Reply to comment on “climate sensitivity, agricultural productivity and the social cost of carbon in fund” by Philip Meyer
Bibliographic record
Abstract
Abstract Meyer (Environ Econ Policy Stud, 2022) questions a number of assumptions behind the social cost of carbon (SCC) calculations in Dayaratna et al. (Environ Econ Policy Stud 22:433–448, 2020), especially the CO 2 fertilization benefit and the climate sensitivity estimate. He recommends against increasing the CO 2 effect and suggests applying a recent climate sensitivity estimate in Lewis, Clim Dyn (2022), but did not calculate the resulting SCC distribution. Herein we critically assess his recommendations and compute the SCC distribution they imply. It has a median SCC value in 2050 of $3.39 and implies a 33.4 percent probability of the optimal carbon tax being negative. While a bit higher than the results in Dayaratna et al. (Environ Econ Policy Stud 22:433–448, 2020), they are not materially different for the purposes of setting optimal climate policy.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".