Assessing the Mitigation Potential of Environmental Impacts From Circular Economy Strategies on an Industrial Sector and Its Value Chain: A Case Study on the Steel Value Chain in Quebec
Bibliographic record
Abstract
This research project aims to evaluate the potential reduction of environmental impacts from circular economy strategies on an industrial sector at a regional scale with a case study on Greenhouse Gas (GHG) emissions in Quebec's steel industry and its value chain. To do so, an integrated model has been created based on the matrix approach, building on material flow analysis (MFA) tracking flows and stocks and on life cycle assessment (LCA) to compute direct (from the activity, e.g., combustion process) and indirect (from the supply chain, e.g., production of raw material inside or outside of region) emissions. This theoretical model is designed to be applied to any emissions or environmental impacts from a specific sector in a given region and enable to model the effects of circularity strategies to both flows and related environmental impacts. The overall mitigation potential of individual or combined circular economy strategies on a specific sector could thus be evaluated across its entire value chain. In the case study, a set of the most promising circular strategies applicable in the Quebec context were identified, and the GHG reduction potential within and outside the province is calculated and compared with actual emissions. Six circular strategies were analyzed acting at three different levers, namely, GHG/material (increase iron recycling rate, switch to hydrogen-based reduction production), material/product (reduce weight of vehicle, limit over-specification in building construction), and product/service (increase buildings and cars lifetime, increase car-sharing), and therefore impact rather direct or indirect emissions on different stages of the steel life cycle. Combining these six strategies into a consolidated scenario shows that a circular-driven economy allows to cut down GHG emissions of the cradle-to-gate steel industry value chain by −55%, i.e., 1.67 Mt CO2e. Taking into account use phase of steel, overall reductions are estimated at −6.03 Mt CO2e, i.e., −30% of the whole life cycle.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 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".