Embodied carbon calculation for geosynthetic products and implication for engineering projects
Bibliographic record
Abstract
Geosynthetic products are widely used in construction projects. Although many studies have demonstrated that geosynthetic solutions result in lower carbon dioxide emissions compared to traditional methods, precise embodied carbon (EC) values for geosynthetic products are scarce. In addition, the EC values of geosynthetics are sometimes substituted with primary raw material data in project carbon footprint calculations, which undermines the credibility of their sustainability claims. This paper reviews EC calculation methods for geosynthetics and provides a geogrid case example. It then extracts EC values from 120 Environmental Product Declarations (EPDs) to propose representative values for geosynthetic products in different regions and recalculates the carbon footprints of geosynthetic-reinforced projects using maximum EC values to assess their impact on project-level estimates. The results show that emissions accumulated during the manufacturing stage of geosynthetic products account for nearly 30% of their total EC. However, the EC of geosynthetic products contributes only a small portion of the total EC of geosynthetic-reinforced projects. Even with higher EC values, geosynthetic solutions remain more sustainable than conventional alternatives. The calculation method presented in this paper enables geosynthetic companies to estimate product EC without commercial life cycle assessment software, while EPD-derived values enhance existing datasets for more accurate carbon footprint calculations in geosynthetic-reinforced projects globally.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".