# Exploring the Feasibility of a Participatory Data Physicalization of CO₂ Emissions for Dietary Choices in Collective Catering Settings

Eugenie Brasier, Yvonne Jansen, Pierre Dragicevic, Martin Hachet

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Eugenie Brasier, Yvonne Jansen, Pierre Dragicevic, Martin Hachet. Exploring the Feasibility of a Participatory Data Physicalization of CO₂ Emissions for Dietary Choices in Collective Catering Settings. VIS 2025 - (poster track), Nov 2025, Vienna, Austria. 2025. [hal-05195257](https://hal.science/hal-05195257v1)

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# Exploring the Feasibility of a Participatory Data Physicalization of CO₂e Emissions for Dietary Choices in Collective Catering Settings

Eugenie Brasier*

Inria, CNRS

Yvonne Jansen†

Univ. Bordeaux, CNRS, Inria

Pierre Dragicevic‡

Inria, CNRS

Martin Hachet§

Inria, CNRS

## ABSTRACT

We present the design and early on-site deployment of a participatory data physicalization in which patrons of a collective restaurant report their dietary choices and view their associated carbon footprint. We describe our design process, our first prototype, and lessons learned from a week-long test. We conclude with practical insights for future iterations and directions for further research.

**Index Terms:** Input visualization, data physicalization, participatory data physicalization, carbon footprint, dietary choices.

## 1 INTRODUCTION

Individual dietary choices, particularly the consumption of animal-based products, play a significant role in global CO₂e emissions. Changing habits can play a major role in reducing global carbon emissions, which can be partly achieved by informing people about the environmental impact of their food choices. The effectiveness of such ecological feedback [2] depends on several factors, including its clarity, its ability to capture users' attention, and how closely it is delivered in time and space to the moment of decision-making. Presenting this type of visualization in a situated manner [6], directly within a restaurant environment where people actively decide which dish to order, may therefore significantly enhance the effectiveness and impact of the data presentation.

Participatory data physicalizations [4, 5]—a form of input visualizations [1, 3]—are one of the possible means of communicating such data, as they actively engage individuals in contributing to the dataset being visualized. Such a participatory approach places users in an active role within the data collection, visualization, and exploration process. It can foster meaningful engagement and group discussions by presenting data that is directly relevant to its local context, rather than broad, impersonal, large-scale information.

Previously, the *Edo* project [5] used this approach with a participatory data physicalization that enables users to indicate their dietary choices and visualize their environmental impact in terms of CO₂e emissions. However, *Edo* was tested only with a small group of colleagues within a research team and is not ready for direct deployment in larger-scale restaurant settings. Several design choices would make it difficult to integrate it in a restaurant environment, most notably, the time-consuming task of identifying the correct tokens for one's meal from among 18 different options.

This poster presents a new participatory data physicalization design. This design is directly inspired by *Edo*, but was specifically developed to operate in high-traffic environments with larger user volumes (Fig. 1 & Fig. 2). We describe our design and lessons from a week-long test deployment, aimed at assessing the feasibility of deploying a participatory physicalization in a high-traffic restaurant and do a basic usability test, in preparation for a future study.

<figure><img src="image_2.png" /><figcaption>Figure 1: Our participatory physicalization where column width encodes the carbon impact of dietary choices, and tokens indicate the number of meals chosen for each option.</figcaption></figure>

## 2 DESIGN CONSIDERATIONS

Our design aims for the following properties to address several key challenges identified by the *Edo* project and specific to the initial design [5]: (1) *encoding information* of environmental impacts of food that is visible even on an empty panel; (2) a *higher number of possible entries* to accommodate a large number of users; (3) a *shorter contribution time* to prevent congestion around the visualization; (4) a lower *complexity of the visualization* to facilitate the understanding of key messages; (5) adding a *scale and annotations* allowing the impacts of different meals to be compared.

## 3 THE NEW DESIGN

The design of the new panel is illustrated in Fig. 2. Similar to *Edo*, it is composed of multiple columns and uses tokens to represent environmental impact. However, here each column stands for a particular type of meal (e.g., beef-based, chicken-based, vegeterian), whereas in *Edo*, food items were encoded in tokens of different types. The width of each column is inversely proportional to the CO₂e emissions associated with the production of the main protein source of the meal. Thus, the environmental impact of different foods is not encoded in the size of tokens, but in the width of the columns, and is thus visible even on an empty panel (1). A meal is represented by *one* token of constant size to be placed in the corresponding column. This makes the number of possible contributions predictable (2), likely shortens the time needed to contribute data (3), and likely reduces the complexity of parsing the visualization (4). Finally, this design enables the addition of a common scale which helps interpret the data (5). Additionally, each token is double-sided (orange and blue), allowing people to answer a question while indicating their meal, adding a playful dimension.

The design is meant to support the intuitive comparison of the environmental impact of different types of meals: high-impact meals, such as those containing beef, quickly fill their respective columns (cf. Fig. 3), visibly exceeding recommended environmental limits.

<footnote>*e-mail: eugenie.brasier@inria.fr</footnote>

<footnote>†e-mail: yvonne.jansen@cnrs.fr</footnote>

<footnote>‡e-mail: pierre.dragicevic@inria.fr</footnote>

<footnote>§e-mail: martin.hachet@inria.fr</footnote>
<figure><img src="image_3.png" /><figcaption>Figure 2: Four columns are displayed on the panel with pictograms placed over each for beef, pork, chicken and fish, and vegetarian. For the purpose of illustration, each column is filled with 20 tokens.</figcaption></figure>

In contrast, columns of lower-impact meals (e.g., vegetarian options) can accommodate many more tokens before reaching such thresholds and thus illustrate the differences in orders of magnitude.

## 4 PRELIMINARY DEPLOYMENT TEST

We installed the panel during five consecutive days in an administrative university restaurant to which only university employees have access. The primary purpose was to assess the feasibility of integrating such a panel into a high-traffic restaurant without disrupting its normal operations. The deployment was done with co-operation from the restaurant staff. We did not collect any data from patrons, and only took photos of the panel at the end of each day.

The panel was approximately 110 cm wide and 85 cm high (Fig. 2) and sized to accommodate about 50 tokens in the *beef and red meat* column, about 150 tokens in the *pork and white meat* column, about 200 tokens in the *fish and chicken* column, and about 700 tokens in the *vegetarian* column. Above the panel, a prompt was displayed asking what people ate that day (Fig. 2). Just below, a daily question was displayed, which participants could answer by choosing the blue or orange side of the token. The panel was placed on a table positioned against a wall, close to the tray return area. The table was organized with a box containing a set of tokens and several documents explaining the panel and detailing the actual quantity of CO₂e emissions associated with each protein.

As most patrons were regulars, the panel and its purpose only needed to be explained on the first day. Three staff members, including one of the authors, were present to distribute tokens in front of the panel, invite people to take part, and answer questions. From the second to the fourth day, tokens were distributed at the cashier station. On the last day, the panel was left for autonomous use without any facilitator.

The number of daily patrons varied between 184 and 254, of which only a few chose to ignore the installation. Everyone else placed the token they received in one of the columns of the panel. While we did not collect any data from individual patrons, we informally received numerous comments on the panel suggesting that people would like to continue using it.

<figure><img src="image_4.png" /><figcaption>Figure 3: Examples of how the panel was filled during two lunches. On the right, the beef consumption created an overflow.</figcaption></figure>

## 5 PRELIMINARY LESSONS LEARNED AND DISCUSSION

Some patrons placed their token immediately after collecting their meal and before taking their seat. This suggests that the timing of token distribution may need to be considered in the design, as it could potentially disrupt the restaurant flow. When the number of tokens placed in the beef and red meat column exceeded the available space, some people expressed surprise at the high associated CO₂e emissions, and attempted to stack their token somewhere on top (see Fig. 2-right) or left it simply in front of the column. The autonomous use on the last day noticeably lowered the number of tokens added but did not eliminate participation—there were about half as many tokens as the previous days. Several people commented on the playful aspect of the panel, describing it as fun and engaging. Some expressed feeling proud having chosen a better alternative with lower CO₂e emissions. Conversely, patrons who had chosen high CO₂e options occasionally experienced negative reactions from bystanders. It would be interesting to study the social dynamics around such participatory visualizations in future work. A remaining design challenge observed is that not all meals served did fit neatly into one of the columns. This is due to the properties of the underlying data which can have widely different data values for related food items, notably in the case of shrimps which are closer to beef than fish. This caused confusion among patrons regarding the interpretation of categories and the appropriate placement of tokens. Adapting to this idiosyncrasy and communicating it clearly will be a challenge for future iterations of this work.

## ACKNOWLEDGMENTS

This work was partly supported by grant ANR-19-CE33-0012 and grant ANR-20-IDES-0001. We acknowledge Univ. Bordeaux and CROUS for their support throughout this project.

## REFERENCES

[1] N. Bressa, J. Louis, W. Willett, and S. Huron. Input Visualization: Collecting and Modifying Data with Visual Representations. In *ACM CHI '24*, 2024. doi: 10.1145/3613904.3642808 1

[2] J. Froehlich, L. Findlater, and J. Landay. The design of eco-feedback technology. In *Proc. CHI '10*, pp. 1999–2008, 2010. doi: 10.1145/1753326.1753629 1

[3] S. Huron and W. Willett. Visualizations as Data Input? In *alt.VIS '21 Workshop*, Sept. 2021. 1

[4] Y. Jansen, P. Dragicevic, P. Isenberg, J. Alexander, A. Karnik, J. Kildal, S. Subramanian, and K. Hornbæk. Opportunities and Challenges for Data Physicalization. In *Proc. CHI '15*, pp. 3227–3236. ACM, New York, NY, USA, 2015. doi: 10.1145/2702123.2702180 1

[5] K. Sauvé, P. Dragicevic, and Y. Jansen. Edo: A Participatory Data Physicalization on the Climate Impact of Dietary Choices. In *In Proc. TEI '23*. ACM, New York, NY, USA, 2023. doi: 10.1145/3569009.3572807 1

[6] W. Willett, Y. Jansen, and P. Dragicevic. Embedded Data Representations. *IEEE TVCG*, 23(1):461–470, 2017. doi: 10.1109/TVCG.2016.2598608 1