Determinants of the Use of Circular Economy Strategies by Stakeholders in the Wood–Forestry Sector in Benin
Bibliographic record
Abstract
Although the circular economy (CE) has emerged as an innovative approach to address the challenges of protecting natural resources, the use of its strategies remains in its infancy, particularly in West Africa. This study examines the factors influencing the use of CE strategies in the wood and forestry sector in Benin. This study relied on a methodological approach based on surveys, using interview guides to collect information in both the southern and northern zones of the country. This information was collected at the level of the different actors directly involved in this sector, to identify the factors that influence the use of CE strategies using Probit models. The results show that access to information, the number of years of professional experience, the age of the actors and the type of training received are the determining factors in the use of these strategies (the models statistically significant at the 1% level). Other factors, such as knowledge of the costs and benefits of different strategies, are also identified as fundamental. Furthermore, a high financial capacity and an excess or overload of information are identified as the limiting factors for the use of these strategies.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".