Barriers to limiting fossil fuel supply in UNFCCC negotiations: insights from Bangladesh
Bibliographic record
Abstract
Constraining fossil fuel supply is imperative to curtail fossil CO2 emissions and keep the global mean temperature below 1.5°C, thereby accomplishing the Paris Agreement to avoid dangerous climate change. New research highlights that the Least Developed Countries Group (LDCG) faces multiple barriers limiting its ability to play a robust role in the United Nations (UN) climate negotiations to develop international policies curbing fossil fuel supply. However, there is limited evidence on the barriers preventing Bangladesh, an LDCG member, from meaningfully contributing to the UN climate negotiations to advance these vital policies. This article addresses this gap by drawing insights from 24 exclusive interviews with Bangladeshi negotiators and observers actively participating in the UN climate negotiations. This study identifies three underlying barriers that hinder the country’s ability to effectively argue in climate negotiations with powerful fossil fuel-producing parties to curb their fuel supply. These barriers reflect politico-economic, institutional, and nonmaterial factors. Bangladesh’s constructive role in promoting supply-side policies to regulate fossil fuels in international climate diplomacy is largely obstructed by these barriers, as they shape negotiation dynamics and negatively affect Bangladesh’s proactive participation in climate negotiations. These barriers have thus delayed a consensus on developing policies restricting fossil fuel supply. This study suggests that surmounting these barriers is indispensable in enhancing Bangladesh’s negotiation capacity and ensuring its more substantial role in expediting supply-side climate policy progress and implementation under the United Nations Framework Convention on Climate Change (UNFCCC).
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".