Econundrum: Would You Reconsider Your Diet When You See the Climate Impact?


  • Type: Final Master Project
  • Goal: Research how a shared physicalization of dietary choice and climate impact could create awareness and/or behavior change.
  • Competencies: DRP | US | TR | CA | MDC | BE

Key learning points

1. Acting as the link between disciplines in a multidisciplinary project.
2. Applying the Trans-Theoretical model.
3. Developing an intelligent system.




Climate change might perhaps be the biggest global challenge we face nowadays as global warming because of carbon emissions is fundamentally changing the planet. Food production and transport are a big contribution to this as a quarter of all man-made carbon emissions are associated with the food system. However, for many people there is not a clear connection between climate impact and the food they consume every day. In order to reduce man-made emissions, it is important that new generations get accustomed to sustainable lifestyle choices. The purpose of this research project is to examine how a shared physicalization of food consumption can create awareness on climate impact and empower a community to change behavior. To this end, we designed Econundrum, a physicalization that visualizes how dietary choices create different carbon footprints per member of a community. The field study showed how a physical representation is supportive in creating awareness within a community and facilitated in first steps towards change.


As I was situated at Lancaster University during the first 3 months, which offers a broad range of studies, I wanted to exploit the opportunity to meet experts from other research areas such as shape-changing interfaces, data visualization and above all sustainability. Being able to discuss and question knowledge I gained from literature in face-to-face meetings was very helpful in comprehending a lot of information in a short time and quickly iterate on ideas. Moreover, I was able to transfer knowledge from one discipline towards experts from another. For example, during the brainstorm with experts from data visualization, sustainability and shape-changing interfaces, I communicated the information I got from an expert on carbon foot printing in such a way that they could build upon this.

Once I was back in the Netherlands I focused more on transforming the concept into a final realization and evaluation. I think the two months previous to the field study have been the most stressful and exciting months of the whole project as I wanted to create a fully working prototype. I had to obtain knowledge in varied fields such as electronics, mechanics and coding, but also make sure that my method and target group were well prepared before the start of the study. It required me to do a myriad of activities simultaneously and reaching out to many experts about all the topics mentioned before. I guarded my process by keeping an overview of all the activities that should be done in order to finish, from small to large, and kept prioritizing them to not get myself overwhelmed. Eventually, the to-do lists became shorter and I was able to finish the prototype in time, develop a method to evaluate the system and a gather a group of people that were willing to participate.

Setting up this evaluation, this was the first time I directly applied a theoretical model in my project, in this particular case the Trans-Theoretical Model (TTM). The model functioned both as inspiration for the visualization design as well as for the quantitative pre- and post-measurements of behavior change. Creating the measurement instrument was educational in itself, as originally the TTM is used for health-related problem behavior and therefore I had to make an adaptation of it, suitable for this particular context. Although this method eventually resulted in few significant results, applying a theoretical model in my project allowed me to experience the whole process of creating, using and analyzing it. I see now how it could be complementary to qualitative measurements, especially in case of a long-term study, for example in clustering the behaviors observed.