Advancing Municipal Natural Asset Management through Standardized Evaluation
Notice bibliographique
Résumé
In Canada, many urban and near-urban ecosystems are in decline. As well, engineered infrastructure is aging, its capital and operating costs are rising, and municipal service delivery is strained. Local governments are searching for new strategies to deliver services in financially and environmentally sustainable ways. They are also looking to incorporate ecosystems and ecosystem services into their understanding of service delivery. Unfortunately, many municipalities struggle to view these ecosystems as green infrastructure that can provide local communities with a wide range of important services such as stormwater management. However, some Canadian municipalities are beginning to incorporate ecosystems and the services they provide into their asset management planning and service delivery frameworks, an approach known as municipal natural asset management. To conduct municipal natural asset management, municipalities should restore, conserve, inventory, and track ecosystems under their jurisdiction. \n \nAs more municipalities incorporate municipal natural asset management, evidence of its efficacy is required to upscale and mainstream this approach. Therefore, the purpose of this research is to evaluate municipal natural asset management programs. Evidence from this evaluation will contribute to a broadening database of the beneficial outcomes of a municipal natural asset management program. To do this, this research created a rigorous evaluation framework for municipal natural asset management and has applied it to a national cohort of five case studies. This evaluation framework includes a Program Logic Model and an Evaluation Matrix as two common evaluation tools. As well, evaluation questions, indicators, benchmarks, and a five-point, colour-coded scoring system were created for program outcomes based on four distinct outcome streams in the Program Logic Model. These four outcome streams are (i) Awareness, Capacity and Education Outcomes, (ii) Implementation Outcomes, (iii) Ecosystem Rehabilitation and Restoration Outcomes and (iv) Service Delivery Outcomes. Findings from the evaluation showed that the five municipalities received high scores for Awareness, Capacity and Education Outcome indicators and some Implementation Outcome indicators. However, the municipalities did not receive high scores in later Implementation Outcome indicators, Ecosystem Rehabilitation and Restoration Outcome indicators, and Service Delivery Outcome indicators. \n \nThese findings reveal that municipalities are aligning municipal natural asset management with existing municipal climate action initiatives. Moving forward, Canadian local governments should focus on partnerships and champions to enable municipal natural asset management, recognize municipal natural asset management as a full municipal program, and use existing tools to identify sites for ecosystem rehabilitation and restoration. Findings from the evaluation also provide insights on complex and complicated Program Logic Models, nested outcomes, and outcome streams. This evaluation framework should be improved upon so more municipalities can be evaluated simultaneously and automatically. Finally, local governments should explore using funding from COVID-19 Pandemic Recovery to integrate municipal natural asset management.
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.
Comment cette classification a été obtenuedéplier
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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 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 | 0,002 | 0,001 |
| 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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, 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 ».