Strengthening the Role of Forests in Climate Change Mitigation through the European Union Forest Law Enforcement, Governance and Trade Action Plan
Bibliographic record
Abstract
Forests influence climate change by either increasing or decreasing the atmospheric concentration of greenhouse gases. When unsustainably managed, forests can release more greenhouse gases than they can absorb, intensifying their atmospheric concentration with adverse climate and human health outcomes. Several frameworks and institutional arrangements have been adopted globally to help strengthen political commitment and actions to promote the sustainable management of forests and enhance forests’ contribution to climate change mitigation. Nevertheless, the destruction of tropical forests is accelerating at an alarming rate, making it an uphill battle to stop forest sector carbon emissions, limit the global average temperature rise to well below 2°C, and build a sustainable and climate-resilient future for all. The continuous release of forest-related emissions indicates that humanity has fallen short of meeting global forest and climate-related goals and, hence, reiterates the need to develop and apply additional tools to support international cooperation on mitigating forest carbon emissions. Considering deforestation’s challenge for voluntary frameworks such as the UN Forest Instrument and REDD+, opportunities/actions beyond non-legally binding agreements and principles must be harnessed promptly. Trade-related measures, such as the Forest Law Enforcement, Governance and Trade Action Plan, are relevant in addressing deforestation. Arguably, the Forest Law Enforcement, Governance and Trade Action Plan provides the most appropriate framework for future regional/global forest law reform.
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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.012 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".