries of the Former Soviet Union," IMF Working Papers 02/60, International Monetary Fund.
    4. Badunenko, Oleg & Kumbhakar, Subal C., 2016. "When, where and how to estimate persistent and transient efficiency in stochastic frontier panel data models," European Journal of Operational Research, Elsevier, vol. 255(1), pages 272-287.
    5. Richard E. Ericson, 1991. "The Classical Soviet-Type Economy: Nature of the System and Implications for Reform," Journal of Economic Perspectives, American Economic Association, vol. 5(4), pages 11-27, Fall.
    6. Orea, Luis & Llorca, Manuel & Filippini, Massimo, 2014. "Measuring energy efficiency and rebound effects using a stochastic demand frontier approach: the US residential energy demand," Efficiency Series Papers 2014/01, University of Oviedo, Department of Economics, Oviedo Efficiency Group (OEG).
    7. Griffiths, William E. & Hajargasht, Gholamreza, 2016. "Some models for stochastic frontiers with endogeneity," Journal of Econometrics, Elsevier, vol. 190(2), pages 341-348.
    8. Dermot Gately & Hiliard G. Huntington, 2002. "The Asymmetric Effects of Changes in Price and Income on Energy and Oil Demand," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1), pages 19-55.
    9. Aigner, Dennis & Lovell, C. A. Knox & Schmidt, Peter, 1977. "Formulation and estimation of stochastic frontier production function models," Journal of Econometrics, Elsevier, vol. 6(1), pages 21-37, July.
    10. Greene, William, 2005. "Reconsidering heterogeneity in panel data estimators of the stochastic frontier model," Journal of Econometrics, Elsevier, vol. 126(2), pages 269-303, June.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    Electricity Consumption; Transition Economies; Energy Efficiency; Stochastic Frontier;

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • Q49 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Other
    • P20 - Economic Systems - - Socialist Systems and Transition Economies - - - General

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