From Smokes to Smokestacks:Lessons from Tobacco for the Future of Climate Change Liability
Bibliographic record
Abstract
In this Article, we imagine a future, circa 2030, wherein the world has managed to avoid the worst climate change, yet has begun to experience considerable warming. Governments of all levels, especially at the state and provincial-level, are incurring unprecedented costs to mitigate the effects of climate change and adapt to new and uncertain climatic regimes. We consider how legislatures might respond to these imagined challenges. In our view, the answer may lie in the unprecedented story of tobacco liability, and especially the promulgation of state and provincial legislation specifically designed to enable the recovery of the public healthcare costs of tobacco-related diseases in the 1990s. This Article delves into the legally-relevant differences and similarities between the tobacco industry and the fossil-fuel industry. It also sets out the main elements of a potential Climate Change Damages and Adaptation Costs Recovery Act, mirroring similar legislation passed to combat tobacco-related issues. As will be seen, the design of such legislation engages several complex legal issues, implicating not only tort doctrine but also questions of legislative competence and private international law. Nevertheless, our initial assessment is that such legislation is both likely and feasible. Our analysis focuses primarily on Canadian law but is relevant to other jurisdictions grappling with the increasing costs of climate change mitigation and adaptation.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.032 |
| Scholarly communication | 0.011 | 0.018 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.017 | 0.018 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".