A CGE Framework for Modelling the Economics of Flooding and Recovery in a Major Urban Area
Bibliographic record
Abstract
Coastal cities around the world have experienced large costs from major flooding events in recent years. Climate change is predicted to bring an increased likelihood of flooding due to sea level rise and a higher frequency of severe storms. In order to plan future development and adaptation, cities must know the magnitude of losses associated with these events, and how they can be reduced. Often losses are calculated by adding up insurance claims or surveying flood victims. However, this largely neglects the loss due to the interruption of economic activity caused by a flood. There have been some attempts to account for the output losses using input-output techniques, but these do not account for the mitigation achieved through flexible prices, changes in output composition, and factor substitution. Here, we use a computable general equilibrium (CGE) model to study how a local economy responds to a flood, focusing on the subsequent recovery/reconstruction. Initial damage is modelled as a shock to the capital stock and recovery requires rebuilding that stock. We apply the model to Metro Vancouver by considering a flood scenario causing total capital damage of $14.6 billion spread across five municipalities. Transportation and Warehousing are most severely impacted, followed by Manufacturing and Wholesale Trade. We find that the GDP loss relative to a scenario with no flood is 1.9% ($2.07B) in the first year after the flood, 1.7% ($1.97B) in the second year, 1.5% ($1.70B) in the fifth year and 1.1% ($1.42B) in the twentieth year. We also find that the losses tend to scale approximately linearly with the damage rate.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".