Bibliographic record
Abstract
We develop a two–country model of foreign aid and cross–border pollution resulting from production activities in the recipient country. There is both private and public abatement of pollution, the latter being financed through emissions tax revenue and foreign aid. We characterize a Nash equilibrium in which the donor country chooses the amount of aid and the recipient chooses the fraction of aid allocated to pollution abatement and the emission tax rate. At this equilibrium, an increase in the donor’s perceived rate of cross–border pollution reduces emission levels. JEL Classification: Q28, F35, H41 Est–ce que la pollution trans–frontière peut réduire le niveau de pollution? Les auteurs développent un modèle à deux pays d’aide à l’étranger et de pollution trans–frontière résultant d’activités de production dans le pays qui reçoit l’aide. Il existe des efforts privés et publics pour réduire la pollution, ces derniers étant financés par les rentrées fiscales d’une taxe sur la pollution et par l’aide étrangère. On définit un équilibre à la Nash pour lequel le pays donateur choisit le montant de l’aide, et le pays récipiendaire choisit la fraction de l’aide étrangère qu’il allouera à la lutte à la pollution ainsi que le taux de taxation sur la pollution. A cet équilibre, un accroissement dans le taux de pollution trans–frontière perçu par le donataire réduit le taux de pollution.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 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".