De-energization as maladaptation: Uneven residential exposure to wildfire Public Safety Power Shutoffs and compound heat
Bibliographic record
Abstract
In response to growing levels of wildfire destruction, electric utility companies are adopting powerline de-energization as an adaptation strategy intended to prevent wildfire ignitions. While reducing wildfire risk, planned de-energizations also expose residents to electricity loss, potentially causing harmful consequences. We investigated the extent to which planned de-energization can be considered a form of maladaptation , in which an adaptive response to a climate-related hazard results in unintended, concurrent harms. To do so, we examined the co-occurrence of Public Safety Power Shutoffs (PSPSs) with extreme heat (temperature ≥ 32 °C) in California between October 2021 and September 2024. Our analysis revealed compound heat-PSPS outages throughout this period, including extreme temperatures exceeding 40 °C, during power shutoffs. Compound heat-PSPS events were geographically concentrated in census block groups with higher proportions of older adults and mobile home residents, both populations which may be at increased risk of heat-related morbidity and mortality. While they affected a relatively small proportion of customers de-energized by PSPSs, compound heat-PSPS outages raise concerns over extreme heat exposure when access to electricity-based cooling strategies is curtailed. Evaluating the maladaptive effects of institutional responses to climate change hazards is critical for comprehensively weighing both the benefits and harms of emerging adaptation strategies.
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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 | 0.000 | 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) | 0.001 | 0.001 |
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".