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A Framework to Navigate Eco-Labels in the Textile and Clothing Industry

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  • Paula Ziyeh

    (Decision Engineering for Sustainability and Resilience Laboratory, Leiden University College, Leiden University, 2595DG The Hague, The Netherlands)

  • Marco Cinelli

    (Decision Engineering for Sustainability and Resilience Laboratory, Leiden University College, Leiden University, 2595DG The Hague, The Netherlands)

Abstract

Considering the increasing demand for more sustainable products across many industries, eco-labels are a useful tool for communicating the sustainability-related performance of a product to the eco-conscious consumer. However, the abundance of different eco-labels and a lack of harmonization concerning their assessment methods can hamper their effectiveness. To address these shortcomings, this paper considers the methods employed by eco-labels in the textile and clothing industry to assess the sustainability-based performance of products. Using a sample of 10 eco-labels from the Ecolabel Index, a new framework for classifying eco-labels based on their assessment methods is developed. The framework includes two categories of label assignments ((i) binary and (ii) different levels of performance) and six types of assessment methods. These types are characterized according to the decision support features employed by the labels, such as lists of mandatory criteria, minimum (average) scores, percentage scores, and the weighting of sub-categories. The proposed framework shows the benefits of cascading decision science notions in the eco-labeling domain. It provides a harmonized vocabulary of components (i.e., a roadmap) to perform a consistent and traceable advancement of eco-labels. Consequently, it can be expanded at present to allow for the classification of other eco-labels in the textile and clothing industry and beyond.

Suggested Citation

  • Paula Ziyeh & Marco Cinelli, 2023. "A Framework to Navigate Eco-Labels in the Textile and Clothing Industry," Sustainability, MDPI, vol. 15(19), pages 1-29, September.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:19:p:14170-:d:1247290
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