Unconventional Heavy Oil Growth and Global Greenhouse Gas Emissions
Bibliographic record
Abstract
Enormous global reserves of unconventional heavy oil make it a significant resource for economic growth and energy security; however, its extraction faces many challenges especially on greenhouse gas (GHG) emissions, water consumption, and recently, social acceptability. Here, we question whether it makes sense to extract and use unconventional heavy oil in spite of these externalities. We place unconventional oils (oil sands and oil shale) alongside shale gas, coal, lignite, wood and conventional oil and gas, and compare their energy intensities and life cycle GHG emissions. Our results reveal that oil shale is the most energy intensive fuel among upgraded primary fossil fuel options followed by in situ-produced bitumen from oil sands. Lignite is the most GHG intensive primary fuel followed by oil shale. Based on future world energy demand projections, we estimate that if growth of unconventional heavy oil production continues unabated, the incremental GHG emissions that results from replacing conventional oil with heavy oil would amount to 4-21 Gt-CO2eq GtCO2eq over four decades (2010 by 2050). However, prevailing socio-economic, regional and global energy politics, environmental and technological challenges may limit growth of heavy oil production and thus its GHG emissions contributions to global fossil fuel emissions may be smaller.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".