The Effect of Recycling versus Trashing on Consumption: Theory and Experimental Evidence
Bibliographic record
Abstract
This article proposes a utilitarian model in which recycling could reduce consumers’ negative emotions from wasting resources (i.e., taking more resources than what is being consumed) and increase consumers’ positive emotions from disposing of consumed resources. The authors provide evidence for each component of the utility function using a series of choice problems and formulate hypotheses on the basis of a parsimonious utilitarian model. Experiments with real disposal behavior support the model hypotheses. The findings suggest that the positive emotions associated with recycling can overpower the negative emotions associated with wasting. As a result, consumers could use a larger amount of resources when recycling is an option, and more strikingly, this amount could go beyond the point at which their marginal consumption utility becomes zero. The authors extend the theoretical model and introduce acquisition utility and the moderating effect of the costs of recycling (financial, physical, and mental). From a policy perspective, this research argues for a better understanding of consumers’ disposal behavior to increase the effectiveness of environmental policies and campaigns.
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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 | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".