Improving robustness of LCA results through stakeholder engagement: A case study of emerging oil sands technologies
Bibliographic record
Abstract
Life cycle assessments can help inform decision-making about greenhouse gas (GHG) emission reduction opportunities but are often not embraced by stakeholders associated with industries where study results are highly scrutinized and often contentious. This project was motivated by stakeholder interest in understanding open source life cycle models (the Oil Production Greenhouse Gas Emissions Estimator, OPGEE, and the Petroleum Refinery Life Cycle Inventory Model, PRELIM) and how accurately they can estimate emissions for existing oil sands projects and emerging technologies. We evaluate the robustness of these models and improve them using data from three existing oil sands projects (mining + upgrading, mining + dilution, and steam assisted gravity drainage, SAGD, + dilution). The models are then applied to estimate the GHG emissions reduction potential for two emerging in situ oil sands technologies. We find that, when boundaries are aligned, OPGEE can generate upstream GHG emissions estimates for the projects modeled within 1-4% of company reported GHG emissions data. Extending the boundary to include indirect (life cycle) emissions can lead to a doubling in upstream GHG emissions intensity. The two emerging technologies evaluated in the study can reduce upstream emissions by 14-19% compared to a SAGD project operating at the same reservoir, or 1.4-1.9% on a well-to-wheel basis. This work contributes a revised process of conducting LCAs that includes stakeholder input throughout and results in more robust and transparent estimates of emissions from deploying existing and emerging technologies.
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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 | 0.001 | 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) | 0.000 | 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".