Eliciting attachment security with social norm messages is linked to reduced energy consumption in extreme heat in the United Arab Emirates
Bibliographic record
Abstract
Social norms effectively reduce household energy use, yet research often focuses on moderate climates. Extreme heat could hinder energy-saving behaviors, potentially requiring extra motivational triggers. Here, we examined whether eliciting attachment security—a psychological mechanism triggering bonding and empathy—combined with a social norm message is linked to reduced energy consumption in extreme heat. In a preregistered field experiment in the United Arab Emirates (100 households, 26,400 observations over 9 months, from September 2019 to May 2020), we compared a standard social norm message against one enhanced with secure attachment priming (vs a control group) in the campus housing of an international university. Results showed that households receiving the combined message saved more electricity (9.98%) than those receiving the standard message (6.11%), showed greater efficacy in already efficient households, had heightened effectiveness on hotter days, and the follow-up effect lasted twice as long post-intervention. During the study’s final months, the COVID-19 lockdown occurred, revealing no significant usage differences between experimental groups from lockdown onwards. Given that this study was conducted in only one location with particular characteristics, results may not be generalizable and should be interpreted with caution. Energy savings improve when messages evoking social expectations also promote empathy, especially in extreme heat, according to a randomized controlled field experiment on a United Arab Emirates campus housing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".