Institutional and environmental effectiveness: Will the Paris Agreement work?
Bibliographic record
Abstract
The 2015 Paris Agreement (PA) has been widely hailed as a diplomatic triumph and a breakthrough in global climate cooperation. However, it is commonly accepted that the PA's collective goal—keeping global warming “well below” 2°C above preindustrial levels—remains ambitious. Making matters even more challenging, in 2017, global CO2emissions resumed growth after 3 years of near standstill. In 2018, this growth accelerated. It is therefore extremely important that the PA's institutional architecture meet expectations concerning its ability to induce member countries to promise and deliver emissions reductions. This study offers a review of the rapidly growing literature on the PA, to assess its strengths and weaknesses, its significance, and its prospects. We focus on evaluations of its institutional structure and its ability to induce member countries to implement policies. We frame the issues as a trilemma: the challenge of simultaneously satisfying all three main conditions for effectiveness—broad participation, deep commitments, and satisfactory compliance rates. Based on our review, we conclude that the key challenge for the PA will likely be to facilitate sufficiently fast ratcheting‐up of nationally determined contributions, while keeping compliance rates high. This article is categorized under: Policy and Governance > Multilevel and Transnational Climate Change Governance
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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 | 0.033 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.013 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".