Barriers and Enablers to Blue Carbon Projects in Africa: A Horizon Scan Analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
ABSTRACT Africa's ‘blue carbon ecosystems’ are increasingly recognised for their role in climate change mitigation, biodiversity conservation and sustainable livelihoods, with existing carbon offset projects showcasing their potential to sequester carbon and support community livelihoods. Despite this promise, blue carbon (BC) projects remain scarce across Africa. Understanding the barriers to BC implementation is therefore critical for unlocking their potential across the continent. Through a horizon scan and expert solicitation involving 41 participants from 20 countries, this study identified 13 major barriers spanning social, technical, economic, environmental, and policy domains. Governance obstacles, such as weak law enforcement, complex land tenure, and unclear carbon rights, emerged as the most significant reflecting Africa's diverse regulatory landscapes and often unstable political contexts. Socio‐economic challenges, such as few sustainable livelihood options for those involved in/impacted by BC projects, further constrain progress. Economic barriers, particularly limited funding for project design, monitoring, and delivery, also featured prominently. Technical and environmental factors, including low scientific capacity, fragmented ecosystem distribution, and climate‐driven impacts, further complicate project design and scalability. The barriers identified varied significantly across regions and ecosystem types. To overcome them, we propose targeted policy reforms, innovative financing, capacity building, and integrated management approaches that align local priorities with national climate goals. Collectively, these strategies can unlock Africa's BC potential, delivering substantial climate, biodiversity and socio‐economic benefits.
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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 | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 it