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Spatio-temporal analysis of car distance, greenhouse gases and the effect of built environment: A latent class regression analysis

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  • Zahabi, Seyed Amir H.
  • Miranda-Moreno, Luis
  • Patterson, Zachary
  • Barla, Philippe

Abstract

This work examines the temporal–spatial variations of daily automobile distance traveled and greenhouse gas emissions (GHGs) and their association with built environment attributes and household socio-demographics. A GHGs household inventory is determined using link-level average speeds for a large and representative sample of households in three origin–destination surveys (1998, 2003 and 2008) in Montreal, Canada. For the emission inventories, different sources of data are combined including link-level average speeds in the network, vehicle occupancy levels and fuel consumption characteristics of the vehicle fleet. Urban form indicators over time such as population density, land use mix and transit accessibility are generated for each household in each of the three waves. A latent class (LC) regression modeling framework is then implemented to investigate the association of built environment and socio-demographics with GHGs and automobile distance traveled. Among other results, it is found that population density, transit accessibility and land-use mix have small but statistically significant negative impact on GHGs and car usage. Despite that this is in accordance with past studies, the estimated elasticities are greater than those reported in the literature for North American cities. Moreover, different household subpopulations are identified in which the effect of built environment varies significantly. Also, a reduction of the average GHGs at the household level is observed over time. According to our estimates, households produced 15% and 10% more GHGs in 1998 and 2003 respectively, compared to 2008. This reduction can be associated to the improvement of the fuel economy of vehicle fleet and the decrease of motor-vehicle usage – e.g., a decrease of 4% is observed for fuel efficiency rates and 12% for distance according to the raw average estimates from 1998 with respect to 2008. A strong link is also observed between socio-demographics and the two travel outcomes. While number of workers is positively associated with car distance and GHGs, low and medium income households pollute less than high-income households.

Suggested Citation

  • Zahabi, Seyed Amir H. & Miranda-Moreno, Luis & Patterson, Zachary & Barla, Philippe, 2015. "Spatio-temporal analysis of car distance, greenhouse gases and the effect of built environment: A latent class regression analysis," Transportation Research Part A: Policy and Practice, Elsevier, vol. 77(C), pages 1-13.
    • Handle: RePEc:eee:transa:v:77:y:2015:i:c:p:1-13
    DOI: 10.1016/j.tra.2015.04.002
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    3. Kim, Sung Hoo & Mokhtarian, Patricia L., 2023. "Finite mixture (or latent class) modeling in transportation: Trends, usage, potential, and future directions," Transportation Research Part B: Methodological, Elsevier, vol. 172(C), pages 134-173.
    4. Zhong, Shaopeng & Bushell, Max, 2017. "Impact of the built environment on the vehicle emission effects of road pricing policies: A simulation case study," Transportation Research Part A: Policy and Practice, Elsevier, vol. 103(C), pages 235-249.
    5. Bel, Germà & Rosell, Jordi, 2017. "The impact of socioeconomic characteristics on CO2 emissions associated with urban mobility: Inequality across individuals," Energy Economics, Elsevier, vol. 64(C), pages 251-261.
    6. Chowdhury, Tufayel & Scott, Darren M., 2020. "An analysis of the built environment and auto travel in Halifax, Canada," Transport Policy, Elsevier, vol. 94(C), pages 23-33.
    7. Olivieri, Cecilia & Fageda, Xavier, 2021. "Urban mobility with a focus on gender: The case of a middle-income Latin American city," Journal of Transport Geography, Elsevier, vol. 91(C).
    8. Seyed Amir H. Zahabi & Luis Miranda-Moreno & Zachary Patterson & Philippe Barla, 2017. "Impacts of built environment and emerging green technologies on daily transportation greenhouse gas emissions in Quebec cities: a disaggregate modeling approach," Transportation, Springer, vol. 44(1), pages 159-180, January.
    9. Michał Czepkiewicz & Áróra Árnadóttir & Jukka Heinonen, 2019. "Flights Dominate Travel Emissions of Young Urbanites," Sustainability, MDPI, vol. 11(22), pages 1-35, November.
    10. Jing Li & Kevin Lo & Meng Guo, 2018. "Do Socio-Economic Characteristics Affect Travel Behavior? A Comparative Study of Low-Carbon and Non-Low-Carbon Shopping Travel in Shenyang City, China," IJERPH, MDPI, vol. 15(7), pages 1-11, June.
    11. Cecilia Olivieri & Xavier Fageda, 2019. "Determinants of urban mobility with a focus on gender: a multilevel analysis in the Metropolitan Area of Montevideo, Uruguay," Documentos de Trabajo (working papers) 0419, Department of Economics - dECON.

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    JEL classification:

    • R42 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Government and Private Investment Analysis; Road Maintenance; Transportation Planning
    • R48 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Government Pricing and Policy
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming
    • Q58 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environmental Economics: Government Policy

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