Consideration of Uncertainties in Environmental Protection Plans and Follow-up Programs in Canadian EIA
Bibliographic record
Abstract
The rationale for project-based environmental impact assessment (EIA) is to provide stakeholders and decision-makers with a complete understanding of a proposed project as well as a realistic representation of impacts on environmental processes. Environmental processes are known to be unstable, complex and sometimes hard to predict leading to the uncertainties about impacts. In project-based EIA, environmental processes tend to be simplified. Classifying uncertainties and evaluating their implications have been identified as an urgent need. Predictions about the kinds and severity of a project’s impacts are often wrong and mitigation measures less effective than anticipated. This study aims to evaluate the extent to which uncertainty is considered and addressed in Canadian EIA practice. Environmental protection plans (EPPs) and follow-up programs present opportunities for proponents to disclose and address uncertainties raised during the environmental impact predictions. Twelve Canadian Environmental Impacts Statements (EISs), post the Canadian Environment Assessment Act in 1995 and prior to the 2012 Canadian environmental legislation Act, were reviewed. This study shows that in the EPPs and follow-up programs, uncertainty is never discussed in depth. There is a lack of suitable terminology and consistency in how uncertainty is disclosed reflecting the need for explicit guidance. When uncertainty is acknowledged, the authors took various approaches to address it. Seven kinds of approaches were identified in the reports. However, uncertainties were still never addressed in depth. This research clearly demonstrates that project-based Environmental Protection Plans and follow-up programs in Canadian EIA are not as transparent with respect to uncertainties as they should be, and that uncertainties generally need to be better considered and communicated to stakeholders and decision-makers.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".