Learning about negative externalities and support for a carbon tax
Bibliographic record
Abstract
Climate change is one of the most pressing issues facing the world and its negative impacts have already started materializing. However, despite the urgency, climate action has not been adequate and the issue has become one of the most polarized across the political spectrum in many countries, including Canada and the United States. While polarization may not be easy to overcome with political messages or on ethical grounds, making it about pocketbook evaluations may reduce some of the opposition. This is particularly relevant for a case like climate change, since at the basis of climate action is the understanding of the economic concept of negative externality. Since the extreme polarization on climate change makes any communication on the issue potentially ineffective, one strategy would be to illustrate the concept of negative externality using an alternative framing, for example emphasizing pollution rather than climate change. For these reasons, this framing might help the climate change cause, both through increasing support for action against air pollution and potentially also increasing support for a carbon tax, once the welfare effects of a corrective tax in the presence of a negative externality become clear. In this paper I intend to use a survey experiment in Canada and the US where I manipulate information on the negative externalities of pollution to then infer its effects on policy support for both an air pollution tax and a carbon tax, the latter being the one most often associated with climate action. My hypothesis is that those who learn about and understand the mechanisms of negative externalities in the context of pollution should then be more likely to not only support an air pollution tax, but also a carbon tax. The mechanisms that I anticipate would explain the latter effect are an increase in the salience of the economic dimension of climate change mitigation, and a change in the perceived costs and benefits of a corrective tax. Finally, I am also interested in testing if there is a a relationship between zero-sum thinking on the environment and support for corrective taxes, such as a carbon tax.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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; both teacher heads agree on what is shown here.
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".