Bibliographic record
Abstract
Abstract Here, we present a methodology to improve environmental assessment of Europe based facilities, industries and regions by linking the European Pollutant Release and Transfer Register and the USEtox, a scientific consensus model for characterizing human and ecotoxicological impacts of chemicals. Such environmental assessment is an increasingly important need in policy and finance. In this paper we measure human, cancer and non-cancer toxicity and ecotoxicity risks of more than 10,000 companies from point source pollutant releases in Europe from 2007 to 2017. We discuss water and air emissions of dozens of pollutants in urban, rural, coastal and inland areas. There are clusters of toxicity in the most industrialized regions of North-England, North-Italy, the German Ruhr-area, South-Poland, in the Benelux states, and in coastal areas of Spain, Portugal and Nordic countries. There is an overlap of areas of the largest emissions of human toxicity and ecotoxicity. We confirm toxicity potential of major pollutants in previous research papers (Hg accounting for 71% of the total human toxicity and Zn accounting for 55% of total ecotoxicity). Human toxicity is estimated to be mostly non-cancer type in Europe. Companies in the electricity production sector are estimated to have the largest human toxicity potential (52% of total) in the European Union 2017 and companies in the sewerage sector have the largest ecotoxicity impact potential (41%). Total human toxicity almost halved from 2001 to 2017, although the downward trend reversed in 2016. Ecotoxicity increased by 20% in the same period. A key advantage of our methodology and indicators is that they can be used to measure progress towards the United Nation's Sustainable Development Goals and the environmental objectives in the EU Taxonomy regulation on the company facility level and regionally.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".