Quantifying land use of oil sands production: a life cycle perspective
Bibliographic record
Abstract
Methods for the inclusion of land use in life cycle assessment are not well established. Here, we describe an approach that compares land disturbance between spatially compact and diffuse activities that contribute to the life cycle of a single product, in this case synthetic crude from Alberta's oil sands. We compare production using surface mining and in situ extraction technologies. In situ technologies disturb less land per unit of production than surface mining, but the spatial footprint of in situ production is more dispersed—increasing landscape fragmentation—and in situ production requires more natural gas which increases land use due to gas production. We examine both direct and peripheral land use of oil sands development by quantifying land disturbance using a parameterized measure of fragmentation that relies on 'edge effects' with an adjustable buffer zone. Using a life cycle perspective, we show that the land area influenced by in situ technology is comparable to land disturbed by surface mining when fragmentation and upstream natural gas production are considered. The results suggest that land disturbance due to natural gas production can be relatively large per unit energy. This method could be applied to other energy developments, for example, a comparison between coal mining and natural gas production when both fuels are used to generate electricity.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".