From government to market? A discrete choice analysis of policy instruments for electric vehicle adoption

Lixu Li (First Author), Zhiqiang Wang (Participant Author), Xiaoqing Xie (Participant Author)

Research output: Contribution to journalJournal

7 Citations (Web of Science)


This paper conducts a stated preference (SP) survey by integrating vehicle attributes, policy attributes, and psychological characteristics to explore the mechanisms of personal carbon trading (PCT) and tradable driving credit (TDC), as well as consumer preferences for different types of policy instruments. Using a hybrid choice model (HCM), the empirical results obtained from 1546 Chinese consumers reveal that PCT and TDC are more powerful than most existing policy instruments in encouraging consumers to adopt electric vehicles (EVs). Moreover, although psychological characteristics (i.e., environmental awareness, perceived environmental benefits, and subjective norms) play an important role in explaining consumers’ EV adoption behaviour, PCT and TDC primarily alter consumers’ EV adoption decisions through economic motivations rather than through psychological motivations. Most importantly, the implementation of PCT and TDC enables consumers to adopt EVs while having no substantial impact on their intention to purchase conventional fuel vehicles (CFVs), thereby laying the groundwork for PCT and TDC to function effectively. This paper contributes to the EV adoption literature by systematically investigating the combined effects of different factors, especially market-oriented policy instruments, on consumers’ vehicle adoption decisions. The findings also provide new insights for policymakers to design novel policy instruments to promote EV adoption.
Original languageEnglish
Pages (from-to)143-159
Number of pages17
JournalTransportation Research, Part A: Policy and Practice
Publication statusPublished - Jun 2022


  • Electric vehicles
  • Vehicle attributes
  • Psychological characteristics
  • Personal carbon trading
  • Tradable driving credits
  • Hybrid choice model


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