Predictors of Pro-Environmental Behavior: A Comparison of University Students in the United States and China
Bibliographic record
Abstract
Understanding and managing University student’s environmental and sustainable behavior is critical to reducing global environmental problems and influencing pro-environmental behaviors. Survey data obtained from University students in different cities in the United States (n = 75) and Chinese Students in Jiangsu Province, China (n = 109) were investigated and analyzed to identify major predictors of pro-environmental behaviors using the Comprehensive Action Determination Model. The results confirmed multiple factors such as Social norms, Intention to Act, Information Need and Situational factors significantly influence and shape the nature of pro-environmental behavior in the US and sets of Social norms, Intention to act, Environmental awareness, Information need and Situational factor in China. These findings are in consonance with the tenets of theory of planned behavior, norm activation theory; though the loading and effects differ in their local environment. University students in US showed higher level of pro-environmental behavior despite their individualistic society compared to Chinese students in China. The findings confirms the complexity of human behavior through the robustness of the comprehensive action determination model by showing that using unitary construct to predict environmental behavior is context specific and using different combinations of predictor variables exert significant influences in different local environments.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".