A Supervised Machine Learning Classification Framework for Clothing Products’ Sustainability

Chloe Satinet,François Fouss

doi:10.3390/su14031334

Abstract

These days, many sustainability-minded consumers face a major problem when trying to identify environmentally sustainable products. Indeed, there are a variety of confusing sustainability certifications and few labels capturing the overall environmental impact of products, as the existing procedures for assessing the environmental impact of products throughout their life cycle are time consuming, costly, and require a lot of data and input from domain experts. This paper explores the use of supervised machine learning tools to extrapolate the results of existing life cycle assessment studies (LCAs) and to develop a model—applied to the clothing product category—that could easily and quickly assess the products’ environmental sustainability throughout their life cycle. More precisely, we assemble a dataset of clothing products with their life cycle characteristics and corresponding known total environmental impact and test, on a 5-fold cross-validation basis, nine state-of-the-art supervised machine learning algorithms. Among them, the random forest algorithm has the best performance with an average accuracy of 91% over the five folds. The resulting model provides rapid environmental feedback on a variety of clothing products with the limited data available to online retailers. It could be used to quickly provide interested consumers with product-level sustainability information, or even to develop a unique and all-inclusive environmental label.

Highlights

IntroductionOur current consumption pattern is a major cause of damage to the natural environment (see, e.g., [1])
Introduction and Related WorkOur current consumption pattern is a major cause of damage to the natural environment
We performed principal component analysis (PCA) and factor analysis (FA) on our rescaled features to analyze the potential of reducing the dimensionality of our data prior to modeling, but the results indicated that more than 24 factors are needed to explain at least

Summary

Introduction

Our current consumption pattern is a major cause of damage to the natural environment (see, e.g., [1]). The consumption of fashion products in particular has changed with the emergence of fast fashion—the sale of cheap and widely available garments—as a dominant business model. As shown in [2,3], our current fashion consumption has a disastrous impact on the environment: the fashion industry accounts for around 10%. Following [2], a change in clothing buying habits could be a first step in the right direction. A growing number of consumers seems to be aware of this issue and willing to make efforts to limit their consumption and adopt proenvironmental consumption habits (see, e.g., [4,5]). References [6,7] mention there is an intention–

Methods

Results

Discussion

Conclusion