Exploring the Application of the Social Cost of Carbon in Loss-and-Damage and Impact Assessment
Bibliographic record
Abstract
Abstract In this tribute to our friend and colleague, Dr Meinhard Doelle, we bring together three strands of work that we each undertook in collaboration with Meinhard: social cost of carbon, climate change loss and damage, and climate impact assessment. We first introduce social cost of carbon estimates as a form of carbon valuation used in decision-making processes in Canada and the United States. We then introduce legal approaches to climate change loss and damage and related challenges of economic and non-economic valuation. After contemplating the potential for social cost of carbon to contribute to valuation of loss and damage, and vice versa, we examine the integration of climate change in impact assessment law. Ultimately, we tentatively consider whether application of the social cost of carbon for the valuation of loss and damage in impact assessment processes might help to centre justice concerns. In conclusion, we pose questions for future research.
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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 | 0.020 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".