Reflections on the Kigali Amendment Implementation in the GCC
Bibliographic record
Abstract
Since activating the implementation of the Kigali Amendment to the Montreal Protocol in 2019, many countries are striving to adopt the amendments to sustain a green environment. The Gulf Cooperation Council (GCC) region has implemented the Kigali Enabling Activities project, which paves the way for future amendments. This paper explores the hurdles faced when implementing this Amendment through three factors: natural, economic, and cultural problems. By adopting the GAP analysis, the paper first presents the ideal procedures of the Kigali Amendment as suggested by the environmental organizations of the United Nations (UN), and this scenario is used to benchmark introducing this Amendment in the GCC region. Then the paper considers the different variations of weather and culture, which comprise the main hurdles that the Amendment is facing. In addition, the paper explores the other problems to conclude with solutions and recommendations that enable the governmental bodies and UN offices to implement the Kigali amendments with minimum errors and lowest cost, thereby promoting the same model in the GCC states.
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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 | 0.024 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.015 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.012 | 0.013 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".