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Watershed Planning within a Quantitative Scenario Analysis Framework
Published on: July 24, 2016
Environmental impacts of jeans: life cycle assessment and sustainable washing scenarios
Mouna Haj Nasr1, Hassen Hedfi2, Romain Benkirane3
1Textile Materials and Processes Research Unit MPTex, UR17ES33, National Engineering School of Monastir, University of Monastir, Monastir, 5019, Tunisia. hadjnasrmouna@gmail.com.
Abstract:
The jeans industry remains a significant polluter in the textile sector, with the production phase usually overlooked despite its crucial contribution to environmental impact. This study assessed the environmental performance of producing a pair of jeans in Tunisia, focusing on key processes like garment manufacture, washing and finishing. A gate-to-gate life cycle assessment (LCA) was performed. It begins with a characterization analysis of the assessed jeans (baseline scenario), highlighting the contribution of the key jeans production stages to the selected environmental impact categories (i.e. climate change and water use) defined by the ReCiPe2016 (V.1.1) Midpoint (H) method. The total impact of the assessed pair of jeans was 5.14 kg CO2 eq and 81.68 × 10-3 m3 in terms of climate change and water use, with industrial washing being the main hotspot. Six alternative scenarios were then selected to explore potential strategies for improvement of the washing process. The evaluation results of the alternative scenarios were normalized and combined into a Single Environmental Performance Indicator, identifying the optimal scenario with the best overall results. Scenario six, combining 100% solar energy with stone washing, proved most effective, reducing climate impact by 15.2% and water use by 10%. Next, a data quality assessment (DQA) was carried out, focusing on data with a considerable influence on the LCIA results. Finally, two sensitivity analyses were conducted and revealed that climate change was most sensitive to heat use, while water use was most sensitive to tap water use. It also revealed consistent results across LCIA methods variation.
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