Leveraging mangroves to advance climate action in Africa: Zooming in on Nationally Determined Contributions (NDCs)
Bibliographic record
Abstract
Mangroves are vital for climate change adaptation and mitigation due to their efficient carbon sequestration and coastal protection roles providing often untapped opportunities for countries to enhance their national climate commitments (including the so-called Nationally Determined Contributions) under the Paris Agreement adopted at the United Nations Framework Convention on Climate Change (UNFCCC) COP21 in 2015. In Africa, several countries possess mangroves and have signed the Paris Agreement, however, the level of integration of mangrove actions into the Nationally Determined Contributions (NDCs) has not been adequately explored. Using a systematic review methodology, 33 African countries possessing mangroves were selected and their NDCs were reviewed, scored, and ranked to assess their level of integration of mangrove ecosystems into their NDCs. Countries like Equatorial Guinea, Nigeria, Senegal, and Sudan demonstrate commendable progress and leadership in integrating mangroves within their NDCs. However, gaps in budgetary commitments, spatial planning, and temporal specificity for mangrove actions among a majority of the countries hinder broader mangrove inclusion. The study also revealed a disconnect between research outputs and policy frameworks, where countries with increased mangrove research output in Africa failed to integrate mangrove actions into their NDCs. To increase African countries' resilience to climate risks and contributions to global climate goals, there is a need to increase effective collaboration between mangrove researchers, local communities, and policymakers in mangrove-possessing countries. This will bolster public education on mangroves and their inclusion of mangrove actions in their NDCs, ultimately enhancing the implementation of mangrove actions on the ground.
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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 | 0.032 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".