Preparing Legal Frameworks for Environmental Disasters: Practical Considerations for Host States
Bibliographic record
Abstract
Projects in the extractives sector carry risks of lasting, and sometimes irreversible, damage to the environment. Nonetheless, these projects are important for accelerating the economic development of host countries. Governments seeking to mitigate the adverse effects of foreign investment often face pushback from investors that are unwilling to change their practices in order to avert environmental disaster. This report sets forth certain steps that host-governments can take during the pre-investment, operation, and enforcement phases of extractives projects to provide financial and other protection in the context of environmental disasters associated with private sector investments. Upon comparative review of five Case Study Countries (Canada, Chile, the UAE, Indonesia, and Uganda), the authors of this report found that the most important factor impacting a host government’s ability to hold developers accountable for environmental harm is a gap between applicable environmental legislation and the enforcement of this legislation. The report then provides several recommendations for addressing this discrepancy, with an emphasis on the role of planning and robust legal and regulatory frameworks, as well as an analysis of a variety of safeguards (including, but not limited to, impact assessments, stakeholder engagement, and financial penalties) to be deployed at each of the three phases of a project. It also discusses the role of financial institutions in promoting best practices and mitigating risks and minimizing the fallout from investment-related environmental disasters.
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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 | 0.073 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.012 | 0.016 |
| Scholarly communication | 0.027 | 0.026 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.024 | 0.017 |
| Insufficient payload (model declined to judge) | 0.012 | 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".