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Record W4289667456 · doi:10.1139/er-2021-0117

Impacts of plastic waste management strategies

2022· article· en· W4289667456 on OpenAlexvenueno aff
Levi T. Helm, Erin L. Murphy, Alexis McGivern, Stephanie B. Borrelle

Bibliographic record

VenueEnvironmental Reviews · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicMicroplastics and Plastic Pollution
Canadian institutionsnot available
Fundersnot available
KeywordsPlastic wasteSustainabilityPlastic pollutionContext (archaeology)BusinessCleaner productionProduction (economics)Environmental impact assessmentEnvironmental scienceWaste managementNatural resource economicsEnvironmental resource managementEnvironmental planningMunicipal solid wasteEnvironmental economicsPollutionEngineeringEconomicsEcology

Abstract

fetched live from OpenAlex

The ecological and societal impacts of plastics production, use, and waste are a complex global challenge. Management strategies to mitigate the impacts of plastics, such as recycling, waste-to-energy, and replacement with alternative materials have impacts of their own. Achieving long-term sustainability of plastics use therefore requires considering the externalized impacts of such management strategies. Here, we assessed the literature on the most common plastic waste management strategies to identify their impacts in relation to the sustainable development goals. We reviewed impacts of bans, levies, and taxes; alternative products; recycling; waste-to-energy; plastic recovery; and extended producer responsibility. Our analysis identified a total of 259 measured impacts of plastic waste mitigation strategies, from 113 papers. Ninety-three impacts were negative, 104 were positive, 11 were neutral, and 51 depended on the context of implementation. Consideration of the impacts of both plastic materials and management strategies is necessary to avoid perverse outcomes of plastic pollution mitigation efforts.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.809
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0100.001

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.

Opus teacher head0.010
GPT teacher head0.212
Teacher spread0.202 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations47
Published2022
Admission routes1
Has abstractyes

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