How does local context matter? Assessing the heterogeneous impact of electric vehicle incentive policies in China

This study reassesses the effectiveness of electric vehicle (EV) incentive policies in China and explores potential disparities in policy impact across different local contexts. Initially, employing panel data analysis, it evaluates policy effectiveness using data from 80 Chinese pilot cities spanning 2010 to 2022. Subsequently, cities are stratified based on varying income levels, traffic conditions, population density, and administrative hierarchy to facilitate an in-depth examination of the heterogeneous impacts of EV policies. Additionally, the Difference-inDifferences (DID) estimation method is employed to scrutinize the dynamic effects of these policies over time, enhancing the credibility of the panel model results. The findings reveal that purchase subsidies, parking benefits, driving privileges, and charging infrastructure have significant positive impacts on EV market share. Specifically, financial incentives are more effective in low-income, low-traffic, low-density cities, as well as in provincial capital cities while driving privileges are more suitable for high-income, high-traffic, and high-density areas. This study also finds that usage-phase policies increasingly drive EV adoption over time, offsetting the negative impact of subsidy reductions. The results of this study hold substantial implications for policymakers, providing insights to design context-based EV policies tailored to specific local conditions.