The long-term effects of the Lacey Act Amendment on high-risk timber species: insights from 15 years of interrupted time series analysis
Bibliographic record
Abstract
Despite comprising a small portion of US wood consumption, tropical hardwoods like Keruing and Meranti are highly valued for their aesthetic and physical properties. However, their sustainability is threatened by illegal logging and over-harvesting, compounded by the use of generic names that obscure species identities, complicating trade monitoring and regulation. Enacted in May 2008, the Lacey Act Amendment (LAA) aims to ensure the legality of plant and plant product sourcing in the US. This study evaluates the LAA’s impact on the import of these tropical hardwoods from Indonesia and Malaysia, hypothesizing that the amendment has curtailed illegal imports, thereby reducing import volumes and increasing prices. Using data from 1990 to 2023, we employed (i) intervention multiple regression analysis with autoregressive error and (ii) intervention auto-regressive integrated moving average models with step transfer function to analyze changes in import trends. Findings indicate that while LAA has significantly impacted import trends as anticipated, the effects are complex and evolving over time, highlighting the need for ongoing regulation analysis and enforcement monitoring. This research underscores the critical role of targeted legal frameworks in promoting sustainable trade practices and conservation, offering valuable insights for policymakers aiming to combat exploitative and illegal logging globally.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".