Bibliographic record
Abstract
I examine the risk/return tradeoff for environmental investments, and its implications for policy choice.Consider a policy to reduce carbon emissions.To what extent does the value of such a policy depend on the expected future damages from global warming versus uncertainty over those damages, i.e., on the expected benefits from the policy versus their riskiness?And to what extent should the policy objective be a reduction in the expected temperature increase versus a reduction in risk?Using a simple model of a stock externality (e.g., temperature) that evolves stochastically, I examine the ``willingness to pay" (WTP) for alternative policies that would reduce the expected damages under ``business as usual" (BAU) versus the variance of those damages.I also show how one can compute ``iso-WTP" curves (social indifference curves) for combinations of risk and expected returns as policy objectives.Given cost estimates for reducing risk and increasing expected returns, one can compute the optimal risk-return mix for a policy, and the policy's social surplus.I illustrate these results by calibrating the model to data for global warming.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".