Electricity Dispatch and EGU Emission Remapping Algorithm 19 To determine added electricity generation unit (EGU) emissions in the 30% electric all transport 20 (eAT) scenario, we employ an electricity dispatch algorithm that provides a first-order approximation of 21 altered EGU demand. We begin by determining the vehicle miles traveled (VMTs) for each vehicle type 22 (k) in each U.S. county from the EPA's National Emissions Inventory 1. For our 30% EV adoption 23 scenario, eAT, we convert 30% of VMTs in each category k to electric VMTs (eVMTs) using Eq.1: 24 í µí±’í µí±‰í µí±€í µí±‡ í µí±,í µí±˜ = í µí±‰í µí±€í µí±‡ í µí±,í µí±˜ · í µí±“ í µí°¸í µí±‰ (1) 25 where í µí±’í µí±‰í µí±€í µí±‡ í µí±,í µí±˜ is a proportion of a county's VMTs that will demand electricity for battery charging for 26 vehicle type (k), í µí±‰í µí±€í µí±‡ í µí±,í µí±˜ is a county's total VMTs for vehicle type (k), and í µí±“ í µí°¸í µí±‰ is the fractional EV 27 adoption rate (0.3 in the eAT scenario). Given the variability in driving habits among vehicle owners, we 28 note that simulating the electrification of 30% of VMTs is likely to be different from 30% of vehicles. 29 Newly converted eVMTs are then translated into increased electricity demand from each CONUS 30 county via Eq. 2: 31 í µí±¡í µí°¸íµí°¸í µí± = ∑ í µí±’í µí±‰í µí±€í µí±‡ í µí±,í µí±˜ · í µí° ¶í µí°¸íµí°¸í µí±˜ í µí±˜ 1 · (1 − í µí°ºí µí°ºí µí°¿) −1 (2) 32