Probabilistic refunds increase beverage container recycling behaviour in British Columbia and Alberta, Canada
Bibliographic record
Abstract
Of the two trillion beverage containers produced globally every year, most are not recycled. To increase recycling rates, the bottle deposit refund system has been proposed and implemented in some regions of the world with varying degrees of success. To improve the refund system, we leverage a classic decision-making phenomenon where low-probability high rewards are preferred over small certain rewards with the same expected payoff. Specifically, we turned an established certain but small refund for recycling beverage containers (i.e., 100 % chance of getting $0.10 per bottle) into a probabilistic one (e.g., 0.01 % chance of getting $1,000 per bottle) with the same expected payoff. In three pre-registered field and lab studies (N = 975 total), we showed that participants preferred a probabilistic refund option (0.01 % chance of getting $1,000) over the certain option (100 % chance of getting $0.10), felt happier about the opportunity to get money when they chose the probabilistic refund option, and brought 47 % more bottles to recycle when the probabilistic refund was offered. These findings highlight the value of probabilistic refunds in increasing recycling behaviour and provide theoretical and practical implications for recycling policies and programs.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".