The cost of degradation of the Dutch North Sea environment - update 2024 : a study into the cost of avoiding degradation and the applicability of the Ecosystem Services approach
Bibliographic record
Abstract
This report provides an insight into the cost of degradation of the marine environment of the Dutch part of the North Sea by calculating the annual current (2022) costs of measures that avoid or minimise degradation.In addition to this, insight is provided into the potential applicability of the ecosystem services approach to calculate ecosystem benefits gained when Good Environmental Status is reached, in comparison to a Business-as-Usual scenario.The total costs of measures that avoid degradation of the Dutch North Sea environment have been calculated to be in the range of approximately at least €7.19-2.02bn in 2022.In terms of the applicability of the Ecosystem Services Approach methodology, it is concluded that the methodology and empirical application are not mature enough yet and that the data needed are too limited to be applied within the context of the Marine Strategy Framework Directive.Dit rapport geeft inzicht in de huidige (2022) jaarlijkse uitgave om aantasting van het mariene milieu van het Nederlandse deel van de Noordzee te voorkomen of te minimaliseren.Daarnaast wordt inzicht gegeven in de potentiële toepassing van de ecosysteembenadering als methode om de waarde te berekenen van extra ecosysteemdiensten bij een scenario waarbij de Goede Milieutoestand (GMT) gerealiseerd is ten opzichte van een Business as Usual-scenario.De jaarlijkse totale kosten van maatregelen die aantasting van het Nederlandse Noordzeemilieu voorkomen, zijn voor 2022 berekend op ten minste €7,19-2,02 mld.Voor wat betreft de toepassing van de ecosysteemdienstenbenadering is geconcludeerd dat de methodologie en empirische toepassing nog niet ontwikkeld genoeg zijn en er nog niet voldoende benodigde gegevens beschikbaar zijn om deze binnen de context van de Kaderrichtlijn Mariene Strategie toe te passen.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".