Avoiding leakage from nature-based offsets by design
Bibliographic record
Abstract
Leaky offsets are old news. As the world embraces nature-based solutions as a core strategy for critical near-term climate change mitigation, transactions of nature-based offsets in both compliance and voluntary markets reflect an underlying assumption that current approaches to managing leakage at the project level are working. We argue that this is not the case: leading third-party certification standards appear to vastly understate leakage compared to the research literature, and the tools available for project-level crediting cannot deliver the accuracy needed in practice. We propose an alternative, conservative, approach for avoiding leakage by design, based on understanding the ‘duality’ between additionality and leakage in a system at equilibrium. We then identify three principles that offset developers, certifiers, and consumers should implement at the project level now to improve the credibility of nature-based offset markets, while also allowing for increasing ambition and investment in nature-based solutions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.010 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".