Insurance Coverage in a Climate Changed Canada: How Can Canada Pay for Loss and Damage from Anthropogenic Climate Change?
Bibliographic record
Abstract
This article looks at the impact of anthropogenic climate change and its associated costs in the Canadian context. It begins by outlining how climate change is predicted to alter the Canadian climate before evaluating how this will affect the insurance industry. It determines that insurers’ response to the unpredictable risks and high costs associated with climate change will lead to significant gaps in coverage. How litigation of major carbon polluters could help cover some of the costs associated with climate change by holding polluters accountable is considered. State-led climate litigation can overcome some of the litigation obstacles identified and it may be preferable to civil litigation. The current state of civil and state-led litigation will be inadequate to address the mounting costs associated with climate change. Thus, the article considers the use of legislation to assist state-led litigation against major carbon polluters, which would be modeled after the tobacco legislation first used in British Columbia. The article contemplates how these funds could be disbursed into disaster relief and no-fault insurance schemes to compensate for climate loss and damage, as well as briefly discussing the international concerns relevant to these domestic issues. Ultimately, it is determined that there are viable combinations of legislation, litigation, taxation, compensation, mitigation, adaptation, and insurance that can better prepare Canada for managing the high costs associated with anthropogenic climate change.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".