GreenDB -- A Dataset and Benchmark for Extraction of Sustainability Information of Consumer Goods

TL;DR

GreenDB is a new dataset of European online products with expert-evaluated sustainability labels, enabling machine learning models to accurately predict product sustainability and promote sustainable consumption.

Contribution

The paper introduces GreenDB, a large, high-quality dataset with sustainability labels, and demonstrates ML models achieving high accuracy in predicting product sustainability.

Findings

ML models trained on GreenDB achieve 96% F1 score in predicting sustainability labels.

GreenDB extends schema.org, facilitating integration into existing e-commerce platforms.

The dataset supports development of sustainable recommendation systems.

Abstract

The production, shipping, usage, and disposal of consumer goods have a substantial impact on greenhouse gas emissions and the depletion of resources. Machine Learning (ML) can help to foster sustainable consumption patterns by accounting for sustainability aspects in product search or recommendations of modern retail platforms. However, the lack of large high quality publicly available product data with trustworthy sustainability information impedes the development of ML technology that can help to reach our sustainability goals. Here we present GreenDB, a database that collects products from European online shops on a weekly basis. As proxy for the products' sustainability, it relies on sustainability labels, which are evaluated by experts. The GreenDB schema extends the well-known schema.org Product definition and can be readily integrated into existing product catalogs. We present initial results demonstrating that ML models trained with our data can reliably (F1 score 96%) predict the sustainability label of products. These contributions can help to complement existing e-commerce experiences and ultimately encourage users to more sustainable consumption patterns.