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Uncertainty analysis of physical-based carbon accounting in cotton T-shirt manufacturing.


ABSTRACT: The textile sector contributes significantly to global greenhouse gas emissions, yet product-level carbon accounting in this industry remains constrained by data gaps and methodological uncertainty. This study quantifies the uncertainty embedded in process-based carbon accounting for the cradle-to-gate production of a cotton T-shirt. We evaluated secondary activity data and emission factors using a pedigree matrix across five data quality indicators: precision, completeness, and temporal, geographical, and technological representativeness. The total cradle-to-gate carbon footprint was [Formula: see text] per T-shirt, with an overall uncertainty of ±13.81 %. Fabric production was the largest source of emissions and uncertainty, contributing over 60 % of total emissions and nearly 70 % of total uncertainty, driven by energy-intensive processes such as weaving, dyeing, and sanforising. Yarn spinning was a secondary hotspot, while the sewing stage had comparatively minor impacts. The results show significant variation in data reliability across production stages and highlight the need for improved primary data, updated emission factors, and harmonised databases to strengthen the robustness and comparability of process-based carbon accounting in the textile sector.

SUBMITTER: Olugbemi E 

PROVIDER: S-EPMC12936201 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature

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Uncertainty analysis of physical-based carbon accounting in cotton T-shirt manufacturing.

Olugbemi Emmanuel E   Bolson Natanael Favero NF  

Scientific reports 20260206 1


The textile sector contributes significantly to global greenhouse gas emissions, yet product-level carbon accounting in this industry remains constrained by data gaps and methodological uncertainty. This study quantifies the uncertainty embedded in process-based carbon accounting for the cradle-to-gate production of a cotton T-shirt. We evaluated secondary activity data and emission factors using a pedigree matrix across five data quality indicators: precision, completeness, and temporal, geogra  ...[more]

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