Accounting for forest fire risks: global insights for climate change mitigation
Bibliographic record
Abstract
Abstract Fire is an important risk in global forest loss and contributed 20% to 25% of the global anthropogenic greenhouse gas emissions between 1997 and 2016. Forest fire risks will increase with climate change in some locations, but existing estimates of the costs of using forests for climate mitigation do not yet fully account for these risks or how these risks change inter-temporally. To quantify the importance of forest fire risks, we undertook a global study of individual country fire risks, combining economic datasets and global remote sensing data from 2001 to 2020. Our estimates of forest fire risk premia better account for the risk of forest burning that would be additional to the risk-free and break-even price of credits or offsets to promote carbon sequestration and storage in forests. Our results show the following: (1) forest fire risk premia can be much larger than the historical forest area burned; (2) for some countries, forest fire risk premia have a large impact on the relative country-level break-even price of carbon credits or offsets; (3) a large spatial and inter-temporal heterogeneity of forest fires across countries between 2001 and 2020; and (4) the importance of properly incorporating forest fire risk premia into carbon credits/offset programs. As part of our analysis, and to emphasise the possible sub-national scale differences, our results highlight the heterogeneity in fire risk premia across 10 Canadian provinces.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".