Assessing the suitability of economic policy instruments for urban flood risk management
Bibliographic record
Abstract
Although modern technology has improved stormwater management practices, municipalities remain susceptible to urban flooding. One common method for addressing flood risk is through the application of economic policy instruments, which facilitate risk reduction by way of incentivising stakeholders to engage in activities that eliminate risk. To date, several studies have analysed costs and benefits of economic policy instruments, but there are still limited insights regarding the selection and evaluation of economic policy instruments by municipal public managers. As a result, this study explored how Canadian municipal public managers assess the suitability of economic instruments for flood risk management. The economic policy instruments examined in this study included corrective taxes, special surcharges, subsidies, compassionate grants, stormwater credits and stormwater charges. Semi-structured interviews were employed and asked participants to evaluate the suitability of the instruments based on seven evaluation criteria. Thematic content analysis was utilised to identify themes among the interviewees’ evaluations and resulted in a total of eighteen individual axial codes, collated under three broader suitability themes (efficiency, legitimacy and resiliency). This study concluded that municipal public managers evaluate the suitability of economic instruments for flood risk management through the use of a hierarchical framework which organises the seven evaluation criteria from most preferred to least preferred. Thus, the criteria are ordered as such; municipal capacity, effectiveness, political viability, fairness, economic efficiency, flexibility and coherence.
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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 | 0.069 | 0.090 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".