Estimation of Economic Impacts of Climate-Driven Hazards Using Stochastic Process Model
Bibliographic record
Abstract
Projections using global climate models indicate that climate change will influence the patterns of natural hazards, such as thunderstorms, atmospheric river landfalls, extreme droughts, and ocean waves. The frequency and intensity of these hazards are expected to increase gradually in proportion to global temperature. The design principles based on the philosophy of cost optimization need to be updated to accommodate the nonstationarity of the load processes, primarily because the prevalent cost analysis methods in the literature predominantly assume that the loads are stationary. This study provides a novel methodology for calculating the first two moments and the distribution of the economic losses for nonstationary loading processes. Here, the load processes are modeled as a nonhomogeneous Poisson process (NHPP) with time-dependent rates. The presented methodology is applied to estimate the losses due to tornadoes in Ontario, Canada and heat waves in US cities. It was found that if adaptive measures are applied to increase the capacity of structures, the losses due to these climate-driven hazards can be significantly reduced. For example, if mitigation strategies are employed in Ontario, such that the effect of tornadoes with wind speeds lower than 50.3 m/s becomes negligible, then the tornado losses until 2100 can be reduced by 66%.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".