Evaluating flood risk governance with geospatial technologies
Notice bibliographique
Résumé
The concept of governance for flood resilience has gained traction in scholarship and practice over the last 15 years. Governance is distinct from government as it recognizes that flood risk management (FRM) is carried out by actors that transcend government and which include the private sector and civil society. Though there is a theoretical notion that effective FRM entails the involvement of a diverse set of actors and measures, there is a notable lack of studies on the development and evaluation of tools designed to foster greater integration between public and private actors to solve FRM problems of common concern. \nThis dissertation primarily focuses on the Canadian context and the problem of residential properties that are high-risk and “uninsurable” for flood damages, and which are subject of current national public policy discourse. Current public policy discussions revolve around the relocation of these properties away from high-risk flood zones and establishing a public program for extending affordable flood insurance coverage to these higher risk properties. This dissertation focuses on an alternative strategy by evaluating the role that a public-private data-sharing partnership could play in reducing and managing the number of high-risk “uninsurable” properties. \nThrough three interrelated studies, this dissertation demonstrates how, in the absence of a common approach to flood risk identification, insurers and municipalities have a conflicting understanding of which properties are at risk of flood. The lack of channels for information-exchange between municipalities and insurers are also prohibiting municipal officials from knowing why insurers classify a property as “high-risk”, while also posing difficulties for insurers to become aware of public flood risk mitigation investments happening across Canada. To foster collaboration between insurers and municipal officials for resolving the flood insurability problem, a data-sharing platform is proposed and evaluated via interviews with municipal officials and insurers. While this process of evaluation confirmed there is a “missing link” between municipalities and insurers, the proposed data platform faces many obstacles for its adoption and evidence gathered suggest it is unlikely to lead to greater availability and affordability of flood insurance products. \nInstead, findings support an appeal process that would establish a three-way communication line between insurers, government officials and individual homeowners who are experiencing issues finding affordable flood insurance coverage for their property. This process of appeal would (1) reveal the reasons why insurers classify a specific property as “high risk” of flood, (2) address modelling and technical limitations that may be influencing insurers’ risk classification such as the omission of flood protection infrastructure, (3) agree upon additional measures that could be implemented to reduce physical flood risk by either the homeowner and/or responsible government agencies that could further promote that flood insurance remains available and affordable to the homeowner. \nMore broadly, this dissertation concludes that in a time of increasing economic losses from floods and climate risk uncertainties, a concerted multi-level government effort is necessary to collect data about flood protection infrastructure. This database can inform the process of appeal but it can also be used to raise awareness and provide confidence among civil society and the private sector about the effectiveness of public infrastructure investments in preventing flood damages. Finally, this dissertation presents novel approaches for evaluating flood risk governance that leverage geospatial data, methods and tools which could be replicated and tested in other international contexts.
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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 | 0,006 | 0,023 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,007 | 0,006 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,005 | 0,005 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 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 ».