文章编号： 1672-5328（2026）03-0123-05

黄知微1, 2
(1. 同济大学交通学院，上海201804；2.同济大学城市交通研究院，上海201804)

摘要： 选取来自国际学术期刊的论文，以概述形式对城市交通理论方法、实证分析等研究成果进行 总结性介绍，旨在增强城市交通业界与学界对国际学术动向及研究热点的关注，促进学术交流。 《眼见为实：社会与基础设施可见性如何影响电动汽车的普及》一文以美国西雅图-塔科马都市区为 研究对象，基于大规模出行数据，构建了包含特征选择、非线性因果结构识别、双重机器学习因果 效应估计方法的三阶段分析框架，系统识别了电动汽车普及过程中社会可见性与充电设施可见性的 作用机制。研究发现：社会可见性直接促进电动汽车普及，而充电设施主要通过提升可见性间接发 挥作用；社会经济因素显著影响“可见性—采用”的转化效率。政策模拟进一步表明，基础设施均 衡布局可提升电动汽车总体采用水平，但会加剧不平等；定向补贴低收入社区则能改善公平性，但 整体普及效率相对较低。

关键词： 电动汽车；社会可见性；充电设施可见性；采用率；因果推断；双重机器学习

中图分类号： U491

文献标识码：A

Academic Dynamics on Electric Vehicle Adoption Mechanisms Based on Social and Infrastructure Exposure

Huang Zhiwei1, 2
(1. College of Transportation, Tongji University, Shanghai 201804, China; 2. Urban Mobility Institute, Tongji University, Shanghai 201804, China)

Abstract： A review of selected papers from international academic journals is presented to summarize research findings, theoretical approaches, and empirical analyses of urban transportation. The aim is to enhance the communication between industrial and academic fields in urban transportation, highlight international research focuses, and promote academic exchange. The paper Seeing Is Believing: How Social and Infrastructure Exposure Shape Electric Vehicle Adoption focuses on the Seattle–Tacoma metropolitan area in the United States as the study area. Based on large-scale travel data, the paper develops a three-stage analytical framework that integrates feature selection, nonlinear causal structures identification, and dual machine learning for causal effects estimation method, systematically identifying the mechanisms through which social and charging infrastructure exposure influence the adoption of electric vehicles (EV). The findings reveal that social exposure directly promotes EV adoption, while charging infrastructure primarily exerts an indirect effect by enhancing EV exposure; socioeconomic factors significantly influence the conversion efficiency of the "visibility–adoption" relationship. Policy simulations further indicate that a balanced distribution of infrastructure can increase overall EV adoption rates but exacerbate inequality; in contrast, targeted subsidies for low-income communities improve equity at the cost of lower overall adoption efficiency.

Keywords： electric vehicles; social exposure; charging infrastructure exposure; adoption rate; causal inference; dual machine learning