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The Dynamics of the California Electric Grid Mix and Electric Vehicle Emission Factors

Author

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  • Xingrui Zhang

    (Civil and Environmental Engineering, University of California, Berkeley, CA 94704, USA
    Department of Civil Engineering, The University of Hong Kong, Hong Kong)

  • Elliot Martin

    (Transportation Sustainability Research Center, University of California, 2150 Allston Way, Berkeley, CA 94704, USA)

  • Susan Shaheen

    (Civil and Environmental Engineering and Transportation Sustainability Research Center, University of California, 408 McLaughlin Hall, Berkeley, CA 94720, USA)

Abstract

Electric vehicle (EV) emissions occur when a vehicle is charged and are based on the mix of power sources used during that period. The rapid growth of solar and wind energy has introduced a high degree of variability in the emissions of the grid for both diurnal and annual periodicities. Growth in solar energy has been particularly prominent in California. Using grid mix data spanning April 2018 to April 2023 from the California Independent System Operator (CAISO) and power source emission factors, we evaluate the grid mix at 5 min intervals and estimate the emission factors based on the mix of power sources, the transmission losses, and the AC/DC conversion. We compare the emission factors derived from this analysis to other static factors that can be used for estimating EV emissions. We find that, when using Low Carbon Fuel Standard (LCFS) emission factors, the average emissions rate was 301 g CO 2 -e per kWh over five years. However, depending on the time of year, the maximum hourly grid emission factors in California can be between 50% and 300% higher than the minimum factors. This difference suggests that the accurate measurement of emissions from electric vehicles would be improved with better data characterizing energy by the time of day and year in which the vehicle was charged.

Suggested Citation

  • Xingrui Zhang & Elliot Martin & Susan Shaheen, 2025. "The Dynamics of the California Electric Grid Mix and Electric Vehicle Emission Factors," Energies, MDPI, vol. 18(4), pages 1-23, February.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:4:p:895-:d:1590250
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    References listed on IDEAS

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    2. Akhil Kadiyala & Raghava Kommalapati & Ziaul Huque, 2016. "Evaluation of the Life Cycle Greenhouse Gas Emissions from Hydroelectricity Generation Systems," Sustainability, MDPI, vol. 8(6), pages 1-14, June.
    3. Sovacool, Benjamin K. & Kester, Johannes & Noel, Lance & Zarazua de Rubens, Gerardo, 2020. "Actors, business models, and innovation activity systems for vehicle-to-grid (V2G) technology: A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
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    Cited by:

    1. Zhenkun Gan & Peiwu Dong & Zhengtang Fu & Yanbing Ju & Yajun Shen, 2025. "Optimizing Electric Cold-Chain Vehicle Scheduling for Sustainable Urban Logistics: A Novel Framework Balancing Freshness and Vehicle Charging," Energies, MDPI, vol. 18(7), pages 1-18, March.

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