Predicting Project Environmental Performance under Market Uncertainties: Case Study of Oil Sands Coke
Bibliographic record
Abstract
A method combining life cycle assessment (LCA) and real options analyses is developed to predict project environmental and financial performance over time, under market uncertainties and decision-making flexibility. The method is applied to examine alternative uses for oil sands coke, a carbonaceous byproduct of processing the unconventional petroleum found in northern Alberta, Canada. Under uncertainties in natural gas price and the imposition of a carbon price, our method identifies that selling the coke to China for electricity generation by integrated gasification combined cycle is likely to be financially preferred initially, but eventually hydrogen production in Alberta is likely to be preferred. Compared to the results of a previous study that used life cycle costing to identify the financially preferred alternative, the inclusion of real options analysis adds value as it accounts for flexibility in decision-making (e.g., to delay investment), increasing the project's expected net present value by 25% and decreasing the expected life cycle greenhouse gas emissions by 11%. Different formulations of the carbon pricing policy or changes to the natural gas price forecast alter these findings. The combined LCA/real options method provides researchers and decision-makers with more comprehensive information than can be provided by either technique alone.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 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".