Elasticities as a lens for international sustainable governance: tropical sawnwood trade between the European Union and Sub-Saharan Africa
Bibliographic record
Abstract
This study evaluates the import demand elasticities for tropical sawnwood between major European Union (EU) importing countries—France, Germany, and Italy—and two key Sub-Saharan African exporters, Cameroon, and Ghana. Using the autoregressive distributed lag error correction approach and two-stage least squares, the analysis reveals that France exhibits elastic demand for Cameroonian sawnwood (−1.20) and nearly unitary elastic demand for Ghanaian sawnwood (−0.96) in the long run. In contrast, Ghanaian sawnwood demand is elastic in Germany (−1.22) and highly elastic in Italy (−2.24). These findings hold significant implications for policymakers and international forest governance. Elastic trade relationships highlight the potential for trade diversion and market shifts that may undermine regulatory efforts such as the Forest Law Enforcement, Governance, and Trade (FLEGT) Voluntary Partnership Agreement and the EU Regulation on Deforestation-Free Products Act (EUDRs). Conversely, relatively inelastic demand may allow for stricter enforcement of deforestation-free supply chains with minimal market disruption.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.000 | 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".