Proposed Study on the Factors Influencing Electric Vehicle Adoption in the Urban Public Sector of Hunan Province, China

Zhao Binliang

Asian Social Science, 2025, vol. 21, issue 1, 86

Abstract: The low rate of electric vehicle (EV) adoption in Hunan Province represents a multifaceted issue, stemming from a convergence of technical, economic, and governance-related challenges. Technically, critical obstacles include the limited availability and accessibility of EV charging infrastructure, suboptimal performance of EVs in various operational conditions, and a pronounced lack of technical literacy and knowledge among key stakeholders concerning EV technology and its benefits. Economically, the high upfront cost associated with EV ownership, elevated electricity tariffs for vehicle charging, and the uncertainty regarding the long-term financial returns pose substantial deterrents to adoption. Governance issues further complicate these challenges, as the diffusion of responsibilities across institutional actors, insufficient coordination between national and local governments, and regulatory weaknesses contribute to delays in infrastructure development and maintenance critical for EV adoption. Addressing these barriers requires a strategically coordinated approach, including investments in infrastructure, the introduction of financial incentives, and the strengthening of governance frameworks, all of which are essential to foster wider EV adoption within Hunan's public transportation sector. This studyapplies quantitative methodologies to evaluate the influence of technical and economic determinants on the adoption of EVs in Hunan, China. Through survey data collection and the application of advanced statistical techniques, the study aims to yield objective, reliable, and generalizable insights that can inform both policy formulation and strategic decision-making. Employing a systematic sampling method, the study will target a sample of 459 respondents from government bodies and public institutions, anticipating an adequate response rate to enable robust analysis using Smart PLS.

Date: 2025
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