Environmental agreements as clubs: Evidence from a new dataset of trade provisions
Bibliographic record
Abstract
Abstract Creating intergovernmental environmental clubs is a prominent policy proposal for addressing global environmental problems. According to their proponents, environmental clubs provide an incentive to join them and accept their environmental obligations by generating exclusive “club goods” for their members. Yet, the existing literature considers environmental clubs as a theoretical idea that still has to be put into practice. This article asks whether, in fact, the numerous international environmental agreements (IEAs) containing trade-related provisions provide club goods to their parties. It does so by investigating the effects of these provisions on trade flows among parties compared to flows with non-parties. We introduce an original dataset on 48 types of trade provisions in 2,097 IEAs that we make available with the publication of this article. Based on this new data and a panel of worldwide bilateral trade flows, we find evidence that existing IEAs and their trade-liberalizing content are associated with increased trade among their parties relative to trade with non-parties. We conclude from this finding that systems of IEAs provide club goods to their parties. Uncovering the existence of environmental clubs has significant methodological and policy implications. It is an important first step for future research on the actual effectiveness of clubs in attracting participation and raising environmental standards.
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".