How to convince lawyers? Sweden vs EU for 20 years, claiming scientific evidence of nitrogen removal in lakes
Notice bibliographique
Résumé
This presentation will tell the story of how policy-makers and scientists interacted during three decades for improved water status on a national level in co-production and mutual understanding, but how the pieces of evidence was then met by mistrust when joining the European union. Environmental policies based on ecohydrology, science and specific environmental characteristics seem difficult to maintain and convey in a larger union of conform legislation across diverse countries. High lake densities are located in the northern parts of the Northern Hemisphere like Northern Canada, Scandinavia, Russia and Alaska. Sweden is ranked as number five among the world countries having most lakes (>0.1 km2) and they cover 9 % of the land area. More than 20% of the EU waterbodies and 40% of the lakes in EU are on Swedish territory. These lakes contribute with many ecosystem services, of which one is nitrogen (N) removal during the riverine transport due to water retention in lakes on its way towards the sea. This N removal is caused by natural bacterial processes in lake water and sediments, so called denitrification, converting soluble nitrate to atmospheric non-reactive N gas. Thus, the aquatic ecosystem cleans the water from inland N pollution before the rivers reach the sea. This ecosystem service of Swedish lakes is important as the Swedish coastal waters and the Baltic sea suffers from eutrophication caused by extensive nutrient emissions. Sweden has a very long coast, being surrounded by seas in the east, south and southwest. The coast is characterised of archipelago and semi-enclosed bays, which are highly influenced by inflow from rivers and land-based emissions. When introducing the EU water framework directive (2000/60/EG), it was associated with the ten-year older directive concerning urban waste-water treatment (91/271/EEC) prescribing 80 % phosphorus (P) reduction and 70 % N reduction in treatment plants. However, already during the 1970’s it was well-known that Swedish waters are highly vulnerable to P emissions, and the treatment plants were already removing 96 % of urban P emissions. The new legislation on P was thus by far less effective than the Swedish law at the time. For N, on the other hand, Sweden applied treatment requirements in consideration to the natural ecosystem services from the numerous lakes in the river networks for 12 inland plants. Scientific methods combining monitoring and modelling were elaborated and used to quantify the N removal downstream of each wastewater treatment plant, to allow for individual restrictions on these inland emissions. Despite the rigorous scientific literature supporting the Swedish environmental policy, the European Commission has repeatedly questioned the evidence in the EU court for two decades. This presentation will give insight in how the science-policy interface in hydrology is dependent on culture, history and participatory processes, leveraging the know-how. We will show the numerous efforts in science communication over the decades and we welcome suggestions on how to further advance understanding and ultimately close the gap between scientists and legal experts.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,051 | 0,136 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,004 | 0,002 |
| Études des sciences et des technologies | 0,010 | 0,018 |
| Communication savante | 0,032 | 0,018 |
| Science ouverte | 0,002 | 0,013 |
| Intégrité de la recherche | 0,036 | 0,019 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».