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VMT, energy consumption, and GHG emissions forecasting for passenger transportation

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  • Rentziou, Aikaterini
  • Gkritza, Konstantina
  • Souleyrette, Reginald R.
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    Abstract

    Globalization, greenhouse gas emissions and energy concerns, emerging vehicle technologies, and improved statistical modeling capabilities make the present moment an opportune time to revisit aggregate vehicle miles traveled (VMT), energy consumption, and greenhouse gas (GHG) emissions forecasting for passenger transportation. Using panel data for the 48 continental states during the period 1998–2008, the authors develop simultaneous equation models for predicting VMT on different road functional classes and examine how different technological solutions and changes in fuel prices can affect passenger VMT. Moreover, a random coefficient panel data model is developed to estimate the influence of various factors (such as demographics, socioeconomic variables, fuel tax, and capacity) on the total amount of passenger VMT in the United States. To assess the influence of each significant factor on VMT, elasticities are estimated. Further, the authors investigate the effect of different policies governing fuel tax and population density on future energy consumption and GHG emissions. The presented methodology and estimation results can assist transportation planners and policy-makers in determining future energy and transportation infrastructure investment needs.

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    Bibliographic Info

    Article provided by Elsevier in its journal Transportation Research Part A: Policy and Practice.

    Volume (Year): 46 (2012)
    Issue (Month): 3 ()
    Pages: 487-500

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    Handle: RePEc:eee:transa:v:46:y:2012:i:3:p:487-500

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    Related research

    Keywords: VMT; Energy consumption; GHG; Passenger transportation; SURE; Random parameters;

    References

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    1. Southworth, Frank, 2001. "On the potential impacts of land use change policies on automobile vehicle miles of travel," Energy Policy, Elsevier, vol. 29(14), pages 1271-1283, November.
    2. Hsiao,Cheng, 2003. "Analysis of Panel Data," Cambridge Books, Cambridge University Press, number 9780521818551, April.
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    7. Choo, Sangho & Mokhtarian, Patricia L., 2007. "Telecommunications and travel demand and supply: Aggregate structural equation models for the US," Transportation Research Part A: Policy and Practice, Elsevier, vol. 41(1), pages 4-18, January.
    8. Su, Qing, 2010. "Travel demand in the US urban areas: A system dynamic panel data approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 44(2), pages 110-117, February.
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    11. Chatman, Daniel G., 2008. "Deconstructing development density: Quality, quantity and price effects on household non-work travel," Transportation Research Part A: Policy and Practice, Elsevier, vol. 42(7), pages 1008-1030, August.
    12. Liddle, Brantley, 2009. "Long-Run Relationship among Transport Demand, Income, and Gasoline Price for the US," MPRA Paper 52080, University Library of Munich, Germany.
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    Cited by:
    1. Zhang, Chuanguo & Nian, Jiang, 2013. "Panel estimation for transport sector CO2 emissions and its affecting factors: A regional analysis in China," Energy Policy, Elsevier, vol. 63(C), pages 918-926.
    2. Al-Ghandoor, Ahmed & Jaber, Jamal & Al-Hinti, Ismael & Abdallat, Yousef, 2013. "Statistical assessment and analyses of the determinants of transportation sector gasoline demand in Jordan," Transportation Research Part A: Policy and Practice, Elsevier, vol. 50(C), pages 129-138.

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