Simple Pricing Schemes for Pollution Control under Asymmetric Information
Bibliographic record
Abstract
Abstract Most policies for pricing pollution under asymmetric information proposed in the literature to date are rarely if ever used in practice. This is likely due to their complexity. We investigate the scope for using somewhat simpler policies that are more closely related to pricing schemes already used by regulators in many jurisdictions. These schemes have a discrete block pricing (DBP) structure whereby a given unit price for pollution is applied up to a specified level of pollution for any given polluter, and a higher unit price is applied to any pollution from that polluter above the specified level. If the same price schedule is applied uniformly to all firms, we call it UDBP. We derive the optimal UDBP schedule for any given number of price blocks. We also derive the optimal limiting case of the UDBP schedule (with an infinite number of price blocks) as a uniform linear increasing marginal price schedule (ULIMP). The optimal ULIMP scheme strikes a balance between the information-related benefits of increasing marginal prices on one hand, and an increase in aggregate abatement cost, due to the non-equalization of marginal abatement costs across firms, on the other. In particular, the optimal schedule is steeper with larger aggregate uncertainty about marginal abatement costs, and flatter with more observable heterogeneity across firms. We then compare our price schemes with those proposed by Weitzman (1978) and Roberts and Spence (1976).
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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".