Disagreement and Design: Searching for Consensus in the Climate Policy and Intergenerational Discounting Debate
Bibliographic record
Abstract
Current approaches to discounting in climate policy present a seemingly intractable problem. While it is widely recognized that choice of discount rate in climate models can easily dwarf the effect of other parameter inputs, there is at present a very wide disagreement, both in law and in economics, about the appropriate discount rate to use. This Paper provides a framework for achieving a workable consensus range for acceptable discount rates in climate models. It does so by emphasizing three factors previously ignored in the literature. First, it demonstrates that the choice of discount rate should be tailored to the type of climate model at issue, distinguishing particularly between policy evaluation models and optimization models. Second, it suggests that some disagreement in these debates is fundamental (reflecting deep unbridgeable differences in views about the proper scope of the market), while some disagreement is not. By focusing attention on the non-fundamental sorts of disagreement, it becomes possible to shrink the consensus range of plausible discount rates. Third, this Paper argues that some of the current disagreement about the choice of discount rate for modeling purposes on the front-end can actually be better addressed through elements of program design on the back-end.
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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 | 0.213 | 0.301 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.040 |
| Scholarly communication | 0.015 | 0.023 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.015 | 0.016 |
| Insufficient payload (model declined to judge) | 0.004 | 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".