IDEAS home Printed from https://ideas.repec.org/a/ove/journl/aid10233.html
   My bibliography  Save this article

Applications of the stochastic frontier approach in Energy Economics

Author

Listed:
  • Massimo Filippini
  • Luis Orea

Abstract

In this paper we discuss some of the issues that energy researchers and regulators could address in two different areas of the application of stochastic frontier analysis (SFA) in Energy Economics: i) the estimation of productive or cost efficiency of electricity and gas distribution networks, and ii) the estimation of energy demand frontier models. Our review examines common problems that often appear using standard frontier models, such as the selection of input (cost) and output variables, the integration of quality incentives in cost benchmarking, the impact of distributed energy resources, or the effect of both unobserved heterogeneity and observed (e.g. environmental, socioeconomic, etc.) variables on firms’ cost or energy consumption. We also point out that the SFA can be used not only to measure the level of efficiency in the use of energy, but also “rebound effects†associated with improvements in energy efficiency.

Suggested Citation

  • Massimo Filippini & Luis Orea, 2014. "Applications of the stochastic frontier approach in Energy Economics," Economics and Business Letters, Oviedo University Press, vol. 3(1), pages 35-42.
  • Handle: RePEc:ove:journl:aid:10233
    as

    Download full text from publisher

    File URL: http://www.unioviedo.es/reunido/index.php/EBL/article/view/10233
    Download Restriction: no

    References listed on IDEAS

    as
    1. Alberini, Anna & Filippini, Massimo, 2011. "Response of residential electricity demand to price: The effect of measurement error," Energy Economics, Elsevier, vol. 33(5), pages 889-895, September.
    2. Massimo Filippini & Lester C. Hunt, 2011. "Energy Demand and Energy Efficiency in the OECD Countries: A Stochastic Demand Frontier Approach," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2), pages 59-80.
    3. 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.
    4. Orea, Luis & Growitsch, Christian & Jamasb, Tooraj, 2012. "Using Supervised Environmental Composites in Production and Efficiency Analyses: An Application to Norwegian Electricity Networks," EWI Working Papers 2012-18, Energiewirtschaftliches Institut an der Universitaet zu Koeln (EWI).
    5. Haney, Aoife Brophy & Pollitt, Michael G., 2009. "Efficiency analysis of energy networks: An international survey of regulators," Energy Policy, Elsevier, vol. 37(12), pages 5814-5830, December.
    6. Yu, William & Jamasb, Tooraj & Pollitt, Michael, 2009. "Does weather explain cost and quality performance? An analysis of UK electricity distribution companies," Energy Policy, Elsevier, vol. 37(11), pages 4177-4188, November.
    7. Mehdi Farsi & Massimo Filippini, 2004. "Regulation and Measuring Cost-Efficiency with Panel Data Models: Application to Electricity Distribution Utilities," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 25(1), pages 1-19, August.
    8. Saunders, Harry D., 2000. "A view from the macro side: rebound, backfire, and Khazzoom-Brookes," Energy Policy, Elsevier, vol. 28(6-7), pages 439-449, June.
    9. Sorrell, Steve & Dimitropoulos, John, 2008. "The rebound effect: Microeconomic definitions, limitations and extensions," Ecological Economics, Elsevier, vol. 65(3), pages 636-649, April.
    10. Kumbhakar, Subal C., 1990. "Production frontiers, panel data, and time-varying technical inefficiency," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 201-211.
    11. Per J. Agrell & Mehdi Farsi & Massimo Filippini & Martin Koller, 2013. "Unobserved heterogeneous effects in the cost efficiency analysis of electricity distribution systems," CER-ETH Economics working paper series 13/171, CER-ETH - Center of Economic Research (CER-ETH) at ETH Zurich.
    12. Flaig, Gebhard, 1990. "Household production and the short- and long-run demand for electricity," Energy Economics, Elsevier, vol. 12(2), pages 116-121, April.
    13. Mehdi Farsi & Massimo Filippini & William Greene, 2005. "Efficiency Measurement in Network Industries: Application to the Swiss Railway Companies," Journal of Regulatory Economics, Springer, vol. 28(1), pages 69-90, July.
    14. Jamasb, Tooraj & Orea, Luis & Pollitt, Michael, 2012. "Estimating the marginal cost of quality improvements: The case of the UK electricity distribution companies," Energy Economics, Elsevier, vol. 34(5), pages 1498-1506.
    15. 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).
    16. Chu Wei & Jinlan Ni & Manhong Shen, 2009. "Empirical Analysis of Provincial Energy Efficiency in China," China & World Economy, Institute of World Economics and Politics, Chinese Academy of Social Sciences, vol. 17(5), pages 88-103.
    17. Mundlak, Yair, 1978. "On the Pooling of Time Series and Cross Section Data," Econometrica, Econometric Society, vol. 46(1), pages 69-85, January.
    18. Saunders, Harry D., 2008. "Fuel conserving (and using) production functions," Energy Economics, Elsevier, vol. 30(5), pages 2184-2235, September.
    19. Antonio Alvarez & Christine Amsler & Luis Orea & Peter Schmidt, 2006. "Interpreting and Testing the Scaling Property in Models where Inefficiency Depends on Firm Characteristics," Journal of Productivity Analysis, Springer, vol. 25(3), pages 201-212, June.
    20. Hu, Jin-Li & Wang, Shih-Chuan, 2006. "Total-factor energy efficiency of regions in China," Energy Policy, Elsevier, vol. 34(17), pages 3206-3217, November.
    21. Growitsch, Christian & Jamasb, Tooraj & Wetzel, Heike, 2012. "Efficiency effects of observed and unobserved heterogeneity: Evidence from Norwegian electricity distribution networks," Energy Economics, Elsevier, vol. 34(2), pages 542-548.
    22. Po, Rung-Wei & Guh, Yuh-Yuan & Yang, Miin-Shen, 2009. "A new clustering approach using data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 199(1), pages 276-284, November.
    23. Coelli, Tim J. & Gautier, Axel & Perelman, Sergio & Saplacan-Pop, Roxana, 2013. "Estimating the cost of improving quality in electricity distribution: A parametric distance function approach," Energy Policy, Elsevier, vol. 53(C), pages 287-297.
    24. Llorca, Manuel & Orea, Luis & Pollitt, Michael G., 2014. "Using the latent class approach to cluster firms in benchmarking: An application to the US electricity transmission industry," Operations Research Perspectives, Elsevier, vol. 1(1), pages 6-17.
    25. 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)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ilya A. Dolmatov & Vladimir V. Dvorkin & Igor V. Maskaev, 2015. "Parametric and Non-Parametric Cost Efficiency Benchmarking of Water Utilities in Russia," HSE Working papers WP BRP 42/MAN/2015, National Research University Higher School of Economics.
    2. Llorca, Manuel & Jamasb, Tooraj, 2017. "Energy efficiency and rebound effect in European road freight transport," Transportation Research Part A: Policy and Practice, Elsevier, vol. 101(C), pages 98-110.
    3. Agrell, Per J. & Brea-Solís, Humberto, 2017. "Capturing heterogeneity in electricity distribution operations: A critical review of latent class modelling," Energy Policy, Elsevier, vol. 104(C), pages 361-372.
    4. Agrell, P & Brea-Solís, H., 2015. "Stationarity of Heterogeneity in Production Technology using Latent Class Modelling," CORE Discussion Papers 2015047, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ove:journl:aid:10233. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Francisco J. Delgado) or (Christopher F. Baum). General contact details of provider: http://edirc.repec.org/data/deovies.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.