Role of science and scientists in public environmental policy debates: The case of EU agrochemical and Nature Restoration Regulations
Bibliographic record
Abstract
Abstract Halting biodiversity loss, mitigating global warming and maintaining the long‐term viability of rural and urban areas requires urgent policy action. However, environmental policies often trigger resistance and highly polarised public debates, with some actors employing pseudo‐scientific claims. This raises concern about the increasing impact of misinformation on policymaking. Here, we analyse the role of science and scientists in the public debate around two pieces of legislation that were proposed in 2022 by the European Commission as part of the Green Deal, namely the Nature Restoration Regulation (NRR) and the Sustainable Use Regulation (SUR) of plant protection products. First, we examine key claims against these two legislative proposals and contrast them with scientific evidence. We show that these claims fail to consider ample scientific evidence that restoring nature and reducing the use of agrochemicals are essential for maintaining long‐term agricultural production and enhancing food security. Critics further failed to acknowledge that the NRR and SUR may generate new employment opportunities and stimulate innovation, with high return rates and multiple beneficiaries across society, fostering a transition to sustainable production and consumption models. Second, we examine how the publication of an open letter, signed by 6000 scientists, may have influenced the public debate. We contrast the role that scientific evidence played in the fate of the NRR, which was adopted, against the fate of the SUR, which was rejected by the European Parliament. We draw lessons from these two cases that illustrate the global tension between environmental protection and economic‐driven interests to spread misinformation. We argue that scientists should play an important role in making scientific evidence more accessible and available to the general public and policymakers for informed decision‐making. We recommend that scientists be proactive and unbiased in providing information and data and that policymakers use scientific evidence and engage scientists in developing much needed, well informed environmental policies. Read the free Plain Language Summary for this article on the Journal blog.
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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 | 0.139 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.031 | 0.061 |
| Scholarly communication | 0.038 | 0.021 |
| Open science | 0.004 | 0.022 |
| Research integrity | 0.065 | 0.026 |
| 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".