Trade and biodiversity loss: disentangling the complexities for effective policy action
Bibliographic record
Abstract
There is a growing policy emphasis on “harnessing trade for biodiversity” and “biodiversity-friendly trade.” However, achieving this requires a full understanding of the complexities involved. Biodiversity is a complex concept that encompasses a wide range of factors and influences. It cannot be measured in a straightforward manner and its evaluation depends on the specific context, scale, and time frame. Additionally, there are inherent tensions between the benefits of trade and the importance of maintaining biodiversity. The pursuit of trade often leads to greater agrosystem specialization, intensification, and concentration, which can harm ecosystem functions. Indeed, it is important to avoid oversimplification and exercise caution in advocating for “biodiversity-friendly” production and trade. This article critically examines the idea of “harnessing trade for biodiversity” and scrutinizes the intricate interplay between trade and biodiversity. It approaches the topic from an interdisciplinary perspective, considering trade policy vis-à-vis commodity trade economics and social-ecological system frameworks. By linking trade policy to various approaches in the “beyond growth” debate, we present an overview of different pathways to reconcile trade and biodiversity, ranging from fundamental reform to incremental changes.
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 | 0.020 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.006 | 0.038 |
| Scholarly communication | 0.023 | 0.037 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.015 | 0.017 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".