Editorial: Forest carbon credits as a nature-based solution to climate change?
Bibliographic record
Abstract
▪ Additionalitywhether the activities that generate forest carbon credits (i.e., avoided deforestation, improved forest management or restoration) have happened only because of the targeted economic incentives enabled by the ability to sell credits. ▪ Duration or Permanencewhether the generation of forest carbon credits and their use as offsets leads to long-term climate benefits. ▪ Leakagewhether the generation of forest carbon credits in one area leads to increased emissions elsewhere, so the net climate gains are reduced or converted to net losses.Some proponents of forest carbon credits argue that additionality can be assessed with some confidence on large scales; that credits can be durable over the necessary timeframe to result in climate benefits, particularly with appropriate buffers and/or requirements for replacement; and that both duration and leakage can be better addressed on jurisdictional geographic scales.We also need to enhance our understanding of the co-benefits of forest carbon crediting for coupled ecosystem services (e.g., biodiversity, water quality, etc.), as well as impacts on social outcomes such as improved employment, and on equity outcomes (e.g., indigenous people, local communities).In this special issue, in order of publication date, you will find theoretical and applied contributions to the forest carbon credits debate.Badgley et al., analyze the current performance of California's forest carbon credits buffer pool and conclude that it is currently undercapitalized to secure the 100-year permanence requirements for forest carbon projects.Mei and Clutter, conduct cost-benefit analyses for a representative landowner supplying forest carbon credits in voluntary carbon markets and run sensitivity tests for different interest rates, and timber and carbon prices. The editors fully recognize that forest carbon credits are a complement to, not a substitute for, the deep decarbonization process that countries and companies must rapidly undertake to meet global commitments under the Paris Agreement. However, protecting and restoring forests are critical parts of any pathway that achieves global climate goals and forest carbon credits could play an important role in motivating and funding those activities. The papers in this special topic help further our understanding of forest carbon credits as a tool towards that goal.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.019 | 0.028 |
| Insufficient payload (model declined to judge) | 0.017 | 0.010 |
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".