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

arXiv: https://arxiv.org/abs/2207.10733 · HTML: https://mlsanity.com/paper/2207.10733/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 16 references that are in the map.
- First posted on arXiv 21 Jul 2022.

## Builds on (most cited first, 2 of 2)

- [AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data](https://mlsanity.com/paper/2003.06505/) (2020)
- [GreenDB: Toward a Product-by-Product Sustainability Database](https://mlsanity.com/paper/2205.02908/) (2022)

## Built on by (most cited first, 1 of 1)

- [Automated Extraction of Fine-Grained Standardized Product Information from Unstructured Multilingual Web Data](https://mlsanity.com/paper/2302.12139/) (2023)

## Abstract (arXiv)

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.