The Green Consumption Assistant (GCA) supports consumers in making more sustainable decisions during online shopping. The GCA displays green product alternatives on the search engine Ecosia and provides information about more sustainable alternatives, for example, references to repair, rental, or sharing options. In addition, sustainable websites will be highlighted on Ecosia and the climate commitments of the organisations and companies will be made transparent in a ranking. For the recommendations of the GCA, a comprehensive product database (Green Database) with ecological and social sustainability information is being built up using machine learning techniques.
The GCA is a collaboration project between the Technische Universität Berlin, the Berliner Hochschule für Technik, and the green search engine Ecosia and is funded by the Bundesumweltministerium as a lighthouse project for artificial intelligence in use for ecological challenges. The project embodies a new, interdisciplinary partnership that combines sustainable and behavioural research with machine learning, user-centered design, and digital product development.
In the project, we rely on cooperation and exchange with various sustainability actors, scientists, and label organisations or online shops, to ensure a reliable and comprehensive data set for the recommendations of the Green Consumption Assistant.
Tuesday, 31th August 2021 our second beta version of the Green Consumption Assistant went online in the Chrome Store named „Koala – Ecosia Assistant”! Now, you can install the browser extension and test it for free. What’s new? A smart design: The browser extension contrasts with the design used by Ecosia and the „Koala” assistant…
In our second publication, Robin Jadkowski looks at psychological determinants that lead to more eco-friendly purchasing decisions. This involves developing a method that matches user tracking data from our GCA with survey data and investigating the relationship of different variables to each other that could intensify eco-friendly behaviour. Abstract This study evaluates psychological and socio-demographic…
Improving the data quality of applications that use machine learning (ML) helps to increase their performance and enables the use of more efficient models. One of the most common problems of data quality is missing values. In this peer-reviewed article, Sebastian Jäger, Arndt Allhorn, and Felix Bießmann evaluated different methods based on different data sets…
We are proud to publish the first working paper for the Green Consumption Assistant project. In this paper, our colleague Cathérine Lehmann summarises our decision-making processes for sustainable product recommendations, as well as possible approaches for scaling up. Abstract Data availability on the sustainability of products is low which poses challenges for actors from all…