Assessing the long term effects on climate change of metallurgical slags valorization as construction material: a comparison between static and dynamic global warming impacts
Bibliographic record
Abstract
<abstract> <p>The interest in circular economy for the construction sector is constantly increasing, and Global Warming Potential (GWP) is often used to assess the carbon footprint of buildings and building materials. However, GWP presents some methodological challenges when assessing the environmental impacts of construction materials. Due to the long life of construction materials, GWP calculation should take into consideration also time-related aspects. However, in the current GWP, any temporal information is lost, making traditional static GWP better suited for retrospective assessment rather than forecasting purposes. Building on this need, this study uses a time-dependent GWP to assess the carbon footprint of two newly developed construction materials, produced through the recycling of industrial residues (stainless steel slag and industrial goethite). The results for both materials are further compared with the results of traditional ordinary Portland cement (OPC) based concrete, presenting similar characteristics. The results of the dynamic GWP (D_GWP) are also compared to the results of traditional static GWP (S_GWP), to see how the methodological development of D_GWP may influence the final environmental evaluation for construction materials. The results show the criticality of the recycling processes, especially in the case of goethite valorization. The analysis shows also that, although the D_GWP did not result in a shift in the ranking between the three materials compared with S_GWP, it provides a clearer picture of emission flows and their effect on climate change over time.</p> </abstract>
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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 | 0.000 | 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".