Toward behavioural climate policy: Framing of carbon pricing policy has deep effects on Canadian attitudes and behaviours
Bibliographic record
Abstract
This study looked at the effects of framing and payment structure on the public reception of acarbon pricing policy. The study closely mirrored the design of the Federal backstop of the Pan-Canadian Framework, under which consumers receive a direct payment to offset the carbon taxes collected in their province. There were four key findings. First, framing the payment as an “incentive” increased the likelihood of consumers spending their return on green renovations as opposed to everyday purchases. Alternatively, calling the payment a “rebate” pushed people towards spending the money on everyday expenses.Second, if the policy was designed so that payments were disbursed monthly in smaller amounts, they were more likely to be spent on everyday purchases. Conversely, annual lump sum payments were more likely to be allocated towards savings. Third, there was an interaction effect between framing the payment as a “dividend” and the lump sum annual payment structure; in this case, consumers were much more likely to put the money towards savings. Finally, in terms of public perception of the policy, framing it as an “incentive” led to the most positive response, notably in terms of how well the policy reflects on Canada’s image and its alignment with Canadian values. These findings offer insight into how the use of different language to describe a carbon pricing policy, and the use of different payment schedules, can affect what consumers would spend their rebates on and their perception of the policy. How to frame public policy issues advantageously is an important part of political communication. These preliminary findings offer insight into how a carbon pricing policy can be framed and structured so as to promote the changes in consumer behaviour that the policy seeks to bring about.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".