Simulation Model for Assessing the Impact of Climate Change on Transportation and the Economy in Canada
Bibliographic record
Abstract
It is widely argued that severe weather events and episodes of poor weather conditions (cold snaps and heat waves) have significant impact on regional economies and transportation systems. Several studies have focused on quantifying this relation from observed data. However, little has been done to simulate and assess the long-term impacts of climate change on regional transportation systems and economies. This is because of the lack of simulation models that are able to link changes in weather events to transportation system performance and interregional trade flows. This paper reports on the development of CLIMATE-C, a tool for simulation of the assessment of the impact of climate on transportation and the economy in Canada. Linkages between transportation and the economy are handled through a random utility-based multiregional input– output model (RUBMRIO), which predicts interregional trade flows by truck and rail among the 76 economic regions of Canada for 43 commodities. But the influence of weather on transportation is handled through speed adjustment factors that account for the reduction in travel speeds because of changes in the frequency of various weather events. Therefore, changes in the frequency of weather events translate into travel delays, which in turn influence trade flows between regions. Sensitivity analysis with the implemented model illustrated its ability to assess the impact of climate change on transportation and the economy in Canada.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".