Addressing Global Environmental Externalities: Transaction Costs Considerations
Bibliographic record
Abstract
Is there a way to understand why some global environmental externalities are addressed effectively, whereas others are not? The transaction costs of defining the property rights to mitigation benefits and costs is a useful framework for such analysis. This approach views international cooperation as a contractual process among country leaders to assign those property rights. Leaders cooperate when it serves domestic interests to do so. The demand for property rights comes from those who value and stand to gain from multilateral action. Property rights are supplied by international agreements that specify resource access and use, assign costs and benefits including outlining the size and duration of compensating transfer payments, and determining who will pay and who will receive them. Four factors raise the transaction costs of assigning property rights: (i) scientific uncertainty regarding mitigation benefits and costs; (ii) varying preferences and perceptions across heterogeneous populations; (iii) asymmetric information; and (iv) the extent of compliance and new entry. These factors are used to examine the role of transaction costs in the establishment and allocation of property rights to provide globally valued national parks, implement the Convention on the International Trade in Endangered Species of Wild Fauna and Flora, execute the Montreal Protocol to manage emissions that damage the stratospheric ozone layer, set limits on harvest of highly-migratory ocean fish stocks, and control greenhouse gas emissions. ( JEL D23, P14, Q22, Q51, Q54, Q58)
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.006 | 0.015 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.019 | 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".