Policy Brief—Facilitating Retrospective Analysis of Environmental Regulations
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.
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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 | 0.022 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.020 | 0.008 |
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".