Overview of factors influencing consumer engagement with plastic recycling
Bibliographic record
Abstract
Abstract Many semi‐durable and durable consumer goods are composed of plastic. Yet, plastic pollution is one of the most pressing environmental issues as it harms oceans and marine biodiversity. This state of affairs is worsened because plastic recycling rates remain low. Therefore, one commonly proposed solution is to improve plastic waste management to create a circular plastics economy. However, focusing on recycling management alone overshadows the consumption component and how consumers might contribute to recycling efforts. Although not alone in the overall recycling process, consumers are critical stakeholders in this because through their disposal behavior, they determine the responsible discarding of plastic through recycling. The significance of consumer engagement in driving circularity has been strongly emphasized in extant research and practice. Shifting from a linear plastic economy toward a circular one requires the active contribution of all stakeholders, especially the consumer. Hence, given the centrality of consumers' role, this paper provides an overview of the themes related to consumer engagement with plastic recycling. More specifically, the paper reveals three layers of influence on consumer plastic recycling behavior: (1) macroenvironmental factors, (2) situational factors, and (3) individual factors. This review provides scholars, practitioners, and decision‐makers with better insights into the themes to consider in order to spur consumer engagement in plastic recycling. This article is categorized under: Human and Social Dimensions > Behavioral Science Emerging Technologies > Materials Climate and Environment > Circular Economy
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".