Predicting Project Environmental Performance under\nMarket Uncertainties: Case Study of Oil Sands Coke
Bibliographic record
Abstract
A method combining life cycle assessment\n(LCA) and real options\nanalyses is developed to predict project environmental and financial\nperformance over time, under market uncertainties and decision-making\nflexibility. The method is applied to examine alternative uses for\noil sands coke, a carbonaceous byproduct of processing the unconventional\npetroleum found in northern Alberta, Canada. Under uncertainties in\nnatural gas price and the imposition of a carbon price, our method\nidentifies that selling the coke to China for electricity generation\nby integrated gasification combined cycle is likely to be financially\npreferred initially, but eventually hydrogen production in Alberta\nis likely to be preferred. Compared to the results of a previous study\nthat used life cycle costing to identify the financially preferred\nalternative, the inclusion of real options analysis adds value as\nit accounts for flexibility in decision-making (e.g., to delay investment),\nincreasing the project’s expected net present value by 25%\nand decreasing the expected life cycle greenhouse gas emissions by\n11%. Different formulations of the carbon pricing policy or changes\nto the natural gas price forecast alter these findings. The combined\nLCA/real options method provides researchers and decision-makers with\nmore comprehensive information than can be provided by either technique\nalone.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.037 | 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".