IDEAS home Printed from https://ideas.repec.org/a/eee/juecon/v65y2009i1p91-98.html
   My bibliography  Save this article

The impact of residential density on vehicle usage and energy consumption

Author

Listed:
  • Brownstone, David
  • Golob, Thomas F.

Abstract

We specify and estimate a joint model of residential density, vehicle use, and fuel consumption that accounts for both self selection effects and missing data that are related to the endogenous variables. Our model is estimated on the California subsample of the 2001 U.S. National Household Travel Survey (NHTS). Comparing two California households that are similar in all respects except residential density, a lower density of 1000 housing units per square mile (roughly 40% of the weighted sample average) implies an increase of 1200 miles driven per year (4.8%) and 65 more gallons of fuel used per household (5.5%). This total effect of residential density on fuel usage is decomposed into two paths of influence. Increased mileage leads to a difference of 45 gallons, but there is an additional direct effect of density through lower fleet fuel economy of 20 gallons per year, a result of vehicle type choice.

Suggested Citation

  • Brownstone, David & Golob, Thomas F., 2009. "The impact of residential density on vehicle usage and energy consumption," Journal of Urban Economics, Elsevier, vol. 65(1), pages 91-98, January.
  • Handle: RePEc:eee:juecon:v:65:y:2009:i:1:p:91-98
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0094-1190(08)00109-5
    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. Hausman, Jerry, 2015. "Specification tests in econometrics," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 38(2), pages 112-134.
    2. Fang, Hao Audrey, 2008. "A discrete-continuous model of households' vehicle choice and usage, with an application to the effects of residential density," Transportation Research Part B: Methodological, Elsevier, vol. 42(9), pages 736-758, November.
    3. Marlon G. Boarnet & Sharon Sarmiento, 1998. "Can Land-use Policy Really Affect Travel Behaviour? A Study of the Link between Non-work Travel and Land-use Characteristics," Urban Studies, Urban Studies Journal Limited, vol. 35(7), pages 1155-1169, June.
    4. 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.
    5. Bhat, Chandra R. & Eluru, Naveen, 2009. "A copula-based approach to accommodate residential self-selection effects in travel behavior modeling," Transportation Research Part B: Methodological, Elsevier, vol. 43(7), pages 749-765, August.
    6. Bhat, Chandra R. & Guo, Jessica Y., 2007. "A comprehensive analysis of built environment characteristics on household residential choice and auto ownership levels," Transportation Research Part B: Methodological, Elsevier, vol. 41(5), pages 506-526, June.
    7. Matthew E. Kahn, 2000. "The environmental impact of suburbanization," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 19(4), pages 569-586.
    8. Kevin A. Bryan & Brian D. Minton & Pierre-Daniel G. Sarte, 2007. "The evolution of city population density in the United States," Economic Quarterly, Federal Reserve Bank of Richmond, vol. 93(Fall), pages 341-360.
    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. Kim, Jinwon & Brownstone, David, 2010. "The impact of residential density on vehicle usage and fuel consumption," University of California Transportation Center, Working Papers qt31m0w2x3, University of California Transportation Center.
    2. Andrew Perumal & David Timmons, 2017. "Contextual Density and US Automotive CO2 Emissions across the Rural–Urban Continuum," International Regional Science Review, , vol. 40(6), pages 590-615, November.
    3. Watanabe, Hajime & Maruyama, Takuya, 2024. "A Bayesian sample selection model with a binary outcome for handling residential self-selection in individual car ownership," Journal of choice modelling, Elsevier, vol. 51(C).
    4. Heres-Del-Valle, David & Niemeier, Deb, 2011. "CO2 emissions: Are land-use changes enough for California to reduce VMT? Specification of a two-part model with instrumental variables," Transportation Research Part B: Methodological, Elsevier, vol. 45(1), pages 150-161, January.
    5. Kim, Jinwon, 2012. "Endogenous vehicle-type choices in a monocentric city," Regional Science and Urban Economics, Elsevier, vol. 42(4), pages 749-760.
    6. Cao, Jason & Ettema, Dick, 2014. "Satisfaction with travel and residential self-selection: How do preferences moderate the impact of the Hiawatha Light Rail Transit line?," The Journal of Transport and Land Use, Center for Transportation Studies, University of Minnesota, vol. 7(3), pages 93-108.
    7. Xinyu Cao & Patricia L. Mokhtarian, 2012. "The connections among accessibility, self- selection and walking behaviour: a case study of Northern California residents," Chapters, in: Karst T. Geurs & Kevin J. Krizek & Aura Reggiani (ed.), Accessibility Analysis and Transport Planning, chapter 5, pages 73-95, Edward Elgar Publishing.
    8. Ipek Sener & Chandra Bhat, 2012. "Modeling the spatial and temporal dimensions of recreational activity participation with a focus on physical activities," Transportation, Springer, vol. 39(3), pages 627-656, May.
    9. Kim, Jinwon & Brownstone, David, 2013. "The impact of residential density on vehicle usage and fuel consumption: Evidence from national samples," Energy Economics, Elsevier, vol. 40(C), pages 196-206.
    10. Sabreena Anowar & Naveen Eluru & Luis F. Miranda-Moreno, 2014. "Alternative Modeling Approaches Used for Examining Automobile Ownership: A Comprehensive Review," Transport Reviews, Taylor & Francis Journals, vol. 34(4), pages 441-473, July.
    11. Campbell, Randall C. & Nagel, Gregory L., 2016. "Private information and limitations of Heckman's estimator in banking and corporate finance research," Journal of Empirical Finance, Elsevier, vol. 37(C), pages 186-195.
    12. Kamruzzaman, Md. & Baker, Douglas & Washington, Simon & Turrell, Gavin, 2013. "Residential dissonance and mode choice," Journal of Transport Geography, Elsevier, vol. 33(C), pages 12-28.
    13. Andreas Wagner & Denise Fischer‐Kreer, 2024. "The role of CEO regulatory focus in increasing or reducing corporate carbon emissions," Business Strategy and the Environment, Wiley Blackwell, vol. 33(2), pages 1051-1065, February.
    14. Verbeek, M.J.C.M. & Nijman, T.E., 1992. "Incomplete panels and selection bias : A survey," Discussion Paper 1992-7, Tilburg University, Center for Economic Research.
    15. David Bendig & Andreas Wagner & Kevin Lau, 2023. "Does it pay to be science‐based green? The impact of science‐based emission‐reduction targets on corporate financial performance," Journal of Industrial Ecology, Yale University, vol. 27(1), pages 125-140, February.
    16. Malmendier, Ulrike M. & Botsch, Matthew J., 2020. "The Long Shadows of the Great Inflation: Evidence from Residential Mortgages," CEPR Discussion Papers 14934, C.E.P.R. Discussion Papers.
    17. Boswijk, H. Peter & Franses, Philip Hans & van Dijk, Dick, 2010. "Cointegration in a historical perspective," Journal of Econometrics, Elsevier, vol. 158(1), pages 156-159, September.
    18. Massimiliano Bratti & Stefano Staffolani, 2007. "Effort‐based career opportunities and working time," International Journal of Manpower, Emerald Group Publishing Limited, vol. 28(6), pages 489-512, September.
    19. Martin Beznoska, 2014. "Estimating a Consumer Demand System of Energy, Mobility and Leisure: A Microdata Approach for Germany," Discussion Papers of DIW Berlin 1374, DIW Berlin, German Institute for Economic Research.
    20. Hall, George & Rust, John, 2021. "Estimation of endogenously sampled time series: The case of commodity price speculation in the steel market," Journal of Econometrics, Elsevier, vol. 222(1), pages 219-243.

    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:juecon:v:65:y:2009:i:1:p:91-98. 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/inca/622905 .

    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.