Undermining environmental assessment laws: post-assessment amendments for mines in British Columbia, Canada, and potential impacts on water resources
Bibliographic record
Abstract
In British Columbia (BC), Canada, there is increased attention on mines and their impacts on water resources. In BC, many proposed mines undergo provincial environmental assessment (EA), which predicts a mine’s risks and involves government oversight and public engagement. After approval, mines can apply for amendments that alter the project’s undertakings, including in ways that may harm water resources. We examined all amendment documents for mines undergoing provincial EA in BC from 2002 to 2020. Of the 23 approved mines, 15 (65%) requested a total of 49 amendments, of which 98% were approved. Most mines applied for their first amendment within 3 years of approval. We deemed 20 of the approved amendments (associated with 10 projects) likely to have negative impacts on water resources, including changes to effluent discharge, increased volume of water extraction, or degradation of fish habitat. Amendment applications and approval documents lacked specific, quantitative information to reinforce claims or decisions. We present the first known summary of EA amendments in any jurisdiction. Given that most mines in BC receive amendments, and many are related to water, we express concern that amendment processes increase risk to water resources without meeting standards of evidence and public scrutiny required by the regular EA process.
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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 | 0.025 | 0.118 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.014 | 0.008 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".