GreenDB: Toward a Product-by-Product Sustainability Database
This addresses the problem of limited sustainability data for retail platforms and ML applications, though it is incremental as it builds on existing schemas and data collection methods.
The authors tackled the lack of open sustainability data for consumer products by creating GreenDB, a product-by-product database that integrates sustainability information, enabling ML systems to support sustainable consumption patterns.
The production, shipping, usage, and disposal of consumer goods have a substantial impact on greenhouse gas emissions and the depletion of resources. Modern retail platforms rely heavily on Machine Learning (ML) for their search and recommender systems. Thus, ML can potentially support efforts towards more sustainable consumption patterns, for example, by accounting for sustainability aspects in product search or recommendations. However, leveraging ML potential for reaching sustainability goals requires data on sustainability. Unfortunately, no open and publicly available database integrates sustainability information on a product-by-product basis. In this work, we present the GreenDB, which fills this gap. Based on search logs of millions of users, we prioritize which products users care about most. The GreenDB schema extends the well-known schema.org Product definition and can be readily integrated into existing product catalogs to improve sustainability information available for search and recommendation experiences. We present our proof of concept implementation of a scraping system that creates the GreenDB dataset.