Bibliographic record
Abstract
Abstract Canada plans to phase out internal combustion engine vehicle (ICEV) sales in favour of electric vehicles (EVs) by 2035 as part of its climate policy. Herein I examine the economic implications of a phased‐in electric vehicle mandate. I show using partial equilibrium analysis that when both types of cars are available, auto companies will overproduce electric vehicles and earn scarcity rents on internal combustion engine vehicles that partially offset the revenue loss on electric vehicles. I then present a numerical general equilibrium model of the Canadian economy to assess the overall macroeconomic consequences of the policy. The results depend critically on the assumed pace at which electric vehicles achieve cost parity with internal combustion engine vehicles on a quality‐adjusted basis. An electric vehicle mandate will have manageable economic consequences if technology improves so rapidly that the mandate is unnecessary. If the mandate outpaces achievement of cost parity the economic consequences can be severe and would likely cause the auto manufacturing sector to shut down. The cost per tonne of emission reductions are at least 10 times the Canadian carbon tax rate while the mandate is binding. The analysis provides insight into why automakers have been willing hitherto to develop and sell electric vehicles even though they currently lose money on them.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".