The EU Green Deal's ambition for a toxic-free environment: Filling the gap for science-based policymaking
Bibliographic record
Abstract
Abstract Around the world, many ambitious environmental conventions and regulations have been implemented over recent decades. Despite this, the environment is still deteriorating. An increase in the volume and diversity of chemicals is one of the main drivers of this deterioration, of which biodiversity loss is a telling indicator. In response to this situation, in October 2020, a chemicals strategy for sustainability (CSS) was published in the EU. The CSS is the first regional framework aiming to address chemical pollution in a holistic manner. The CSS covers the complete lifecycle of a chemical, including the design of better substances and remediation options, to remove chemicals from the environment. The strategy contains terms, such as a “toxic-free environment,” for which no clear definition exists, potentially hampering the implementation of the CSS. In this paper, a definition for a “toxic-free environment” is proposed on the basis of a survey and a discussion held at the 2020 SETAC Europe Annual Meeting. In addition, key issues that are absent from the CSS but are considered to be key for the realization of a toxic-free environment are identified. To achieve the policy goals, it is recommended to align the definition of risk across the different chemical legislations, to establish a platform for open data and data sharing, and to increase the utility and use of novel scientific findings in policymaking, through the development of a strong science to regulation feedback mechanism and vice versa. The paper concludes that environmental scientists have the tools to address the key challenges presented in the CSS. However, an extra step is needed by both policymakers and scientists to develop methods, processes and tools, to increase the robustness and transparency of deliberation processes, and the utility of science. Integr Environ Assess Manag 2021;17:1105–1113. © 2021 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC). KEY POINTS A definition for a ‘toxic-free environment’ is proposed based on an analysis of a survey and a discussion held at the 2020 SETAC Europe Annual Meeting. Key issues are identified based on discussions that are absent from the European Commission's chemicals strategy for sustainability but are considered to be key for the realisation of a toxic-free environment. Scientists have the tools to address key environmental challenges, but an extra step is needed by both policymakers and scientists to make these applicable.
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 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 | 0.054 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.034 | 0.015 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.036 | 0.020 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".