Unlocking private investment in Canadian flood resilient home retrofits: a return on investment research summary
Notice bibliographique
Résumé
Canadian governments, businesses, and residents currently spend a combined average of $2.9 billion every year repairing damage to homes caused by fluvial, pluvial, and coastal flooding. The distribution of losses is particularly concentrated in homes exposed to the highest levels of flood hazard. For example, homes within the top 10% (approximately 1.5 million) experience 89.3% of all losses per year whereas the top 1% (approximately 109,000) experience 34.1% (Public Safety Canada, 2022). To date very few Canadian households have invested in flood resilient retrofits before flooding occurs, opting instead to wait for government disaster recovery programs to provide support if and when flooding occurs. This reactive approach unnecessarily exposes Canadians, particularly socially vulnerable households within high-risk zones, to dire social and economic consequences (Wright et al., 2022). Without implementing adequate voluntary and regulatory flood risk reduction measures experts project that annual costs associated with repairing home flood damages may triple by 2030 (Ziolecki et al., 2020). This paper examines the grey and peer-reviewed literature that is currently available to help drive proactive private side investment in flood resilient home retrofits, identifies where gaps exist, and points to additional research that is needed to bolster retrofit adoption. The majority of research describes home flood mitigation approaches using the PARA framework (protect, accommodate, retreat, avoid) and expresses results as a benefit cost ratio (BCR). A BCR compares the total expected benefits to society for the life of a building (75 years for new builds and 50 years for retrofits) as a result of completing mitigative actions compared to related installation and maintenance costs (Doberstein et al., 2019). The research consistently documents the power of public and private incentives to increase the BCR of flood resilient retrofits and drive preventative action. It also emphasizes the importance of building all new homes to meet flood resilient standards to maximize the long-term benefits to society (Porter & Yuan, 2020; Porter et al., 2023). BCR estimates for dry flood proofing (protect) range from 11:1 for flood resilient new home construction (Porter & Scawthorn, 2020) to 6:1 for flood resilient retrofits (Porter et al., 2019). Wet flood proofing (accommodate) BCR estimates range from 6:1 for 1’ (30cm) elevation above the 100-year flood plain to 5:1 for 5’ (150cm) elevation above the 100-year flood plain (American Flood Coalition, 2022). BCR estimates for retreat range from 6:1 over 20 years (Porter et al., 2019) to 6:1 over 30 years (Nelson & Camp, 2020). Two studies proposed a methodology for estimating BCR associated with land use planning initiatives to eliminate flood risk (avoid), but did not generate any BCR results (Mechler et al., 2014; De Risi et al., 2018). To unlock private side investment in proactive flood resilient retrofits and new builds the research identifies opportunities for all participants in the real estate value chain (builders, developers, retailers, insurers, lenders, and local governments) to work together to raise public awareness of flood risks and incentivize improved flood resilience (Porter & Yuan, 2020; Porter et al., 2023; Giannitsos, 2023; Krueger, 2022; Minano & Peddle, 2018). Support for flood resilient home retrofits in high-risk zones should be prioritized, particularly for homes that provide the country’s most affordable housing units, including basement apartments, social housing, rental homes, and apartment buildings. This is critical to ensure access to safe, reliable and attainable housing for Canada’s vulnerable populations (Wright et al., 2022). Updating building codes and eliminating perverse legislation and policies that incentivize development in high-risk flood zones is also necessary to fortify the long-term flood resilience of Canada’s housing stock (Porter & Scawthorn, 2020).
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 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 | 0,009 | 0,026 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,004 | 0,007 |
| Études des sciences et des technologies | 0,006 | 0,003 |
| Communication savante | 0,011 | 0,004 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,012 | 0,001 |
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 ».