Scientific dimensions of cumulative effects assessment: toward improvements in guidance for practice
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Notice bibliographique
Résumé
Cumulative effects assessment (CEA) became an increasingly important component of environmental impact assessment (EIA; or simply environment assessment (EA)) shortly after formal processes for EIA were established in North America in the 1970s. Despite a growing body of literature addressing science requirements of exemplary EIA and CEA, practice remains contested. Our mission in preparing this review was to provide a critical update on progress in scientific developments associated with CEA and also to guide practitioners to a broad selection of the recent relevant peer-reviewed formal literature on CEA. In addition, we point to ways in which guidance for CEA practice could be improved. The study canvassed widely for refereed papers in journals and edited books as far back as 2000. On the matter of key concepts related to CEA, the paper addresses the definition of other activities to be assessed, establishment of time and space bounds, impact thresholds, methods for impact prediction, and stressor-based versus effect-based approaches. Definitions of cumulative effect are reviewed, with encouragement for continued work to elaborate the concept. Contributions from science to CEA practice are identified as follows: retrospective and prospective investigative protocols; basic ecological knowledge; effects knowledge; tools and methods; ecological grounds for threshold establishment; and analytically competent practitioners. We observe that the plethora of CEA frameworks populating the scientific literature offer practitioners helpful ways to think about the CEA process. CEA methods are then reviewed, with specific emphasis on geographic information systems, scenario-building, thresholds, indicators, simulation, and public engagement. Several case examples of CEA in practice are summarized, with the observation that none of the published case studies arises from work done to support CEA that is part of the regulated EIA process. The paper reflects on the role of CEA in project-specific EIA (or project EA) as well as class EA, strategic EA, and regional EA. CEA is needed in all forms of EA, but it seems to be particularly difficult to implement well in project-specific EIAs. Recommendations for improvements in guidance materials for practitioners address definitions, scenarios, analytical methods, collaborative methods, thresholds, knowledge accumulation, accidents and malfunctions, project scale, and knowledge integration. We conclude that competent CEA is a vital requirement for securing the sustainability of valued ecosystems and their components.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 découle