Land use change and related carbon emissions from metal mines in Canada: An industry-level review
Bibliographic record
Abstract
During mining, native vegetation, dead organic matter, and soil are stripped from the landscape to accommodate mine infrastructure. Carbon emissions increase in response to rapid land-use change (LUC) because the carbon storage (i.e., in living and dead biomass) capacity of the site is reduced or lost for the life of mine. New and expanding mines need to account for these carbon impacts during net zero planning for their operations. This analysis reviewed LUCs for 85 metal mine sites in Canada in 2001 ± 1 and 2019 ± 1. LUC was estimated using satellite imagery and publicly available operations information. Greenhouse gas emissions were determined based on a Government of Canada accounting method. A total of 27,000 hectares of land were disturbed. The associated 12.6 million tonnes of carbon dioxide equivalent emitted during the study period represented approximately 15% of Scope 1 emissions from hydrocarbon-based fuel consumption at these operations. The impact on the carbon sink estimated for select sites was up to 20% of the carbon emissions from LUCs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.025 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".