Economic Evaluation of Climate Change Impacts on Ground Transportation in Atlantic Canada: General Equilibrium Framework
Bibliographic record
Abstract
Climate change has become one of the most serious consequences of human activities. According to the IPCC Fourth assessment report, the emission of greenhouse gases (GHG) generated by human activities is the primary cause of the climate change and especially global warming. In general, transportation is one of the areas where climate change problem is the most severe. On the one hand, with passage of time vehicle usage will result in more greenhouse gases, which would contribute to further climate change; on the other hand, climate change itself makes transportation infrastructure, vehicles and operations vulnerable to various impacts. Unfortunately, very little has been done in order to estimate economic losses due to climate change impacts on transportation. THis study tries to capture this gap.In this study, climate change impacts and their economic consequences for Atlantic Canada ground transportation network are analyzed. First, major climate change impacts in the area are identified. Second, the best economic model to evaluate consequences of the climate change impacts is chosen. Third, using the existing literature and studies that describe future climate changes in the region, various scenarios of future challenges for the regional ground transportation network are specified. Finally, economic consequences of the regional climate change impacts on the ground transportation network are evaluated. The above specified consequences are imposed on a dynamic general equilibrium model and their cumulative impacts are traced over time.Climate change impacts could reduce the volume of transpiration associated with analyzed regional network by 0.07 to 0.14 million vehicles per year compared to the current volume of 4.89 million. It could lead to the loss in value added by 11.136 million per year.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".