Using repositories to store product design information can provide additional and extensive design knowledge to the global design community. Using repository data in the design of new products can be especially impactful for DfX design objectives, such as product sustainability, about which many engineering designers have limited knowledge. Furthermore, repositories can prove useful for product trend identification. In this thesis, I present the origination of a sustainable design repository— a collection of product data that includes environmental impact information—and the use of the repository to explore novel research in product eco-labeling and identification of product function as it relates to environmental impact. Through the initialization of a 47-product repository, we seek to create data-driven design processes that can influence designers to consider environmental sustainability. We found, for example, that in the first year of a product’s life, 29-64% of the environmental impact occurs during the product’s use phase, and that uncertainty in input data (such as component manufacturing location and disposal method) can significantly contribute to variation in reported environmental impact. Eco-labeling—implications on products or product packaging that the product has a reduced environmental impact—does, in fact, have a positive correlation with improved sustainability. Lastly, I use a machine learning approach to relate specific product functions to environmental impact. The creation of this sustainable design repository highlights the need for the consideration of input uncertainties, and has proven useful in other sustainable design research. The sustainable design repository and the manuscripts presented in this thesis will continue to enable subsequent data-driven design research in that the repository provides a large dataset on which various novel research approaches can operate.