This Working Paper Series is published by the Green
Consumption Assistant (GCA) Research Project.
The Green Consumption Assistant 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.
All issues of the GCA Working Papers Series can be downloaded free of charge at: https://green-
consumption-assistant.de/en/publications/#working-paper
The GCA Working Paper Series serves to publish initial results from the ongoing research project
and is intended to promote the exchange of ideas and academic discourse.
More information:
Website: green-consumption-assistant.de
Contact: info@green-consumption-assistant.de
Citation: Cathérine Lehmann [2021]: #01 Scaling sustainability advice. Options for generating
large-scale green consumption recommendations. GCA Working Paper Series, Berlin.
https://doi.org/10.14279/depositonce-18623
DOI: https://doi.org/10.14279/depositonce-18623
Author for this issue: Cathérine Lehmann
This research was funded by the German Federal Ministry for the Environment, Nature
Conservation, Nuclear Safety and Consumer Protection, grant number 67KI2022A.
License: This work is licensed under a Creative Commons Attribution 4.0 International
License. more information, see https://creativecommons.org/licenses/by/4.0/
Scaling sustainability advice
Options for generating large-scale green consumption recommendations
Working Paper for the GCA Research Project
2021
Cathérine Lehmann (TU Berlin)
Abstract
Data availability on the sustainability of products is low which poses challenges for actors from
all sectors dealing with promoting sustainable consumption. We describe how we currently
provide users of a Chrome browser extension with general sustainability advice and with
recommendations of best-in-class products in terms of sustainability. Then we outline a possible
concept towards more automatisation and thus scalability of the current approach. For the
latter, we discuss six different schemes for generating large-scale green recommendations on a
product level, finding that currently product sustainability can be best evaluated in terms of data
availability when resorting to lists of labelled products. In the future, Product Environmental
Footprints and similar data should be more easily available in order to have quantifiable data for
research and for showing more information to users. Overall, an integrated approach, including
e.g. aspects of organizational sustainability, might help to fill data voids and/or to provide a more
complete picture of a product’s sustainability level.
Keywords
sustainability information, sustainable consumption, online consumption, automation of
product evaluation, sustainable product recommendations
TABLE OF CONTENTS
Introduction ............................................................................................................................ 1
The process for generating advice needs to balance several objectives: ............................... 1
Description of current approach .............................................................................................. 2
General advice ..................................................................................................................... 2
Best-in-class products (BICs) ............................................................................................... 3
Approaches for Scaling ........................................................................................................... 5
General advice ..................................................................................................................... 5
Best-in-class products ........................................................................................................ 6
Recommendation .................................................................................................................. 10
Loading more pages...