Proposed Study on the Factors Influencing Electric Vehicle Adoption in the Urban Public Sector of Hunan Province, China
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".