Color prediction and color management for digital printed fabrics remain a challenging task. Accurate prediction of color appearances of digital printed fabrics would enable designers and manufacturers to better fulfill their design requirements and creative visions. We propose a color prediction method for silk digital printing utilizing a Pix2Pix Generative Adversarial Network (GAN) framework. This method aims to generate predicted images that possess the same stylistic and color characteristics as their actual fabrics after production. To develop and validate the method, color data and images are collected and processed from 5252 sets of paired original Pantone TPX color card and actual print sample fabrics. The results of this study demonstrate that the method can predict the colors of silk digital print samples while effectively reproducing the effects of inkjet printing in silk fabrics including silk crepe satin and silk twill. The method exhibits high prediction accuracy to an average CIEDE2000 value of 2.372 for silk crepe satin and 1.846 for silk twill. The findings of this research not only enhance the efficiency and accuracy of color management in fabric digital printing technology but also contribute to the exploration and development of high-fidelity color prediction techniques within the textile industry.

In global trade, the manufacture and consumption of cotton textile products are intervening in the hydrological cycle. To address the relationship between the product system and the unsustainable use of local water resources, a spatial water footprint assessment is needed. This study presents a spatial water footprint method that was demonstrated in the case of domestically produced cotton T-shirts in three recycle scenarios. The results showed that the water scarcity footprint of conventional T-shirt, eco T-shirt, recycled T-shirt is 2.45 H₂O eq, 1.74 H₂O eq, 8 × 10⁻² H₂O eq, respectively, and the water-eutrophication footprint of conventional T-shirt, eco T-shirt, recycled T-shirt is 1.18 × 10⁻² PDF·m²·yr⁻¹, 9.47 × 10⁻³ PDF·m²·yr⁻¹, 4.04 × 10⁻³ PDF·m²·yr⁻¹, respectively. Two interesting results have been found. Firstly, the hydrosphere has been affected by manufacture and consumption; thus any choice made by manufacturers and consumers may lead to an impact on the water resource in a region that is far from the location. Secondly, the effect of water footprint reduction will be more apparent in places with severe water stress. The spatial water footprint offers a transparent result of each phase’s potential contribution to the local environment and could make a quantitative comparison between product stages, products, and local impacts. Thus, spatial water footprint will be a critical component in the sustainability management improvement of the supply chain.

Colored yarns interweaving in accordance with specific weave structure provides the opportunity for textile designers to create an enormous number of mixing color effects on fabric surface. The key point of this idea is to search an appropriate model to match the optical mixing color of a fabric with the targeted or desired color. Based on the present prediction models such as K/S, Log (K/S), S-N and Friele model for colored yarn mixing, this study proposes a new color mixing model with a variable parameter to predict optical mixing color values of yarn-dyed fabrics. In order to optimize the model and improve the adaptive capacity for different kinds of yarn materials, a constant σ is introduced. Using a fitting approximation algorithm, the optimal value of constant σof the model can be found per different yarns and interweave density of fabrics. Compared with previous models, the new model has much lower color differences.

In this study, to accurately calculate the carbon footprint of silk products, key issues were analysed and discussed, including accounting boundary, accounting data, sequestration of greenhouse gases (GHG) and calculation of results. The results support the feasibility of "cradle to gate" or "gate to gate" as the accounting boundary for the carbon footprint of silk products. Accordingly, the quality of accounting data may be improved by determining the key emission sources of GHG within the accounting boundary, selecting the appropriate data allocation methods and maintaining the consistency of the allocation methods within the accounting boundary when necessary. The GHG sequestration of silk is explained separately in the results, which report on the carbon footprint of silk products. The carbon-neutral actions taken by silk enterprises are also discussed and quantified in the results of the carbon footprint calculation. Ultimately, it is found that a consistent accounting boundary and emission factors constitute two key prerequisites for the feasibility of carbon footprint quantification of various silk products.