Allocation Methodology of Process-Level Carbon Footprint Calculation in Textile and Apparel Products

Abstract

Textile and apparel industrial processes generate a huge amount of greenhouse gas emissions, which is a severe environmental issue for China. Aiming at greenhouse reduction, a carbon footprint calculation method is presented. In carbon footprint calculations, allocation methodology is among the most significant and controversial issues; it can be a major reason for the LCA uncertainty and robustness caused. What is more, allocation methodology impacts directly on the preparation of data collection and system boundary. Different outcomes can be achieved even for apparently similar systems by using a different allocation approach. Textile production has a large range of production process. During textile production process, it may be a single product production with co-products. The current CF calculation only evaluates GHGs emissions at product or plant level, so the difference of the technology on different processes cannot be deduced. Hence, the choice of proper allocation methodology is a crucial issue to be considered in textile and apparel industry. In this paper, based on characteristics of textile and apparel industry, process-level allocation methodology in textile and apparel industry was put forward. The application of allocation methodology was investigated and analyzed with a case study on cotton T-shirts. Firstly, case study results show that greenhouse gases of the ironing and sewing process are the two largest emissions (ironing, 40.82%, and sewing, 34.85%, respectively). Energy-saving refrigeration equipment needs to be chosen to reduce the greenhouse gases significantly. Secondly, for most processes, CF of S2 (auxiliary CF) accounts for the highest proportion of total CF. Preferred to S1, more attention should be paid to reduce the S2 emissions. Thirdly, GHGs emissions of the polo shirt in the sewing process are significantly higher than that of the T-shirt in the sewing stage (T-shirt, 0.167 kg CO2 eq/piece, and polo shirt, 0.371 kg CO2 eq/piece, respectively). This is the consequence that polo shirt’s style and structure determine the complexity of its sewing process. Finally, based on the pearson correlation coefficient, T-shirt production (kg) has a significant negative linear correlation (correlation coefficient: −0.868) with the CF (kg CO2 eq/kg T-shirts), the similar with that (correlation coefficient: −0.963) of all production. Improving the textile and garment production efficiency is significant to reduce the CF of products (per mass) by technological innovation and management optimization. In this study, we demonstrate that the process-level allocation is a feasible method, and can serve as the basis for a textile-specific allocation approach in LCA. Process-level allocation may help to address textile allocation problems and might lead to more detailed LCA results for products. We recommend broad applications and testing of this new allocation approach.