Making the Paris Agreement: Historical Processes and the Drivers of Institutional Design
Bibliographic record
Abstract
After a decade-long search, countries finally agreed on a new climate treaty in 2015. The Paris Agreement has attracted attention both for overcoming years of gridlock and for its novel features. Here, we build on accounts explaining why states reached agreement, arguing that a deeper understanding requires a focus on institutional design. Ultimately, it was this agreement, with its specific provisions, that proved acceptable to states rather than other possible outcomes. Our account is multi-causal and draws methodological inspiration from the public policy and causes of war literatures. Specifically, we distinguish between background, intermediate, and proximate conditions and identify how they relate to one another, jointly producing the ultimate outcome we observe. Our analysis focuses especially on the role of scientific knowledge, non-state actor mobilization, institutional legacies, bargaining, and coalition-building in the final push for agreement. This case-based approach helps to understand the origins of Paris, but also offers a unique, historically grounded way to examine questions of institutional design.
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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 | 0.017 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.007 | 0.028 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".