This study explores the influence of Thai government policy perceptions on the adoption of electric vehicles (EVs). Transitioning to EVs is vital for reducing greenhouse gas emissions and combating climate change, aligning with global sustainability goals. This study addresses gaps in understanding how multidimensional perceptions of government policies influence EV adoption intentions in emerging markets, particularly in Thailand. A questionnaire was distributed to 3770 respondents across Thailand between January and March 2024. The survey assessed multiple dimensions of government policy, including commitment and efficiency, welfare, communication, policy effectiveness, and tax benefits. Using statistical techniques such as Exploratory Factor Analysis (EFA), second-order confirmatory factor analysis (CFA), and structural equation modeling (SEM), this study validated the constructs of government support perception and examined their influence on EV adoption intentions. The findings highlight that tangible government policies, particularly those improving EV infrastructure and providing clear regulatory support, alongside effective communication about these policies, significantly influence public willingness to adopt EVs. The results also emphasize the critical role of perceived government commitment and fiscal incentives in shaping consumer decisions. Based on these insights, this study recommends prioritizing the expansion of EV infrastructure, enhancing the visibility of government commitment, and improving direct financial incentives to accelerate EV adoption. These findings contribute to the growing body of knowledge on EV adoption in emerging markets and offer practical implications for policymakers seeking to promote sustainable transportation solutions.

This paper aims to investigate the differences between temporal stability of factors influencing driver-injury severities in crashes involving speeding and non-speeding driving using a six-year (2012–2017) crash data in Thailand. With two possible driver injury severity outcomes (no/minor and severe/fatal), random parameter binary logit models, that allow for heterogeneity in means and variances, were estimated to fully account for unobserved heterogeneities (i.e., allow crash-level factors to vary across crashes and to influence random parameter distribution). While most factors were unstable over time, speeding crash models result showed that stable factors decreasing probability of severe/fatal injury were restraint, van, passenger car, pickup truck, running-off-road on straight and hitting guardrail and mounting traffic island; whereas stable factor increasing probability of severe/fatal injury were central/eastern/southern regions. In non-speeding driving crash model, stable factors decreasing probability of severe/fatal injury were restraint, truck, and running-off-road on straight and hitting guardrail; whereas stable factors increasing probability of severe and fatal injury were under influence of alcohol and van. The findings of this research could potentially be utilized to improve highway safety and facilitate the development of more effective crash injury mitigation policies. Practical-related recommendation based on the results is also provided.