# Measurement of Regional Electric Vehicle Adoption Using Multiagent Deep Reinforcement Learning
## 1 Introduction  
The adoption of Electric Vehicles (EVs) is experiencing rapid growth, propelled by governmental policies that position them as the future of transportation. According to the U.S. Department of Energy [1], there was a substantial increase in EV sales from 2020 to 2021, when overall sales of light-duty vehicles also rose. On a global scale, EV sales saw a 55% and 60% increase in 2021 and 2022, respectively, compared to the preceding year's sales [2]. Major investment firms, including Morgan Stanley [2], project that by 2050, EVs will constitute 90% of global auto sales. The promotion and growth of EVs primarily stem from their environmental benefits and cost efficiency. Transportation is one of the largest contributors to Greenhouse Gas (GHG) emissions [3], which disturb the Earth's energy balance and significantly increase the risk of global warming and climate change. Battery Electric Vehicles (BEVs), which are fully electrified, produce zero tailpipe emissions. Hence, replacing conventional Internal Combustion Engine Vehicles (ICEVs) with EVs is viewed as an effective strategy to reduce GHG emissions, aligning to achieve net zero emissions. EVs can convert two to six times more energy from the grid to power at the wheels than gas-fueled vehicles ([PERSON] et al., 2012). This efficiency is further enhanced when the electricity used to charge EVs is derived solely from sustainable renewable energy sources [4; 5]. Factors contributing to the rise in EV sales include decreased battery prices, increased charging options, a broader range of EV models, growing consumer trust [6], and heightened environmental awareness among consumers [7; 8]. However, early-stage analyses of transportation equity in EV adoption have revealed disparities across different races and income levels. In particular, deploying and accessing EV chargers present persistent inequalities [9; 10; 11]. [PERSON] and [PERSON] [11] analyzed public EV charger access across racial and income groups in California, the leading state in the U.S. for the EV market. Their findings indicated that communities of color, specificallyAfrican-American and Hispanic majority communities, had less access to EV chargers compared to predominantly White communities. This disparity is even more pronounced in low-income communities within the communities of color. Consequently, EVs are still considered luxury vehicles, with their primary owners being high-income, highly educated [10; 12], and predominantly male homeowners [10]. A survey by [PERSON] et al. [12] of 1257 EV owners found that they are generally older and socioeconomically more stable than non-EV buyers. Incentives for EVs often need an equity discourse [10; 13]. For instance, when census tracts are grouped by income, the top 12.5% receive 25% of the total rebate amount, while the bottom 75% only receive 38% [13]. Moreover, residents living in and owning Single-family (SF) housing units have a greater capacity to install personal chargers than those living in Multi-family (MF) housing units [14]. Given that the ability to purchase an EV involves not only the upfront cost but also the associated costs of maintaining the vehicle [14], ensuring equitable distribution of incentives for EV purchase and access to the related infrastructure is critical. The promotion of EVs fundamentally hinges on policy implications. Researchers have primarily analyzed subsidy policies and their impacts on the EV market, informed by governmental incentives and tax rebate programs. Subsidies can influence the market segmentation of automobile vehicles, but their effects vary between high- and low-income individuals [15]. High-income individuals are more likely to purchase high-quality EVs, while low-income individuals opt for lower-tier EVs [15]. This outcome seems plausible, as price-sensitive consumers are 1.6 times more susceptible to subsidy policies than price-insensitive consumers. However, without subsidies, the overall preference for purchasing EVs could decrease by 35% [16].