Carbonated Fodder: The Social Cost of Carbon in Canadian and U.S. Regulatory Decision-Making
Bibliographic record
Abstract
For decades, cost-benefit analysis has been criticized while it has simultaneously been a core analytical tool in regulatory decision-making. Scrutiny of a relatively new component of cost-benefit analysis used to measure environmental benefits—the “social cost of carbon” (“SCC”)—provides fresh evidence that confirms long-standing concerns around bias, manipulation, uncertainty, and moral judgment. This Article is the first to examine and compare use of the social cost of carbon in Canadian and U.S. carbon emissions regulatory decision-making. It reveals that despite fundamental differences between the two countries, regulators on either side of the border use similar or identical dollar values and modelling. This practice erroneously assumes commonalities across the two countries that have not been empirically proven, including with respect to risk tolerance, moral judgment, value of ecosystem services, and the value of a human life. Additionally, the analysis finds differences in the way SCC values are selected and used by Canadian and U.S. decision-makers, demonstrating that another example of cost-benefit analysis is susceptible to bias and arbitrary decisions. This in turn undermines the credibility of regulatory decisions in the carbon emissions realm at a critical time in global climate governance. The Article goes on to suggest that SCC represents an opportunity for cost-benefit analysis to evolve and contribute to sound regulatory decision-making, but further improvement is needed to do so.
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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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.012 | 0.017 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| 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".