Evaluating flood risk governance with geospatial technologies
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".