Absolute Environmental Sustainability of Materials Dissipation: Application for Construction Sector
Bibliographic record
Abstract
The materials used globally in the construction sector are projected to more than double in 2060, causing some to deplete. We argue that access to the services that the resources provide must be protected, thus implying that a carrying capacity (CC) for resource dissipation must be set. Dissipation accrues when the resource becomes inaccessible to users. The CC allows defining a maximum dissipation rate that allows to maintain those resources’ availability in the future. The CC of the dissipation of the resource may be operationalized to characterize the resource use impact, using absolute environmental sustainability assessments principles. The study makes it possible to determine a dissipation CC as the world dissipation rate that would enable all users to adapt to using an alternative resource before the material’s reserve is entirely dissipated. The allocation of a fraction of this CC to the building sector was performed using equal per capita and grandfathering sharing principles. Finally, we applied the method to the case of steel in a school life cycle. The results show that the actual dissipation rates of iron, copper and manganese in the building sector exceed the dissipation CC by 70%, 56% and 68%, respectively. However, aluminum dissipation is 90% less than the assigned CC. The allocation to schools shows that the results are influenced by the choice of allocation principle. The application in the case of steel use of the school life cycle shows an exceedance of the CC that decreases when increasing the building life span.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 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) | 0.006 | 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".