The Influence of Environmental Issues on Ecological Concerns and Pro-environmental Behaviour Intention Employing the VBN Model
Bibliographic record
Abstract
The environmental concerns identified concentrate on climate change; other concerns include: pesticide use, burning fossil fuels for energy consumption, rising of sea levels, and that Nature’s balance, once lost, will never be recovered. Ecological worldviews are tested in this thesis. The main research targets Canadian respondents, because Canada is one of the greatest carbon dioxide emitters and has planned to implement new sets of regulations aimed at its industries and the impact of individual citizens. In the face of climate change, Canada fails to emerge as a leader in policy and environmental protection. The purpose of this study is, first, to empirically test the VBN research model through conducting a survey questionnaire regarding the pro-environmental behaviour of Canadians. Second, the study uses six datasets to identify and then compare Canadians to other population samples in terms of pro-environmental behaviour intention, and their relationships between constructs. The VBN constructs was confirmed and most causal relationship hypotheses were supported pointing out that more emphasis should be given to Egoistic reasons for PBI. The most significant socio-demographic variables differentiating respondents’ pro-environmental behaviour intention was found to be the respondents’ perception of ‘climate change’, ‘transit use’, ‘income’, and ‘car access’. The thesis concludes with managerial implications and future research recommendations.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".