Optimizing the Use of a Constrained Resource to Minimize Regional Greenhouse Gas Emissions: The Case Study of Slag in Ontario’s Concrete
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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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".