Responsible Risk: How putting a price on environmental risk makes disasters less likely
Bibliographic record
Abstract
From a policy perspective, managing risk means getting incentives right. Firms already want to avoid disasters and environmental damage, given costs to their reputation and their bottom line. But those incentives are sometimes insufficient. Gaps in existing policies — we call them “liability gaps” — mean that firms are not always held fully accountable. These gaps can shift risk—and any related costs of environmental damage—away from firms and onto taxpayers. For example, firms that declare bankruptcy might be unable to pay the full costs of clean up, leaving society to cover the rest of the bill. When firms do not bear the full cost of potential environmental damage, they have less incentive to reduce risk. As a result, industrial disasters might be more likely to occur. Better financial assurance policies— for example, cash deposits, insurance, and industry funds — address this problem by putting a price on environmental risk. They create incentives for firms to reduce risk. They ensure that taxpayers do not end up bearing the costs of environmental damage, should those unlikely disasters occur. And they support economic activity by harnessing market forces to achieve these objectives at lowest cost. The report today unpacks both the problem of un-priced environmental risk and potential solutions. It identifies five types of liability gaps that can result in firms’ not bearing the full cost of potential environmental damage. It shows how financial assurance can address these gaps, and considers the tradeoffs across different financial assurance tools. And it develops a detailed case study of financial assurance in Canada’s mining sector, evaluating current provincial policies in Yukon, British Columbia, Alberta, Ontario, and Quebec.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.062 | 0.004 |
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".