The Use of a Social Cost of Carbon in Canadian Cost-Benefit Analysis
Bibliographic record
Abstract
The Social Cost of Carbon (SCC) is being adopted for systematic use in cost-benefit analysis (CBA) conducted by the Government of Canada. Although there are potential efficiency gains from its application, we argue that the SCC may be inappropriate for use in CBA for three reasons. First, as currently calculated, the SCC typically excludes the potential for catastrophes and certain types of climate damages, and assumes perfect substitutability between natural and human capital. For these reasons, it is likely to be biased downwards, and as such would provide misleading advice to policy-makers. Second, the SCC is a global measure of benefits, whereas standard practice in CBA is to include only domestic costs and benefits. Accounting for costs borne outside of Canada and along only one dimension (carbon damage) risks reducing economic efficiency and confusing the users of CBA studies. Third, SCC-based decision-making is unlikely to be consistent with Canadian commitments to international partners on emissions reductions; so its adoption risks institutionalizing non-delivery of those commitments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".