A First Look at Ontario’s Electric Vehicle Incentive Program: Who Are Ontario’s Green Drivers?
Bibliographic record
Abstract
This article provides a first look at Ontario’s Electric Vehicle Incentive Program, which was designed to support the adoption of plug-in electric vehicles, reward early adopters, and stimulate market demand. Under this program, the Ontario government offered financial incentives to plug-in electric vehicle buyers. Although the market share of plug-in electric vehicles in Ontario followed an upward trend, it remained below 1 percent of the new car market for most of the program’s duration. Using multivariate regression, the fraction of program applicants in a postal code forward sortation area was found to be positively associated with the incidence of higher education and negatively associated with the fraction of individuals with short commutes. Financial incentives provided to install home charging stations may have been ineffective in promoting the adoption of plug-in electric vehicles. The effects of being married or living in a common-law relationship and median household income are not definitive because they depend on the approach (ordinary least squares or fractional probit) used to estimate the empirical model.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".