Identifying summer energy poverty and public health risks in a temperate climate
Bibliographic record
Abstract
• 72% of survey participants reported overheating; 63% of these stated adverse health effects. • Sleep and mental health were more affected than physical health. • Older respondents reported significantly less adverse health effects. • Cooling energy poverty was significantly associated with adverse health effects. • Renters and Māori households are more vulnerable to overheating and health risks. Understanding the health risks associated with indoor overheating and the impacts of cooling energy poverty during summer is becoming increasingly urgent as anthropogenic climate change intensifies heatwave events in many places. We report on results from a cross-sectional postal survey undertaken in Summer 2021/2022, conducted in five regions of New Zealand that typically experience some of the highest temperatures nationally. The study revealed that energy poverty is significant issue during summer, with 43% of the respondents identifying cost as a cooling restriction. Indoor overheating commonly affected the health and wellbeing of participants, with 63% reporting adverse health outcomes. Households citing cost as a cooling restriction were significantly more likely to report adverse health outcomes. Renters and indigenous Māori households were disproportionately affected by indoor overheating and the associated health and energy inequities. These findings highlight the growing health risks from indoor heat exposure in warming climatesparticularly in temperate countries like New Zealand, where inhabitants and infrastructure are not adequately prepared to handle heat-related risks. Relying solely on energy-intensive active cooling exacerbates energy poverty and injustice increasing residential energy demand. Policy interventions should focus on promoting passive, energy-efficient, and sustainable cooling strategies to protect vulnerable populations from heat-related health disparities.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".