Comparing nutritional, economic, and environmental performances of diets according to their levels of greenhouse gas emissions

Abstract

In response to climate change, reduction of GHGEs (greenhouse gas emissions) from food systems is required. Shifts of agricultural practices and dietary patterns could reduce GHGEs. We aimed to characterize observed diets with different levels of GHGEs and compare their nutritional, economic, and environmental performances. Food consumptions of 34,193 French adults participating in the NutriNet-Santé Cohort were assessed using a food frequency questionnaire. Nutritional, environmental, and economic indicators were computed for each individual diet. Adjusted means of food group intakes, contribution of food groups to dietary GHGEs, nutritional, environmental, and economic indicators were compared between weighted quintiles of GHGEs. Diets with high GHGEs (ranging from 2318 to 4099 kgCO2eq/year) contained more animal-based food and provided more calories. Few differences were found for unhealthy food (alcohol or sweet/fatty food) consumption across the categories of dietary GHGEs. Diets with low GHGEs were characterized by a high nutritional quality. Primary energy consumption and land occupation increased with GHGEs (from Q1: 3978 MJ/year (95%CI = 3958–3997) to Q5: 8980 MJ/year (95%CI = 8924–9036)) and (from Q1: 1693 m²/year (95%CI = 1683–1702) to Q5: 7188 m²/year (95%CI = 7139–7238)), respectively. Finally, participants with lower GHGE related-diets were the highest organic food consumers. After adjustment for sex, age, and energy intake, monetary diet cost increased with GHGEs (from Q1: 6.89€/year (95%CI = 6.84–6.93) to Q5: 7.68€/year (95%CI = 7.62–7.74)). Based on large observational cohort, this study provides new insights concerning the potential of current healthy and emergent diets with low monetary cost and good nutritional quality to promote climate mitigation. However, the question of a large acceptability remains.

Full text

1 23
Climatic Change
An Interdisciplinary, International
Journal Devoted to the Description,
Causes and Implications of Climatic
Change
ISSN 0165-0009
Climatic Change
DOI 10.1007/s10584-018-2195-1
Comparing nutritional, economic, and
environmental performances of diets
according to their levels of greenhouse gas
emissions
Louise Seconda, Julia Baudry, Benjamin
Allès, Christine Boizot-Szantai, Louis-
Georges Soler, Pilar Galan, Serge
Hercberg, et al.

1 23
Your article is protected by copyright and
all rights are held exclusively by Springer
Science+Business Media B.V., part of Springer
Nature. This e-offprint is for personal use only
and shall not be self-archived in electronic
repositories. If you wish to self-archive your
article, please use the accepted manuscript
version for posting on your own website. You
may further deposit the accepted manuscript
version in any repository, provided it is only
made publicly available 12 months after
official publication or later and provided
acknowledgement is given to the original
source of publication and a link is inserted
to the published article on Springer's
website. The link must be accompanied by
the following text: "The final publication is
available at link.springer.com”.

Comparing nutritional, economic, and environmental
performances of diets according to their levels
of greenhouse gas emissions
Louise Seconda
1,2
&Julia Baudry
1
&Benjamin Allès
1
&
Christine Boizot-Szantai
3
&Louis-Georges Soler
3
&
Pilar Galan
1
&Serge Hercberg
1,4
&Brigitte Langevin
5
&
Denis Lairon
6
&Philippe Pointereau
5
&
Emmanuelle Kesse-Guyot
1
Received: 25 July 2017 / Accepted: 28 March 2018
#Springer Science+Business Media B.V., part of Springer Nature 2018
Abstract In response to climate change, reduction of GHGEs (greenhouse gas emissions)
from food systems is required. Shifts of agricultural practices and dietary patterns could reduce
GHGEs. We aimed to characterize observed diets with different levels of GHGEs and compare
their nutritional, economic, and environmental performances. Food consumptions of 34,193
French adults participating in the NutriNet-Santé Cohort were assessed using a food frequency
questionnaire. Nutritional, environmental, and economic indicators were computed for each
individual diet. Adjusted means of food group intakes, contribution of food groups to dietary
GHGEs, nutritional, environmental, and economic indicators were compared between weight-
ed quintiles of GHGEs. Diets with high GHGEs (ranging from 2318 to 4099 kgCO
2eq
/year)
contained more animal-based food and provided more calories. Few differences were found
Climatic Change
https://doi.org/10.1007/s10584-018-2195-1
Electronic supplementary material The online version of this article (https://doi.org/10.1007/s10584-018-
2195-1) contains supplementary material, which is available to authorized users.
*Louise Seconda
l.seconda@eren.smbh.univ-paris13.fr
Julia Baudry
j.baudry@eren.smbh.univ-paris13.fr
Benjamin Allès
b.alles@eren.smbh.univ-paris13.fr
Christine Boizot-Szantai
Christine.Boizot@ivry.inra.fr
Louis-Georges Soler
lgsoler@ivry.inra.fr
Pilar Galan
p.galan@uren.smbh.univ-paris13.fr
Author's personal copy

for unhealthy food (alcohol or sweet/fatty food) consumption across the categories of dietary
GHGEs. Diets with low GHGEs were characterized by a high nutritional quality. Primary
energy consumption and land occupation increased with GHGEs (from Q1: 3978 MJ/year
(95%CI = 3958–3997) to Q5: 8980 MJ/year (95%CI = 8924–9036)) and (from Q1: 1693 m
2
/
year (95%CI = 1683–1702) to Q5: 7188 m
2
/year (95%CI = 7139–7238)), respectively. Finally,
participants with lower GHGE related-diets were the highest organic food consumers. After
adjustment for sex, age, and energy intake, monetary diet cost increased with GHGEs (from
Q1: 6.89€/year (95%CI = 6.84–6.93) to Q5: 7.68€/year (95%CI = 7.62–7.74)). Based on large
observational cohort, this study provides new insights concerning the potential of current
healthy and emergent diets with low monetary cost and good nutritional quality to promote
climate mitigation. However, the question of a large acceptability remains.
Keywords Climate change .Dietary pattern .Greenhouse gas emissions .Organic food
Abbreviations
AMAPs Associations supporting small farming
ANCOVA Analysis of covariance
BMI Body Mass Index
CI Confidence intervals
CU Consumption unit
CH
4
Methane
CO
2
Carbon dioxide
GHG Greenhouse gas
Serge Hercberg
s.hercberg@uren.smbh.univ-paris13.fr
Brigitte Langevin
b.langevin@laposte.net
Denis Lairon
denis.lairon@orange.fr
Philippe Pointereau
philippe.pointereau@solagro.asso.fr
Emmanuelle Kesse-Guyot
e.kesse@eren.smbh.univ-paris13.fr
1
Equipe de Recherche en Epidémiologie Nutritionnelle (EREN), Inserm (U1153), Inra (U1125),
Centre d’Epidémiologie et Statistiques Paris Cité, Cnam, COMUE Sorbonne-Paris-Cité, Université
Paris 13, 74 rue Marcel Cachin, 93017 Bobigny, France
2
Agence de l’Environnement et de la maîtrise de l’Energie, 20, Avenue du Grésillé, 49004 Angers
Cedex 01, France
3
INRA Aliss UR 1303, 94200 Ivry sur Seine, France
4
Département de Santé Publique, Hôpital Avicenne, 93017 Bobigny, France
5
Solagro, 75, Voie TOEC, 31000 Toulouse, France
6
Nutrition, Obésité et Risque Thrombotique (NORT), INSERM, UMR S 1062, INRA 1260, Aix
Marseille Université, 13005 Marseille, France
Climatic Change
Author's personal copy

GHGEs Greenhouse gas emissions
INSEE National Institute of Statistical and Economic Studies
IPAQ International Physical Activity Questionnaires
IPCC Intergovernmental Panel on Climate Change
LCA Life Cycle Assessment
mPNNS-GS modified Programme National Nutrition Santé Guidelines Score
N
2
O Nitrous oxide
Org-FFQ Organic Food Frequency Questionnaire
PANDiet Diet Quality Index Based on the Probability of Adequate Nutrient Intake
Q Quintile
UK United Kingdom
WHO World Health Organization
1 Introduction
The last IPCC (Intergovernmental Panel on Climate Change) report concluded with 95%
certainty that human activities have been the primary cause of global warming since the middle
of the twentieth century (Fifth Assessment Report Climate Change 2013). In response to the
temperature rise and its harmful consequences, deep reduction of greenhouse gas emissions
(GHGEs) is required, notably in food systems which account for 19–29% of global anthro-
pogenic GHGEs (Vermeulen et al. 2012). Three gases contribute for almost 91.5% to the
agricultural total emissions, nitrous oxide (N
2
O), methane (CH
4
), and carbon dioxide (CO
2
).
CH
4
and N
2
O emissions are generally limited to the agricultural phase, while CO
2
emissions
are spread out amongst the whole food chain (Camanzi et al. 2017). At the agricultural stage,
some measures and innovative processes (e.g. enhancing carbon removals, optimizing nutrient
use) have emerged to reduce the GHGEs (Garnett 2011). However, several studies concluded
that agricultural technical options are not sufficient, and important shifts in dietary patterns by
a large proportion of the world population will be required to achieve the necessary climate
mitigation (Garnett 2011; Bryngelsson et al. 2016; Bryngelsson et al. 2017).
In particular, the modelled French scenario Afterres2050 plans a mandatory 50% cut in
agricultural GHGEs by 2050, by changing the French diet, implementing agro ecological
practices, and reducing energy consumption (Solagro_afterres2050-v2-web.pdf n.d.). This
scenario applies the road map of the European Commission for a low carbon economy with
the objective of −42 to −49% GHGEs (without CO
2
)in2050comparedto1990
(Communication de la commission au parlement européen n.d.).
Low GHGE diets have already been estimated using modelling approaches or depicted
using observational data (Communication de la commission au parlement européen n.d.;
Perignon et al. 2017;Aleksandrowiczetal.2016).
However, due to the small size of the samples restraining the variety of dietary patterns, or
the constraints included in the models when performing optimization or simulation studies,
issues as regards the reality and the acceptability of such diets in large populations remain
(Perignon et al. 2017). Moreover, the consistency between eco-friendly dietary practices and
nutritional requirements were scarcely evaluated. A high nutritional quality diet has previously
been associated with greater environmental impact in some studies (Perignon et al. 2017;
Masset et al. 2014; van Dooren et al. 2014; van Dooren et al. 2015). Conversely, a recent meta-
analysis concluded that a shift from Western to sustainable dietary patterns generally provided
Climatic Change
Author's personal copy

benefits for the environment and health (Aleksandrowicz et al. 2016). Finally, published
findings about the associations between monetary costs and low GHGE diets are contradictory
(Perignon et al. 2017; Donati et al. 2016).
As primary production is responsible for a large proportion of carbon footprints (Hyland
et al. 2017), and given the high diversity of the current farming systems, the model of food
production (conventional or organic) seems an important factor to take into account, for the
assessment and comparison of the environmental performances of various diets (Tuomisto
et al. 2012; Reganold and Wachter 2016;Meieretal.2015). Also, most existing studies
considered a single environmental indicator (Auestad and Fulgoni 2015) while the assessment
of several environmental indicators can contribute to consolidate the validity of results for
different environmental dimensions (Auestad and Fulgoni 2015).
In that context, we aimed to depict the diets observed in a large sample of adults with
different levels of GHGEs in terms of food composition while accounting for the mode of food
production. We also compared their nutritional, environmental, and economic characteristics.
2 Materials and methods
2.1 Study design and participants
The NutriNet-Santé Study is an ongoing web-based prospective observational cohort of
French adult volunteers, launched in May 2009 with a scheduled follow-up of 10 years. The
design has been comprehensively described elsewhere (Hercberg et al. 2010). The inclusion in
the cohort is based on a set of self-administered web-based questionnaires on dietary intake,
health and anthropometric, sociodemographic, and lifestyle characteristics. Included volun-
teers are regularly invited to update their data and to fill in optional complementary
questionnaires.
2.2 Standard protocol approvals, registrations, and participant consents
The study was conducted observing the guidelines from the Declaration of Helsinki, and all
protocols were approved by the Institutional Review Board of the French Institute for Health
and Medical Research (IRB INSERM no. 0000388FWA00005831) and the Commission
Nationale de l’Informatique et des Libertés (CNIL no. 908450 and no. 909216). Participant
informed consents were signed by all volunteers with an electronic signature. The NutriNet-
Santé Study is registered in ClinicalTrials.gov (NCT03335644).
2.3 Data collection
2.3.1 Assessment and treatment of dietary data
Usual dietary food intake was assessed using an organic semi-quantitative food frequency
questionnaire (Org-FFQ) (Baudry et al. 2015), based on a previously validated questionnaire
(Kesse-Guyot et al. 2010). Participants had to report their frequency (yearly, monthly, weekly,
or daily units) of consumption over the past year for 264 items (food and beverage). Standard
portion sizes were described as typical household measurements or using color-standardized
and validated photographs. Food intakes in grams per day were obtained by multiplying the
Climatic Change
Author's personal copy

portion size and frequency. Food items were grouped into 15 food groups for descriptive
purpose. Nutrient intakes were estimated using nutritional values from the published NutriNet-
Santé food composition table (Nutrinet-Santé 2013).
Moreover, to assess the level of organic food consumption, participants were asked for each
item except those that do not exist in organic form how often they came from organic source
(identified through label certification) through a 5-point ordinal scale ranging from Bnever^to
Balways^. Aweight of 0, 0.25, 0.5, 0.75, and 1 was applied to the five respective frequencies to
obtain the share of organic food in the diet for each item and overall (without water). The over-
reporters and under-reporters were identified by a ratio of energy intake to energy requirement
(estimated with the Schofield equations (Schofield 1984) according to sex, age, weight, and
height) below or above the cut-offs (0.35 and 1.93).
Finally, three dietary indicators were also computed to assess the overall nutritional quality
of the diet: (1) the energy density of the diet, (2) the nutrient-based PANDiet score (Verger
et al. 2012) measures the probability of adequate nutrient intake based on current nutrient
reference values, and (3) the food-based mPNNS-GS (Chauliac et al. 2009) (modified
Programme National Nutrition Santé Guideline-Score) assesses adherence to the French
official nutrition guidelines. A detailed description of these scores is proposed in the
Supplemental Material 1.
2.3.2 Assessment of the environmental impacts of the diet
A database gathering the environmental indicators associated with the Org-FFQ items ac-
counting for the method of production (conventional or organic) was developed. To do that, we
used DIALECTE, a comprehensive tool developed by Solagro (Toulouse, France) (Pointereau
et al. 2012) which aims to describe French farming systems and to assess the environmental
performance of farms, which contained environmental impacts related to 60 raw products. The
selected indicators were the GHGEs (in kg CO
2eq
/kg), the primary energy consumption (in
MJ/kg), and land occupation (in m
2
/kg). As detailed in the first part of the Supplemental
Material 2, the perimeter of DIALECTE environmental impact assessment included the
upstream processes such as the production of inputs or energy provision, while conditioning,
transport, processing, storage, and recycling were excluded due to missing information for the
organic sector. The database was completed with published literature data, to obtain the
environmental impact in organic and conventional of 92 raw agricultural products. As our
objective was to assess the environmental impacts of diets, it was necessary to conduct a set of
conversions (described in the Supplemental Material 2) from the environmental impacts
assessed for raw agricultural products in order to estimate environmental impacts for food
items of the food frequency questionnaire. Briefly, the items were decomposed into ingredi-
ents. The environmental impacts of ingredients (organic and conventional) were assessed from
raw products by applying an economic allocation (accounting for co-products) and cooking
and edibility coefficients (Yield and Retention: USDA ARS n.d.; Bognár 2002). The individ-
ual environmental impacts of diet were estimated by multiplying the environmental impacts by
food quantity consumed (g/day) accounting for the method of food production.
2.3.3 Assessment of the monetary cost of diet
Volunteers were also invited to fill in a complementary questionnaire focusing on attitudes and
motivations as regards food choices inquiring the place of purchase.
Climatic Change
Author's personal copy

A database gathering the prices of the 264 items of the Org-FFQ accounting for the place of
purchase and the food production mode (organic vs. conventional) was developed (Seconda
et al. 2017). The 2012 KANTAR database was used to collect the prices from supermarkets
and specialized stores (Attitudes - Français - Kantar Worldpanel n.d.). Additionally, members
of the Bioconsom’acteurs association collected 1100 additional prices in autumn 2014 and 862
prices in spring 2015 over nine French metropolitan regions, in short supply chains (local
markets or associations supporting small farming (AMAPs)). The individual monetary cost of
diet was calculated by multiplying price (€/g) by food quantity consumed (g/day) accounting
for the place of purchase and the method of food production.
2.3.4 Covariates
Sociodemographic and lifestyle data were collected using the inclusion set and yearly update
questionnaires. Data used was the closest to the date of the completion of the Org-FFQ.
Sociodemographic data included sex, age (over 18), education (< high school diploma, high
school diploma, and post-secondary graduate), place and area of residence (rural community,
urban units with a population smaller than 20,000 inhabitants; between 20,000 and 200,000
inhabitants; and higher than 200,000 inhabitants), and monthly income per household unit (<
1200 euros, between 1200 and 1800 euros, between 1800 and 2700 euros, and > 2700 euros
per household unit) obtained using the income by month in the household and the composition
of the household.
Lifestyle variables were smoking status (former, occasional, current, or non-smoker), level
of physical activity (as measured by the IPAQ (International Physical Activity questionnaires
(Craig et al. 2003;Hallal2004; Hagströmer et al. 2006)), weight and height assessed by a
health operator, medical doctor, or from self-measurement guided by standardized procedures.
Body Mass Index (BMI) (kg/m
2
) was computed.
2.3.5 Statistical analysis
A total of 37,685 adult participants completed the Org-FFQ. We excluded participants who
were under/over-reporters (n= 2109), as well as those with missing covariates (n= 391), living
abroad (n= 743) or in overseas territories (n= 249), leading to a sample of 34,193 volunteers.
Data concerning monetary cost of diet were collected from a subsample of volunteers (N=
29,210) who completed the questionnaire on place of purchase.
The sample was weighted, in order to improve representativeness of the population
identified, using the SAS Calmar macro, developed by the National Statistics and Economic
Studies Institute (INSEE) (La macro SAS CALMAR | Insee n.d.). The weighting was made by
gender, taking into account the age, educational level, area of residence, and whether or not the
household included any children. We used the 2009 national Census data as reference. Then,
participants were divided into weighted quintiles according to diet-related GHGEs.
Sociodemographic and lifestyle characteristics were compared across quintiles: means with
standard deviation or percentages were presented and overall differences were tested using
Mantel-Haenszel trend χ
2
or linear contrast tests.
The adjusted means for sex, age, and energy intake with the residual method (Willett and
Stampfer 1986) and 95%CI of food group consumption by diet according to GHGEs were
calculated. The contribution of each food group to the total dietary GHGEs were also assessed
across quintiles.
Climatic Change
Author's personal copy

Finally, analyses of covariance (ANCOVA) models according to the observed margins (this
option changes the coefficients to be proportional to those observed in the input data set) were
performed to identify the associations between GHGEs and dietary, health, and other envi-
ronmental and economic indicators. When appropriate, a log-transformation was applied to
improve the normality of continuous variables (namely for expenditure, primary energy
consumption, and land occupation). Post hoc differences across categories were evaluated
after adjustment for multiple testing using the Dunnett’s correction. Pvalues for linear trend
across quintiles were estimated using linear contrast tests. The type I error was set at 5% for all
statistical tests.
SAS 9.4 software (SAS Institute Inc., Cary, NC, USA) was used to perform all analyses.
3Results
3.1 Participant characteristics
The sample was composed of 34,193 volunteers with a mean age of 53.3 (SD = 14.0), and
75.5% women (before weighting).
Table 1shows the sociodemographic and lifestyle characteristics of participants across
quintiles of diet-related GHGEs. Participants in Q1 were more often women, younger, as well
as more often large town inhabitants, physically active, never smokers, and post-secondary
graduate. The level of income per household unit did not seem associated with GHGEs. Lower
dietary GHGEs were associated with a greater part of organic food in the diet.
3.2 Food intakes according to quintiles of dietary GHGEs
Concerning the dietary characteristics, the prominent gap across quintiles of diet-related
GHGEs concerned the intake of red meat (Table 2). Red meat intake was positively associated
with GHGEs. A similar trend was observed to a lesser extent for white meat, mixed dishes, and
dairy products. The average consumption of sweet and fatty products was not significantly
different across quintiles of GHGEs. Participants exhibiting the lower GHGEs consumed more
starches, whole grains, fruits, vegetables, and soya products.
The relative contributions of food groups to total GHGEs showed strong disparities across
quintiles (Fig. 1). The contribution of red meat to dietary GHGEs increased across quintiles,
while opposite trends were observed for fruit and vegetables and starchy foods.
3.3 Nutritional characteristic of participants
Tab le 3shows the nutritional characteristics of participants according to the diet-related
GHGEs. First, caloric intake linearly increased with the GHGEs of the diet as well as the
energy density. Participants in Q4 and Q5 showed the lowest dietary quality (mPNNS-GS and
PANDiet). However, for the first three quintiles, the differences in the nutritional quality
of diet are less obvious. Indeed, the participants in Q3 exhibited the better compliance
with the French nutritional guidelines, while Q1 participants presented the highest
PANDiet score, reflecting a good adequacy match to nutrient guidelines. However, the
highest PANDiet score in Q1 was mostly explained by higher moderation sub-score than
high adequacy sub-score which was higher in Q3.
Climatic Change
Author's personal copy

Tab le 1 Sociodemographic and lifestyle characteristics by level of greenhouse gas emissions (GHGEs) from diets of French adults (weighted sample), NutriNet-Santé Study, 2014
(N=34,193)
a
Tot al Q1 (347–816
kgCO
2eq
/year)
Q2 (891–1190
kgCO
2eq
/year)
Q3 (1229–1589
kgCO
2eq
/year)
Q4 (1639–2158
kgCO
2eq
/year)
Q5 (2318–4099
kgCO
2eq
/year)
Ptrend
b
Wei ghted d is tribu tion N=34,193 N=7837 N=7919 N=7238 N=6190 N=5009
Male (%) 47.64 34.23 36.08 47.75 54.26 65.83 < 0.0001
Age 48.14 (16.25) 45.43 (16.03) 48.39 (15.27) 49.91 (15.06) 48.25 (17.05) 48.74 (18.15) < 0.0001
Location (%) <0.0001
Rural community 25.05 22.33 25.45 27.17 23.97 26.34
Urban unit with a population
of < 20,000 inhabitants
15.81 14.57 13.34 16.53 16.21 18.42
Urban unit with a population
of 20,000–200,000 inhabitants
16.85 16.66 19.90 12.86 19.08 15.76
Urban unit with a population
of > 200,000 inhabitants
42.29 46.45 41.31 43.44 40.75 39.48
Physical activity (%) <0.0001
Low (< 30 min/day) 21.08 20.57 22.88 16.96 22.81 22.23
Medium (30–60 min/day) 30.63 30.19 32.78 34.47 29.00 26.71
High (> 60 min/day) 33.95 38.82 31.43 35.27 30.47 33.73
Missing data
c
14.34 10.43 12.91 13.30 17.72 17.33
Tob acc o st atu s (%) <0.0001
Current smoker 9.49 10.41 6.00 11.02 9.46 10.53
Occasional smoker 3.2 3.06 3.30 3.02 3.13 3.47
Former smoker 39.98 33.42 38.88 41.65 40.95 44.97
Never smoker 47.34 53.11 51.82 44.31 46.45 41.04
Graduation (%) <0.0001
<High-schooldiploma 24.89 28.02 29.04 25.20 22.97 19.25
High-school diploma 15.55 18.80 16.06 13.53 13.35 16.01
Post-secondary graduate 59.56 53.18 54.90 61.26 63.69 64.75
Monthly income per
household unit (%)
0.75
Refuse to declare 7.75 10.06 7.69 6.22 9.54 5.23
<1200euros 24.44 23.89 23.38 24.36 24.95 25.60
Climatic Change
Author's personal copy

Tab le 1 (continued)
Tot al Q1 ( 347 –816
kgCO
2eq
/year)
Q2 (891–1190
kgCO
2eq
/year)
Q3 (1229–1589
kgCO
2eq
/year)
Q4 (1639–2158
kgCO
2eq
/year)
Q5 (2318–4099
kgCO
2eq
/year)
Ptrend
b
Wei ghted d is tribu tion N=34,193 N=7837 N=7919 N=7238 N=6190 N=5009
1200–1800 euros 28.66 28.29 27.18 31.37 26.96 29.48
1800–2700 euros 24.18 24.42 25.30 23.84 23.64 23.73
>2700euros 14.97 13.34 16.45 14.21 14.91 15.96
Organic food ratio
d
0.26 (0.27) 0.42 (0.32) 0.28 (0.25) 0.21 (0.22) 0.19 (0.22) 0.19 (0.25) <0.0001
Qweighted quintile
a
Valu es p r es e nt ed ar e pe rce nt a ge s , me ans ( SD )
b
Pvalues referred to linear contrast test or Mantel-Haenszel trend χ
2
test as appropriate
c
As some questions were optional, data are missing
d
Organic food ratio was obtained by dividing the total organic food intake (g/d) by the total foodintakeexcludingwater(g/d)
Climatic Change
Author's personal copy

Tab le 2 Food group intakes by level of greenhouse gas emissions (GHGEs) from diets of French adults (weighted sample), NutriNet-Santé Study, 2014 (N=34,193)
a
Q1 (347–816
kgCO
2eq
/year)
Q2 (891–1190
kgCO
2eq
/year)
Q3 (1229–1589
kgCO
2eq
/year)
Q4 (1639–2158
kgCO
2eq
/year)
Q5 (2318–4099
kgCO
2eq
/year)
Pfor trend
b
Wei ghted d is tribu tion N=7837 N=7919 N=7238 N=6190 N=5009
Fish
c
(g/day) 36.6 (35.5–37.7) 41.8 (40.7–42.9) 52.6 (51.6–53.6) 48.1 (47–49.2) 48.5 (47.3–49.7) < 0.0001
Red meat
c
(g/day) 9.9 (9.0–10.7) 25.9 (25.1–26.7) 42.2 (41.4–43) 63 (62.2–63.8) 122.3 (121.4–123.3) < 0.0001
White meat
c
(g/day) 35.3 (33.7–36.9) 59.6 (58–61.1) 82.2 (80.7–83.7) 96.5 (94.9–98) 135.1 (133.3–136.8) < 0.0001
Eggs
c
(g/day) 11.0 ( 10.5–11. 5) 13.5 (13–13.9) 15.0 (14.6–15.5) 10.4 (10–10.9) 9.7 (9.1–10.2) <0.0001
Dairy products
c
(g/day) 141 (136–147) 225 (220–230) 257 (252–262) 251 (246–256) 261 (255–267) <0.0001
Cheese
c
(g/day) 31.3 (30.2–32.4) 40.0 (38.9–41) 46.2 (45.2–47.1) 50.7 (49.6–51.7) 54.0 (52.8–55.2) < 0.0001
Mixed dishes
c
(g/day) 37.3 (33.8–40.8) 38.2 (34.8–41.6) 32.1 (28.8–35.4) 30.6 (27.2–34) 55.2 (51.3–59.1) 0.0006
Dressings
c
(g/day) 22.1 (21.6–22.6) 18.2 (17.7–18.6) 16.9 (16.5–17.4) 15.1 (14.7–15.6) 14.0 (13.5–14.5) < 0.0001
Fat
c
(g/day) 33.1 (32.7–33.6) 31.9 (31.4–32.3) 29.8 (29.4–30.2) 27.6 (27.2–28) 22.5 (22–23) <0.0001
Sweet and fatty products
c
(g/day) 71.0 (69.7–72.4) 79.7 (78.4–81.1) 73.0 (71.7–74.3) 73.5 (72.2–74.9) 74.0 (72.4–75.5) 0.95
Starches
c
(g/day) 203 (200–206) 189 (186–191) 185 (183–188) 202 (200–205) 166 (162–169) <0.0001
Whole grain
c
(g/day) 94.8 (92.8–96.8) 72.6 (70.7–74.5) 57.2 (55.3–59) 32.3 (30.4–34.3) 14.8 (12.5–17.0) < 0.0001
Fruits and vegetables
c
(g/day) 905 (895–915) 775 (765–784) 726 (716–735) 676 (666–685) 524 (512–535) < 0.0001
Soya
d
(g/day) 89.1 (89.5–91.8) 32.1 (29.5–34.7) 25.6 (23.0–28.2) 10.0 (7.4–12.6) 7.3 (4.7–10.0) <0.0001
Alcoholic beverages
c
(ml/day) 119 (116–123) 112 (108–115) 11 0 (106–113) 105 (101–109) 108 (104–113) <0.0001
All food groups (except zero-calorie beverages) were included. Fish: fish and seafood; Red Meat: beef, veal, and lamb; White meat: pork, poultry, sausage, and charcuterie; Dairy
products: milk, yogurt, and other milky-based desserts; and Fat: oil, butter, and other fat
Qweighted quintile
a
Valu es pr es e nt e d ar e adj us t ed m e an s (95 % CI ) c om p ut ed ac co r di ng to o bse rv e m ar gin s
b
Valu es b a se d o n li nea r con tr a st t e st s
c
Adjustment for sex, age and energy intake with the residual method
d
Adjustment for sex and age
Climatic Change
Author's personal copy

Finally, the mean BMI of participants increased along with the level of diet
GHGEs (Q1 to Q5).
3.4 Environmental and economic characteristics of diet
Table 4shows the data for environmental and economic indicators according to the level of
GHGEs. Two adjusted models are presented with and without adjustment for energy intake.
Land occupation, primary energy consumption to produce foods, and diet purchase increased
with the level of GHGEs from the diet.
All these associations remained significant after the adjustment for energy intake, although
the magnitudes of the differences were reduced.
4 Discussion
The present study showed, from a large adult cohort, that diet-related with low GHGEs are
characterized by a low intake of food from animal origin and provided fewer calories. They
were also characterized by a high nutritional quality and a higher proportion of organic food.
No or few differences in consumption of unhealthy food (alcohol or sweet and fatty products)
across categories of dietary GHGEs were observed. Concerning environmental indicators, a
diet with low GHGEs was produced with a minimum primary energy consumption and land
occupation.
Recent studies, based on modelled or observed diets, have reported the major contribution
of animal products to diet-related GHGEs (Aston et al. 2012; Scarborough et al. 2014;Soret
et al. 2014; Temme et al. 2015). At the individual level, a decrease in animal product
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Contribu!on to greenhouse gases emissions(%)
Fat
Alcoholic beverages
Soya
Mixed Dishes
Non-alcoholic beverages
Whole grain
Starches
Sweet and/or fa!y products
Dairy products
Eggs
Cheese
White meat
Red meat
Fish
Fruit and vegetable
Q1 Q5
Q3
Q2 Q4
Fig. 1 Contribution of food groups to the greenhouse gas emissions of individual diets by level of greenhouse
gas emissions (GHGEs) in French adults (weighted sample), NutriNet-Santé Study, 2014 (N= 34,193). Q
weighted quintile. Fish: fish and seafood; Red meat: beef, veal, and lamb; White meat: pork, poultry, processed
meat; Dairy products: milk, yogurt, and other milky-based desserts; and Fat: oil, butter, and other fats
Climatic Change
Author's personal copy

Tab le 3 Diet quality scores and nutritional characteristics by level of greenhouse gas emissions (GHGEs) of French adults (weighted sample), NutriNet-Santé Study, 2014 (N=
34,193)
a
Q1 (347–816
kgCO
2eq
/year)
Q2 (891–1190
kgCO
2eq
/year)
Q3 (1229–1589
kgCO
2eq
/year)
Q4 (1639–2158
kgCO
2eq
/year)
Q5 (2318–4099
kgCO
2eq
/year)
Pfor trend
b
Wei ghted d is tribu tion N=7837 N=7919 N=7238 N=6190 N=5009
Energy intake
c
(kcal/day) 1634 (1620–1649) 1843 (1829–1857) 1998 (1985–2012) 2280 (2266–2293) 2781 (2767–2795) < 0.0001
mPNNS-GS
d
(/13.5) 8.39 (8.35–8.43) 8.48 (8.45–8.52) 8.64 (8.61–8.68) 8.28 (8.24–8.32) 7.94 (7.89–7.98) < 0.0001
PAN Di et
d
(/100) 68.62 (68.45–68.78) 68.04 (67.88–68.2) 66.63 (66.48–66.79) 63.80 (63.64–63.96) 60.70 (60.52–60.88) < 0.0001
-Adequacysub-score
d, e
(/100) 70.14 (69.88–70.41) 73.99 (73.74–74.25) 75.92 (75.67–76.17) 74.22 (73.96–74.48) 71.31 (71.01–71.6) < 0.0001
-Moderationsub-score
d, e
(/100) 67.09 (66.89–67.3) 62.09 (61.89–62.29) 57.35 (57.15–57.54) 53.37 (53.17–53.57) 50.10 (49.87–50.33) < 0.0001
Energy density
c
(kcal/100 g) 72.46 (71.87–73.05) 79.08 (78.49–79.66) 80.52 (79.95–81.09) 86.79 (86.21–87.37) 90.51 (89.93–91.09) < 0.0001
BMI
d
(kg/m
2
)23.52 (23.38–23.66) 24.74 (24.6–24.87) 26.01 (25.87–26.14) 25.69 (25.55–25.83) 26.59 (26.43–26.75) < 0.0001
Qweighted quintile, mPNNS-GS modified Programme National Nutrition Santé Guideline Score, PAN D i e t Diet Quality Index based on the Probability of Adequate Nutrient Intake,
BMI Body Mass Index
a
Valu es pr es e nt e d ar e adj us t ed m e an s (95 % CI) c om p ut ed ac co r di n g to o bse rv e m ar g in s or pe rc e nt a ge s
b
Valu es b a se d o n li nea r co n tr ast t est s
c
Adjustment for sex and age
d
Adjustment for sex, age, and energy intake
e
Adequacy and moderation sub-score arethebothcomponentsofPANDiet.Adequacy sub-score is the average of the probability of adequacy for 21 items for which the usual intake
should be above a reference value, multiplied by 100. As moderation sub-score is the average of the probability of adequacy of adequacy for six items forwhichtheusualintakeshould
not exceed a reference value and penalty values, multiplied by 100
Climatic Change
Author's personal copy

Tab le 4 Environmental and economic indicators of diets depending on the level of greenhouse gas emissions (GHGEs) in French adults (weighted sample), NutriNet-Santé Study,
2014 (N=34,193)
a
Q1 (347–816
kgCO
2eq
/year)
Q2 (891–1190
kgCO
2eq
/year)
Q3 (1229–1589
kgCO
2eq
/year)
Q4 (1639–2158
kgCO
2eq
/year)
Q5 (2318–4099
kgCO
2eq
/year)
Pfor trend
b
Wei ghted d is tri bu tion N=7837 N=7919 N=7238 N=6190 N=5009
Land occupational
c
(m
2
/year) Model 1
d
1622 (1614–1631) 2659 (2645–2672) 3629 (3610–3648) 4862 (4835–4890) 7676 (7627–7725) <0.0001
Model 2
e
1693 (1683–1702) 2714 (2700–2727) 3628 (3609–3646) 4742 (4715–4769) 7188 (7139–7238) <0.0001
Primary energy
consumption
c, d
(MJ/year)
Model 1
d
3600 (3581–3619) 5113 (5086–5140) 6290 (6255–6324) 7699 (7653–7744) 10,477 (10,408–10,547) <0.0001
Model 2
e
3978 (3958–3997) 5364 (5340–5389) 6285 (6256–6314) 7259 (7222–7296) 8980 (8924–9036) <0.0001
Expenditure
c
(€/day) N=29,210 Model1
d
5.81 (5.76–5.85) 6.44 (6.39–6.49) 7.21 (7.16–7.27) 8.26 (8.19–8.33) 10.00 (9.90–10.09) <0.0001
Model 2
e
6.89 (6.84–6.93) 6.99 (6.95–7.03) 7.20 (7.16–7.25) 7.47 (7.42–7.52) 7.68 (7.62–7.74) <0.0001
Abbreviations: Q, weighted quintile
a
Valu es pr es e nt e d ar e adj us t ed m e an s ( 95 % CI) c om p ut ed ac cor di n g to o bse rv e m ar gin s
b
Valu es b ase d on l ine ar c ont ra st t e st s
c
Va r i ab l e s w e r e n o t n o r m a l l y di s t r i b u t e d ; a l o g ar i t h m t r a n s f o r m a t io n w a s u s e d t o o b t a i n t he r e s u l t s
d
Adjustments for sex and age
d
Adjustments for sex, age and energy intake
Climatic Change
Author's personal copy

consumption, especially from ruminants, remains a necessary key challenge to maintain global
temperature increase below 2 °C (Bryngelsson et al. 2016). Moreover, a lower level of animal
production does not reduce only GHGEs, but it is also of land occupation and energy saving
(Perignon et al. 2017;Hallströmetal.2015; Fazeni and Steinmüller 2011).
In our study, the average dietary energy intake increased across quintiles of diet-related
GHGEs. This finding is consistent with previous studies reporting a linear relationship
between energy intake and dietary GHGEs (Temme et al. 2015; Monsivais et al. 2015).
Based on these observational data, it appears that the lowest levels of energy intake are
observed amongst some subgroups of the population (Q1 and Q2) while others have higher
energy intakes and animal food-based dietary patterns (Q4 and Q5).
Moreover, we observed that participants with low GHGE diet consumed more plant-based
products, in line with findings from other observational studies (Soret et al. 2014;Temmeetal.
2015). However, for consumption of sweet or fatty products, no difference was detected across
quintiles. In contrast to the study of Temme et al. (Temme et al. 2015), participants with lower
GHGE diets had slightly higher intakes of alcoholic beverages. Of note, participants of our
cohort presented low consumption of unhealthy food (alcohol and sweet or fatty products)
(Andreeva et al. 2016). This may be explained by a potential desirability bias often observed in
self-reported dietary records or by specific profiles of volunteers in the cohort. Even though
alcohol and sweet or fatty food have low impacts on the environment compared to animal
products, their intakes provide low nutritionally benefits, and their consumption is thus limited
in nutritional recommendations (Estaquio et al. 2009).
It is now well-documented that the overall quality of the diet (as assessed herein with two
dietary scores) decreases with increasing GHGEs (van Dooren et al. 2014; Monsivais et al.
2015;Temmeetal.2013). In our study, the highest PANDiet score observed in low GHG
‘emitters’was mostly explained by the highest sub-score related to moderation (for fat, sugar,
salt). Besides, the Q3 exhibited the highest sub-score related to nutrient adequacy, meaning
participants generally presented the lowest probability of having nutrient deficiency. The
mPNNS-GS scores of Q1 and Q2 were lower than Q3. This result may be explained by the
low consumption of animal-based products in these groups. Indeed, the 2001 French nutrition
guidelines promotes a consumption of animal products (one or two servings of meat, fish, or
egg per day and three servings of dairy products) although vegetarians and low-meat con-
sumers can still meet nutritional needs through appropriate alternative dietary choices (Melina
et al. 2016). Concerning nutrition-health status, low GHG ‘emitters’exhibited the lowest BMI
(Q1 and Q2). A number of hypotheses may be proposed to explain such observation including
healthier dietary patterns as lower overall caloric intake and higher ratio of plant-based to
animal-based foods (Lassale et al. 2012). Other studies have identified individual health
benefits of low GHGEs or meat diets (Aston et al. 2012;Milneretal.2015; Tilman and
Clark 2014).
Interestingly, participants in the lowest GHG ‘emitter’quintiles showed the highest con-
sumption of organic food, while the available data of GHGEs in current organic food
production showed limited or doubtful benefits (Meier et al. 2015) depending on the indicators
considered. In particular, the organic animal-based products, these products presented some-
times a greater carbon footprint (mainly due to the longer cycle of production and to lower
growth rate (Nijdam et al. 2012;Kooletal.2009;Leinonenetal.2012)). However, our results
seem to show that heavy organic consumers have a less GHG-emitting diet. That can be
explained by the overall higher intake of low-GHG foods such as plant-based foods, which is a
main characteristic of the organic diet and may more than compensate for the potential
Climatic Change
Author's personal copy

additional GHGEs from some organic production. For other environmental indicators as
energy efficiency biodiversity, organic farming may present environmental benefits
(Reganold and Wachter 2016; Meier et al. 2015). Muller et al. concluded that organic
agriculture can contribute to decrease environmental impacts only if adequately high propor-
tions of legumes are produced concomitantly with significant reductions of food-competing
feed use, livestock product quantities, and food wastage (Muller et al. 2017).
Some limitations should be mentioned. Firstly, participants in the study exhibited specific
profiles, as they are volunteers in a long-term cohort focusing on nutrition and health. Participants
are likely to display healthier behaviors than in general population. This may have led to an
underestimation of unhealthy dietary patterns. However, this specific sample provided an interesting
large diversity of pro-environmental dietary behavior profiles. Secondly, the assessment of food
consumption was based on a food frequency questionnaire, which is, as other self-administered
methods, prone to measurement error and desirability bias. It is as illustrated, at least partly, by
elevated consumption of fruits or vegetables and low levels of unhealthy foods such as sweet or fatty
foods and alcohol. Thirdly, environmental database was based on farms registered in DIALECTE on
avolunteerbasisleadingtoapotentialunderrepresentation of farms which are not sensitive to
environmental issues and whom the pressures on the environment could be greater. This may have
led to an underestimation of environmental impact.However,thehighnumberoffarmsandtheuse
of the median value may have partly overcome this limitation. Only the agricultural production was
included in the Life Cycle Assessment; thus, transformation, packaging, and transport were not
taken into account. This limitation should be considered as relative since the major part of
environmental impacts generally occur from the agricultural phase (Clune et al. 2017;Weidema
and Meeusen 2000;Muñozetal.2010). Some exceptions should be noted such as the alcoholic
beverages. However, these foods generally contribute poorly to the total food consumption. Finally,
the three environmental indicators assessed do not sufficiently reflect all environmental pressures.
Other indicators such as eutrophication or biodiversity are important. However, the used of three
indicators is an advance because previous studies generally used a single environmental indicator
(Auestad and Fulgoni 2015). Besides, the study exhibited important contributions. Indeed, scientific
literature about the environmental impacts of the diet is growing (Perignon et al. 2017;
Aleksandrowicz et al. 2016), but our study is the first that distinguished modes of production.
In conclusion, based on observed individual data in a large cohort of adults, a low GHG-
emitting diet appears to be healthier in terms of nutrition, presents environmental benefits, and
is less expensive. Other environmental indicators as the biodiversity footprints or water use are
also major indicators which should be accounted in future research including farming
practices.
Acknowledgments We especially thank Younes Esseddik, Paul Flanzy, Thi Hong Van Duong, Veronique
Gourlet, Fabien Szabo, Nathalie Arnault, Laurent Bourhis and Stephen Besseau, Cédric Agaësse, Claudia
Chahine, and the Bioconsom’acteurs’members. We warmly thank all of the dedicated and conscientious
volunteers involved in the Nutrinet-Santé Cohort.
Funding The NutriNet-Santé Study is supported by the French Ministry of Health (DGS), the national public
health agency (Santé Publique France), the National Institute for Health and Medical Research (INSERM), the
National Institute for Agricultural Research (INRA), the National Conservatory of Arts and Crafts (CNAM), and
the University of Paris 13. This study is supported by the BioNutriNet project which is a research project
supported by the French National Research Agency (Agence Nationale de la Recherche) in the context of the
2013 Programme de Recherche Systèmes Alimentaires Durables (ANR-13-ALID-0001). Louise Seconda is
supported by a doctoral fellowship from the French Environment and Energy Management Agency (ADEME)
and the National Institute for Agricultural Research (INRA).
Climatic Change
Author's personal copy

Compliance with ethical standards The study was conducted observing the guidelines from the Declaration of
Helsinki, and all protocols were approved by the Institutional Review Board of the French Institute for Health
and Medical Research (IRB INSERM no. 0000388FWA00005831) and the Commission Nationale de
l’Informatique et des Libertés (CNIL no. 908450 and no. 909216). Participant informed consents were signed
by all volunteers with an electronic signature. The NutriNet-Santé Study is registered in ClinicalTrials.gov
(NCT03335644).
References
Aleksandrowicz L, Green R, Joy EJM, Smith P, Haines A (2016) The impacts of dietary change on greenhouse
gas emissions, land use, water use, and health: a systematic review. Wiley AS, editor. PLoS One 11(11):
e0165797
Andreeva VA, Deschamps V, Salanave B, Castetbon K, Verdot C, Kesse-Guyot E et al (2016) Comparison of
dietary intakes between a large online cohort study (etude NutriNet-Santé) and a nationally representative
cross-sectional study (etude Nationale Nutrition Santé) in France: addressing the issue of generalizability in
E-epidemiology. Am J Epidemiol 184(9):660–669
Aston LM, Smith JN, Powles JW (2012) Impact of a reduced red and processed meat dietary pattern on disease
risks and greenhouse gas emissions in the UK: a modelling study. BMJ Open 2(5):e001072
Attitudes - Français - Kantar Worldpanel (n.d.) [Internet]. [cited 2017 Jul 19]. Available from: https://www.
kantarworldpanel.com/fr/Nos-solutions/attitudes
Auestad N, Fulgoni VL (2015) What current literature tells us about sustainable diets: emerging research linking
dietary patterns, environmental sustainability, and economics. Adv Nutr Int Rev J 6(1):19–36
Baudry J, Méjean C, Allès B, Péneau S, Touvier M, Hercberg S et al (2015) Contribution of organic food to the
diet in a large sample of French adults (the NutriNet-Santé Cohort Study). Nutrients 7(10):8615–8632
Bognár A. (2002) Tables on weight yield of food and retention factors of food constituents for the calculation of
nutrient composition of cooked foods (dishes). Bundesforschungsanstalt für Ernährung
Bryngelsson D, Wirsenius S,Hedenus F, Sonesson U (2016) How can the EU climate targets be met? A combined
analysis of technological and demand-side changes in food and agriculture. Food Policy 59:152–164
Bryngelsson D, Hedenus F, Johansson D, Azar C, Wirsenius S (2017) How do dietary choices influence the
energy-system cost of stabilizing the climate? Energies 10(2):182
Camanzi L, Alikadic A, Compagnoni L, Merloni E (2017) The impact of greenhouse gas emissions in the EU
food chain: a quantitative and economic assessment using an environmentally extended input-output
approach. J Clean Prod 157:168–176
Chauliac M, Razanamahefa L, Choma C, Boudot J, Houssin D (2009) National health and nutrition program:
challenges of a global action plan. Rev Prat 59(1):10–12
Clune S, Crossin E, Verghese K (2017) Systematic review of greenhouse gas emissions for different fresh food
categories. J Clean Prod 140(Part 2):766–783
Communication de la commission au parlement européen (n.d.) au conseil, au comité économique et social
européen et au comité des régions. Feuille de route vers une économie compétitive à faible intensité de
carbone à l’horizon 2050 [Internet]. Commission européenne; 2011 [cited 2017 Jul 10]. Available from:
http://www.eurosfaire.prd.fr/7pc/doc/1301388482_com_2011_112_fr.pdf
Craig CL, Marshall AL, Sjostrom M, Bauman AE, Booth ML, Ainsworth BE et al (2003) International physical
activity questionnaire: 12-country reliability and validity. Med Sci Sports Exerc 35(8):1381–1395
Donati M, Menozzi D, Zighetti C, Rosi A, Zinetti A, Scazzina F (2016) Towards a sustainable diet combining
economic, environmental and nutritional objectives. Appetite 106:48–57
Estaquio C, Kesse-Guyot E, Deschamps V, Bertrais S, Dauchet L, Galan P et al (2009) Adherence to the French
Programme National Nutrition Santé Guideline Score is associated with better nutrient intake and nutritional
status. J Am Diet Assoc 109(6):1031–1041
Fazeni K, Steinmüller H (2011) Impact of changes in diet on the availability of land, energy demand, and
greenhouse gas emissions of agriculture. Energy Sustain Soc 1:6
Fifth Assessment Report Climate Change (2013) [Internet]. [cited 2017 Jul 4]. Available from: http://www.ipcc.
ch/report/ar5/wg1/
Garnett T (2011) Where are the best opportunities for reducing greenhouse gas emissions in the food system
(including the food chain)? Food Policy 36:S23–S32
Hagströmer M, Oja P, Sjöström M (2006) The International Physical Activity Questionnaire (IPAQ): a study of
concurrent and construct validity. Public Health Nutr [Internet]. 2006 Sep [cited 2016 Apr 28];9(06).
Available from: http://www.journals.cambridge.org/abstract_S1368980006001261
Climatic Change
Author's personal copy

Hallal PC (2004) Victora CG Reliability and validity of the International Physical Activity Questionnaire (IPAQ).
Med Sci Sports Exerc 36(3):556
Hallström E, Carlsson-Kanyama A, Börjesson P (2015) Environmental impact of dietary change: a systematic
review. J Clean Prod 91:1–11
Hercberg S, Castetbon K, Czernichow S, Malon A, Mejean C, Kesse E et al (2010) The Nutrinet-Santé Study: a
web-based prospective study on the relationship between nutrition and health and determinants of dietary
patterns and nutritional status. BMC Public Health 10:242
Hyland JJ, Henchion M, McCarthy M, McCarthy SN (2017) The role of meat in strategies to achieve a
sustainable diet lower in greenhouse gas emissions: a review. Meat Sci [Internet]. 2017 Apr [cited 2017
May 23]; Available from: http://linkinghub.elsevier.com/retrieve/pii/S0309174017302176
Kesse-Guyot E, Castetbon K, Touvier M, Hercberg S, Relative Validity GP (2010) Reproducibility of a food
frequency questionnaire designed for French adults. Ann Nutr Metab 57(3–4):153–162
Kool A, Blonk H, Ponsioen T, Sukkel W, Vermeer H, De Vries J, et al. (2009) Carbon footprints of conventional
and organic pork : assessments of typical production systems in the Netherlands, Denmark, England and
Germany carbon footprints of conventional and organic pork : assessments of typical production systems in
the Netherlands, Denmark, England and Germany. Blonk Milieuzdvies BV
La macro SAS CALMAR | Insee (n.d.) [Internet]. [cited 2018 Jan 9]. Available from: https://www.insee.
fr/fr/information/2021902
Lassale C, Fezeu L, Andreeva VA, Hercberg S, Kengne A-P, Czernichow S et al (2012) Association between
dietary scores and 13-year weight change and obesity risk in a French prospective cohort. Int J Obes 36(11):
1455–1462
Leinonen I, Williams AG, Wiseman J, Guy J, Kyriazakis I (2012) Predicting the environmental impacts of
chicken systems in the United Kingdom through a life cycle assessment: broiler production systems. Poult
Sci 91(1):8–25
Masset G, Soler L-G, Vieux F, Darmon N (2014) Identifying sustainable foods: the relationship between
environmental impact, nutritional quality, and prices of foods representative of the French diet. J Acad
Nutr Diet 114(6):862–869
Meier MS, Stoessel F, Jungbluth N, Juraske R, Schader C, Stolze M (2015) Environmental impacts of organic
and conventional agricultural products—are the differences captured by life cycle assessment? J Environ
Manag 149:193–208
Melina V, Craig W, Levin S (2016) Position of the academy of nutrition and dietetics: vegetarian diets. J Acad
Nutr Diet 116(12):1970–1980
Milner J, Green R, Dangour AD, Haines A, Chalabi Z, Spadaro J et al (2015) Health effects of adopting low
greenhouse gas emission diets in the UK. BMJ Open 5(4):e007364
Monsivais P, Scarborough P, Lloyd T, Mizdrak A, Luben R, Mulligan AA et al (2015) Greater accordance with
the dietary approaches to stop hypertension dietary pattern is associated with lower diet-related greenhouse
gas production but higher dietary costs in the United Kingdom. Am J Clin Nutr 102(1):138–145
Muller A, Schader C, El-Hage Scialabba N, Brüggemann J, Isensee A, Erb K-H, et al (2017) Strategies for
feeding the world more sustainably with organic agriculture. Nat Commun [Internet]. 2017 Dec [cited 2017
Nov 24];8(1). Available from: http://www.nature.com/articles/s41467-017-01410-w
Muñoz I, Canals LM i, Fernández-Alba AR (2010) Life cycle assessment of the average Spanish diet including
human excretion. Int J Life Cycle Assess 15(8):794–805
Nijdam D, Rood T, Westhoek H (2012) The price of protein: review of land use and carbon footprints
from life cycle assessments of animal food products and their substitutes. Food Policy 37(6):760–
770
Nutrinet-Santé E (2013) Table de composition des aliments de l’étude Nutrinet-Santé. Paris Econ
Perignon M, Vieux F, Soler L-G, Masset G, Darmon N (2017) Improving diet sustainability through evolution of
food choices: review of epidemiological studies on the environmental impact of diets. Nutr Rev 75(1):2–17
Pointereau P, Langevin B, Gimaret M (2012) DIALECTE, a comprehensive and quick tool to assess the agro-
environmental performance of farms (PDF Download Available) [Internet]. ResearchGate. 2012 [cited 2017
Jun 16]. Available from: https://www.researchgate.net/publication/265288497_DIALECTE_a_
comprehensive_and_quick_tool_to_assess_the_agro-environmental_performance_of_farms
Reganold JP, Wachter JM (2016) Organic agriculture in the twenty-first century. Nat Plants 2(2):15221
Scarborough P, Appleby PN, Mizdrak A, Briggs ADM, Travis RC, Bradbury KE et al (2014) Dietary greenhouse
gas emissions of meat-eaters, fish-eaters, vegetarians and vegans in the UK. Clim Chang 125(2):179–192
Schofield W (1984) Predicting basal metabolic rate, new standards and review of previous work. Hum Nutr Clin
Nutr 39(Suppl 1):5–41
Seconda L, Baudry J, Allès B, Hamza O, Boizot-Szantai C, Soler L-G et al (2017) Assessment of the
sustainability of the Mediterranean diet combined with organic food consumption: an individual behaviour
approach. Nutrients 9(1):61
Climatic Change
Author's personal copy

Solagro_afterres2050-v2-web.pdf n.d. [Internet]. [cited 2017 Jun 6]. Available from: http://afterres2050.solagro.
org/wp-content/uploads/2015/11/Solagro_afterres2050-v2-web.pdf
Soret S, Mejia A, Batech M, Jaceldo-Siegl K, Harwatt H, Sabaté J (2014) Climate change mitigation and health
effects of varied dietary patterns in real-life settings throughout North America. Am J Clin Nutr
100(Supplement 1):490S–495S
Temme EH, van der Voet H, Thissen JT, Verkaik-Kloosterman J, van Donkersgoed G, Nonhebel S (2013)
Replacement of meat and dairy by plant-derived foods: estimated effects on land use, iron and SFA intakes
in young Dutch adult females. Public Health Nutr 16(10):1900–1907
Temme EH, Toxopeus IB, Kramer GF, Brosens MC, Drijvers JM, Tyszler M et al (2015) Greenhouse gas
emission of diets in the Netherlands and associations with food, energy and macronutrient intakes. Public
Health Nutr 18(13):2433–2445
Tilma n D, Clark M (2014) Global die ts link environmental sustainability a nd human h ealth. Nature 515(7528):
518–522
Tuomisto HL, Hodge ID, Riordan P, Macdonald DW (2012) Does organic farming reduce environmental
impacts—a meta-analysis of European research. J Environ Manag 112:309–320
van Dooren C, Marinussen M, Blonk H, Aiking H, Vellinga P (2014) Exploring dietary guidelines based on
ecological and nutritional values: a comparison of six dietary patterns. Food Policy 44:36–46
van Dooren C, Tyszler M, Kramer G, Aiking H (2015) Combining low price, low climate impact and high
nutritional value in one shopping basket through diet optimization by linear programming. Sustainability
7(9):12837–12855
Verger EO, Mariotti F, Holmes BA, PaineauD, Huneau J-F (2012) Evaluation of a diet quality indexbased on the
probability of adequate nutrient intake (PANDiet) using National French and US dietary surveys. PLoS One
7(8):e42155
Vermeulen SJ, Campbell BM, Ingram JSI (2012) Climate change and food systems. Annu Rev Environ Resour
37(1):195–222
Weidema B, Meeusen MJG (2000) Agricultural data for life cycle assessments. La Hague, Pays-Bas 189 p
Willett W, Stampfer MJ (1986) Total energy intake: implications for epidemiologic analyses. Am J Epidemiol
124(1):17–27
Yield and Retention: USDA ARS (n.d.) [Internet]. [cited 2018 Jan 26]. Available from: https://www.ars.usda.
gov/northeast-area/beltsville-md/beltsville-human-nutrition-research-center/nutrient-data-
laboratory/docs/yield-and-retention/
Climatic Change
Author's personal copy