Summarizing the impacts of policies that limit the use of single-use plastic items: a systematic literature review
Bibliographic record
Abstract
Abstract Single-use plastic (SUP) waste negatively impacts human health. While emerging jurisdictional policies target consumption of SUPs, their effects on environmental and human health remain uncertain. A systematic review of peer-reviewed and grey literature databases, using plastic and policy search terms, generated 16,684 articles. Subsequently, screening selected 51 articles, which were critically appraised. Data characterizing the types of policy and plastic, changes in consumption, and other impacts were descriptively and statistically analysed. The results span 21 countries, addressing SUP bags (49), straws (1), or mix (1). 28 papers represented national, and 23 subnational, policies that use tax-based, ban, mixed, or default-choice modification approaches. Reduction in SUP use averaged 62 %. Median reduction in bag use appeared higher for subnational (75 %) than national policies (66 %, p =0.31) and in G20 countries (75 %, vs. others, 56 %, p =0.40). Some co-benefits and unintended consequences include increased tax revenue, and increased garbage bag consumption, respectively. Considering the dataset’s limitations, policies effectively reduce SUP consumption, optimized through bottom-up policy implementation. However, G20 countries contribute most plastic pollution, which is transboundary, leaving lower-income nations astray with regulatory challenges, thereby perpetuating inequity. While co-benefits encourage policy development as a tool to reduce SUP waste, the unintended consequences must be mitigated. Additionally, knowledge gaps for certain regions, SUPs, and secondary impacts warrant further research. Ultimately, plastic pollution requires global collaboration to strive towards environmental justice.
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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 | 0.017 | 0.072 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.023 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".