Environmental standards: Examining a regulator’s strategy for setting a deadline
Bibliographic record
Abstract
One of the most common approaches that regulators use to improve the environmental performance of firms is to enact a standard that firms must comply with before a set deadline or face a penalty. In this study, we examine how a regulator should set the deadline for a new standard in a market with two competing firms that make technology development and production decisions. We show that when the firms are differentiated by development capability, as the difference between firms’ development capabilities increases, the regulator must be careful as her effort to reduce the lower capability firm’s cost may inadvertently lead to the higher capability firm decreasing his development investment. When the firms are instead differentiated by production capability, as the difference between firms’ capabilities increases, this can create an opportunity for the regulator to take advantage of the higher capability firm’s motivation to gain market share and set an earlier deadline. Extending our model, we find that (i) the regulator should use a development assistance program as a complimentary lever to a deadline for decreasing firms’ compliance times and costs, (ii) when firms can collaborate, the regulator should almost always set a more aggressive deadline, and (iii) the lower capability firm’s attempt to gain a first mover advantage can increase the regulator’s total cost for the standard.
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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 | 0.028 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".