Past and Future Land Use Impacts of Canadian Oil Sands and Greenhouse Gas Emissions
Bibliographic record
Abstract
The Canadian oil sands underlie 142,000 km2 of the boreal forest in northeastern Alberta. Oil sands production greenhouse gas (GHG) emissions increased from 15 million tonnes (Mt) to 55 Mt between 1990 and 2011. Their production represents the fastest-growing source of GHG emissions in Canada. A large body of studies show that oil sands industries have large environmental impacts, including effects on climate, land, water, and air quality but GHG emissions from oil sands land use disturbance and future land use impacts have yet to be examined in detail and the associated literature is scarce and incomplete. Our paper examines the historical and potential land use change and GHG emissions associated with oil sands development in Canada. Disturbance occurred between 1985 and 2009 from oil sands development were identified using remote sensing technique and mapped onto spatially explicit soil, biomass and peatlands carbon maps. We found that land use and GHG disturbance of oil sands production, especially in-situ technology that will be the dominant technology of choice for future oil sands development, are greater than previously reported. We estimate additional 500 km2 and 2,400 km2 of boreal forest including carbon-rich peatlands would be disturbed from surface mining and in-situ production, respectively, between 2012 and 2030; releasing additional 107–182 million tonnes of GHG from land use alone. Future efforts to monitor land use impacts of in-situ production are needed to reduce landscape impacts and associated GHG emissions. In addition, land reclamation after oil sands projects needs to be enforced for broad ecological benefits together with GHG benefits.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".