Floods and Water Service Disruptions: Eliciting Willingness-to-Pay for Public Utility Pricing and Infrastructure Decisions
Bibliographic record
Abstract
Devastating floods and ongoing droughts throughout the world highlight the infrastructure and management challenges facing water and wastewater utilities. While increasing variability in climate has been identified as the immediate culprit, the severity of events has been exacerbated by years of underinvestment in infrastructure improvements due to inadequate pricing of services. The American Water Works Association (2015) identifies a crucial step for decision-makers: identify clearly communities’ priorities with respect to water and wastewater management, both of which provide public or community-level goods and services. Using data from two separate choice experiments, collected in the same survey of Canadian households, the current paper estimates household willingness-to-pay (WTP) to reduce the likelihood of flood events and water service disruptions. Results from both choice experiments show a strong preference for policy scenarios that reduce these risks, although there is a substantial amount of heterogeneity. People living in the Prairie region, rural residents, and people living in areas with higher home values have a higher WTP. Although the results lend support to public infrastructure programs for addressing flood and water disruption risks, the degree to which these are preferred to private action is not clear.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".