IDEAS home Printed from https://ideas.repec.org/a/eee/enepol/v38y2010i11p6574-6585.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0301-4215(10)00491-X
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Huang, Wen-Hsiu, 2015. "The determinants of household electricity consumption in Taiwan: Evidence from quantile regression," Energy, Elsevier, vol. 87(C), pages 120-133.
    2. Tilov, Ivan & Farsi, Mehdi & Volland, Benjamin, 2020. "From frugal Jane to wasteful John: A quantile regression analysis of Swiss households’ electricity demand," Energy Policy, Elsevier, vol. 138(C).
    3. Rahman, Mustafizur & Al-Hasan, Md., 2018. "Male-Female wage gap and informal employment in Bangladesh: A quantile regression approach," MPRA Paper 90131, University Library of Munich, Germany.
    4. Marco Capasso & Tania Treibich & Bart Verspagen, 2015. "The medium-term effect of R&D on firm growth," Small Business Economics, Springer, vol. 45(1), pages 39-62, June.
    5. Bassam Fattouh & Laurence Harris & Pasquale Scaramozzino, 2008. "Non-linearity in the determinants of capital structure: evidence from UK firms," Empirical Economics, Springer, vol. 34(3), pages 417-438, June.
    6. Kaushal, Kaushalendra Kumar, 2014. "Pathway from nutrition intake to wage among elementary workers in India," MPRA Paper 56652, University Library of Munich, Germany.
    7. Daria Ciriaci & Pietro Moncada-Paternò-Castello & Peter Voigt, 2016. "Innovation and job creation: a sustainable relation?," Eurasian Business Review, Springer;Eurasia Business and Economics Society, vol. 6(2), pages 189-213, August.
    8. Ben Tomlin & Loretta Fung, 2015. "Exchange Rate Movements and the Distribution of Productivity," Review of International Economics, Wiley Blackwell, vol. 23(4), pages 782-809, September.
    9. Daniel Pollmann & Thomas Dohmen & Franz Palm, 2020. "Robust Estimation of Wage Dispersion with Censored Data: An Application to Occupational Earnings Risk and Risk Attitudes," De Economist, Springer, vol. 168(4), pages 519-540, December.
    10. E. Pastrapa & C. Apostolopoulos, 2015. "Estimating Determinants of Borrowing: Evidence from Greece," Journal of Family and Economic Issues, Springer, vol. 36(2), pages 210-223, June.
    11. Tsani, Stella, 2015. "On the relationship between resource funds, governance and institutions: Evidence from quantile regression analysis," Resources Policy, Elsevier, vol. 44(C), pages 94-111.
    12. Li, Ming-Yuan Leon, 2009. "Value or volume strategy?," Finance Research Letters, Elsevier, vol. 6(4), pages 210-218, December.
    13. Zhang, Jian & Mishra, Ashok K. & Hirsch, Stefan & Li, Xiaoshun, 2020. "Factors affecting farmland rental in rural China: Evidence of capitalization of grain subsidy payments," Land Use Policy, Elsevier, vol. 90(C).
    14. Medina, Eva & Vicéns, José, 2011. "Factores determinantes de la demanda eléctrica de los hogares en España: una aproximación mediante regresión cuantílica/Determinants of Household Electricity Demand in Spain: An Approach through Quant," Estudios de Economia Aplicada, Estudios de Economia Aplicada, vol. 29, pages 515-538, Agosto.
    15. LI, Tao & SUN, Laixiang & ZOU, Liang, 2009. "State ownership and corporate performance: A quantile regression analysis of Chinese listed companies," China Economic Review, Elsevier, vol. 20(4), pages 703-716, December.
    16. Joachim Zietz & Emily Zietz & G. Sirmans, 2008. "Determinants of House Prices: A Quantile Regression Approach," The Journal of Real Estate Finance and Economics, Springer, vol. 37(4), pages 317-333, November.
    17. Pedro Pires & João Pedro Pereira & Luís Filipe Martins, 2015. "The Empirical Determinants of Credit Default Swap Spreads: a Quantile Regression Approach," European Financial Management, European Financial Management Association, vol. 21(3), pages 556-589, June.
    18. Uematsu, Hiroki & Mishra, Ashok K., 2012. "The Impact of Natural Amenity on Farmland Values: A Quantile Regression Approach," 2012 Annual Meeting, February 4-7, 2012, Birmingham, Alabama 119804, Southern Agricultural Economics Association.
    19. Parshall, Lily & Gurney, Kevin & Hammer, Stephen A. & Mendoza, Daniel & Zhou, Yuyu & Geethakumar, Sarath, 2010. "Modeling energy consumption and CO2 emissions at the urban scale: Methodological challenges and insights from the United States," Energy Policy, Elsevier, vol. 38(9), pages 4765-4782, September.
    20. Koh, SzeKee & Durand, Robert B. & Limkriangkrai, Manapon, 2015. "The value of Saints and the price of Sin," Pacific-Basin Finance Journal, Elsevier, vol. 35(PA), pages 56-72.

    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:eee:enepol:v:38:y:2010:i:11:p:6574-6585. See general information about how to correct material in RePEc.

    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 bibliographic 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.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/enpol .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.