Modelling the energy conservation behaviour among Chinese households under the premises of value-belief-norm theory
Bibliographic record
Abstract
Energy conservation is a necessary form of green behaviour, as energy production and consumption gravely affect the climate. The current study aimed to explore energy conservation behaviour among Chinese households based on the value-belief-norm framework. This study used a cross-sectional design and collected quantitative data from 1671 respondents through an online survey. The hybrid analysis techniques of partial least squares structural equation modelling and artificial neural network analysis were used to analyse the data. Findings revealed that biospheric values have a positive and significant effect on pro-environmental beliefs, awareness of consequences, and ascription of responsibility, which ultimately explains 46.3% of the change in personal norms and 42.6% of the change in green trust. The results shed light on the significant positive impact of green trust and personal norms on the energy conservation intention. Green trust and personal norms can elucidate 27.3% of the change in energy conservation intention. The energy conservation intention (39.1%) explains the energy conservation behaviour. The results of ANN analysis revealed energy conservation intention, personal norms, green trust, and awareness of consequences as the four most significant contributors to the formation of energy conservation behaviour. The current study extended the VNB model with the green trust. It offered empirical evidence on the effects of pro-environmental belief, awareness of consequences, and ascription of responsibility concerning energy conservation intention. Energy policies should thus concentrate on addressing energy conservation behaviour, promoting energy-efficient household appliances, and rewarding energy conservation by lowering energy prices for low-energy users.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".