Future wildfires increase the risk of the residential insurance gap
Bibliographic record
Abstract
Altered fire regimes pose unprecedented threats to residential properties in many parts of the world. Consequently, insurers are less willing to insure properties from the threat of wildfire or will do so at an inflated premium. Uninsurance or under insurance (the insurance gap) may have cascading impacts on property values, stranding residential assets, and amplifying economic inequalities. Here, we aim to quantify impacts of future climate-driven wildfires on residential properties, and the risk of the insurance gap from 2024 to 2099. We determine how this differs to historical wildfire impacts, in relation to socio-economic context, and spatial planning schemes. We ran spatially explicit wildfire regime simulations for five case study areas within Southeastern Australia. We compared the simulated wildfire impacts to data on residential properties, socio-economic status, spatial planning schemes, and the historical wildfire impacts from the preceding 75 years. Across all regions, a total of 274,657 houses (16.7 %) were projected to be burnt by wildfire within the next 75 years. Almost all of these houses, 96 %, were projected to experience an increase of at least one fire compared to the last 75 years. Most houses (86.6 %) projected to burn are currently occupied by low or middle class and a quarter were in the bounds of current fire plan building schemes. We suggest that transformative change may be required to help mitigate the potential increases to the residential insurance gap, both in the financial tools available to insure residential assets from wildfire, and the planning of where residences can safely be built into the future.
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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 | 0.001 | 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 | 0.001 | 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".