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Record W2884312308 · doi:10.1142/s2382624x18500200

Reliability of Drinking Water: Risk Perceptions and Economic Value

2018· article· en· W2884312308 on OpenAlexaffabout
Alfred Appiah, Wiktor Adamowicz, Patrick Lloyd‐Smith, Diane Dupont

Bibliographic record

VenueWater Economics and Policy · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Environmental Valuation
Canadian institutionsGlobal Institute for Water SecurityBrock UniversityUniversity of SaskatchewanUniversity of Alberta
Fundersnot available
KeywordsWillingness to payEndogeneityRisk perceptionContingent valuationInvestment (military)Actuarial scienceReliability (semiconductor)EconomicsBusinessEconometricsPerceptionPsychology

Abstract

fetched live from OpenAlex

This paper estimates the monetary value of drinking water supply reliability in Alberta, Canada. We use the results of an online stated preference survey that elicited respondents’ experiences with, and numerically expressed future risk perceptions of, three types of water outages: short-term outages, longer-term outages and boiled water advisories. Respondents with non-zero risk perceptions were presented with alternative programs that reduced these risks, but increased their water bills. Using cost and other program attributes as explanatory variables, we measured the probability of supporting the programs. The survey results indicated that respondents have not experienced many water outages in the last 10 years, but expect outages to be more frequent over the next 10 years. Using the sample of respondents with non-zero beliefs in the likelihood of future water outages, we calculated a mean willingness to pay (WTP) per household of $71 per year for at least a 50% reduction in the likelihood of a short-term water outage. Results from spike models using responses of all respondents, regardless of their expressed risk perceptions, indicate a WTP of $46 per year for at least a 50% reduction in the risk of short-term water outages. We also used a control function approach to control for potential endogeneity associated with the use of elicited perceived risks in the model and found small differences in WTP estimates. These values provide policy makers with quantified benefits that can be compared to investment costs in traditional water treatment or source water protection.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.159
Threshold uncertainty score0.963

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.029
GPT teacher head0.220
Teacher spread0.191 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2018
Admission routes2
Has abstractyes

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