Optimizing urban electric vehicle incentive policy mixes in China: Perspective of residential preference heterogeneity. (1st May 2022) - Qiu, Y.Q.; Tsan Sheng Ng, Adam; Zhou, P.
Highlights: Study the optimization of the EV incentive policy mixes at city level. Residential preference heterogeneity has been examined in the research. A stated preference experiment is conducted in the EV pilot cities. Cities are grouped into three clusters by the compositions of the preference segments. Demand-side EV policy mixes are tailored for the three city groups. Abstract: The large-scale diffusion of electric vehicles (EVs) helps pave the way towards carbon peak and neutrality goals, while it is affected by the governmental incentive policies at different levels. This paper studies the optimization of demand-side policy mixes from the perspective of residential preference heterogeneity with 18 EV pilot cities in China as the case. A latent class model is used to investigate urban residents' preferences for demand-side policies and characterization of urban heterogeneities of the preferences. Valid survey data from 1455 respondents were collected from a stated preference experiment, which segmented urban residents into a "policy-sensitive" group (8.25% of the sample), "policy-indifferent" group (27.97%), and "policy-cognitive" group according to their preferences. The 18 pilot cities were grouped into three clusters according to the different compositions of the preference segments. Our analysis shows that it may not be necessary for the municipal governments to provide subsidies for the installations of home chargers currently. Besides, different optimized policy mixes of bus lane access privileges, replacement subsidies, toll discounts, and preferential parking fees for charging were proposed for the three clusters of cities. Our research paradigm applies to cities in other countries that need to optimize EV policy mixes.