Optimizing the Use of a Constrained Resource to Minimize Regional Greenhouse Gas Emissions: The Case Study of Slag in Ontario’s Concrete
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Green policies currently incentivize concrete producers to replace portland cement with industrial byproducts to reduce their greenhouse gas (GHG) emissions. However, policies are based on attributional life cycle assessments (LCAs) that do not account for market constraints and consider byproducts either available burden-free to the user (cutoff approach) or partially responsible for the emissions generated in the upstream processes (allocation). The goal of this study was to investigate whether these approaches (and incentives) could lead to a mismanagement of byproducts and to suboptimal solutions in terms of regional GHG emissions. The use of ground granulated blast-furnace slag (GGBS) in Ontario was studied, and an optimization model to find the least GHG-intense way of using GGBS was developed. Results showed that producers should replace 30 to 40% of portland cement in high-strength concrete to minimize the regional GHG emissions associated with concrete. However, traditional LCA approaches do not suggest this solution and are estimated to lead to up to a 10% increase in concrete GHG emissions in Ontario. The substitution method, which assigns emissions or credits to byproducts based on emissions associated with the products they may displace, can yield decisions consistent with the regional emission optimization model. A revision of current policies is recommended to include market constraints.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it