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<p>How do social exposure to electric vehicles (EVs), charging infrastructure visibility, and socioeconomic characteristics jointly shape adoption patterns across urban communities? This study develops a framework to disentangle these interconnected mechanisms using large-scale mobility data from 142,000 points of interest and anonymized movement patterns across 862 census tracts in the Seattle–Tacoma metropolitan area. Through a three-stage analytical approach combining supervised feature selection, nonlinear causal discovery algorithms, and double machine learning, we causally identify the complex pathways linking infrastructure deployment to adoption outcomes while accounting for socioeconomic heterogeneity. Our analysis yields four key insights. First, consensus causal structures reveal that socioeconomic factors operate through multiple channels: constraining adoption, shaping exposure opportunities, and moderating infrastructure effectiveness. Second, exposure mechanisms demonstrate fundamentally different causal effects: EV exposure directly increases adoption by 15.3 percentage points per standard deviation, while charging infrastructure operates primarily through visibility amplification, with 41.8% of its impact mediated through increased EV encounters and insignificant direct utility effects. Third, heterogeneous treatment analysis exposes disparities, with high-income communities converting exposure into adoption at rates 2.7–4.4 times higher than low-income areas, indicating that uniform infrastructure deployment amplifies rather than reduces adoption gaps. Fourth, policy simulations quantify an equity-efficiency trade-off where uniform deployment maximizes aggregate adoption but maintains disparities, while targeted low-income investment improves equity but achieves only one-third of the total impact on EV adoption. These findings demonstrate that infrastructure functions as a visibility mechanism rather than a functional necessity, and achieving equitable EV transitions requires complementary policies addressing the economic and informational barriers that prevent exposure from translating into adoption in disadvantaged communities.</p>

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Seeing Is Believing: How Social and Infrastructure Exposure Shape Electric Vehicle Adoption

  • 期刊论文

  • 发表日期：

    2025年10月31日

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  • Jacob Jordan,
  • Nicolas Cooker,
  • Hossein Gazmeh,
  • Xinwu Qian

摘要

How do social exposure to electric vehicles (EVs), charging infrastructure visibility, and socioeconomic characteristics jointly shape adoption patterns across urban communities? This study develops a framework to disentangle these interconnected mechanisms using large-scale mobility data from 142,000 points of interest and anonymized movement patterns across 862 census tracts in the Seattle–Tacoma metropolitan area. Through a three-stage analytical approach combining supervised feature selection, nonlinear causal discovery algorithms, and double machine learning, we causally identify the complex pathways linking infrastructure deployment to adoption outcomes while accounting for socioeconomic heterogeneity. Our analysis yields four key insights. First, consensus causal structures reveal that socioeconomic factors operate through multiple channels: constraining adoption, shaping exposure opportunities, and moderating infrastructure effectiveness. Second, exposure mechanisms demonstrate fundamentally different causal effects: EV exposure directly increases adoption by 15.3 percentage points per standard deviation, while charging infrastructure operates primarily through visibility amplification, with 41.8% of its impact mediated through increased EV encounters and insignificant direct utility effects. Third, heterogeneous treatment analysis exposes disparities, with high-income communities converting exposure into adoption at rates 2.7–4.4 times higher than low-income areas, indicating that uniform infrastructure deployment amplifies rather than reduces adoption gaps. Fourth, policy simulations quantify an equity-efficiency trade-off where uniform deployment maximizes aggregate adoption but maintains disparities, while targeted low-income investment improves equity but achieves only one-third of the total impact on EV adoption. These findings demonstrate that infrastructure functions as a visibility mechanism rather than a functional necessity, and achieving equitable EV transitions requires complementary policies addressing the economic and informational barriers that prevent exposure from translating into adoption in disadvantaged communities.