Barriers and Enablers to Blue Carbon Projects in Africa: A Horizon Scan Analysis
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".