“If it is not made easy for me, I will just not bother”. A qualitative exploration of the barriers and facilitators to recycling plastics
Bibliographic record
Abstract
Despite significant investment to increase recycling facilities and kerbside collection of waste materials, plastic packaging is frequently discarded as litter, resulting in significant environmental harm. This research uses qualitative methods to explore the contextual and psychological factors that influence plastic waste disposal behaviour from the perspectives of consumers. This research also reports key results from a brief online survey exploring consumer perspectives toward plastics and plastic recycling. A total of N = 18 adults living in Northern Ireland (NI) participated in a semi-structured interview and N = 756 adults living in NI took part in an online survey. Interview data was analysed via a semi-directed content analysis approach, using the COM-B behaviour change model as a guiding framework. Survey data underwent descriptive and frequency analysis. Collectively, the findings suggest that environmental concern exists among consumers generally, but there is a degree of ambivalence toward recycling that reflects a gap between intentions to recycle and actual recycling behaviour. Plastic recycling behaviour is hindered by three common barriers: 1. confusion and uncertainty about which plastic materials can be recycled (exacerbated by the abundance of plastic products available) 2. perceiving plastic recycling to be less of a personal priority in daily life 3. perceiving that local government and manufacturers have a responsibility to make plastic recycling easier. As recycling is simply not a priority for many individuals, efforts should instead be placed on providing greater scaffolding to make the process of recycling less tedious, confusing, and more habitual. Visual cues on product packing and recycling resources can address ambiguity about which plastic materials can/cannot be recycled and increasing opportunities to recycle (via consistent availability of recycling bins) can reduce the physical burden of accessing recycling resources. Such interventions, based on environmental restructuring and enablement, may increase motivations to recycle by reducing the cognitive and physical burden of recycling, supporting healthier recycling habits.
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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 | 0.012 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".