1 PRELIMINARY AND INCOMPLETE DRAFT- PLEASE DO NOT CITE OR QUOTE Subjective Benefits from Climate Change Mitigation: Intermediate Results from a Household Survey
Bibliographic record
Abstract
This paper reports some intermediate results from an in-progress household mail survey that will eventually reach 8000 households throughout the US and Canada over a complete one-year weather cycle between February of 2001 and January of 2002. There are three main facets to the overall study. In the first section, we undertake a thorough formal analysis of the response/nonresponse decision by individual households who receive a copy of the survey. In the second section, we incorporate this response/nonresponse decision process into a joint specification that also includes a model of respondents ’ choices concerning climate change mitigation policies, either business-as-usual, or a program that will be ambitious enough to sustain the climate at approximately its current characteristics. In the third section, we will use the fitted indirect utilitydifference function underlying the selectivity corrected model for the policy choice and solve for estimates of the implied willingness to pay (WTP) for climate change mitigation programs with different characteristics (i.e. different avoided subjective climate change impacts, different costs, and different uncertainty about costs, and different domestic and international distributions of these costs).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.180 | 0.043 |
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".