Integration of Just Transition Strategies into Nationally Determined Contributions (NDCs)
Bibliographic record
Abstract
Climate policies have traditionally emphasized economic and technical effectiveness, often overlooking their social impacts. Recently, the concept of just transition has emerged as a significant approach to support countries to assist countries in minimising the adverse effects of the transition and maximising its benefits, ultimately enabling more ambitious climate actions. Despite the adoption of just transition by some countries, research on its integration into Nationally Determined Contributions (NDCs) remains limited. This study developed a just transition framework that interconnects five justice dimensions with a set of core just transition elements and actions, using it as a benchmark to analyse NDCs of G20 members, Kenya, and Nigeria, and conducting case studies for Canada, Mexico, Nigeria, and the UK. The findings highlight several pivotal factors, including the need for countries to adopt a comprehensive understanding of just transition, better acknowledge existing inequalities, take concrete actions to identify vulnerability, improve mechanisms for policy integration and synergy, and establish legal and institutional frameworks. Additionally, ensuring meaningful public engagement and bridging the gap between policy commitments and practical application are crucial. Moreover, applying restorative justice to redress historical climate damages and achieving a global consensus on the meaning of fairness are vital. Improvements to the UNFCCC reporting framework, mandating just transition in NDCs, are also essential. This research underscores the heightened challenges faced by developing countries in implementing just transition, necessitating increased attention to integrating just transition strategies into NDCs and effective policy implementation.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| 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".