Cost-Benefit Analysis Case Study on Regulations to Lower the Level of Sulphur in Gasoline
Bibliographic record
Abstract
The Canadian Cost-Benefit Analysis Guide: Regulatory Proposals, sets out the general methodology and analytical steps to perform a cost-benefit analysis of proposed regulatory changes. To make the Guide operational, this case study has been prepared following the analytical approach recommended by the Guide. In 1994 the sulphur content of Canadian gasoline was found to be high and varied widely across the country. Scientists and health experts have found evidence that emissions of pollutants from vehicles cause considerable harm to the health of Canadians and to the environment. In order to derive the net economic benefits, we integrate the economic benefits with the economic costs for each of the alternative scenarios. In the cost-benefit analysis, all private costs must be measured in terms of their economic opportunity costs. The results indicate that reducing the sulphur in gasoline for any scenario under consideration would generate substantial net health benefits or well-being for Canadians as a whole. Estimates of the net present value (at an eight percent discount rate) range from $1,809 million to $2,663 million.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| 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".