Estimating willingness to pay for a hypothetical earthquake early warning systems
Bibliographic record
Abstract
The development of reliable, accessible, and transparent earthquake early warning systems (EEWSs) for disaster reduction have been given increased priority at local, national, and international levels. Accurately quantifying the social and economic benefits accrued to households and businesses from EEWSs are a challenging and difficult task. In this paper, the Contingent Valuation Method (CVM) is used to evaluate the benefits of a hypothetical EEWS to the citizens of Tehran Metropolitan. This study clarifies public willingness to pay (WTP) for EEWS in Tehran, and the dominant factors involved in WTP through a CVM analysis. The survey, completed by more than 504 households, showed that on average households are willing to pay 367,471 Rials (~38 US$) per month for the hypothetical EEWS. Those willing to pay the most for EEWS are households, which currently possess a fire alarm. Also the more educated the respondents and the more children the respondents have, the more willing they are to pay for EEWS. These results could be used by policy makers and technology firms in order to determine the optimal investments in early warning systems for earthquake disaster reduction.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".