Measuring the Value of Blockchain Traceability in Supporting LCA for Textile Products
Bibliographic record
Abstract
The efficiency of sustainability assessments of textile products is generally prevented because of a lack of available and reliable data across complex and globalized supply chains. The purpose of this study is to evaluate how blockchain traceability data can improve the Life Cycle Assessment (LCA) of textile products and to measure the actual value of exploiting this specific traceability data. To do so, a case study consisting of two LCAs modeling the production of wool top lots in China was conducted. A first LCA was conducted with generic data and the second with the added value of specific blockchain traceability data. Based on the second LCA, different wool top lot composition scenarios were then modeled to account for the environmental impact of different farming practices. Two main results were obtained: the environmental impact of wool top lots can vary up to +118% between two batches depending on their composition, and the specific data changes drastically from the impact calculated with generic data, with +36% calculated impact for the same wool composition of batches. Therefore, it was concluded that blockchain traceability data could be a strong asset for conducting LCA at the batch level by providing differentiated data on batch composition and origin and providing readily available specific data for a more representative assessment.
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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 | 0.007 | 0.002 |
| 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 | 0.001 |
| 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".