Impact of Environmental Labelling on Food Choices : a Randomized Controlled Trial in a Virtual Reality Supermarket
Phase: NA
Conditions: Diet, Healthy, Food Selection
Sponsor: Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement

 
 
1 
 
PROTOCOL AND STATISTICAL ANALYSIS PLAN 
 
Study title: Impact of Environmental Labelling on Food Choices : a Randomized Controlled Trial in a 
Virtual Reality Supermarket 
 
Short title: Environmental labelling in a virtual supermarket 
 
Investigators from CSGA: 
- 
Lucile Marty 
- 
Laura Arrazat 
- 
Gaëlle Arvisenet 
- 
Sophie Nicklaus 
- 
Stéphanie Chambaron 
 
Date: May 24th, 2021 
NCT number: not yet assigned 
 
 

 
 
2 
 
Table of content 
 
PROTOCOL AND STATISTICAL ANALYSIS PLAN ........................................................................................ 1 
BACKGROUND AND RATIONALE .............................................................................................................. 3 
OBJECTIVES, OUTCOMES MEASURES AND HYPOTHESES ........................................................................ 4 
EXPERIMENTAL DESIGN ........................................................................................................................... 5 
PARTICIPANTS AND RECRUITMENT ......................................................................................................... 6 
Recruitment ......................................................................................................................................... 6 
Inclusion criteria .............................................................................................................................. 6 
METHODS ................................................................................................................................................ 7 
Online shopping tasks ......................................................................................................................... 7 
Shopping tasks ................................................................................................................................. 7 
Experimental virtual supermarket ................................................................................................... 7 
Intervention (environmental label) ................................................................................................. 9 
Questionnaires after the shopping experience ................................................................................. 12 
STUDY FLOW .......................................................................................................................................... 13 
STATISTICAL ANALYSES .......................................................................................................................... 14 
Definition of the population for analysis ........................................................................................... 14 
Participant’s characteristics .............................................................................................................. 14 
Overview of the statistical analysis ................................................................................................... 14 
Description of variables ..................................................................................................................... 16 
Dependent variables ..................................................................................................................... 16 
Independent variables ................................................................................................................... 18 
Other variables .............................................................................................................................. 18 
Missing data ...................................................................................................................................... 19 
Primary analyses ................................................................................................................................ 19 
Secondary analyses ........................................................................................................................... 19 
Sensitivity analyses ............................................................................................................................ 20 
Exploratory analyses.......................................................................................................................... 20 
Sample size ........................................................................................................................................ 22 
REFERENCES .......................................................................................................................................... 22 
 
 
 

 
 
3 
 
BACKGROUND AND RATIONALE 
 
Food systems have been recognised to play a major role in climate change and it was estimated that 
they contributed to 34% of total anthropogenic greenhouse gas emissions (GHGe) in 2015 (Crippa, 
2021). A French study evaluated that food accounted for 24% of the carbon footprint of households 
(Barbier et al., 2019). The largest contributor of these emissions is agriculture since it represents more 
than two-thirds of the food system’s GHGe (Barbier et al., 2019). The remaining third of the GHGe is 
due to transportation, consumption, industrial processes and packaging. A recent study showed that 
even if we were to stop fossil fuel emissions, the GHGe from food systems would not allow to stay 
under the targeted 1.5°C increase in global temperatures (Clark et al., 2020) highlighting the 
importance of cutting down agricultural GHGe. It has also been shown that the structure of our diets 
can be modified to reduce their carbon footprint and remain within a “safe operating space for 
humanity” as modelled by planetary boundaries (Willett et al., 2019). In particular, the reduction of 
meat and dairy consumption and the increase of plant-based food consumption are major levers to 
reduce the environmental impact of our diets (Hedenus et al., 2014; Scarborough et al., 2014).  
In developed countries particularly, where meat and dairy consumption is high, a shift towards more 
plant-based diets has been advocated by scientists for its beneficial impacts on health and the 
environment (Willett et al., 2019). However, this shift may not be easily achieved by consumers 
(Hartmann & Siegrist, 2017). This can be explained by the fact that eating meat is associated with 
pleasure and has strong cultural, social and personal values in our societies (Macdiarmid et al., 2016). 
The reluctance to reduce meat consumption may also be associated with the lack of knowledge of the 
environmental footprint of meat (Hartmann & Siegrist, 2017; Macdiarmid et al., 2016). The 
underestimation of the GHGe of different types of foods, especially animal products, has been 
previously highlighted and was considered as a potential area for interventions (Camilleri, 2019). In 
line with this idea, studies have shown that consumers with a higher knowledge regarding the impact 
of human behaviours on climate change declared higher intentions to adopt environmentally-friendly 
behaviours (Truelove & Parks, 2012). 
Environmental labels on food products have been described as potential drivers of the choice of 
environmentally-friendly products, as they may increase consumers’ knowledge about the 
environmental impact of the food products allowing consumers to make more informed decisions 
when choosing food products (Camilleri, 2019). In addition, consumers wish to be informed about the 
environmental footprint of the products that they consume (Gadema & Oglethorpe, 2011). However, 
when presented with those labels, many consumers found them hard to understand and to use 
(Gadema & Oglethorpe, 2011). Yet, the use of an environmental label is directly linked to its 
understanding (Grunert et al., 2014). Thus, there is a need of a clear environmental label that conveys 
key information regarding the environmental impact of foods and that is easily understood by 
consumers. 
Interventional studies using gold-standard design (i.e., randomised controlled trials) in realistic food 
purchasing environments are needed to demonstrate the effect of environmental labels on food 
choices (Vyth et al., 2012). Supermarkets are key environments to test the effect of food labelling 
interventions since they represented roughly 70% of the total food sales in 2019 in France (Insee, 
2019). Studies testing the effect of environmental labels on food choices carried out in realistic food 
purchasing settings, such as online supermarkets (Muller & Lacroix, 2019), real supermarkets (Pelletier 
et al., 2016; Vanclay et al., 2011; Vlaeminck et al., 2014) or restaurants (Brunner et al., 2018) are scarce. 
To our knowledge, only one randomised controlled trial, consisting in an online food shopping task for 
soups, tested the effect of an environmental label on food choices (Camilleri, 2019). 

 
 
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We will conduct a 2-arm randomised controlled trial (with and without labels) to test the effects of an 
environmental label on food choices in a virtual supermarket. Participants will take part in two 
shopping tasks: 1/ selection of 3 products to prepare a home-made dish, and 2/ selection of a ready-
to-eat dish. These two tasks will be repeated for two scenarios: 1/ participants will be asked to select 
the foods as they would do for a usual day (everyday scenario), and 2/ participants will be asked to 
select the foods for an environmentally friendly meal (environmentally-friendly scenario). This 
experimental design will allow to compare food choices in the presence vs. the absence of an 
environmental label and to investigate whether the label is informative and likely to help individuals 
to choose more environmental-friendly food options when explicitly asked to do so. In line with 
previous results showing that participants were more likely to choose pulses for a meal at a restaurant 
compared to a home-cooking scenario (Melendrez-Ruiz et al., 2019), we also hypothesise that it might 
be easier for a consumer to choose more environmentally friendly ready-to-eat dish than more 
environmentally friendly ingredients to cook it from scratch. 
On top of the lack of knowledge regarding the link between food systems and climate change, it has 
been emphasized that food choice motivations are key barriers for a shift towards more plant-based 
diets (Grunert et al., 2014). Many criteria are considered when purchasing foods and differ among 
consumers. In the French population taste, price and geographical origin of products are the key 
declared criteria influencing food choice (European Commission. Directorate General for Health and 
Food Safety, 2020). Environmental considerations such as the carbon footprint were poorly rated 
among food choice motives by UK supermarket shoppers (Gadema & Oglethorpe, 2011). Similarly, the 
environment was declared by only 15% of European respondents as an important driver influencing 
food choice (European Commission. Directorate General for Health and Food Safety. et al., 2020). An 
additional aim of the present study is to investigate the relationships between food choice motives 
and the effect of an environmental label on food choices.  
OBJECTIVES, OUTCOMES MEASURES AND HYPOTHESES 
 
OBJECTIVES 
OUTCOME MEASURES 
HYPOTHESES 
Primary objectives 
1.1 
To study the effect of an 
environmental label on 
the environmental impact 
of ingredients and ready-
to-eat dish choice in a 
virtual supermarket. 
 
Mean of PEF1 score for 100g of 
each ingredient selected and PEF 
score for 100g of the ready-to-eat 
dish in the “everyday scenario”.  
 
▪ 
An environmental label will lower 
the environmental impact of the 
food choices made by the 
participants compared to no 
label. 
▪ 
The effect of the environmental 
label will be greater when 
choosing a ready-to-eat dish 
compared to when choosing 
ingredients.  
1.2 
To investigate whether 
the environmental label is 
informative and likely to 
help individuals to choose 
environmentally friendly 
food options when they 
Mean PEF score for 100g of each 
ingredient selected and PEF score 
for 100g of the ready-to-eat dish in 
the “everyday scenario” and the 
“environmentally-friendly 
scenario”. 
▪ 
The reduction of the 
environmental impact of the 
foods chosen in the 
“environmentally-friendly 
scenario” compared to the 
“everyday scenario” will be 
greater for participants in the 
                                                          
1 PEF score is the “Product Environmental Footprint”. Details regarding its calculation is described in the 
paragraph “Creation of an environmental score” (page 9).  

 
 
5 
 
are encouraged to make 
environmentally friendly 
food choices. 
environmental label condition 
than in the no label condition.  
Secondary objectives 
2.1 
To study the effect of an 
environmental label on 
the nutritional quality, 
liking, familiarity, level of 
process and price of 
ingredients and ready-to-
eat dish choices in a 
virtual supermarket.  
The following variables will be 
calculated for the 3 ingredients 
(average) and for the ready-to-eat 
dish chosen in the “everyday 
scenario”: 
▪ 
FSA scores  
▪ 
Liking  
▪ 
Familiarity  
▪ 
NOVA score (only for the 
average of the 3 ingredients) 
▪ 
Price per calorie  
(continuous variables) 
 
▪ 
The environmental label will have 
an impact on the nutritional 
quality (FSA score), liking, 
familiarity, NOVA score and price 
of the foods chosen by the 
participants. 
2.2 
To investigate the 
relationships between 
food motives and the 
effect of an 
environmental label on 
ingredients and ready-to-
eat dish selection in a 
virtual supermarket. 
▪ 
Mean PEF score for 100g of 
each ingredient selected 
and PEF score for 100g of 
the ready-to-eat dish in the 
“everyday scenario”.  
▪ 
Level of ethical concern 
(questionnaire score) 
▪ 
The environmental label will be 
more effective in reducing the 
environmental impact of food 
choices in participants with 
higher levels of ethical concern 
when making food choices. 
EXPERIMENTAL DESIGN 
 
This study will be a randomised controlled trial with two experimental arms:  
1/ No label (no label condition) 
2/ Environmental label (label condition) 
 
A 2-block randomisation sequence (male and female) of 1:1 will be generated to allocate each 
participant to one of the experimental arm before recruitment using the Random Allocation software 
(Saghaei, 2004). 
 
Each participant will be asked to perform two hypothetical shopping tasks in a virtual shopping 
environment, first in an “everyday scenario”: 1/ choice of 3 ingredients for an everyday home-cooked 
dish among 66 products, 2/ choice of a ready-to-eat dish for an everyday meal among 30 products. 
The order of presentation of the two tasks will be counterbalanced. Then, the participants will be asked 
to perform the same two tasks while choosing foods for a dish that is “good for the planet” 
(“environmentally-friendly scenario”). For a given participant, the order of presentation of the two 
tasks will remain the same as in the everyday scenario. 
 
The experimental design is described Figure 1. 
 

 
 
6 
 
 
 
Figure 1. Experimental design for the virtual reality tasks performed by participants 
PARTICIPANTS AND RECRUITMENT 
 
Recruitment 
Participants will be recruited thanks to the “PanelSens” database from the “Chemosens” platform. This 
database was declared to the relevant authority (Commission Nationale Informatique et Libertés; CNIL; 
n°1,148,039). Participants will be invited to take part in a lab study that aims to study food choices in 
a virtual supermarket. After completing the study, participants will receive a 10-euro voucher. 
 
The recruitment will be stratified on sex: 
o 50% male 
o 50% female 
and on age: 
o ~33% between 18 and 35 years old 
o ~33% between 36 and 50 years old  
o ~33% over 50 years old 
 
This recruitment structure also aims to include a diverse sample including single participants, 
participants with children and participants with children that have left the house (estimated around 
18 years of age) as we hypothesise that age, gender and the structure of the household may have an 
impact on food choice behaviour. 
 
Inclusion criteria 
• 
Age ≥ 18 years old 
• 
Responsible for a substantial proportion of household grocery shopping 
• 
Fluent in French 
• 
No dietary restrictions (vegetarian, vegan, allergies, intolerance, …) 
• 
No uncorrected eye problems 
• 
No known symptoms of nausea and dizziness when wearing a virtual reality headset 
 

 
 
7 
 
METHODS 
 
Online shopping tasks 
 
Shopping tasks 
 
The experimental sessions will take place individually. After participants will have given their informed 
consent to take part in the experiment, they will be asked to sit down and will be equipped with a 
virtual reality headset. A qualified experimenter will be present to assist the participants. 
 
The first step will consist in a test of the virtual environment so that participants can familiarise 
themselves with the virtual supermarket environment. Participants will be asked to navigate and test 
the functionalities of the virtual reality headset. This dummy supermarket will only include cosmetic 
products (shampoos, soaps, etc.).  
 
Then, participants will start the shopping tasks under two scenarios. If participants are allocated to the 
“label condition”, the label will be presented and explained to the participants prior to the shopping 
tasks with a pop-up message. 
 
1) For the first scenario, in the first task, we will ask the participants to navigate in the aisles of a 
virtual environment of 66 foods and to select 3 food items in order to prepare a home-cooked 
dish for an everyday meal. In the second task, the shopping environment will be composed of 
30 foods and participants will be asked to select 1 ready-to-eat dish for an everyday meal. The 
order of these tasks will be balanced. 
2) The second scenario will be broken down in the same two tasks. We will keep the same order 
for the two tasks as in the first scenario. This time we will ask the participants to choose foods 
for a meal that is “good for the planet”.  
 
Experimental virtual supermarket 
This study will be undertaken in a virtual reality environment that mimics the aisles, shelve and position 
of food products of a real supermarket. This virtual environment will be developed by our partner 
STRATEGIR under Oculus Quest 2 (https://www.strategir.com/fr/). The food products available in the 
virtual supermarket will be real products that can be found in three French supermarkets (Intermarché, 
Carrefour and Picard). Participants will see the front of the products on the shelves in the same size as 
if they were in a real supermarket. With a hand controller, participants will be able to navigate in the 
virtual supermarket, come closer, seize and examine closely the products they would like to choose. 
Once a product is selected, they will have the option to either put the product in their shopping cart 
or to put it back on the shelf.  
 
 
Choice of the foods available in the supermarket 
 
Based on a recent study carried out in a virtual supermarket (Melendrez et al. working paper), four 
food groups will be included in our virtual supermarket: “Meat, egg, fish and substitutes”, “legumes”, 
“vegetables” and “starchy foods”. The number of foods composing each category, for each task of the 
shopping scenarios (“everyday scenario” and “environmentally-friendly scenario”), is described in 
Table 2. The foods included in each category are either canned, dry, fresh, or frozen. 
 

 
 
8 
 
As participants will be asked to select foods for a main dish only, the items included are all savoury 
foods. They are typical foods that can be part of a main dish. We will focus on the main dish as it is the 
meal item where we found the greater variability in terms of environmental impact, as compared with 
starters and desserts. Food sub-categories were chosen with distinct environmental impacts to 
maximise the likelihood of substitutions between food products of contrasted environmental impact. 
 
Table 2. Foods included in the virtual supermarket 
Tasks for 
each 
shopping 
scenario 
Main Food Groups 
Food Sub-groups 
Number of products included 
 
Task 1: 
Home-cooked 
dish 
Starchy foods  
- 
12 
Vegetables 
- 
12 
Legumes 
- 
12 
Meat, eggs, fish and substitutes 
Beef and lamb 
6 
Other meat 
6 
Fish 
6 
Other animal products 
6 
Plant-based meat substitutes 
6 
Task 2: Ready 
to eat dish 
Beef and lamb 
/ 
6 
Other meat 
/ 
6 
Fish 
/ 
6 
Vegetarian dishes (with animal 
products) 
/ 
6 
Vegan dishes (without animal 
products) 
/ 
6 
 
 
Availability and placement of the products in the virtual supermarket 
 
The first rule we adopted to create our virtual environment was to keep the same types of shelves as 
in a real supermarket. The shelves are defined by the storage method of the products. 
4 types of shelves have been identified: 
• 
With canned products 
• 
With dry products 
• 
With fresh products 
• 
With frozen products 

 
 
9 
 
 
As in a real supermarket, within each shelf, we grouped products from the same food category (Table 
3). To control for availability effect, we chose to allocate the same space to each product. 
 
Table 3. Number of products composing each shelf of the virtual supermarket.  
Task 1: Home-cooked dish 
Task 2: Ready to eat dish 
Food categories 
Canned 
products 
Dry products 
Fresh 
products 
Ready-to-eat 
dish 
category 
Frozen 
products 
Fresh 
products 
Meat, egg, 
fish and 
substitutes 
Dairy 
products 
0 
2 
4 
Dairy 
products 
4 
2 
Poultry and 
pork 
0 
0 
6 
Poultry and 
pork 
3 
3 
Cow or 
sheep meat 
0 
0 
6 
Cow or 
sheep meat 
3 
3 
Sea food  
2 
0 
4 
Sea food  
4 
2 
Plant-based 
substitutes 
0 
2 
4 
Plant-based 
substitutes 
4 
2 
  
Vegetables 
5 
0 
7 
Total 
18 
12 
Legumes 
4 
6 
2 
  
  
  
Starchy 
foods 
1 
10 
1 
  
  
  
Total 
12 
20 
34 
  
  
  
 
 
Intervention (environmental label) 
As mentioned in the design of the experiment, an environmental label will be added to the front of the 
pack of the 96 food items included in the virtual supermarket. 
 
Development of an environmental score: 
 
In order to identify the environmental impact of the 96 foods included in the virtual supermarket, we 
matched each experimental food product with items in Agribalyse database2. To create our 
environmental score, we used the PEF, Product Environmental Footprint, score recommended by the 
European Commission (Pant et al., 2019). This score has the aim to evaluate the environmental impact 
of a food product based on 16 indicators such as climate change (kg CO2 eq), ozone depletion (kg CFC-
11 eq), marine eutrophication (kg N eq), land and water use or acidification (mol H+ eq). These 16 
indicators are calculated throughout the life cycle of the products. In order to obtain a unique PEF 
score, the indicators are weighed according to a method described by the European Commission (Pant 
et al., 2019). PEF scores for the foods present in the Agribalyse database are expressed in mPt for 1 kg 
of product. Pt means “eco-indicator point” and on a scale, 1 mPt represents the mean annual 
environmental load of an average European inhabitant. 
 
In order to highlight PEF score variability across the different products presented in the virtual 
supermarket we created a segmentation of this score into five modalities. We defined the cut-offs 
                                                          
2 Agribalyse is a database with the environmental impact, calculated thanks to a life cycle analysis, of 2480 
different foods created by the French Environmental and Energy Management Agency (ADEME). 
https://agribalyse.ademe.fr/app/aliments  

 
 
10 
 
across modalities as the quintile values from a larger selection of similar foods from Agribalyse 
database. For the first shopping task (choice of 3 ingredients to compose a home-cooked dish), we 
created quintiles for the PEF score of 1655 foods that correspond to the food groups included in the 
virtual supermarket (Table 4). We replicated this method for the second task (choice of a ready-to-eat 
dish among 30 products). The quintiles were therefore calculated for the 281 foods from Agribalyse 
database with corresponding food groups (Table 4). 
 
The process for the creation of the environmental score, explained for the first shopping task, is 
summarised Figure 2.  
 
Table 4. Food groups from the Agribalyse database that are common with the food items included in 
the virtual supermarket 
 
Food groups from Agribalyse used to create a list of foods from which we created 
quintiles 
Task 1: Choice of 3 ingredients to compose a 
home-cooked dish among 66 products 
Task 2: Choice of a ready-prepared dish among 30 
products 
• 
Fruits, vegetables, legumes, nuts and 
seeds  
• 
Starters and dishes 
• 
Cereal products 
  
• 
Milk and dairy products 
  
• 
Meat, egg and fish 
  
These food groups represent: 
N=1655 foods 
N=281 foods 
 
Table 5. Limits of the quintiles created for the PEF scores of the different foods selected.  
The values in the following table are PEF scores expressed in (mPt/kg of product) 
 
Ingredients 
Ready-to-eat dishes 
1655 foods 
281 foods 
0 < Q1 ≤ 0,17 
0 < Q1 ≤ 0,20 
0,17 < Q2 ≤ 0,35 
0,20 < Q2 ≤ 0,36 
0,35 < Q3 ≤ 0,62 
0,36 < Q3 ≤ 0,46 
0,62 < Q4 ≤ 1,36 
0,46 < Q4 ≤ 0,84 
1,36 < Q5 ≤ 6,09 
0,84 < Q5 ≤ 3,23 
 
 
 

 
 
11 
 
 
 
Figure 2. Creation of an environmental score for the label 
 
Creation of an environmental label: 
 
Since no environmental label design is currently being used in France for food products, we created a 
design considering previous findings regarding the most effective label designs.  
 
The ideal label proposed by Carrero and collaborators is “traffic-light labels” (Carrero et al., 2021). It 
has been shown that green is often seen as a validation (“go”) and a positive colour whereas red is 
associated with negative aspects and danger (Schuldt, 2013). This was put forward in research 
preceding the adoption of a nutritional front-of-pack label (Ducrot et al., 2015). 
 
In September 2020, the French Agency for ecological transition (ADEME) published a design for a label 
assessing the environmental impact for the life cycle analysis of products or services (ADEME, 2020), 
see Figure 3. The colour was changed to obtain a “traffic light label” since it is considered as the best 
option to increase understanding and therefore behaviour change in consumers, see Figure 3. The 
label will be placed on the bottom part of the products. 
 
 
Figure 3. Design of the environmental label inspired by the French environmental label of ADEME 

 
 
12 
 
 
Questionnaires after the shopping experience  
 
After the shopping experience, participants will be asked to complete one questionnaire with the 
virtual reality headset and four questionnaires on a separate computer (Qualtrics). These 
questionnaires will be presented in the following order:  
 
1) After the four shopping tasks, participants will be asked to evaluate their familiarity and liking 
of the eight products selected under the virtual reality headset. The eight products will 
successively appear under the headset and participants will give a familiarity score (frequency 
of consumption of a similar product, ranging from “never” to “very often”) and a liking score 
(score ranging from 1 to 10) for each product. 
 
2) An investigation questionnaire will then be presented to participants on a computer to identify 
aim-guessers and record participants’ feedback about their experience in the virtual 
supermarket. We will use questions adapted from the presence questionnaire used in previous 
studies (Schnack et al., 2019; Witmer et al., 2005). We will ask participants to evaluate, for 
each scenario, the importance of the presumed price, taste, packaging size, nutritional quality 
and environmental impact when making their choices (“not influenced” to “very influenced” 
by each criteria). For participants in the “label condition”, we will also investigate if they paid 
attention, understood and used the environmental label while selecting products. Participants 
in the “no label condition” will also be asked if they noticed the presence of an environmental 
label in the virtual supermarket (manipulation check) and if they understand the meaning of 
the environmental label (displayed in the questionnaire). We will also ask questions to 
investigate what participants understood when asked to choose an environmentally-friendly 
dish. 
 
3) From this will follow a food choice motivation questionnaire. We will use the adaptation of the 
Food Choice Questionnaire (Steptoe et al., 1995) for the French population (Cottet et al., 2017). 
This version includes 24 items that are grouped in 9 subscales (health, convenience, sensory 
appeal, natural content, ethical concerns, weight control, mood, familiarity and price). In order 
to have a better understanding of the sustainability motives consumers have for selecting their 
food, we will also ask independently the “Environmental welfare” subscale from the recently 
published Sustainable Food Choice Questionnaire (SUS-FCQ) (Verain et al., 2021). This subscale 
is composed of 5 items and its reliability was considered, by its authors, high enough to be 
used independently. The 5 items were translated in French and back-translated to English. For 
each item, participants will be asked to give a score ranging from 1 to 4 (1= not at all important, 
2= a little important, 3= moderately important and 4 = very important). We will calculate the 
average scores for the “ethical” subscale of the FCQ and for the “environmental welfare” 
subscale of the SUS-FCQ. We will use the score with the highest Cronbach’s coefficient in the 
analyses. The Food Choice Questionnaire will be complemented with questions regarding 
potential changes in food consumption habits that will inform future research work and will 
not be analysed as part of the present study. This latter was studied by (Gadema & Oglethorpe, 
2011)3. Will follow, questions evaluating participants’ declared capacity to make food choices 
based on the environmental impact or the nutritional quality of products. 
                                                          
3 These questions have the purpose to inform an additional research project.  

 
 
13 
 
4) A socio-demographic questionnaire to collect data regarding the participants (age, gender, 
level of qualification, etc.) will be completed by the participants. 
 
STUDY FLOW 
 
RECRUITMENT 
(Chemosens platform) 
▪ 
An email will be sent to individuals in the PanelSens database. 
Inclusion criteria will be mentioned in the email.  
▪ 
If a person wishes to participate in the experiment, a 
questionnaire to assess eligibility will be sent. 
INFORMED CONSENT 
(On paper) 
▪ 
Participants will read the information sheet 
▪ 
Participants will sign the consent form 
RANDOMISATION 
(Random Allocation 
Software) 
▪ 
Participants will be randomly allocated to one of the two 
experimental conditions (1:1) 
No label 
(X) 
Environmental label 
(E) 
SHOPPING TASKS 
(virtual reality 
headset) 
▪ 
Participants will navigate in a dummy virtual supermarket. 
▪ 
Everyday scenario (1): Participants will be asked to go shopping in 
the supermarket twice to select foods for an everyday meal:  
- 
Task 1: Selection of 3 items to compose a home-cooked dish 
among 66 products 
- 
Task 2: Selection of 1 ready-to-eat dish among 30 products 
(The order of these two tasks will be counterbalanced) 
▪ 
Environmentally-friendly scenario (2): Participants will be asked to 
go shopping in the supermarket twice to select environmentally 
friendly ready-to-eat dishes:  
- 
Task 1: Selection of 3 items to compose a home-cooked dish 
among 66 products 
- 
Task 2: Selection of 1 ready-to-eat dish among 30 products 
(The order of these two tasks will be the same as in the everyday 
scenario) 
FAMILIARITY AND 
LIKING OF THE 
PRODUCTS SELECTED 
(virtual reality 
headset) 
▪ 
Under the virtual reality headset, participants will be asked to 
evaluate their familiarity and liking of the 8 products they 
selected during the 4 shopping tasks. 
INVESTIGATION 
QUESTIONNAIRE 
Qualtrics 
▪ 
This questionnaire aims to gather information regarding the 
shopping tasks participants underwent under the virtual reality 
headset. We will identify potential aim-guessers and food 
motives during the online shopping experience. For participants 
in condition E, we will investigate if they paid attention, 
understood and used the environmental labelling.  
FOOD CHOICE 
QUESTIONNAIRE 
Qualtrics 
▪ 
Food choice motives will be assessed through an adaptation of 
the “Food Choice Questionnaire”. 
SOCIO-
DEMOGRAPHIC 
CHARACTERISTICS 
Qualtrics 
▪ 
Participants will complete a questionnaire on socio-demographic 
data. 
 

 
 
14 
 
The complete study flow is represented in Figure 4.  
 
 
Figure 4. Complete study flow.  
 
STATISTICAL ANALYSES 
 
All statistical analyses will be performed using SAS version 9.4. The level of significance will be set at p 
< 0.05 for all pre-registered analyses. 
Definition of the population for analysis 
 
Participants who will be included in the statistical analyses are the ones having completed the shopping 
tasks and answered all of the questionnaires. Aim-guessers will be excluded in the sensitivity analysis. 
 
Participant’s characteristics 
 
Participants’ characteristics, overall and for each condition, will be displayed in a table. We will include 
gender, age, highest educational qualification, current employment, number of people living in the 
household, the perceived financial status of the household, dieting status and BMI. Continuous 
variables will be summarised using means and standard deviations. Categorical variables will be 
summarised using counts and percentages. 
 
Overview of the statistical analysis 
 
Objective 
Dependent variables 
(Y) 
Independent 
variables (x) 
Data used 
Statistical analysis 
1.1.  
To study the 
effect of an 
environmental 
label on the 
environmental 
Mean of PEF scores for 
100g of each ingredient 
selected and PEF score 
for 100g of the ready-
to-eat dish. 
- Environmental 
label (0= no / 1= 
yes) 
Only data from 
scenario 1 (“everyday 
scenario”) 
 
Mixed model analysis with 
random effect of participants.  
PEF = label (0/1) + type_of_food 
(I/RTEd) + label*type_of_food 
 

 
 
15 
 
impact of 
ingredients 
and ready-to-
eat dish choice 
in a virtual 
supermarket. 
 
- Type of food 
(I4 / RTEd5)  
If label*type_of_food interaction 
is not significant, it will be 
removed from the model. 
1.2. 
To investigate 
whether the 
environmental 
label is 
informative 
and likely to 
help 
individuals to 
choose 
environmental-
friendly food 
options when 
they are 
encouraged to 
make 
environmental-
friendly food 
choices. 
Mean of PEF score for 
100g of each ingredient 
selected and PEF score 
for 100g of the ready-
to-eat dish. 
 
- Environmental 
label (0= no / 1= 
yes) 
- Scenario 
(“everyday 
scenario”/ 
“environmental-
friendly 
scenario”) 
- Type of food (I 
/ RTEd) 
Data from the two 
labelling conditions 
and the two scenarios 
 
Mixed model analysis with 
random effect of participants. 
 
Mixed model analysis with 
random effect of participants.  
PEF = label (0/1) + type_of_food 
(I/RTEd) + scenario 
(everyday/environmental) + 
label*type_of_food + 
label*scenario + 
scenario*type_of_food 
 
Non-significant interactions will 
be removed from the model. 
 
If the labelling*type_of_food or 
the scenario*type_of_food 
interaction is statistically 
significant: 
 
• 
For the 3 ingredients 
chosen (Task 1) 
PEF = labelling + scenario + 
labelling*scenario 
 
• 
For the ready-ot-eat 
dish chosen (Task 2) 
PEF = labelling + scenario + 
labelling*scenario  
 
 
2.1. 
To study the 
effect of an 
environmental 
label on the 
nutritional 
quality, liking, 
familiarity, 
level of process 
and price of 
ingredients 
and ready-to-
eat dish choice 
in a virtual 
supermarket. 
- Mean of the FSA 
scores for 100g of each 
ingredient selected and 
FSA score for 100g of 
the ready-to-eat dish. 
- Average liking of the 3 
ingredients selected and 
liking for the RTEd 
(continuous variable) 
- Average familiarity of 
the 3 ingredients 
selected and familiarity 
for the RTEd 
(continuous variable) 
- Average NOVA score of 
the 3 ingredients 
- Environmental 
label (0= no / 1= 
yes) 
- Type of food (I 
/ RTEd) 
Data from scenario 1 
only (everyday 
scenario) 
 
Mixed model analysis with 
random effect of participants. 
- FSA= label (0/1) + type_of_food 
(I/RTEd) + (label*type_of_food) 
- Liking= label (0/1) + 
type_of_food (I/RTEd) + 
(label*type_of_food) 
- Familiarity= label (0/1) + 
type_of_food (I/RTEd) + 
(label*type_of_food) 
- Price per calorie = label (0/1) + 
type_of_food (I/RTEd) + 
(label*type_of_food) 
 
If the label*type_of_food 
interaction in model 1.1 is not 
statistically significant, this 
                                                          
4 Ingredient (3 ingredients are chosen by the participant) 
5 RTEd: ready-to-eat dish 

 
 
16 
 
selected (continuous 
variable) 
- Average price per 
calorie of the 3 
ingredients selected and 
price per calorie for the 
RTEd (continuous 
variable) 
 
interaction will not be included in 
these models.  
 
ANOVA model for the 3 
ingredients chosen (Task 1): 
NOVA score= label (0/1) 
2.2. 
To investigate 
the 
relationships 
between food 
motives and 
the effect of an 
environmental 
label on 
ingredients 
and ready-to-
eat dish 
selection in a 
virtual 
supermarket. 
Mean of PEF scores for 
100g of each ingredient 
selected and PEF score 
for 100g of the ready-
to-eat dish. 
 
 
  
- Environmental 
label (0= no / 1= 
yes) 
- The average 
ratings for items 
in the ethical 
subscale of the 
FCQ 
(Ethic_score6) 
- Type of food (I 
/ RTEd) 
 
Data from scenario 1 
only (everyday 
scenario) 
 
 
Mixed model analysis with 
random effect of participants. 
- PEF = labelling + Ethic_score + 
type_of_food + labelling* 
type_of_food + labelling* 
Ethic_score 
 
If label*type_of_food interaction 
is statistically significant:  
2 ANCOVA models 
• 
For the 3 ingredients 
chosen (Task 1) 
- PEF = labelling + Ethic_score + 
labelling* Ethic_score 
 
• 
For the ready-ot-eat 
dish chosen (Task 2) 
- PEF = labelling + Ethic_score + 
labelling* Ethic_score 
 
 
Description of variables 
 
Dependent variables  
 
Primary outcome 
 
The primary outcome is the environmental impact of the food selected by the participants. The 
environmental impact of the food selection will be quantified using the PEF score in mPt for 100g of 
product throughout the life cycle of the products. 
 
The environmental impact of the 3 ingredients (i) chosen during task 1 of each scenario is calculated 
by the mean of the PEF scores of each product (PEFi) 7.  
 
 
 
 
                                                          
6 We will calculate the average for the ethical subscale of the FCQ (Cottet et al. translation) and for the 
“environmental welfare” subscale of the SUS-FCQ. We will use the score with the highest Cronbach’s 
coefficient.  
7 eCO2 stands for CO2 equivalent  
𝑃𝐸𝐹𝑗=  ∑
𝑃𝐸𝐹𝑖
𝑖=3
𝑖=1
3
 

 
 
17 
 
PEFj is the environmental impact for 100g of the selection of the 3 ingredients (mPt/100g). PEFi is the 
environmental footprint for 100 g of each ingredient i. 
 
The environmental impact of the ready-prepared dish chosen (task 2 of each scenario) is assessed by 
the PEF score for 100g of this product. 
 
Secondary outcomes 
 
▪ 
The nutritional quality of the dishes chosen by participants is assessed by the FSA score (the 
British Food Standards Agency nutrient profiling system) (Rayner et al., 2009), as described for 
each product (Julia & Hercberg, 2017). 
 
The nutritional quality of the home-cooked dish (j) chosen during task 1 of each scenario is calculated 
by the mean of the FSA scores of each of the 3 ingredients (i) composing the dish.  
 
 
 
FSAi scores are calculated for 100g of the ingredient i. 
 
The nutritional quality of the ready-prepared dish chosen (task 2 of each scenario) is assessed by the 
FSA score for 100g of this product. 
 
▪ 
The liking score of the home-cooked dish (j) is the average of the 3 ingredients (i).  
𝐿𝑖𝑘𝑖𝑛𝑔 𝑗 = ∑
𝑙𝑖𝑘𝑖𝑛𝑔 𝑖
3
𝑖=1
 
3
 
For the ready-to-eat dish, we will directly use the liking score given by participants. 
The liking score ranges from 1 to 10, 10 being the highest degree of liking.  
 
▪ 
The familiarity score of the home-cooked dish is the average of the familiarity score of the 3 
ingredients selected.  
𝐹𝑎𝑚𝑖𝑙𝑖𝑎𝑟𝑖𝑡𝑦 𝑗 = ∑
𝑓𝑎𝑚𝑖𝑙𝑖𝑎𝑟𝑖𝑡𝑦 𝑖
3
𝑖=1
 
3
 
The familiarity score of the ready-to-eat dish is directly used.  
Familiarity scores range from 1 to 5 (1=Never and 5=Very often). 
 
▪ 
The NOVA score of the home-cooked dish is the average of the NOVA score of the 3 ingredients 
selected.  
𝑁𝑂𝑉𝐴 𝑠𝑐𝑜𝑟𝑒 𝑗 = ∑
𝑁𝑂𝑉𝐴 𝑠𝑐𝑜𝑟𝑒 𝑖
3
𝑖=1
 
3
 
The NOVA score of the ready-to-eat dish will not be analysed since, by definition, all of these 
foods will be categorised in category 4 (ultra-processed foods).  
 
▪ 
The price per calorie of the home-cooked dish is the average of the price per calorie of the 3 
ingredients selected. The price per calorie of the ready-to-eat dish is directly used.  
𝑃𝑟𝑖𝑐𝑒 ( €
𝑘𝑐𝑎𝑙) 𝑗 = ∑
𝑝𝑟𝑖𝑐𝑒 𝑝𝑒𝑟 𝑐𝑎𝑙𝑜𝑟𝑖𝑒 𝑖
3
𝑖=1
 
3
 
The price per calorie for one product will be calculated with the following equation:  
𝐹𝑆𝐴 𝑠𝑐𝑜𝑟𝑒𝑗=  
∑
𝐹𝑆𝐴𝑖
𝑖=3
𝑖=1
3
 

 
 
18 
 
𝑃𝑟𝑖𝑐𝑒 𝑝𝑒𝑟 𝑐𝑎𝑙𝑜𝑟𝑖𝑒𝑖 ( €
𝑘𝑐𝑎𝑙) = 
𝑃𝑟𝑖𝑐𝑒𝑖(€) × 100
𝑊𝑒𝑖𝑔ℎ𝑡𝑖(𝑔) × 𝐸𝑛𝑒𝑟𝑔𝑦 𝑑𝑒𝑛𝑠𝑖𝑡𝑦𝑖( 𝑘𝑐𝑎𝑙
100𝑔)
 
 
Independent variables 
 
Variables for the primary outcomes 
 
▪ 
Labelling (presence (yes) vs. absence (no) of an environmental label) 
▪ 
Scenario (“everyday scenario” vs. “environmentally friendly scenario”) 
▪ 
Type of food present in the virtual environment (Ingredients vs. Ready-to-eat dish) 
 
Variables for the secondary outcomes 
 
▪ 
Labelling (presence (yes) vs. absence (no) of an environmental label) 
▪ 
Scenario (“everyday scenario” vs. “environmentally friendly scenario”) 
▪ 
Type of food present in the virtual environment (Ingredients vs. Ready-to-eat dish) 
▪ 
Ethical concern scores will be calculated for:  
o the items of the “Environmental Welfare” subscale from the Sustainable Food Choice 
Questionnaire (SUS-FCQ).  
o the items of the “Ethical” subscale from the French translation of the Food Choice 
Questionnaire (SUS-FCQ). 
Each rating for individual items, such as “is produced in an environmentally friendly way”, 
ranges from 1 to 4: 1 = Not at all important; 2 = A little important; 3 = Moderately important; 
4 = Very important. Out of the two ethical concern scores described above, we will use the one 
with the highest Cronbach’s alpha in the analyses. This score will be noted “Ethic_score” and 
will be regarded as a continuous variable.  
 
Other variables 
 
Socio-demographic characteristics 
 
▪ 
Gender: 0=male, 1=female 
▪ 
Age: continuous variable 
▪ 
The level of education: This variable will be coded as the highest educational qualification 
from 1 to 4: 1= “< High school + 2-years diploma”, 2= “high school + 2-years diploma”, 3= “High 
school + 3 or + 4-year diploma” and 4 = “≥ High school + 5-year diploma”.  
▪ 
Employment status: The employment status of the participant will be coded from 1 to 6: 1 = 
“full or part-time”, 2=”student”, 3=”retired”, 4=”looking for a job”, 5=”looking after home” and 
6=”other”.   
▪ 
The number of people living in the household: this will be regarded as a continuous variable.  
▪ 
Dieting status: 0 = no, 1 = yes. 
▪ 
BMI: BMI will be calculated as weight (kg) / height (m2). BMI data will be trimmed for 
implausible values excluding weight for less than 30 kg and more than 250 kg, height for less 

 
 
19 
 
than 145 cm and more than 3m, BMI < 14, or BMI > 48 (Hardy et al., 2016; Miller, 2003). We 
will use the BMI as a continuous variable or use the categories defined by the WHO.  
 
Missing data 
 
We do not anticipate missing data for our analysis. It will be impossible for the participant to finish the 
shopping scenarios without having selected the 8 products. We have calculated the FSA-NPS scores 
and the PEF score for all of the products presented in our virtual supermarket. 
We do not anticipate missing data for the dependent variables because the questionnaires will not 
allow missing answers. Data from participants who start but do not finish the study will not be included. 
Any a posteriori withdrawal will be reported and reasons for withdrawal will be documented (e.g., 
incorrect answers, technical problems). 
 
Primary analyses 
 
MODEL 1.1: Mixed model analysis will be used to test the effect of labelling (categorical variable: yes 
or no), type_of_food (categorical variable: ingredients or ready-to-eat dish) and labelling* 
type_of_food interaction on PEF scores in the “everyday scenario”, with random effect of participants 
to account for correlation between repeated measures. If the labelling*type_of_food interaction is not 
significant, it will be removed from the model. 
 
MODEL 1.2:  
• 
Mixed model analysis will be used to test the effect of labelling (categorical variable: yes or 
no), type_of_food (categorical variable: ingredients or ready-to-eat dish), scenario (categorical 
variable: “everyday scenario” or “environmentally friendly scenario”),labelling*scenario, 
labelling*type_of_food and scenario*type_of_food interactions on PEF scores, with random 
effect of participants to account for correlation between repeated measures. Non-significant 
interactions will be removed from the model. 
• 
If the labelling*type_of_food or the scenario*type_of_food interaction is statistically 
significant, two mixed model analysis will be carried out for the home-cooked dish (average 
of the 3 ingredients) and for the ready-to-eat dish separately. These models will test the effect 
of labelling (categorical variable: yes or no), scenario (categorical variable: “everyday scenario” 
or “environmentally friendly scenario”) and labelling*scenario interaction on PEF scores, with 
random effect of participants to account for correlation between repeated measures.  
 
Secondary analyses 
 
▪ 
For the first secondary analysis (MODEL 2.1), mixed model analysis will be used to test the 
effect of labelling (categorical variable: yes or no), type_of_food (categorical variable: 
ingredients or ready-to-eat dish) and labelling*type_of_food interaction on 4 dependent 
variables, with random effect of participants to account for correlation between repeated 
measures. We will only look at the data from the first scenario (“everyday scenario”). The 5 
mixed model analysis that will be carried out are: 

 
 
20 
 
o FSA = labelling (categorical variable: yes or no), type_of_food (categorical 
variable: ingredients or ready-to-eat dish) and labelling* type_of_food 
interaction 
o Liking = labelling (categorical variable: yes or no), type_of_food (categorical 
variable: ingredients or ready-to-eat dish) and labelling* type_of_food 
interaction 
o Familiarity = labelling (categorical variable: yes or no), type_of_food 
(categorical variable: ingredients or ready-to-eat dish) and labelling* 
type_of_food interaction 
o Price per calories (€/kcal) = labelling (categorical variable: yes or no), 
type_of_food (categorical variable: ingredients or ready-to-eat dish) and 
labelling* type_of_food interaction 
 
If the labelling*type_of_food interaction is not significant, it will be removed from the models.  
 
ANOVA model will be used to test the effect of labelling (categorical variable: yes or no) on NOVA score 
for task 1 (for the 3 ingredients chosen). We will only look at the data from the first scenario (“everyday 
scenario”).   
 
▪ 
For the second secondary analysis (MODEL 2.2), we will only use “everyday scenario” data. 
o Mixed model analysis will be used to test the effect of labelling (categorical variable: yes 
or no), type_of_food (categorical variable: ingredients or ready-to-eat dish), 
labelling*type_of_food interaction, Ethic_score (continuous variable) and labelling* 
Ethic_score interaction on PEF scores, with random effect of participants to account for 
correlation between repeated measures. If the labelling*type_of_food interaction is not 
significant, it will be removed from the model. 
o If the interaction between the labelling condition and the type of food is statistically 
significant., two ANCOVA models will be carried out for the home-cooked dish (average of 
the 3 ingredients) and for the ready-to-eat dish separately. ANCOVA models will be used 
to test the effect of labelling (categorical variable: yes or no), Ethic_score (continuous 
variable) and labelling*Ethic_score interaction on the mean PEF score.  
 
 
Sensitivity analyses 
 
▪ 
Sensitivity analysis will be carried out to analyse if the results found for MODEL 1.1. remain 
the same when aim guessers are excluded.  
▪ 
MODEL 1.1. will be adjusted for age, sex, level of education and BMI. 
▪ 
MODEL 1.1. (raw and adjusted) will be replicated for the GHGe from the Agribalyse database 
instead of the PEF score. 
 
Exploratory analyses 
 
Descriptive analyses of the answers to the investigation questionnaire will be performed. 
 
1/ We will calculate the scores of participants responding to the following questions depending on the 
label condition and the scenarios (“everyday” / “environmentally friendly”): 

 
 
21 
 
• 
How much did taste / nutritional quality / environmental impact / packaging size / presumed 
price influence participant’s food choice in the virtual supermarket? (5 possible answers ranging 
from “strongly disagree” to “strongly agree”) 
 
2/ We will calculate the scores of participants responding to the following questions depending on the 
label condition: 
• 
What participants understood when asked to make food choices that are “good for the planet” 
(8 possible answers).  
• 
Participant’s perception of their capacity to distinguish 2 foods depending on their 
environmental impact (5 possible answers ranging from “strongly disagree” to “strongly agree”). 
 
3/ We will calculate the scores of participants, in the labelling condition, responding to the following 
questions: 
• 
If they noticed the label (0=they did not notice the presence of the label on the products chosen or thought 
that the label was present on less than half of the products chosen /1=the participant thought the label was present 
on all of the products chosen or on more than half of them). 
• 
If they understood the environmental label presented on the foods in the virtual supermarket 
(0=the participant did not answer correctly / 1=the participant answered correctly). 
• 
If they used the environmental label when making food choices (we will categorize the scale ranging 
from 0 to 100 into 4 parts).  
 
Further exploratory analyses will be conducted to have a broader understanding of the effects of the 
environmental label on food choices and are summarised in Table 6.  
 
Table 6. Summary of exploratory analysis.  
 
Data used of 
the analysis 
Dependent variable 
Independent variables  
Statistical analysis 
Both labelling 
conditions and 
the 2 scenarios 
will be 
considered 
Importance of food 
choice motives for 
each scenario  
(taste, nutritional 
quality, 
environmental 
impact, presumed 
price and packaging 
size) 
Continuous variables 
- Environmental label (0= no / 1= yes) 
- Scenario (“everyday scenario”/ 
“environmental-friendly scenario”) 
 
 
 
 
 
 
 
 
 
 
 
 
Mixed model analysis 
Data for 
participants in 
the “label 
condition” only 
 
 
 
 
 
 
Mean PEF scores 
- Attention paid by the participant to the label 
(0=they did not notice the presence of the label on 
the products chosen or thought that the label was 
present on less than half of the products chosen 
/1=the participant thought the label was present on 
all of the products chosen or on more than half of 
them) 
- Type of food (I / RTEd) 
- Scenario (“everyday scenario”/ 
“environmental-friendly scenario”) 
- Use of the label by the participant (continuous 
variable ranging from 0 to 100) 
- Type of food (I / RTEd) 
- Scenario (“everyday scenario”/ 
“environmental-friendly scenario”) 

 
 
22 
 
- Time spent to carry out each shopping task8 
(continuous variable) 
- Environmental label (0= no / 1= yes) 
- Type of food (I / RTEd) 
- Score obtained for the question regarding the 
knowledge about the environmental impact of 
food systems (continuous variable).  
- Environmental label (0= no / 1= yes) 
- Type of food (I / RTEd) 
(knowledge_score*labelling interaction will be 
interesting to analyse in order to test the 
moderating effect of knowledge on the 
environmental impact of food choices) 
Mean FSA scores 
- The average ratings for items in the health 
subscale of the FCQ 
- Environmental label (0= no / 1= yes) 
- Type of food (I / RTEd) 
 
Sample size 
 
We powered primary analyses in order to detect a d=0.50 effect size of labelling on PEF scores based 
the results of a previous randomised control trial that tested the effect of an environmental label on 
food choices (Camilleri, 2019). A sample size of 122 participants will be required for 80% power at α = 
0.05 (SAS 9.4). We will recruit a sample of 130 participants to account for potential data loss due to 
technical problems. 
REFERENCES 
ADEME. (2020, septembre). Mise en oeuvre de l’affichage environnemental des produits et services. 
ADEME. https://www.ademe.fr/mise-oeuvre-laffichage-environnemental-produits-services 
Barbier, C., Couturier, C., Pourouchottamin, P., Cayla, J.-M., Silvestre, M., & Pharabod, I. (2019). 
L’empreinte énergétique et carbone de l’alimentation en France. IDDRI, 24. 
Brunner, F., Kurz, V., Bryngelsson, D., & Hedenus, F. (2018). Carbon Label at a University Restaurant – 
Label Implementation and Evaluation. Ecological Economics, 146, 658‑667. 
https://doi.org/10.1016/j.ecolecon.2017.12.012 
Camilleri, A. R. (2019). Consumers underestimate the emissions associated with food but are aided 
by labels. Nature Climate Change, 9, 7. 
                                                          
8 The time spent in the supermarket is calculated under the virtual reality headset. We can also identify the 
time spent between for each task performed by participants.  

 
 
23 
 
Carrero, I., Valor, C., Díaz, E., & Labajo, V. (2021). Designed to Be Noticed : A Reconceptualization of 
Carbon Food Labels as Warning Labels. Sustainability, 13(3), 1581. 
https://doi.org/10.3390/su13031581 
Clark, M. A., Domingo, N. G. G., Colgan, K., Thakrar, S. K., Tilman, D., Lynch, J., Azevedo, I. L., & Hill, J. 
D. (2020). Global food system emissions could preclude achieving the 1.5° and 2°C climate 
change targets. Science, 370(6517), 705‑708. https://doi.org/10.1126/science.aba7357 
Cottet, P., Ferrandi, J., Lichtlé, M., & Plichon, V. (2017). La compréhension des moteurs des 
comportements alimentaires : Une approche par le food choice questionnaire. 12ème 
Journée du Marketing Agroalimentaire, hal-01900329. 
Crippa, M. (2021). Food systems are responsible for a third of global anthropogenic GHG emissions. 2, 
12. 
Ducrot, P., Méjean, C., Julia, C., Kesse-Guyot, E., Touvier, M., Fezeu, L., Hercberg, S., & Péneau, S. 
(2015). Effectiveness of Front-Of-Pack Nutrition Labels in French Adults : Results from the 
NutriNet-Santé Cohort Study. PLoS ONE, 10(10). 
https://doi.org/10.1371/journal.pone.0140898 
European Commission. Directorate General for Health and Food Safety., Kantar., & European 
Commission. Directorate General for Communication. (2020). Making our food fit for the 
future : Citizens’ expectations. Publications Office. 
https://data.europa.eu/doi/10.2875/826903 
Gadema, Z., & Oglethorpe, D. (2011). The use and usefulness of carbon labelling food : A policy 
perspective from a survey of UK supermarket shoppers. Food Policy, 36(6), 815‑822. 
https://doi.org/10.1016/j.foodpol.2011.08.001 
Grunert, K. G., Hieke, S., & Wills, J. (2014). Sustainability labels on food products : Consumer 
motivation, understanding and use. Food Policy, 44, 177‑189. 
https://doi.org/10.1016/j.foodpol.2013.12.001 

 
 
24 
 
Hardy, R., Park, A., & Johnson, J. (2016). “Harmonised height, weight and BMI user guide,” no. July. 
Closer. Cohort & Longitudinal Studies Enhancement Resources. 1‑17. 
Hartmann, C., & Siegrist, M. (2017). Consumer perception and behaviour regarding sustainable 
protein consumption : A systematic review. Trends in Food Science & Technology, 61, 11‑25. 
https://doi.org/10.1016/j.tifs.2016.12.006 
Hedenus, F., Wirsenius, S., & Johansson, D. (2014). The importance of reduced meat and dairy 
consumption for meeting stringent climate change targets. Climatic Change, 13. 
Insee. (2019). Parts de marché du commerce de détail selon la forme de vente. 
https://www.insee.fr/fr/statistiques/2015133 
Julia, C., & Hercberg, S. (2017). Nutri-Score : Evidence of the effectiveness of the French front-of-pack 
nutrition label. Ernährungs Umschau, 64(12), 158‑165. https://doi.org/10.4455/eu.2017.048 
Macdiarmid, J. I., Douglas, F., & Campbell, J. (2016). Eating like there’s no tomorrow : Public 
awareness of the environmental impact of food and reluctance to eat less meat as part of a 
sustainable diet. Appetite, 96, 487‑493. https://doi.org/10.1016/j.appet.2015.10.011 
Melendrez-Ruiz, J., Chambaron, S., Buatois, Q., Monnery-Patris, S., & Arvisenet, G. (2019). A central 
place for meat, but what about pulses? Studying French consumers’ representations of main 
dish structure, using an indirect approach. Food Research International, 123, 790‑800. 
https://doi.org/10.1016/j.foodres.2019.06.004 
Miller, Y. (2003). “Recommendations for the truncation of Body Mass Index in population data,” NSW 
Centre for Physical Activity and Health. 
Muller, L., & Lacroix, A. (2019). Environmental Labelling and Consumption Changes : A Food Choice 
Experiment. Environmental and Ressource Economics, 28. 
Pant, R., Zampori, L., European Commission, & Joint Research Centre. (2019). Suggestions for 
updating the organisation environmental footprint (OEF) method. 
http://publications.europa.eu/publication/manifestation_identifier/PUB_KJNA29682ENN 

 
 
25 
 
Pelletier, M.-C., A. Sullivan, C., Wilson, P. J., Webb, G., & Egger, G. (2016). Informing Food 
Consumption Choices : Innovations in Measuring and Labelling. Food and Nutrition Sciences, 
07(12), 1149‑1170. https://doi.org/10.4236/fns.2016.712108 
Rayner, M., Scarborough, P., & Stockley, T. (2009). The UK Ofcom Nutrient Profiling Model – defining 
‘healthy’ and ‘unhealthy’ food and drinks for TV advertising to children. 
Http://Www.Dph.Ox.Ac.Uk/Bhfhprg/Publicationsandreports/ Acad-
Publications/Bhfhprgpublished/Nutrientprofilemodel. 
Saghaei, M. (2004). Random allocation software for parallel group randomized trials. BMC Medical 
Research Methodology, 4(1), 26. https://doi.org/10.1186/1471-2288-4-26 
Scarborough, P., Appleby, P. N., Mizdrak, A., Briggs, A. D. M., Travis, R. C., Bradbury, K. E., & Key, T. J. 
(2014). Dietary greenhouse gas emissions of meat-eaters, fish-eaters, vegetarians and vegans 
in the UK. Climatic Change, 125(2), 179‑192. https://doi.org/10.1007/s10584-014-1169-1 
Schnack, A., Wright, M. J., & Holdershaw, J. L. (2019). Immersive virtual reality technology in a three-
dimensional virtual simulated store : Investigating telepresence and usability. Food Research 
International, 117, 40‑49. https://doi.org/10.1016/j.foodres.2018.01.028 
Schuldt, J. P. (2013). Does Green Mean Healthy? Nutrition Label Color Affects Perceptions of 
Healthfulness. Health Communication, 28(8), 814‑821. 
https://doi.org/10.1080/10410236.2012.725270 
Steptoe, A., Pollard, T. M., & Wardle, J. (1995). Development of a Measure of the Motives Underlying 
the Selection of Food : The « Food Choice Questionnaire ». Appetite, 25, 267‑284. 
Truelove, H. B., & Parks, C. (2012). Perceptions of behaviors that cause and mitigate global warming 
and intentions to perform these behaviors. Journal of Environmental Psychology, 32(3), 
246‑259. https://doi.org/10.1016/j.jenvp.2012.04.002 
Vanclay, J. K., Shortiss, J., Aulsebrook, S., Gillespie, A. M., Howell, B. C., Johanni, R., Maher, M. J., 
Mitchell, K. M., Stewart, M. D., & Yates, J. (2011). Customer Response to Carbon Labelling of 

 
 
26 
 
Groceries. Journal of Consumer Policy, 34(1), 153‑160. https://doi.org/10.1007/s10603-010-
9140-7 
Verain, M. C. D., Onwezen, M. C., Sijtsema, S. J., & Dagevos, H. (2016). The added value of 
sustainability motivations in understanding sustainable food choices. Applied Studies in 
Agribusiness and Commerce, 10(2‑3), 67‑76. https://doi.org/10.19041/apstract/2016/2-3/8 
Verain, M. C. D., Snoek, H. M., Onwezen, M. C., Reinders, M. J., & Bouwman, E. P. (2021). Sustainable 
food choice motives : The development and cross-country validation of the Sustainable Food 
Choice Questionnaire (SUS-FCQ). Food Quality and Preference, 104267. 
https://doi.org/10.1016/j.foodqual.2021.104267 
Vlaeminck, P., Jiang, T., & Vranken, L. (2014). Food labeling and eco-friendly consumption : 
Experimental evidence from a Belgian supermarket. Ecological Economics, 108, 180‑190. 
https://doi.org/10.1016/j.ecolecon.2014.10.019 
Vyth, E. L., Steenhuis, I. H., Brandt, H. E., Roodenburg, A. J., Brug, J., & Seidell, J. C. (2012). 
Methodological quality of front-of-pack labeling studies : A review plus identification of 
research challenges. Nutrition Reviews, 70(12), 709‑720. https://doi.org/10.1111/j.1753-
4887.2012.00535.x 
Willett, W., Rockström, J., Loken, B., Springmann, M., Lang, T., Vermeulen, S., Garnett, T., Tilman, D., 
DeClerck, F., Wood, A., Jonell, M., Clark, M., Gordon, L. J., Fanzo, J., Hawkes, C., Zurayk, R., 
Rivera, J. A., De Vries, W., Majele Sibanda, L., … Murray, C. J. L. (2019). Food in the 
Anthropocene : The EAT-Lancet Commission on healthy diets from sustainable food systems. 
Lancet (London, England), 393(10170), 447‑492. https://doi.org/10.1016/S0140-
6736(18)31788-4 
Witmer, B. G., Jerome, C. J., & Singer, M. J. (2005). The Factor Structure of the Presence 
Questionnaire. Presence: Teleoperators and Virtual Environments, 14(3), 298‑312. 
https://doi.org/10.1162/105474605323384654 

 
 
27 
 
Zhou, S., Wang, H., Li, S., Chen, Y., & Wu, J. (2019). Carbon labels and “horizontal location effect” : 
Can carbon labels increase the choice of green product? Global Ecology and Conservation, 
18, e00609. https://doi.org/10.1016/j.gecco.2019.e00609