Policy Brief—Facilitating Retrospective Analysis of Environmental Regulations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Prospective or ex ante studies of the costs, benefits, and distributional impacts of new environmental regulations are now commonly performed in many countries. Retrospective analyses, which aim to document actual outcomes, are far less common. The purpose of this policy brief is to illustrate the value of retrospective analysis of environmental regulations, discuss the main challenges of conducting such studies, and make suggestions for facilitating the conduct of retrospective analyses. We examine recent examples of ex post analyses of three sets of U.S. regulations—the Environmental Protection Agency (EPA) Cluster Rule, the NOx Budget Program, and federal gasoline content regulations—and British Columbia’s carbon tax. Based on this review, we offer some lessons for facilitating future retrospective analysis of environmental regulations.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it