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The production, shipping, usage, and disposal of consumer goods have asubstantial impact on greenhouse gas emissions and the depletion of resources.Machine Learning (ML) can help to foster sustainable consumption patterns byaccounting for sustainability aspects in product search or recommendations ofmodern retail platforms. However, the lack of large high quality publiclyavailable product data with trustworthy sustainability information impedes thedevelopment of ML technology that can help to reach our sustainability goals.Here we present GreenDB, a database that collects products from European onlineshops on a weekly basis. As proxy for the products' sustainability, it relieson sustainability labels, which are evaluated by experts. The GreenDB schemaextends the well-known schema.org Product definition and can be readilyintegrated into existing product catalogs. We present initial resultsdemonstrating that ML models trained with our data can reliably (F1 score 96%)predict the sustainability label of products. These contributions can help tocomplement existing e-commerce experiences and ultimately encourage users tomore sustainable consumption patterns.

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