Disaster risk management tipping points: impacts of extreme wildfire events and the resulting need for layered disaster risk management solutions
Bibliographic record
Abstract
Wildfire regimes are changing globally with an increase in global burned area and changes in fire characteristics. Recent research shows that the number of extreme fire events is increasing exponentially and events such as the most recent fires in Los Angeles in the U.S. (2025), the Hawaii fires (2023), Canada’s record-breaking fires (2023), the largest recorded fires in Greece-Europe (2023) and the 2025 European fire season underpin this observation. Extreme wildfire events (EWEs) thereby pose new challenges and limits to managing disaster risk. This refers not only to response operations but also to “conventional” preventive measures such as the creation of buffer zones that may no longer be effective. This paper depicts several limits of conventional wildfire risk management measures towards EWEs and introduces the concept of disaster risk management tipping points (DRM TPs) as critical thresholds that necessitate a revised set of disaster risk management strategies. Building on a bibliographic review, we depict the novelty of the concept and apply it to selected illustrative examples. We propose that this conceptualisation is useful when developing “layered” or diversified risk management approaches for different types of wildfire events including extremes. It may also leverage and shift the discussion around responsibilities in managing risk in terms of public versus individual contributions, the distribution of investments as well as related aspects of justice.
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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 | 0.002 | 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 | 0.001 |
| Open science | 0.001 | 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".