Textile and clothing is one of the most important industrial sectors, not only due to the significant number of jobs generated, but also because it addresses one of the people’s fundamental needs (clothing). It is, however, a sector with a huge global environmental impact, and also an important negative social impact, especially in developing countries. Sustainability in the textile and clothing value chain is a known issue, concerning both environmental and economic-social facets of sustainability. One way to improve sustainability in this sector is by measuring and monitoring the environmental, economic and social impacts of activities along the value chain and, ultimately, computing an environmental and circular score for each batch of textile and clothing product, and an economic and social score for each involved company, reflected in their products. The consumer will then have the opportunity and responsibility for selecting products with the least negative environmental, economic and social impact. This article aims to propose a decentralized traceability platform for the textile and clothing value chain, based on blockchain technology, for tracing textile product batches and activities, along the value chain, classifying them with a score, which measures their environmental and social impact. The environmental, economic and social impact scores are based on a set of proposed indicators. The results are assessed through two test scenarios, namely a face towel (home textile) and a T-shirt (clothing).

Textile and clothing is one of the most important industrial sectors, not only due to the significant number of jobs generated, but also because it addresses one of the people’s fundamental needs (clothing). It is, however, a sector with a huge environmental impact, all over the world, and also an important social impact, especially in developing countries. Sustainability in the Textile & Clothing value chain is a known issue, concerning both environmental, and economical-social facets of sustainability. One way to improve sustainability in this sector is by measuring the environmental, economic and social impacts of value chain activities and, ultimately, computing an Environmental and Circular score for each batch of textile and clothing product, and an Economical and Social score for each involved firm, reflected in their products. The consumer will then have the opportunity and responsibility for selecting products with the least negative environmental, economical and social impact. This article presents the results of STVgoDigital project - PPS1, developed in Portugal with the aim of implementing the traceability of textile and clothing products, boosting sustainability in this sector, and preparing the ground for the implementation of the circular economy. More exactly, a blockchain-based textile and clothing value-chain traceability platform is presented, for tracing textile product lots and activities, along the value chain, classifying them with a score, which measures their environmental and social impact. The environmental, economic and social impact scores are based on a set of proposed indicators. The results are assessed through the presentation of a case study of a face towel (home textile) and a case study of a T-shirt (clothing).

Transparency in business practices is essential for sustainability, ensuring that resources are used responsibly and that environmental and social impacts are properly measured and monitored, allowing the end consumer to make informed purchasing decisions without feeling cheated. The Digital Product Passport (DPP) promotes transparency by providing detailed information about a product’s origin, composition, and life-cycle activities, enabling more sustainable and responsible choices. The implementation of the DPP for textile and clothing items faces many challenges due to the large number and diversity of companies involved in the value chain of these products, combined with the large amount and variability of information that needs to be collected. Therefore, the integration and standardization of data from these companies is one of the largest present challenges. In this article, we study the use of Machine Learning (ML) algorithms for validating, in a homogeneous way, the quality of the data submitted by each company for the implementation of the DPP. We have studied four solutions that, using datasets organized in different ways and using different ML algorithms, enable selecting the solution that best suits each particular situation.