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Record W3212682950 · doi:10.1177/0734242x211055548

Challenges in the context of single-use plastics and bioplastics in Brazil: A legislative review

2021· review· en· W3212682950 on OpenAlexaff
Laís R. Lima, Rafaela Francisconi Gutierrez, Sandra Andréa Cruz

Bibliographic record

VenueWaste Management & Research The Journal for a Sustainable Circular Economy · 2021
Typereview
Languageen
FieldEnvironmental Science
TopicMicroplastics and Plastic Pollution
Canadian institutionsUniversity of Toronto
FundersFundação de Amparo à Pesquisa do Estado de São Paulo
KeywordsBioplasticContext (archaeology)LegislatureLegislationCommodityBusinessIncentiveEngineeringEconomicsWaste managementPolitical scienceLawMarket economy

Abstract

fetched live from OpenAlex

Plastic has been present in our lives for the past century as an essential material for many commodity items. However, the same properties that make plastic convenient are also responsible for the current dramatic environmental pollution. As an alternative, most of the world has been working with technological innovations, and one of its strategies is the use of bioplastics. Despite being considered environmentally beneficial by some people, there are still developments and discussions that need to be made. This article aims to present a legislative review and discusses the difficulty in implementing policies related to the incentive of the bioplastics market, as well as presenting some state and municipal laws, already prohibiting single-use plastics in Brazil. These laws aim to encourage the substitution of these plastics for biodegradable ones. However, it still has gaps and a lack of clarification on how the banning of disposable plastics and their substitution will be beneficial since composting is still an incipient process in the country. It is also the purpose of this article to discuss the challenges in the context of the Circular Economy, as well as the potential solution based on the creation of public policies aimed at improving waste management, in addition to clearer legislation on alternatives to single-use plastics.

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)0.009
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.952
Threshold uncertainty score0.922

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.132
GPT teacher head0.356
Teacher spread0.224 · 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; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations15
Published2021
Admission routes1
Has abstractyes

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