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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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    Cited by:

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    2. Saket, Mohammad Javad & Maleki, Abbas & Hezaveh, Erfan Doroudgar & Karimi, Mohammad Sadegh, 2019. "Institutional analysis on impediments over fuel consumption reduction at Iran's transportation niches," Energy Policy, Elsevier, vol. 129(C), pages 861-867.
    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.
    4. 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.
    5. Dindarloo, Saeid R. & Siami-Irdemoosa, Elnaz, 2016. "Determinants of fuel consumption in mining trucks," Energy, Elsevier, vol. 112(C), pages 232-240.
    6. 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).
    7. 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.
    8. Lin, Haiyang & Fu, Kun & Wang, Yu & Sun, Qie & Li, Hailong & Hu, Yukun & Sun, Bo & Wennersten, Ronald, 2019. "Characteristics of electric vehicle charging demand at multiple types of location - Application of an agent-based trip chain model," Energy, Elsevier, vol. 188(C).
    9. 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.
    10. 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).
    11. 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.
    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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