How International Law Can Deal with Lack of Sanctions and Binding Targets in the Paris Agreement
Bibliographic record
Abstract
The Paris agreement aims at strengthening how the global community has responded to the change in climate by forming international government bodies that would be committed to reducing emissions to the environment. The lack of sanctions and binding targets in the Paris agreement has made its implementation difficult because countries live in an anarchical scenario where there is no overarching body. The agreement was drafted in November 2015 and signed on 22nd April 2016 by 196 countries but since then the success of the agreement has been in question. Generally, the treaty aims at controlling climate change and this is one of the areas that has been so difficult to control. Climate change is a tricky area to tackle because the developed countries are high pollutants because of the industries. The developed countries are mostly the ones who drive the agenda of the Paris treaty because they have the force and resources to do so. They influence policies and the decisions made. Therefore, the lack of sanctions and binding targets makes the treaty difficult to implement because there are no punishments as such. International treaties are difficult to implement especially when there are no sanctions or binding targets. As an example of international treaty governed by international law, it means that the treaty has loopholes that can be targeted by member states not to comply when the provision does not favour them. This paper critically analyzes how international law can deal with the lack of sanctions and binding targets in the Paris agreement as it operates in the international arena.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".