Comparing costs of the Low Carbon Fuel Standard and Carbon Tax for decarbonizing the Canadian transportation sector
Bibliographic record
Abstract
Economists agree that a uniform, economy-wide carbon tax is the lowest cost policy to reduce greenhouse gas emissions. There is less agreement on the comparative cost of the most likely efficient regulatory alternative to the carbon tax, that being the low carbon fuel standard (LCFS). This study used the energy-economy-environment model gTech to compare the economic efficiency of the carbon tax and LCFS to decarbonize the Canadian transportation sector. My results suggest the economic efficiency of a carbon tax is about 25% better than the LCFS and that a carbon tax would need to rise to $198/tonne CO2 eq. by 2050 for Canada to achieve a 65% reduction in transportation emissions from 2005 levels by 2050. Considering the likely political difficulty in implementing a high carbon price, a flexible regulation approach might offer an alternative that is slightly less economically efficient but may have a better chance of being implemented.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".