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 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.