Cascading marginal emissions signals for green charging with growing electric vehicle adoption

Sonia Martin (), Siobhan Powell and Ram Rajagopal
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Sonia Martin: Stanford University, Department of Mechanical Engineering
Siobhan Powell: ETH Zürich, Department of Management, Technology and Economics
Ram Rajagopal: Stanford University, Department of Civil and Environmental Engineering

Nature Communications, 2025, vol. 16, issue 1, 1-13

Abstract: Abstract Shifting electric vehicle charging to use cleaner electricity can reduce carbon dioxide emissions. Grid emissions factors can inform when to shift demand, but key assumptions behind existing emissions factor methods fail for today’s grids and electric vehicle adoption levels. We combine real charging data with a Western U.S. grid model to test these methods under increasing electric vehicle adoption. We find that following existing average and marginal emissions factor methods to manage charging can inadvertently increase grid emissions when emissions factor signals are noisy, too many electric vehicles follow the same signal, or when high-emitting generators respond. We instead propose an alternative Cascading marginal emissions factor strategy that manages charging in smaller groups. We show that the Cascading strategy reduces added emissions by 10–28% across grid scenarios for at least 2 million electric vehicles. Our research reveals how demand response methods must change to reduce emissions and support the grid transition under wider electric vehicle adoption.

Date: 2025
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DOI: 10.1038/s41467-025-64979-7

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