@article{d11db8c8ef0245cf8f1d34e4ee01ef39,
title = "Disparities and equity issues in electric vehicles rebate allocation",
abstract = "The path towards light-duty vehicle electrification promises benefits like lower costs for drivers and reduced environmental externalities for all. Incentives such as electric vehicle rebates assist with alleviating high capital costs of alternative fuel cars. We uncover distributional effects of plug-in electric vehicle rebates, focusing on a program in the State of California. We use economic attributes representative of populations of census tracts as well as data on rebates distributed to plug-in electric vehicle buyers through the Clean Vehicle Rebate Project from 2010 to 2018. Horizontal and vertical equity measures are evaluated, while measurement of spatial association characterizes spatial patterns of rebates allocation across the State. We evaluate the distributional fairness of rebates allocation between income groups and disadvantaged communities. We find that rebates have been predominantly given to high income electric vehicle buyers. However, the share of rebates distributed to low-income groups and disadvantaged communities increased over time and after an income-cap policy was put into effect. Spatial analysis shows high spatial clustering effects and rebates concentration in major metropolitan regions. We reveal neighborhood effects: communities with lower median income or disadvantaged receive higher rebate amounts when these are geographic neighbors to clusters characterized as high rebate amount receivers.",
keywords = "Electric vehicles, Equity, Incentives, Rebates, Spatial patterns",
author = "Shuocheng Guo and Eleftheria Kontou",
note = "Funding Information: Literature uncovers the role of incentives in the PEV market growth and quantifies policies' impact on the communities that those are implemented at. As an example, Narassimhan and Johnson show that battery electric vehicle sales are partially explained by incentives offered (Narassimhan and Johnson, 2018). Helveston et al., using data collected from surveys, quantify the magnitude of subsidies{\textquoteright} influence with respect to how competitive PEVs are compared to their gasoline-fueled counterparts (Helveston et al., 2015). Munzel et al. find that the availability and magnitude of monetary incentives, such as rebates, positively impact PEVs market penetration (M{\"u}nzel et al., 2019). Langbroek et al. also confirm the PEV rebates efficacy in increasing sales, based on a stated-choice experiment in Stockholm, Sweden (Langbroek et al., 2016). DeShazo et al. examine the California PEV rebate policy and propose a cost-effective design from the perspectives of both consumers and retailers (DeShazo et al., 2017). Their analysis suggests allocating higher rebates to lower income consumers for maximizing PEV sales. Adepetu and Keshav, using data from Los Angeles, showcase that high PEV prices are major barriers for prospective electric vehicle owners. They introduce an agent-based vehicle purchase model to illustrate the relationship of rebates and PEV adoption rates in their region of analysis (Adepetu and Keshav, 2017). Lee et al. analyze the heterogeneity of PEV buyers in California, suggesting that mid/high-income and middle-income groups will dominate the future low-emission vehicle market and that relevant policy-making should consider different demand levels for each income group (Lee et al., 2019). Bansal et al. perform spatial analysis and showcase that fuel-efficient vehicle ownership is significantly associated with household income (Bansal et al., 2015). Literature focusing on CVRP data analytics provides insights on how electric vehicles are distributed between consumer income groups. As an example, we learn that it is crucial to identify and profile consumers who are most influenced by such incentives. Johnson and Williams suggest that funding should be allocated to potential buyers who have lower household income or other PEV purchasing constraints but high willingness to pay (Johnson and Williams, 2017). Rubin and St-Louis study the distribution of CVRP funding across California and uncover that higher income groups are more likely to receive rebates, through a linear regression model (Rubin and St-Louis, 2016). Such analyses highlight the importance of characterizing zero- and low-tailpipe emission vehicle buyers who receive incentives and identifying potential income-based disparities.Disadvantaged communities (DACs) are investment targets through the State's cap-and-trade program in an effort to curb climate change progress by improving both air and life quality. Fig. 2 presents the geographic coverage of the analysis and the distribution of total PEV rebates amount, median household income, and CES 3.0 score, averaged over the analysis period. Higher volume of rebates in US dollars is primarily concentrated in metropolitan regions (e.g., the Bay area, Los Angeles, San Diego) where the median household income is higher compared to central and eastern census tracts. The top quartile's CES 3.0 scores, which designate DACs, are concentrated in central and southeastern California indicating a spatial mismatch of the CVRP funding allocation and areas in need of zero- and low-emission light-duty transportation adoption. Publisher Copyright: {\textcopyright} 2021 The Author(s)",
year = "2021",
month = jul,
doi = "10.1016/j.enpol.2021.112291",
language = "English (US)",
volume = "154",
journal = "Energy Policy",
issn = "0301-4215",
publisher = "Elsevier BV",
}