The Circular Economy: A Lever for the Sustainable Development of the Wood and Forestry Sector in West Africa
Bibliographic record
Abstract
The circular economy (CE) has emerged as an innovative response to the challenges of economic growth and environmental protection. This study aims to establish a portrait of the circular economy within the wood forestry sector in Benin. The methodology includes field surveys through structured interviews in the southern and northern zones and a documentary analysis. Data were collected from direct and political stakeholders to assess their knowledge and practices. A discourse analysis, focusing on internal factors, was used to understand and analyze the motivations of local actors in the use of CE strategies. The results show that the most used strategies are maintenance and repair (52.38%), followed by donation and resale (18%). The motivations mentioned by the actors in the two zones are mainly economic (improving income and limiting expenses) and social (esthetic). However, if most of the actors do not perceive limits to these strategies, others highlight certain weaknesses, including the long process of transforming used goods (19.69%) and the loss of quality of recycled materials (15.44%). To address these weaknesses, alternative strategies, such as eco-design, optimization of operations, loan-exchange, and industrial ecology, are proposed.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".