Environment and Statecraft: The Strategy of Environmental Treaty-Making
Bibliographic record
Abstract
Environmental problems like global climate change and stratospheric ozone depletion can only be remedied if states cooperate with one another. But sovereign states usually care only about their own interests. So states must somehow restructure the incentives to make cooperation pay. This is what treaties are meant to do. A few treaties, such as the Montreal Protocol on Substances that Deplete the Ozone Layer, succeed. Most, however, fail to alter the state behaviour appreciably. This book develops a theory that explains both the successes and the failures. In particular, the book explains when treaties are needed, why some work better than others, and how treaty design can be improved. The best treaties strategically manipulate the incentives states have to exploit the environment, and the theory developed in this book shows how treaties can do this. The theory integrates a number of disciplines, including economics, political science, international law, negotiation analysis, and game theory. It also offers a coherent and consistent approach. The essential assumption is that treaties be self-enforcing-that is, individually rational, collectively rational, and fair. The book applies the theory to a number of environmental problems. It provides information on more than three hundred treaties, and analyses a number of case studies in detail. These include depletion of the ozone layer, whaling, pollution of the Rhine, acid rain, over-fishing, pollution of the oceans, and global climate change. The essential lesson of the book is that treaties should not just tell countries what to do. Treaties must make it in the interests of countries to behave differently. That is, they must restructure the underlying game. Most importantly, they must create incentives for states to participate in a treaty and for parties to comply.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.015 | 0.018 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.023 | 0.004 |
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".