Bibliographic record
Abstract
Consider a two-period transboundary stock pollution game in which countries anticipate an international environmental agreement (IEA) to be in effect in the future (i.e., in period 2). What will be the impact of the future IEA on current emissions (i.e., in period 1)? We show that the answer to this question is ambiguous. We examine a fi rst type of IEA where countries anticipate that the level of emissions in period 2 will be set at an agreed upon target. Assuming that the countries can commit to this policy, we show that when this target is set close to the business-as-usual (BAU) level of emissions, the equilibrium level of emissions in period 1 falls below its BAU level. However, the emission level in period 1 is a decreasing function of the target that will prevail in period 2, hence, the impact of this policy on period 1 emissions may be ambiguous, and in general depend on the targeted emission level. We also examine other types of IEAs where countries cannot commit to an emission level but rather commit to an emission policy rule that depends on the level of pollution stock.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".