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 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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".