Élasticités et effet rebond des véhicules légers au Canada
Bibliographic record
Abstract
Although reducing greenhouse gas emissions is an imperative, environmental policies can run into two pitfalls: the rebound effect and inelastic demand for gasoline. If a vehicle is more fuel-efficient but is used more often, this reduces the expected energy savings (rebound effect). Similarly, increasing the price of gasoline via carbon pricing has little effect on demand if it is not very sensitive (inelastic) to price. To determine the value of these parameters for light vehicles in Canada, I use data for the ten provinces between 2000 and 2019 to estimate a simultaneous three-equation model of aggregate demand for kilometers traveled, vehicle fleet and fuel efficiency. While the rebound effect is worth 9 % in the short term and 30 % in the long term, gasoline demand is found to be inelastic in both cases. These values are broadly consistent with those found in the literature. A rise in gasoline prices therefore reduces purchasing power rather than consumption. Similarly, in the long term, a 10 % increase in fuel efficiency would increase distance travelled by 3 %.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".