<i>But What Does It Mean?</i> Competition between Products Carrying Alternative Green Labels When Consumers Are Active Acquirers of Information
Bibliographic record
Abstract
Programs that certify the environmental (or other social) attributes of firms are common. But the proliferation of labeling schemes makes it difficult for consumers to know what each one means—what level of “greenness” does a particular label imply? We provide the first model in which consumers can expend effort to learn what labels mean. The relationship between information acquisition costs, firm pricing decisions, the market shares obtained by alternatively labeled goods and a brown “backstop” good, and total environmental impact proves complex. Consumer informedness can have perverse implications. In plausible cases a reduction in the cost of information damages environmental outcomes. Our results challenge the presumption that provision of environmental information to the public is necessarily good for welfare or the environment.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.008 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 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; 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".