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Optimizing U.S. Mitigation Strategies for the Light-Duty Transportation Sector: What We Learn from a Bottom-Up Model

Author

Listed:
  • Yeh, Sonia
  • Farrell, Alexander E.
  • Plevin, Richard J
  • Sanstad, Alan
  • Weyant, John

Abstract

Few integrated analysis models examine significant U.S. transportation greenhouse gas emission reductions within an integratedenergysystem.Ouranalysis, usingabottom-upMARKet ALocation (MARKAL) model, found that stringent systemwide CO2 reduction targets will be required to achieve significant CO2 reductions from the transportation sector. Mitigating transportation emission reductions can result in significant changes in personal vehicle technologies, increases in vehicle fuel efficiency, and decreases in overall transportation fuel use.Weanalyze policy-oriented mitigation strategies and suggest that mitigation policies should be informed by the transitional nature of technology adoptions and the interactions between the mitigation strategies, and the robustness of mitigation strategies to long-term reduction goals, input assumptions, and policy and social factors. More research is needed to help identify robust policies that will achieve the best outcome in the face of uncertainties.

Suggested Citation

  • Yeh, Sonia & Farrell, Alexander E. & Plevin, Richard J & Sanstad, Alan & Weyant, John, 2008. "Optimizing U.S. Mitigation Strategies for the Light-Duty Transportation Sector: What We Learn from a Bottom-Up Model," Institute of Transportation Studies, Working Paper Series qt1td1g7qw, Institute of Transportation Studies, UC Davis.
    • Handle: RePEc:cdl:itsdav:qt1td1g7qw
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    Cited by:

    1. Jun Osawa, 2023. "Portfolio Analysis of Clean Energy Vehicles in Japan Considering Copper Recycling," Sustainability, MDPI, vol. 15(3), pages 1-16, January.
    2. Kang, Jidong & Ng, Tsan Sheng & Su, Bin & Milovanoff, Alexandre, 2021. "Electrifying light-duty passenger transport for CO2 emissions reduction: A stochastic-robust input–output linear programming model," Energy Economics, Elsevier, vol. 104(C).
    3. Leighty, Wayne & Ogden, Joan M. & Yang, Christopher, 2012. "Modeling transitions in the California light-duty vehicles sector to achieve deep reductions in transportation greenhouse gas emissions," Energy Policy, Elsevier, vol. 44(C), pages 52-67.
    4. Aryanpur, Vahid & Balyk, Olexandr & Daly, Hannah & Ó Gallachóir, Brian & Glynn, James, 2022. "Decarbonisation of passenger light-duty vehicles using spatially resolved TIMES-Ireland Model," Applied Energy, Elsevier, vol. 316(C).
    5. Kaplan, P. Ozge & Witt, Jonathan W., 2019. "What is the role of distributed energy resources under scenarios of greenhouse gas reductions? A specific focus on combined heat and power systems in the industrial and commercial sectors," Applied Energy, Elsevier, vol. 235(C), pages 83-94.
    6. Chen, Yuche & Zhang, Yunteng & Fan, Yueyue & Hu, Kejia & Zhao, Jianyou, 2017. "A dynamic programming approach for modeling low-carbon fuel technology adoption considering learning-by-doing effect," Applied Energy, Elsevier, vol. 185(P1), pages 825-835.
    7. O'Rear, Eric G. & Sarica, Kemal & Tyner, Wallace E., 2015. "Analysis of impacts of alternative policies aimed at increasing US energy independence and reducing GHG emissions," Transport Policy, Elsevier, vol. 37(C), pages 121-133.
    8. Kyle, Page & Kim, Son H., 2011. "Long-term implications of alternative light-duty vehicle technologies for global greenhouse gas emissions and primary energy demands," Energy Policy, Elsevier, vol. 39(5), pages 3012-3024, May.
    9. Yeh, Sonia & Lutsey, Nicholas P. & Parker, Nathan C., 2009. "Assessment of Technologies for Compliance with the Low Carbon Fuel Standard," Institute of Transportation Studies, Working Paper Series qt5bg831jc, Institute of Transportation Studies, UC Davis.
    10. Chen, Chien-Wei & Fan, Yueyue, 2012. "Bioethanol supply chain system planning under supply and demand uncertainties," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 48(1), pages 150-164.
    11. Dodder, Rebecca S. & Kaplan, P. Ozge & Elobeid, Amani & Tokgoz, Simla & Secchi, Silvia & Kurkalova, Lyubov A., 2015. "Impact of energy prices and cellulosic biomass supply on agriculture, energy, and the environment: An integrated modeling approach," Energy Economics, Elsevier, vol. 51(C), pages 77-87.
    12. Li, Lei & Manier, Hervé & Manier, Marie-Ange, 2019. "Hydrogen supply chain network design: An optimization-oriented review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 103(C), pages 342-360.
    13. McCollum, David & Yang, Christopher, 2009. "Achieving deep reductions in US transport greenhouse gas emissions: Scenario analysis and policy implications," Energy Policy, Elsevier, vol. 37(12), pages 5580-5596, December.
    14. Guo, Changqiang & Hu, Hao & Wang, Shaowen & Rodriguez, Luis F. & Ting, K.C. & Lin, Tao, 2022. "Multiperiod stochastic programming for biomass supply chain design under spatiotemporal variability of feedstock supply," Renewable Energy, Elsevier, vol. 186(C), pages 378-393.
    15. Bahn, Olivier & Marcy, Mathilde & Vaillancourt, Kathleen & Waaub, Jean-Philippe, 2013. "Electrification of the Canadian road transportation sector: A 2050 outlook with TIMES-Canada," Energy Policy, Elsevier, vol. 62(C), pages 593-606.
    16. Kessler, Jeff & Sperling, Daniel, 2016. "Tracking U.S. biofuel innovation through patents," Energy Policy, Elsevier, vol. 98(C), pages 97-107.

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    Keywords

    UCD-ITS-RP-08-44; Engineering;

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