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


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 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.

ICML | DataPerf Benchmarking Data for Data-Centric AI Workshop
Sebastian Jäger
Sebastian Jäger
PhD Student
Alexander Flick
Jessica Adriana Sanchez Garcia
Kaspar von den Driesch
Karl Brendel
Felix Bießmann
Felix Bießmann