Environmental Assessment as a Tool for Managing Impacts on Wetlands: Understanding Current Practice in the Mining Sector
Bibliographic record
Abstract
Wetlands are ecologically rich lands but also heavily impacted. Canada has a large mining sector, with operations impacting the important functions provided by wetlands that benefit humans. Environmental assessment (EA) is the primary regulatory tool for mitigating the impacts of development, including mining, to wetlands. Many jurisdictions in Canada use a hierarchical approach to mitigate wetland loss through avoidance, minimization, restoration. Any remaining loss is offset through compensation as under current federal and provincial policies or protections, development activities should pose “no net loss” of wetland functions. Although the mining industry is an important natural resource sector in Canada, there is limited research on how the potential impacts of mining activity on wetlands are identified and managed through EA processes. In response, this research examined wetland impacts and mitigations in EA. Case studies of mining projects in British Columbia (BC) and Yukon (YT) were examined, as mining is particularly important to the economies of this province and territory, and often occurs in areas of high wetland density. The methodology consisted of an in-depth document analysis of mining project EAs. The results indicated that, in BC, the EA practice tends to default to wetland area as a proxy for wetland function and is the primary measure for assessing impacts to wetlands. There is strong focus on direct impacts, while insufficiently describing baseline wetland functions potentially impacted and to be mitigated. Hydrological and habitat wetland functions were prioritized when described in mitigation measures. In YT, the reviewed EAs contain no information on the impacted wetland area, wetland class, or wetland functions, nor provide information on how the proposed mitigation measures would address potentially impacted wetland functions. The often-poor linkages between proposed wetland mitigation measures and identified project impacts found in this research were attributed to inadequate wetland policies and regulations for mitigating impacts, and poor EA practices to address and mitigate wetland impacts effectively. An exploration of mitigation practices across jurisdictions exposes inconsistencies within the implementation of the mitigation hierarchy, with a focus on minimization in BC and restoration in YT. Compensations approaches, only identified in BC, were creation, enhancement, and off-site restoration. While wetland loss in YT is inconclusive due to information gaps, the EA practice in BC suggests that the mitigation hierarchy is not fully applied, and the province is therefore likely moving toward a net wetland loss. Understanding and addressing the issues highlighted by this thesis will be important to advancing the effectiveness of EA to manage the impacts of mining activities on wetlands.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".