Avoiding carbon leakage from nature-based offsets by design
Bibliographic record
Abstract
With nature-based offsets emerging as a core strategy for meeting near-term climate targets, it is essential they deliver real and verifiable mitigation gains. However, the interventions that generate offsets can have unintended effects that cause carbon leakage and ultimately reduce mitigation. Although leakage is "old news" and various anti-leakage measures have been considered, there is little evidence that current practices to address leakage actually work. In this perspective, we present evidence that leakage is vastly underestimated in practice and argue that current efforts to improve accounting methods are unlikely to deliver the accuracy required. We therefore propose and elaborate an alternative approach to address leakage by design, based on a new conceptual framework for understanding leakage in nature-based interventions. We further outline three principles that offset developers, certifiers, and consumers can implement now to improve the credibility of nature-based offsets, without negating further ambition and investment in nature-based solutions.
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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 | 0.012 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".