Criteria for effective zero-deforestation commitments
Bibliographic record
Abstract
Zero-deforestation commitments are a type of voluntary sustainability initiative that companies adopt to signal their intention to reduce or eliminate deforestation associated with commodities that they produce, trade, and/or sell. Because each company defines its own zero-deforestation commitment goals and implementation mechanisms, commitment content varies widely. This creates challenges for the assessment of commitment implementation or effectiveness. Here, we develop criteria to assess the potential effectiveness of zero-deforestation commitments at reducing deforestation within a company supply chain, regionally, and globally. We apply these criteria to evaluate 52 zero-deforestation commitments made by companies identified by Forest 500 as having high deforestation risk. While our assessment indicates that existing commitments converge with several criteria for effectiveness, they fall short in a few key ways. First, they cover just a small share of the global market for deforestation-risk commodities, which means that their global impact is likely to be small. Second, biome-wide implementation is only achieved in the Brazilian Amazon. Outside this region, implementation occurs mainly through certification programs, which are not adopted by all producers and lack third-party near-real time deforestation monitoring. Additionally, around half of all commitments include zero-net deforestation targets and future implementation deadlines, both of which are design elements that may reduce effectiveness. Zero-net targets allow promises of future reforestation to compensate for current forest loss, while future implementation deadlines allow for preemptive clearing. To increase the likelihood that commitments will lead to reduced deforestation across all scales, more companies should adopt zero-gross deforestation targets with immediate implementation deadlines and clear sanction-based implementation mechanisms in biomes with high risk of forest to commodity conversion.
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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 | 0.042 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".