Avoiding carbon leakage from nature-based offsets by design
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.003 |
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 it