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Understanding the spectrum of residential energy consumption: A quantile regression approach

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  • Kaza, Nikhil

Abstract

Residential energy consumption accounts for 22% of the total energy consumption in the US. However, the impacts of local planning policies, such as increasing density and changing the housing type mix, on residential energy consumption are not well understood. Using Residential Energy Consumption Survey Data from the Energy Information Administration, quantile regression analysis was used to tease out the effects of various factors on entire distribution on the energy consumption spectrum instead of focusing on the conditional average. Results show that while housing size matters for space conditioning, housing type has a more nuanced impact. Self-reported neighborhood density does not seem to have any impact on energy use. Furthermore, the effects of these factors at the tails of the energy use distribution are substantially different than the average, in some cases differing by a factor of six. Some, not all, types of multifamily housing offer almost as much savings as reduction in housing area by 100Â m2, compared to single family houses.

Suggested Citation

  • Kaza, Nikhil, 2010. "Understanding the spectrum of residential energy consumption: A quantile regression approach," Energy Policy, Elsevier, vol. 38(11), pages 6574-6585, November.
    • Handle: RePEc:eee:enepol:v:38:y:2010:i:11:p:6574-6585
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    References listed on IDEAS

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    1. Samuel R. Staley, 2008. "Missing the forest through the trees? Comment on Reid Ewing and Fang Rong's “the impact of urban form on U.S. residential energy use”," Housing Policy Debate, Taylor & Francis Journals, vol. 19(1), pages 31-43, January.
    2. Geroski, P. A., 2000. "Models of technology diffusion," Research Policy, Elsevier, vol. 29(4-5), pages 603-625, April.
    3. Reid Ewing & Fang Rong, 2008. "The impact of urban form on U.S. residential energy use," Housing Policy Debate, Taylor & Francis Journals, vol. 19(1), pages 1-30, January.
    4. Clinton Andrews, 2008. "Greenhouse gas emissions along the rural-urban gradient," Journal of Environmental Planning and Management, Taylor & Francis Journals, vol. 51(6), pages 847-870.
    5. Roger Koenker & Kevin F. Hallock, 2001. "Quantile Regression," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 143-156, Fall.
    6. Heckman, James, 2013. "Sample selection bias as a specification error," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 31(3), pages 129-137.
    7. Linden, Anna-Lisa & Carlsson-Kanyama, Annika & Eriksson, Bjorn, 2006. "Efficient and inefficient aspects of residential energy behaviour: What are the policy instruments for change?," Energy Policy, Elsevier, vol. 34(14), pages 1918-1927, September.
    8. John Randolph, 2008. "Comment on Reid Ewing and Fang Rong's “The impact of urban form on U.S. residential energy use”," Housing Policy Debate, Taylor & Francis Journals, vol. 19(1), pages 45-52, January.
    9. Boarnet, Marlon & Crane, Randall, 2001. "The influence of land use on travel behavior: specification and estimation strategies," Transportation Research Part A: Policy and Practice, Elsevier, vol. 35(9), pages 823-845, November.
    10. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
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