A carbon price by another name may seem sweeter: Consumers prefer upstream offsets to downstream taxes
Bibliographic record
Abstract
Steps to limit greenhouse gas emissions, including putting a “price” on emissions, can be undertaken in a variety of ways, and these policies are associated with different terminology, including carbon “taxes” or “offsets.” Furthermore, in the case of fossil fuels, the emissions can be regulated at different points in the production and usage system: “upstream” regulations are applied to the extraction and importation of fossil fuels, while “downstream” regulations are applied to the usage of products and services. From a conventional economic standpoint, under a range of circumstances, these points of regulation should have effectively equivalent impacts on economic incentives, decisions and resulting carbon emissions. However, the impact of “upstream” vs “downstream” policies on consumer perceptions and preferences is largely unknown. In three studies (two main studies plus one supplemental study) examining consumer preferences in the airline industry, we find that consumers respond significantly more favorably to a description of upstream offsets than to other pricing methods such as downstream taxes. To explain this preference, we find that the upstream offset policy is uniquely perceived to address both the causes and consequences of carbon emissions, which in turn predicts consumer preference and policy support.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| 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".