Accounting for Variability and Uncertainty in Life Cycle Assessments: Oil Sands Case Studies
Bibliographic record
Abstract
Along the life cycle of oil sands-derived products, variability in terms of resource heterogeneity, operating decisions, as well as extraction and processing technologies affect a project’s GHG intensity. Previous LCAs that have quantified emissions from bitumen production and processing have not captured all sources of variability along the life cycle, either by including only some projects or employing simplified refinery modeling or modeling refining only of some crude types. Three studies are completed to address this literature gap. In the first study, a statistically-enhanced version of the GreenHouse gas emissions of current Oil Sands Technologies model (GHOST-SE) is developed. Median lifetime GHG intensities for projects producing synthetic crude oil (SCO) range from 89-137 kg CO2eq/bbl SCO and for the project producing dilbit are 51 kg CO2eq/bbl dilbit. Projects show significant temporal variability. No project reaches steady-state in terms of GHG intensity. Next, GHOST-SE is integrated with a pipeline transportation model (COPTEM) and a refinery model (PRELIM) and variability in life cycle emissions intensities are quantified. Allocation to products affects the relative GHG intensities of different projects (e.g., Project 1 has lowest median life cycle GHG intensity per MJ gasoline but highest per MJ diesel). These results demonstrate that there is no representative project or crude type, even across projects within the same pathway (e.g., Mining SCO pathway). In the final study, expert elicitation methods are employed to assess the potential role for emerging technologies to decrease upstream GHG intensity between 2014 and 2034. Experts surveyed do not expect emerging technologies to play a major role in reducing upstream oil sands energy consumption but are more likely to be applied to access marginal resources not economic with current production technologies. Accurate characterizations of the emissions from the life cycle of oil-sands derived fuels has the potential to assist oil sands operators and policymakers to: set benchmarks, develop projections of future emissions, and identify opportunities for GHG intensity reductions along the life cycle of the fuel. The findings of this thesis can also inform operators and policymakers about the potential unintended consequences of policy decisions.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".