Costs and Benefits of Quebec's Drive Electric Program
Bibliographic record
Abstract
This article presents a cost-benefit analysis (CBA) of the Drive Electric Program enacted by the Quebec government in 2012. This program provides direct rebates to all-electric and plug-in hybrid vehicles according to electric battery capacity. After describing the program, we identify and monetize its main costs and benefits. The costs are primarily related to the government expenses associated with the rebate itself, while the benefits include savings on gasoline purchases and oil changes, as well as reductions in the emissions of different pollutants. Because these emissions have no market price, valuing them reliably presents a challenge. For this purpose, we used values from previous relevant studies. We conduct our CBA for the year 2012 and obtain a net present value of $173,331, with an internal rate of return of 6.54 percent. Our sensitivity analysis shows our conclusion to be fairly robust to various changes in the assumptions and parameters.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".