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Record W4386386448 · doi:10.1002/ieam.4826

The use of alternatives assessment in chemicals management policies: Needs for greater impact

2023· article· en· W4386386448 on OpenAlexfundno aff
Catherine Rudisill, Molly Jacobs, Monika A. Roy, Lauren Brown, Timothy Malloy, Holly Davies, Joel Tickner

Bibliographic record

VenueIntegrated Environmental Assessment and Management · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicEffects and risks of endocrine disrupting chemicals
Canadian institutionsnot available
FundersGöteborgs UniversitetHealth CanadaDepartment of Toxic Substances Control
KeywordsSAFERStakeholderEnforcementAgency (philosophy)Risk analysis (engineering)BusinessRisk assessmentStakeholder engagementRegulatory scienceAuthorizationImpact assessmentTimelineEnvironmental planningProcess managementComputer scienceComputer securityPolitical sciencePublic relationsMedicinePublic administration

Abstract

fetched live from OpenAlex

Abstract Alternatives assessment is a methodology used to identify, evaluate, and compare potential chemical and nonchemical solutions with a substance of concern. It is required in several chemicals management regulatory frameworks, with the objective of supporting the transition to safer chemistry and avoiding regrettable substitutions. Using expert input from symposium presentations and a discussion group hosted by the Association for the Advancement of Alternatives Assessment, four case examples of the use of alternatives assessment in regulatory frameworks were evaluated and compared: (1) the US Environmental Protection Agency Significant New Alternatives Policy (USEPA SNAP), (2) authorization provisions in the EU REACH (Registration, Evaluation, Authorisation, and Restriction of Chemicals) regulation, (3) the California (CA) Safer Consumer Products (SCP) Program, and (4) the Safer Products for Washington (WA) Program. Factors such as the purpose of the alternatives assessment, the timeline of actions, who completes the assessment, the role of stakeholder engagement, and the regulatory response options for each policy are outlined. Through these presentations and expert discussions, four lessons learned about the use of alternatives assessments in regulatory policy emerged: (1) the goal and purpose of the regulatory framework significantly affects its ability to result in safer substitution, (2) existing frameworks struggle with data access and insufficient stakeholder engagement, (3) some frameworks lack clear decision rules regarding what is a safer and feasible alternative, and (4) regulatory response options provide limited authority for enforcement and do not adequately address options where alternatives are unavailable or limited. Five recommendations address these lessons as well as how the application of alternatives assessment in regulatory settings could have greater impact in the future. This synthesis is not meant to be a comprehensive policy analysis, but rather an assessment based on the perspectives from experts in the field, which should be supplemented by formal policy analysis as policies are implemented over time. Integr Environ Assess Manag 2024;20:1035–1045. © 2023 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC). Key Points A discussion group composed of subject matter experts was convened to gather perspectives on chemical alternatives assessment in response to government regulations. Four regulatory frameworks were discussed, and lesson learned were developed about the use of chemical alternatives assessment in regulatory policy. Five recommendations for regulatory alternatives assessment were developed: establish clear decision rules, enforce or incentivize adoption alternatives, ensure adequate funding, engage stakeholders throughout the process, and create a third-party, standard-setting organization. A more formal policy analysis is needed to better understand the impact of regulatory alternatives assessment, particularly newer frameworks, as they're enacted over time.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.169
Threshold uncertainty score0.832

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 science0.0000.001
Research integrity0.0000.000
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 head0.024
GPT teacher head0.372
Teacher spread0.348 · 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 designObservational
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

Citations11
Published2023
Admission routes1
Has abstractyes

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