Framing Effects of Corporate Action on Climate Change: Implications for Consumer Attitudes and Behaviour
Bibliographic record
Abstract
This study looked at the effects of framing on the public perception of corporate environmental compliance and government policy, closely mirroring the policy design of the Federal backstop of the Pan-Canadian Framework on Climate Change. There were three key findings. First, companies that simply pay their taxes as a penalty for emissions (“merely complying” with the policy) are considered to be less moral, to have less-acceptable practices and to be harming the environment. Alternatively, companies that invest to decrease their carbon footprint (“proactively engaging” with the policy) are more likely to be perceived as acting morally, having acceptable practices and helping the environment. Secondly, consumers were more willing to bring their business to proactive companies rather than the ones that were strictly complying. Finally, the response of companies also had an effect on citizens’ perception of the policy itself. If companies were engaging proactively rather than merely complying, consumers were more likely to view the carbon pricing policy as fair, to support the political party that implemented it, to ratethe policy as helping the environment, to rate it as helping Canada’s image and reflecting Canadian values, and to rate the policy as helping the economy. There are key implications for both industry and government stakeholders to draw from these findings. For industry, communicating proactive policy engagement improves public image and increases consumer support. For the government, communicating to industry the positive benefits—environmental, economic and social—of proactive engagement could increase overall private-sector engagement and thereby improve the public’s perception of the policy itself. Overall, this suggests that adherencewith and support for carbon pricing policies is a promising opportunity for the private sector to signal their environmental and social commitments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 teacher head, 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".