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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.

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  • 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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    as
    1. 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.
    2. 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.
    3. 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.
    4. Kaza, Nikhil, 2010. "Understanding the spectrum of residential energy consumption: A quantile regression approach," Energy Policy, Elsevier, vol. 38(11), pages 6574-6585, November.
    5. 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.
    6. 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.
    7. 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.
    8. Peter Grosche & Colin Vance, 2009. "Willingness to Pay for Energy Conservation and Free-Ridership on Subsidization: Evidence from Germany," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2), pages 135-154.
    9. Koenker,Roger, 2005. "Quantile Regression," Cambridge Books, Cambridge University Press, number 9780521845731.
    10. Dennis J. Aigner & Cynts Sorooshian & Pamela Kerwin, 1984. "Conditional Demand Analysis for Estimating Residential End-Use Load Profiles," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3), pages 81-98.
    11. 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.
    12. 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.
    13. 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.
    14. Manuel Frondel and Gerhard Kussel, 2019. "Switching on Electricity Demand Response: Evidence for German Households," The Energy Journal, International Association for Energy Economics, vol. 0(Number 5).
    15. 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.
    16. 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.
    17. Caves, Douglas W, et al, 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.
    18. Koenker, Roger & Bassett, Gilbert, Jr, 1982. "Robust Tests for Heteroscedasticity Based on Regression Quantiles," Econometrica, Econometric Society, vol. 50(1), pages 43-61, January.
    19. Hanne Marit Dalen and Bodil M. Larsen, 2015. "Residential End-use Electricity Demand: Development over Time," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4).
    20. 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.
    21. 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.
    22. 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.
    23. 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.
    24. 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.
    25. Huang, Wen-Hsiu, 2015. "The determinants of household electricity consumption in Taiwan: Evidence from quantile regression," Energy, Elsevier, vol. 87(C), pages 120-133.
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    18. Salem, Mohammed Z. & Ertz, Myriam & Sarigӧllü, Emine, 2021. "Demarketing strategies to rationalize electricity consumption in the Gaza Strip-Palestine," Renewable and Sustainable Energy Reviews, Elsevier, vol. 143(C).
    19. Kostakis, Ioannis & Lolos, Sarantis & Sardianou, Eleni, 2021. "Residential natural gas demand: Assessing the evidence from Greece using pseudo-panels, 2012–2019," Energy Economics, Elsevier, vol. 99(C).
    20. Zhao, Lu-Tao & Xing, Yue-Yue & Zhao, Qiu-Rong & Chen, Xue-Hui, 2023. "Dynamic impacts of online investor sentiment on international crude oil prices," Resources Policy, Elsevier, vol. 82(C).
    21. Groh, Elke D. & Ziegler, Andreas, 2022. "On the relevance of values, norms, and economic preferences for electricity consumption," Ecological Economics, Elsevier, vol. 192(C).
    22. Taneja, Shivani & Mandys, Filip, 2022. "The effect of disaggregated information and communication technologies on industrial energy demand," Renewable and Sustainable Energy Reviews, Elsevier, vol. 164(C).
    23. Felipe Moraes do Nascimento & Julio Cezar Mairesse Siluk & Fernando de Souza Savian & Taís Bisognin Garlet & José Renes Pinheiro & Carlos Ramos, 2020. "Factors for Measuring Photovoltaic Adoption from the Perspective of Operators," Sustainability, MDPI, vol. 12(8), pages 1-29, April.
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    More about this item

    Keywords

    Electricity consumption; Conditional demand approach; Quantile regression methods;
    All these keywords.

    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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