Integration of Impacts on Water, Air, Land, and Cost towards Sustainable Petroleum Oil Production in Alberta, Canada
Bibliographic record
Abstract
This paper intends to develop quantitative indicators for comparative sustainability assessment of petroleum oil pathways in the province of Alberta, Canada. Eighteen pathways of oil production were developed in this study, and the sustainability indicators were assigned for each pathway to cover greenhouse gas (GHG) emissions, water demand, and land use in addition to the cost of supply. The developed sustainability indicators were aligned per functional unit and covered the full life cycle of petroleum oil production. The developed GHG emissions, cost of supply, and land use indicators are found in the range 17.50–226.20 kg of CO2 eq./bbl, 12.28–53.53 USD/bbl, and 0.06–0.178 m2/bbl, respectively. Four scenarios were comparatively conducted and assessed against the business-as-usual scenario within the period horizon 2009–2030. The cost-effective scenario was optimized with the objective function to minimize the cost of supply based on the constraints derived from the business-as-usual scenario. Sustainable scenarios were conducted with the lowest possible impacts on natural resources, GHG emissions, and the cost of supply accompanied by specific assumptions for petroleum oil production from different pathways in Alberta. The average annual savings on water demand and land area were found to be 67 and 30%, respectively, due to the shifting of upgrader feedstock from surface mining to the in-situ steam-assisted gravity drainage (SAGD) pathway. The corresponding increases due to this shifting in upgrader feedstock were found to be 40 and 3% in GHG emissions and cost of supply, respectively.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".