Who Will Save Energy? An Extension of Social Cognitive Theory with Place Attachment to Understand Residents’ Energy-Saving Behaviors
Bibliographic record
Abstract
With environmental concerns gaining prominence, the study of energy-saving behavior (ESB) has captured global expert attention. This research applied the SCT model and utilized survey data collected in Jiangsu Province to explore the factors influencing residents’ energy-saving behavior (ESB). The findings reveal that self-efficacy, attitudes, and social norms are direct positive determinants of ESB. Additionally, these factors mediate the positive relationship between knowledge and ESB. Notably, knowledge enhances self-efficacy, attitudes toward energy saving, and adherence to social norms, while outcome expectations improve attitudes and norms. Place attachment also emerges as a significant predictor of ESB, exerting its influence indirectly through attitudes and social norms. These insights enrich social cognitive theory by incorporating place attachment to examine ESB, substantially contribute to the discourse on environmental protection, and have implications for energy conservation strategies globally.
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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 | 0.002 | 0.004 |
| 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 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".