Can policy measures foster plug-in electric vehicle market diffusion?: Presentation at EVS29 - 29th International Electric Vehicle Symposium, Montréal, Québec, Canada, 19-22, 2016
Bibliographic record
Abstract
Plug-in Electric vehicles (PEV), both as battery electric vehicles (BEV) and plug-in hybrid vehicles (PHEV) have noteworthy potential to reduce global and local emissions. Governments around the world have implemented monetary and non-monetary policy measures to foster PEV market diffusion. However, empirical estimates of their effectiveness are scarce. Here, we analyse data on PEV sales from Europe and the US with the policy measures active in these countries, e.g., direct subsidies, tax rebates, and public charging infrastructure. The aim of the present paper is to contribute empirical evidence to the discussion of policy aided market evolution of electric vehicles. We find income, gasoline prices and both direct and indirect subsidies to positively influence PEV adoption.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.034 | 0.008 |
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; both teacher heads agree on what is shown here.
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".