An evaluation of policy options for reducing greenhouse gas emissions in the transport sector: The cost-effectiveness of regulations versus emissions pricing
Bibliographic record
Abstract
The reduction of greenhouse gas emissions from road transport is a key policy goal that is being pursued by both federal and provincial governments using a range of policies. This paper considers the cost of alternative approaches to reducing emissions from road passenger travel in Canada. Our findings reinforce the widely-held belief that a revenue-neutral carbon tax is the most cost-effective tool to reduce greenhouse gas emissions. Regulatory instruments on their own, such as a low carbon fuel standard, vehicle greenhouse gas intensity regulation, or zero emission vehicle mandate, achieve a given reduction at much higher cost. We show, however, that a combination of regulatory instruments can better approach the cost-effectiveness of a carbon tax than individual regulations. We provide insight about the optimal combination of regulatory instruments in the Canadian context, and find that both a low carbon fuel standard and an zero emission vehicle mandate can be jointly used to reduce GHG emissions from the transport sector. Our analysis is timely, given the rapidly evolving policies in this sector.
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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 | 0.016 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".