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Highly-resolved modeling of personal transportation energy consumption in the United States

Author

Listed:
  • Muratori, Matteo
  • Moran, Michael J.
  • Serra, Emmanuele
  • Rizzoni, Giorgio

Abstract

This paper centers on the estimation of the total primary energy consumption for personal transportation in the United States, to include gasoline and/or electricity consumption, depending on vehicle type. The bottom-up sector-based estimation method introduced here contributes to a computational tool under development at The Ohio State University for assisting decision making in energy policy, pricing, and investment.

Suggested Citation

  • Muratori, Matteo & Moran, Michael J. & Serra, Emmanuele & Rizzoni, Giorgio, 2013. "Highly-resolved modeling of personal transportation energy consumption in the United States," Energy, Elsevier, vol. 58(C), pages 168-177.
  • Handle: RePEc:eee:energy:v:58:y:2013:i:c:p:168-177
    DOI: 10.1016/j.energy.2013.02.055
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    References listed on IDEAS

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    1. Perrels, Adriaan & Weber, Christoph, 2000. "Modelling Impacts of Lifestyle on Energy Demand and Related Emissions," Discussion Papers 228, VATT Institute for Economic Research.
    2. Bin, Shui & Dowlatabadi, Hadi, 2005. "Corrigendum to "Consumer lifestyles approach to US energy use and the related CO2 emissions": [Energy Policy 33 (2005) 197-208]," Energy Policy, Elsevier, vol. 33(10), pages 1362-1363, July.
    3. Bin, Shui & Dowlatabadi, Hadi, 2005. "Consumer lifestyle approach to US energy use and the related CO2 emissions," Energy Policy, Elsevier, vol. 33(2), pages 197-208, January.
    4. Weber, Christoph & Perrels, Adriaan, 2000. "Modelling lifestyle effects on energy demand and related emissions," Energy Policy, Elsevier, vol. 28(8), pages 549-566, July.
    5. Shiau, Ching-Shin Norman & Samaras, Constantine & Hauffe, Richard & Michalek, Jeremy J., 2009. "Impact of battery weight and charging patterns on the economic and environmental benefits of plug-in hybrid vehicles," Energy Policy, Elsevier, vol. 37(7), pages 2653-2663, July.
    6. Muratori, Matteo & Roberts, Matthew C. & Sioshansi, Ramteen & Marano, Vincenzo & Rizzoni, Giorgio, 2013. "A highly resolved modeling technique to simulate residential power demand," Applied Energy, Elsevier, vol. 107(C), pages 465-473.
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    Cited by:

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    3. Selima Sultana & Nastaran Pourebrahim & Hyojin Kim, 2018. "Household Energy Expenditures in North Carolina: A Geographically Weighted Regression Approach," Sustainability, MDPI, vol. 10(5), pages 1-22, May.
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    5. Soto, Esteban A. & Bosman, Lisa B. & Wollega, Ebisa & Leon-Salas, Walter D., 2022. "Comparison of net-metering with peer-to-peer models using the grid and electric vehicles for the electricity exchange," Applied Energy, Elsevier, vol. 310(C).
    6. Fiori, Chiara & Ahn, Kyoungho & Rakha, Hesham A., 2016. "Power-based electric vehicle energy consumption model: Model development and validation," Applied Energy, Elsevier, vol. 168(C), pages 257-268.
    7. O. V. Mazurova & E. V. Gal’perova, 2023. "Energy Consumption in Russia: Current State and Forecast," Studies on Russian Economic Development, Springer, vol. 34(1), pages 105-114, February.
    8. Dindarloo, Saeid R. & Siami-Irdemoosa, Elnaz, 2016. "Determinants of fuel consumption in mining trucks," Energy, Elsevier, vol. 112(C), pages 232-240.
    9. Sahraei, Mohammad Ali & Duman, Hakan & Çodur, Muhammed Yasin & Eyduran, Ecevit, 2021. "Prediction of transportation energy demand: Multivariate Adaptive Regression Splines," Energy, Elsevier, vol. 224(C).
    10. Arslan, Okan & Karasan, Oya Ekin, 2013. "Cost and emission impacts of virtual power plant formation in plug-in hybrid electric vehicle penetrated networks," Energy, Elsevier, vol. 60(C), pages 116-124.
    11. Tang, Yanyan & Zhang, Qi & Mclellan, Benjamin & Li, Hailong, 2018. "Study on the impacts of sharing business models on economic performance of distributed PV-Battery systems," Energy, Elsevier, vol. 161(C), pages 544-558.
    12. Orsi, Francesco & Muratori, Matteo & Rocco, Matteo & Colombo, Emanuela & Rizzoni, Giorgio, 2016. "A multi-dimensional well-to-wheels analysis of passenger vehicles in different regions: Primary energy consumption, CO2 emissions, and economic cost," Applied Energy, Elsevier, vol. 169(C), pages 197-209.
    13. Akansha Jain & Masoud Karimi-Ghartemani, 2022. "Mitigating Adverse Impacts of Increased Electric Vehicle Charging on Distribution Transformers," Energies, MDPI, vol. 15(23), pages 1-26, November.

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