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Heterogeneity in German Residential Electricity Consumption: A quantile regression approach

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  • Frondel, Manuel
  • Sommer, Stephan
  • Vance, Colin

Abstract

In the absence of sufficient coverage of metering data on the electricity consumption of individual devices, this paper estimates the contribution of individual appliances to overall household electricity consumption, drawing on the most recent wave of the German Residential Energy Consumption Survey (GRECS). Moving beyond the standard focus of estimating mean effects, we combine the conditional demand approach with quantile regression methods to capture the heterogeneity in electricity consumption rates of individual appliances. Our results indicate substantial differences in these rates, as well as the end-use shares across households originating from the opposite tails of the electricity consumption distribution. This outcome highlights the added value of applying quantile regression methods in estimating consumption rates of electric appliances and indicates some scope for realizing conservation potentials.

Suggested Citation

  • Frondel, Manuel & Sommer, Stephan & Vance, Colin, 2019. "Heterogeneity in German Residential Electricity Consumption: A quantile regression approach," Energy Policy, Elsevier, vol. 131(C), pages 370-379.
  • Handle: RePEc:eee:enepol:v:131:y:2019:i:c:p:370-379
    DOI: 10.1016/j.enpol.2019.03.045
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    1. Matsumoto, Shigeru, 2016. "How do household characteristics affect appliance usage? Application of conditional demand analysis to Japanese household data," Energy Policy, Elsevier, vol. 94(C), pages 214-223.
    2. Michael Parti & Cynthia Parti, 1980. "The Total and Appliance-Specific Conditional Demand for Electricity in the Household Sector," Bell Journal of Economics, The RAND Corporation, vol. 11(1), pages 309-321, Spring.
    3. Chen, Victor L. & Delmas, Magali A. & Kaiser, William J. & Locke, Stephen L., 2015. "What can we learn from high-frequency appliance-level energy metering? Results from a field experiment," Energy Policy, Elsevier, vol. 77(C), pages 164-175.
    4. Fell, Harrison & Li, Shanjun & Paul, Anthony, 2014. "A new look at residential electricity demand using household expenditure data," International Journal of Industrial Organization, Elsevier, vol. 33(C), pages 37-47.
    5. Kaza, Nikhil, 2010. "Understanding the spectrum of residential energy consumption: A quantile regression approach," Energy Policy, Elsevier, vol. 38(11), pages 6574-6585, November.
    6. Herriges, Joseph A. & Caves, Douglas W. & Train, K. & Windle, R. J., 1987. "A Bayesian Approach to Combining Conditional Demand and Engineering Models of Electricity Usage," Staff General Research Papers Archive 10794, Iowa State University, Department of Economics.
    7. Caves, Douglas W, 1987. "A Bayesian Approach to Combining Conditional Demand and Engineering Models of Electricity Usage," The Review of Economics and Statistics, MIT Press, vol. 69(3), pages 438-448, August.
    8. Koenker, Roger & Bassett, Gilbert, Jr, 1982. "Robust Tests for Heteroscedasticity Based on Regression Quantiles," Econometrica, Econometric Society, vol. 50(1), pages 43-61, January.
    9. repec:aen:journl:ej36-4-larsen is not listed on IDEAS
    10. Valenzuela, Carlos & Valencia, Alelhie & White, Steve & Jordan, Jeffrey A. & Cano, Stephanie & Keating, Jerome & Nagorski, John & Potter, Lloyd B., 2014. "An analysis of monthly household energy consumption among single-family residences in Texas, 2010," Energy Policy, Elsevier, vol. 69(C), pages 263-272.
    11. Halvorsen, Bente & Larsen, Bodil M., 2001. "The flexibility of household electricity demand over time," Resource and Energy Economics, Elsevier, vol. 23(1), pages 1-18, January.
    12. Jones, Rory V. & Fuertes, Alba & Lomas, Kevin J., 2015. "The socio-economic, dwelling and appliance related factors affecting electricity consumption in domestic buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 43(C), pages 901-917.
    13. Larsen, Bodil Merethe & Nesbakken, Runa, 2004. "Household electricity end-use consumption: results from econometric and engineering models," Energy Economics, Elsevier, vol. 26(2), pages 179-200, March.
    14. Koenker,Roger, 2005. "Quantile Regression," Cambridge Books, Cambridge University Press, number 9780521845731, November.
    15. repec:aen:journl:1984v05-03-a06 is not listed on IDEAS
    16. Sorrell, Steve & Dimitropoulos, John & Sommerville, Matt, 2009. "Empirical estimates of the direct rebound effect: A review," Energy Policy, Elsevier, vol. 37(4), pages 1356-1371, April.
    17. Katrina Jessoe & David Rapson, 2014. "Knowledge Is (Less) Power: Experimental Evidence from Residential Energy Use," American Economic Review, American Economic Association, vol. 104(4), pages 1417-1438, April.
    18. Peter C. Reiss & Matthew W. White, 2005. "Household Electricity Demand, Revisited," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 72(3), pages 853-883.
    19. Schleich, Joachim & Klobasa, Marian & Gölz, Sebastian & Brunner, Marc, 2013. "Effects of feedback on residential electricity demand—Findings from a field trial in Austria," Energy Policy, Elsevier, vol. 61(C), pages 1097-1106.
    20. Frondel, Manuel & Ritter, Nolan & Vance, Colin, 2012. "Heterogeneity in the rebound effect: Further evidence for Germany," Energy Economics, Elsevier, vol. 34(2), pages 461-467.
    21. Yao, Xi-Long & Liu, Yang & Yan, Xiao, 2014. "A quantile approach to assess the effectiveness of the subsidy policy for energy-efficient home appliances: Evidence from Rizhao, China," Energy Policy, Elsevier, vol. 73(C), pages 512-518.
    22. Huang, Wen-Hsiu, 2015. "The determinants of household electricity consumption in Taiwan: Evidence from quantile regression," Energy, Elsevier, vol. 87(C), pages 120-133.
    23. Hsiao, Cheng & Mountain, Dean C & Illman, Kathleen Ho, 1995. "A Bayesian Integration of End-Use Metering and Conditional-Demand Analysis," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 315-326, July.
    24. repec:aen:journl:ej40-5-frondel is not listed on IDEAS
    25. repec:aen:journl:2009v30-02-a07 is not listed on IDEAS
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    JEL classification:

    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices

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