Essays in environmental economics
Bibliographic record
Abstract
This thesis investigates the cost-effectiveness of different environmental policies in the automobile market. The focus of the second and third chapters is on the cost-effectiveness of "Mandatory Vehicle Inspection and Maintenance" programs. The results predict that the marginal abatement cost for a major representative program (the Ontario Drive Clean program) is so high that even a small reduction in the abatement target leads to substantial social cost savings. Furthermore, even for relatively high levels of the abatement target, the optimal minimum testing age is substantially higher and the frequency of testing is much lower than what is common in many jurisdictions. The fourth and fifth chapters look at the cost-effectiveness of market-based incentives in automobile market. The results suggest that a higher price of gasoline shifts vehicle holdings towards more fuel efficient vehicles and also reduces annual demand for miles traveled, whereas changes in vehicle prices have little to no impact on annual demand for miles traveled and only shift vehicle holdings. Furthermore, achieving any abatement target through a wide range of fees and/or tax on miles driven is more expensive than a tax on gasoline. The only exceptions are fees on new fuel inefficient vehicles and the Feebate program, where vehicles with low fuel efficiency are charged fees and vehicles with high fuel efficiency receive rebates. However, the maximum amount of abatement that can be achieved by these is relatively small.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.029 | 0.005 |
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".