AN ANALYSIS OF LEGISLATION AND GUIDANCE FOR UNCERTAINTY DISCLOSURE AND CONSIDERATION IN CANADIAN ENVIRONMENTAL IMPACT ASSESSMENT
Bibliographic record
Abstract
Environmental Impact Assessment (EIA) is a process designed to predict, and identify means to mitigate, the potential environmental effects of proposed development actions.As a decisionaiding tool, EIA assists decision-makers by providing them with the information needed to make an informed decision about the acceptability of a proposed development.Uncertainty is inherent in any process that is focused on future conditions, but recent research has shown that EIA practitioners do not fully disclose, consider or communicate uncertainties inherent in the EIAs for their development projects.The result is a reduction in the credibility and efficacy of EIA, and decisions potentially being made without full knowledge of, and thus consideration for, such uncertainties.Agencies and scholars have demanded that proponents and practitioners be more explicit about uncertainties in EIA reporting, but there has been no systematic examination of the requirements or guidance made available to practitioners concerning uncertainty reporting in EIA or the types of uncertainties that should be disclosed.The purpose of this research was to examine the provisions for communicating and addressing uncertainty in Canadian EIA.The research was conducted via review of Canadian federal, provincial, and territorial EIA legislation, regulations, and guidelines, which are publically available on the official websites of the corresponding agencies and authorities.Results indicate that requirements and recommendations for uncertainty disclosure and consideration exist in most of Canadian federal, provincial and territorial EIA legislation, regulations and guidelines, but they vary considerably from one jurisdiction to the next.The difference is reflected in many aspects, including: the extent of requirements and recommendations for addressing uncertainty in EIA; iii variability of the provisions given; differences in location of the requirements in legislation versus regulations versus guidelines; and differences in the clarity of requirements.In addition, the requirements and recommendations to manage uncertainty associated with the different stages and components of EIA are inconsistent.Some of the EIA stages were covered better than others, but none of the jurisdictions provided requirements and recommendations for addressing uncertainty in all stages of EIA.The results further indicate that existing requirements and recommendations for uncertainty treatment in Canadian EIA practice are, overall, inadequate and sometimes confusing.This is likely to result in insufficient communication and treatment of uncertainty and decisions being made without consideration of such uncertainty.Therefore, the development of national, standardized best practices and more systematic guidance are, at a minimum, required to ensure that uncertainty is properly addressed and communicated in EIA across EIA jurisdictions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".