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Citation: Hatjiathanassiadou, M.;
Souza, C.V.S.d.; Vale, D.; Dantas,
N.M.; Batista, Y.B.; Marchioni, D.M.L.;
Lima, S.C.V.C.; Lyra, C.d.O.; Rolim,
P.M.; Seabra, L.M.J. Dietary
Environmental Footprints and Their
Association with Socioeconomic
Factors and Food Purchase Practices:
BRAZUCA Natal Study. Foods 2022,
11, 3842. https://doi.org/10.3390/
foods11233842
Academic Editor: Marie Alminger
Received: 12 October 2022
Accepted: 10 November 2022
Published: 28 November 2022
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foods
Article
Dietary Environmental Footprints and Their Association with
Socioeconomic Factors and Food Purchase Practices: BRAZUCA
Natal Study
Maria Hatjiathanassiadou 1, Camila Valdejane Silva de Souza 2, Diôgo Vale 3, Natalie Marinho Dantas 4,
Yasmim Bezerra Batista 5, Dirce Maria Lobo Marchioni 4, Severina Carla Vieira Cunha Lima 1,5,
Clélia de Oliveira Lyra 1,5, Priscilla Moura Rolim 1,5 and Larissa Mont’Alverne JucáSeabra 1,5, *
1Postgraduate Program in Nutrition, Center for Health Sciences, Federal University of Rio Grande do Norte,
Natal 59078-970, RN, Brazil
2Postgraduate Program in Public Health, Center for Health Sciences, Federal University of Rio Grande do
Norte, Natal 59078-970, RN, Brazil
3Federal Institute of Education, Science and Technology of Rio Grande do Norte, Natal 59015-300, RN, Brazil
4Department of Nutrition, School of Public Health, University of São Paulo, São Paulo 01246-904, SP, Brazil
5Department of Nutrition, Center for Health Sciences, Federal University of Rio Grande do Norte,
Natal 59078-970, RN, Brazil
*Correspondence: larissa.seabra@ufrn.br
Abstract:
The analysis of dietary environmental impacts has proven to be an important tool for
guiding the adoption of healthier and more sustainable diets. This study aimed to estimate the
dietary carbon (CF), water (WF), and ecological (EF) footprints of residents in the city of Natal,
Brazil; the study also aimed to verify their association with socioeconomic factors and food purchase
practices. This is a cross-sectional study that used dietary data from 411 adults and elderlies, which
was collected via a questionnaire that applied to the respondents. The results showed that the
dietary CF was 1901.88 g CO
2
eq/day/1000 kcal, the WF was 1834.03 L/day/1000 kcal, and the EF
was
14.29 m2/day/1000 kcal
. The highest environmental footprint values showed an association
(
p≤0.05
) with the factors of male sex, white ethnicity, and higher income and schooling, whereas the
lowest environmental footprint values were associated with social vulnerability variables such as
female sex, non-white ethnicity, and lower income and schooling (
p≤0.05
). Moreover, people with
lower environmental footprints consumed less fast food, had fewer meals at snack bars, and used
food delivery services less often than those with higher footprints. The foods that most contributed
to the CFs and WFs were beef and chicken, while fish and beef contribute the most to the EFs. The
data in the present study show that a diet with a lower environmental impact is not always equal to
a sustainable diet. This relationship is paradoxical and relates to food justice, as people with lower
environmental footprint values are the same ones with worse socioeconomic conditions. In this sense,
is it essential to consider the influence of the social context when assessing dietary environmental
impacts and when assessing actions that promote healthier and more sustainable diets.
Keywords:
sustainable diet; food consumption; environmental impact; water footprint; carbon
footprint; ecological footprint
1. Introduction
Adopting healthy and sustainable dietary practices is an urgent priority in the face
of current ecological and health challenges [
1
]. These goals require promoting dietary
practices aligned with both individual needs and planetary health [
2
]. Such a need has
become even more evident in the scientific and political fields after the publication that
pointed out the present global syndemic, i.e., the co-existence of undernutrition, obesity,
and climate change pandemics around the world [3].
Foods 2022,11, 3842. https://doi.org/10.3390/foods11233842 https://www.mdpi.com/journal/foods
Foods 2022,11, 3842 2 of 20
The implementation of more sustainable actions has become an international agenda
after the agreement with respect to the Sustainable Development Goals (SDGs) by member
states of the United Nations. The incentive to establish more sustainable food production
and consumption standards are included in the scope of actions for the promotion of global
health, particularly in SDGs 2, 11, 12, 13, 14, and 15 [
4
]. The strategies to reach those goals
begin with the comprehension of dietary indicators in populations and their environmental
impacts. Some studies employ environmental sustainability metrics linked to food systems,
such as food waste [
5
] and the estimated environmental footprints (EnF) of individual food
consumption [6].
In the field of nutrition, studies using ecological (EF) [
7
–
9
], water (WF) [
10
–
14
], and
carbon (CF) [
15
–
18
] footprints to assess the dietary sustainability of population groups
have become frequent. These indicators are interesting as they enable laying the basis for
educational action and public policies aimed at reducing environmental impacts and im-
proving health through diet [
19
,
20
]. EnF estimates in different regions point to differences
according to the dietary standards of the territories. In South America, a study performed
in Argentina showed values of 8910 g CO
2
eq/day for greenhouse gas (GHG) emissions,
54.2 m
2
/day for soil use, and 205 L/day for freshwater [
21
], while a study performed in
Chile showed GHG and soil use values of 4670 g CO
2
eq/person/day and
4177 L/person/day, respectively [
22
]. In Asia, a study performed in Lebanon deter-
mined the water use of food consumption to be 2571 L/day and the GHG emissions to be
4060 g CO2eq/day [23]. In some Mediterranean cities of Europe and Asia, the WF values
of food consumption have been shown to range from 3272 L/per capita/day to 5789 L/per
capita/day [24].
In Brazil, a study with data on household food availability showed an increase by
21%, 22%, and 17% in the CF, WF, and EF, respectively, between 1987 and 2018 [
9
]. Another
study analyzed the EnF of food consumption data of Brazilians and identified averages of
6.76 kg CO
2
eq/person/day for CF, 3478.4 L/person/day for WF, and 67.2 m
2
/person/day
for EF [
25
]. It is noteworthy that Brazil is the fourth-highest contributing global economy
to GHG emissions from the food system, using large amounts of soil and water for food
production [
26
]. In this context, the importance of the advancing analyses in Brazil stands
out, as many of the current estimates focus on the national level [
9
,
25
,
27
,
28
]. These estimates
need to be expanded to the regional level as diets are quite varied in the country, which has
continental dimensions and one of the largest populations in the world.
Brazil has made advancements in promoting sustainable diets. One of the examples is
the Dietary Guidelines for the Brazilian Population, which was one of the first guidelines
in the world to discuss social, cultural, economic, and other aspects of sustainability. Along
with these inclusions, the document discusses the impacts of food processing by using
the NOVA classification system to base the main recommendations, which stimulates the
development of more sustainable and healthy diets by recommending a diet based on in
natura and minimally processed foods of plant origin, with one of its principles being the
sustainability of dietary systems [9,29,30].
In this sense, the present study sought to assess the dietary environmental impacts
in the city of Natal, the capital of the state of Rio Grande do Norte (RN), Brazil. Natal is a
coastal city with approximately 896,708 inhabitants and is the most populous municipality
in the state, the 6th most populous in the Northeast region, and the 20th in Brazil. The
city has a Municipal Human Development Index (MHDI) of 0.763, which is considered
high [
31
,
32
]. In Natal, foods such as fish and seafood, particularly shrimp, and products
from the small- and medium-sized cities of the state such as sun-dried meat and cassava
flour are characteristics of the city’s cuisine [
33
]. On the other hand, RN is the state in the
Northeast region of Brazil that consumes ultra-processed foods the most and the one that
consumes in natura or minimally processed foods the least in the North and Northeast
regions [34].
The importance of this study is justified through the need to further studies on dietary
impacts given the urgent priority associated with the matter and the need to assess the diet
Foods 2022,11, 3842 3 of 20
of different population groups according to their social, economic, and cultural contexts.
Such studies are key to promoting health actions at the national and regional levels, thus
fostering a healthier and more sustainable diet for all. Therefore, the present study sought
to answer the following questions: (1) What are the values of the environmental footprints
of food consumption for adult and elderly residents in Natal? (2) Is there a relation between
the environmental footprints of food consumption and the socioeconomic characteristics
and food purchase practices?
2. Material and Methods
2.1. Study Characterization
This is an applied field research with a cross-sectional, observational, and quantitative
design. The data in the present study come from a research project entitled “Food Inse-
curity, Health and Nutrition Conditions in an Adult and Elderly Population of a Capital
in the Northeast Region of Brazil: BRAZUCA Natal/RN Study” (Insegurança Alimen-
tar, Condições de Saúde e de Nutrição em População Adulta e Idosa de uma Capital
do Nordeste do Brasil: Estudo BRAZUCA Natal/RN), which is part of a population-
based multicenter survey called the Brazilian Usual Consumption Assessment (Estudo
BRAZUCA).
A two-stage (census sectors and households) probabilistic sampling was performed.
Sixty-six effective census sectors and six alternate census sectors were randomly chosen.
The number of households was defined considering the minimum sample size, and the
density of elements of each demographic group per household was calculated from data
from the 2010 Brazilian Census with a 10% correction rate to account for losses from refusals
and closed and vacant households. The final sample size aimed to reach 258 interviews in
each of the four sex and age strata: adults and elderly of either sex (total of 1032 people).
The minimum size of 258 in each stratum allowed for us to estimate a prevalence of 50%
with an 8% error and 95% confidence level. The design effect (deff) factor was 1.5, and 15%
were added as a rate of non-responses and closed households.
This study presents a cropping of data collected from June 2019 to March 2020 in
27 census sectors in the four sanitary districts of the municipality of Natal, RN. Adult
and elderly persons (
≥
20 years) of either sex who were physically and cognitively able
to answer the questionnaires were considered eligible for the research. The present study
considered all interviews conducted during the collection period, with a total of 411 persons
being characterized as a convenience sample.
2.2. Ethical Aspects
The BRAZUCA Natal/RN Study was submitted to and approved by the Committee of
Ethics and Research of the University Hospital Onofre Lopes (CAAE 96294718.4.2001.5292),
under Protocol No. 3531,721. The Estudo BRAZUCA application was also approved
(CAAE 96294718.4.1001.5421). The study was conducted in accordance with the Decla-
ration of Helsinki and Resolution No. 466 of 12 December 2012 of the National Health
Council [
35
]. The individuals eligible to take part in the research were informed about the
objectives, risks, and benefits, and those who accepted to participate signed a term of free
and informed consent.
2.3. Data Collection and Instruments Used
The data were collected in households or at a primary healthcare unit close to the
residence of participants. The interviews were performed using a standardized and revised
questionnaire based on the protocols of the National Health Survey (Pesquisa Nacional
de Saúde—PNS 2013) applied via the Epicollect5 platform. All steps were guided by
manuals and standard operating procedures (SOPs) developed by professors and Ph.D.
students. All interviewers were properly trained and qualified. The interview collected
information regarding the demographic and socioeconomic data, food consumption, and
food purchase practices.
Foods 2022,11, 3842 4 of 20
Food consumption data were obtained using a propensity questionnaire (PQ). The
questionnaire aimed to analyze the intake of 41 food groups over the 12 months prior
to the interview. For each group, participants were asked to indicate the frequency of
consumption (in days, weeks, months, or years) and the number of times (from 1 to 10).
The questionnaire did not assess portion intake or amounts. The estimates of per capita
amounts for each individual were taken from the information collected in a 24-h Dietary
Recall (24 HR), which was applied on the same day as the interview using the GloboDiet
software [36].
2.4. Study Variables
2.4.1. Hypotheses
Based on the research questions “What is the value of the environmental footprints
of the food of adults and elderly residents in the city of Natal/RN?” and “Is there a
relationship between the environmental footprints of food consumption and socioeconomic
characteristics and food purchasing practices?”, the following hypotheses were elaborated
(Table 1).
Table 1. Hypotheses.
Hypotheses Metrics References
Socioeconomic characteristics
influence the dietary
environmental footprints of
adults and elderly people
living in Natal, RN, Brazil.
Dietary environmental footprints (CF
estimated in gCO2/person/day/1000 kcal,
WF estimated in L/person/day/1000 kcal,
EF estimated in m2/person/day/1000 kcal)
and their relationship with socioeconomic
variables (sex, age, ethnicity, schooling, and
monthly per capita family income). [3,14,18,19,25,
28,37,38]
Food purchase and
consumption practices
influence the dietary
environmental footprints of
adults and elderly living in
Natal, RN, Brazil.
Dietary environmental footprints (CF
estimated in gCO2/person/day/1000 kcal,
WF estimated in L/person/day/1000 kcal,
EF estimated in m2/person/day/1000 kcal)
and their relationship with food purchase
practices (food purchase frequency from
street fairs, fast food restaurants, use of food
delivery services and food consumption at
snack bars).
2.4.2. Socioeconomic Characteristics
Information was gathered on the biological sex (female and male), age group (adults
20–59 years; elderly
≥
60 years), ethnicity (white and non-white: black, yellow, pardo
(official term used by the IBGE census referring to people of mixed race), indigenous),
schooling (0–5 years: elementary school; 6–9 years: middle school; 10–13 years: high school;
and
≥
14 years: higher education), and monthly per capita family income (BRL), which was
split into quintiles: <249.50 (Q1); 249.50–449.45 (Q2); 449.46–762.67 (Q3); 762.68–1559.99
(Q4); ≥1560.00 (Q5).
2.4.3. Assessment of Food Purchase Practices
The information regarding food purchase frequency refers to purchases from street
fairs, fast food restaurants, and the use of food delivery services, which was split into usage
categories of “never”, “sometimes”, and “often”. The frequency of having lunch or dinner
at snack bars was expressed as “never”, “hardly ever”, and “at least once a week” (the
original response options of “1–2 days a week”, “3–4 days a week”, “5–6 days a week’ and
“every day” have been condensed into “at least once a week”).
Foods 2022,11, 3842 5 of 20
2.4.4. Estimated Environmental Footprints of Food Consumption Frequency
The footprints of food consumption frequency were estimated using the PQ. As the PQ
did not assess portion intake or amounts, these data were estimated based on the per capita
values from the 24 HR. For each food group, the median of the number of times it was
consumed was estimated, and a weighted average was calculated. Some foods need to be
diluted, as the same food was estimated in milliliters (mL) and/or in grams (g), depending
on how each individual reported consumption. This was the case with powdered milk and
chocolate powder. For dilution, we used the rules according to Araújo and Guerra [
39
]. On
the other hand, foods consumed in liquid form (mL or L) such as coffee and tea needed to
be transformed into kilograms, as this is the unit of measurement used to analyze the EnF.
Thus, these were also converted following the same dilution rules (Table S1).
After estimating the per capita amount of each food group, the WF, CF, and EF values
were estimated. For this calculation, the frequency of daily consumption was considered
for each food group from the PQ. To that end, the consumption frequency data collected
in years, months, or weeks were converted into daily consumption: the values estimated
in a year were divided by 365; those estimated by month were divided by 30; and those
estimated by week were divided by 7.
After the daily consumption frequency was obtained, the values were multiplied by
the per capita amount to obtain the estimated food consumption (EFC), as described in
Equation (1) [28,40]:
EFC =dail y consum ptio n f re quency ×per capita (g)
1000 (1)
The per capita consumption value was converted from grams into kilograms to correct
the unit of measurement. The final value was expressed as kilograms per day (kg/day).
To estimate the mean environmental footprint value of each food for each person,
the EFC (Equation (1)) was multiplied by the environmental footprint values for foods
and preparations consumed in Brazil, as described by Garzillo et al. [
40
], which takes
into account the whole lifecycle (from farm to fork), using the life cycle assessment (LCA)
methodology. Therefore, the values of prepared foods consider correction factors, cooking
factors, and estimated carbon emissions associated with the different types of preparation.
The footprint values of raw and prepared foods were considered, and the weighted
average was performed considering the EnF values and number of times the food was
consumed according to what was observed in the 24 HR. Adaptations were made when
foods reported in the 24 HR were not present in the footprint database (Table S2). For
example: For the fruit group, all fruit consumed by the participants according to the 24 HR
and their respective number of times and way of consumption/preparation were compiled.
After the compilation, the weighted average was calculated for each footprint, yielding the
mean values of CF, WF, and EF for the fruit group.
To obtain the final footprint of each individual, the environmental footprint values
calculated from all food groups consumed were added up, as shown in Equation (2) [
23
,
28
]:
Fin al f oot print o f the in dividu al =
Food groups consumed
∑
i=1
EFCi×impacti(2)
where i is each food group consumed and “Food groups consumed” is the total amount
of groups present in the PQ, considering the consumption informed by the individual.
The EFC
i
refers to the EFC of each food group (Equation (1)), while impact refers to the
environmental footprint values for each group according to the type of footprint. This
formula was used to calculate the CF, which was measured in grams of CO
2
equivalent
(g CO
2
eq), the WF, which was measured in liters (L), and the EF, which was measured in
square meters (m2).
After the estimated environmental footprint of each individual was calculated
(
Equation (2)
), the values were equalized to 1000 kcal per person. To this end, the to-
Foods 2022,11, 3842 6 of 20
tal caloric value was calculated considering the estimated daily consumption (
Equation (1)
)
and the caloric estimated from the PQ. The caloric estimative from the PQ was calculated
considering the EFC value in grams of each food group and the caloric value from the Brazil-
ian Table of Food Composition (Tabela Brasileira de Composição de
Alimentos—TBCA
) [
41
]
considering a 100 g portion. For each food group, the arithmetic mean was calculated
considering all the foods present in the TBCA that fit the respective group. The calculation
is explained in Equation (3) [23,28]:
Ekal =
Food groups consumed
∑
i=1
EFCi×Kcali(3)
where i is each food group consumed and “Food groups consumed” is the total amount of
groups present in the PQ, considering the consumption informed by the individual. The
EFC
i
refers to the EFC of each food group (Equation (1)), while Kcal
i
refers to the average
value of kilocalories per 100 g of food in each group.
With the per capita caloric consumption estimated (Equation (3)), the environmental
footprint values were equalized for each 1000 kcal per capita per day, as described in
Equation (4) below [23,28]:
EEF =Fina l f o otpr int o f the individua l ×1000 kcal
EKcal (4)
The final values can be expressed as g CO
2
/person/day/1000 kcal for CF, L/person/
day/1000 kcal for WF, and m2/person/day/1000 kcal for EF.
2.5. Statistical Analyses
The main variables used were the estimated dietary environmental footprints (CF,
WF, and EF) adjusted to 1000 kcal. The secondary variables were the socioeconomic
characteristics and assessment of food purchase practices.
The results were expressed as the median, mean, interquartile interval, maximum
and minimum values, and CI 95% frequency. Data normality was assessed using the
Kolmogorov–Smirnov test. Because the data presents a non-normal distribution (p< 0.05),
non-parametric tests (Mann–Whitney U test and Kruskal–Wallis test) were used to assess
the differences between the EnF medians. The missing data answered as “doesn’t know or
didn’t answer” by the participants were ignored in the data analysis and were indicated in
the respective tables and figures.
The environmental footprint values were also assessed, taking tertiles into account;
food contribution per tertile was analyzed, with the values expressed as percentages. The
correspondence analysis (CA) test was performed to verify the association between the
environmental footprints (main variables) and the secondary variables. The first, second,
and third tertiles were identified as low, medium, and high footprints, respectively.
The CA plots show the graphical representation of the associations, where the tertiles
are represented by ellipses and the other variables are represented by points. An association
exists when those points are within one or more ellipses. The association is considered
significant when the p-value is below 0.05 and when the chi-squared value observed is
lower than the critical chi-squared.
The CA test is an exploratory statistical technique that enables the graphical visual-
ization of the relationships of a large set of variables amongst themselves. In this sense,
the technique allows for the identification of associations or similarities between the quali-
tative variables or categorized continuous variables. The relationship among variables is
seen with no need to assign a causal relationship and without assuming a distribution of
likelihoods, which makes it appropriate for use in populational studies [42–45].
The data were tabulated in the software Microsoft Excel
®
and analyzed using IBM
®
SPSS
®
Statistics and XLSTAT software. The statistical significance was defined as
p-value ≤0.05. Figure 1presents an overall flowchart of methodology.
Foods 2022,11, 3842 7 of 20
Foods2022,11,xFORPEERREVIEW7of20
associationexistswhenthosepointsarewithinoneormoreellipses.Theassociationis
consideredsignificantwhenthep‐valueisbelow0.05andwhenthechi‐squaredvalue
observedislowerthanthecriticalchi‐squared.
TheCAtestisanexploratorystatisticaltechniquethatenablesthegraphical
visualizationoftherelationshipsofalargesetofvariablesamongstthemselves.Inthis
sense,thetechniqueallowsfortheidentificationofassociationsorsimilaritiesbetween
thequalitativevariablesorcategorizedcontinuousvariables.Therelationshipamong
variablesisseenwithnoneedtoassignacausalrelationshipandwithoutassuminga
distributionoflikelihoods,whichmakesitappropriateforuseinpopulationalstudies[42–
45].
ThedataweretabulatedinthesoftwareMicrosoftExcel
®
andanalyzedusingIBM
®
SPSS
®
StatisticsandXLSTATsoftware.Thestatisticalsignificancewasdefinedasp‐value
≤0.05.Figure1presentsanoverallflowchartofmethodology.
Figure1.Flowchartofthemethodology.
3.ResultsandDiscussion
3.1.EstimatedDietaryEnvironmentalFootprints
Table2presentstheestimateddietaryenvironmentalfootprintsofadultsandelderly
peopleparticipatinginthestudy.Thetotalvaluesof2678.53gCO
2
eq/dayforCF,2702.53
L/dayforWF,and20.47m
2
/dayforEFwerefound.Withtheadjustmentto1000kcal,the
valuesare1901.88gCO
2
eq/day/1000kcalforCF,1834.03L/day/1000kcalforWF,and
14.28m
2
/day/1000kcalforEF.
Figure 1. Flowchart of the methodology.
3. Results and Discussion
3.1. Estimated Dietary Environmental Footprints
Table 2presents the estimated dietary environmental footprints of adults and el-
derly people participating in the study. The total values of 2678.53 g CO
2
eq/day for
CF,
2702.53 L/day
for WF, and 20.47 m
2
/day for EF were found. With the adjustment to
1000 kcal,
the values are 1901.88 g CO
2
eq/day/1000 kcal for CF, 1834.03 L/day/1000 kcal
for WF, and 14.28 m2/day/1000 kcal for EF.
Table 2.
Estimated environmental footprints (water, carbon, and ecological) of daily per capita
food consumption of adults and elderly people living in the municipality of Natal/RN, Brazil,
BRAZUCA Study.
Variables Median IQR Min–Max
CF (gCO2eq/person/day)
Estimated footprint 12678.53 1970.70–3636.07 462.13–17,066.11
Adjusted to 1000 kcal 1901.88 1493.11–2503.21 830.62–6394.40
WF (L/person/day)
Estimated footprint 12702.53 2107.00–3436.06 469.01–14,597.74
Adjusted to 1000 kcal 1834.03 1536.60–2264.23 777.45–5430.45
EF (m2/person/day)
Estimated footprint 120.47 15.45–27.24 4.26–102.26
Adjusted to 1000 kcal 14.29 10.83–18.50 5.78–66.19
1
The median of calories was 1289.08. IQR = interquartile range; CF = carbon footprint; WF = water footprint;
EF = ecological footprint; gCO
2
eq = grams of CO
2
equivalent; L = liters; m
2
= square meters; kcal = kilocalories.
Foods 2022,11, 3842 8 of 20
We compared the results of this study with previous studies performed in Brazil and
in other countries and regions that have standardized environmental footprints to 1000 kcal.
Some studies presented values similar to ours. This is evident in the case of a Canadian
study where the dietary CF was estimated at 2150 g CO
2eq/day/1000 kcal [17]
and a study
performed in the United States [
18
] where the CF was 2210 g CO
2
eq/per capita/day/
1000 kcal. Some studies presented higher values; in a study performed in Sweden, the
dietary CF in 2016 was 3380 g CO
2
eq/1000 kcal/day [
46
]. Some studies presented lower
values, as in a study conducted in Lebanon, where the WF was 951.68 L/day/1000 kcal
and CF of 1530 g CO
2
eq/day/1000 kcal in adult diets [
23
]. Another study also conducted
in Lebanon assessed the consumption of the Mediterranean diet by Lebanese adults and
reported WF of 995.79 L/day/1000 kcal and CF of 0.68 kg CO2eq/day/1.000 kcal [47].
In Brazil, according to household food purchase data from 2017–2018, the CF was
1866 g
CO
2
eq/day/1000 kcal, the WF was 1769 L/day/1000 kcal, and the EF was
11.36 m2/
1000 kcal [9]. These values were close to those found in our study.
Estimates at the national and regional levels are equally important for understanding
the environmental impacts associated with diets, given the difference in eating habits
between populations. In the Brazilian context, analyses at the regional level become even
more important, as habits differ according to region and location. Brazil is a country of
continental dimensions, with a land area of approximately 8.5 million km
2
, close to the size
of the European continent (10.2 km
2
) [
48
,
49
]. In this sense, diets heavily vary in Brazil as
these habits were and are influenced by several factors, such as social context and historical
factors. Understanding how different diets relate to environmental impact is essential for
guiding actions aimed at mitigating the advance of climate change. It is also important to
highlight that the differences in EnF values between the studies may be associated with the
eating habits of each location, source of the footprint values used, and system boundaries
according to the LCA methodology.
Given the division into tertiles, the median values were also estimated for each foot-
print. The low CF group had a median of 1372.99 gCO
2
eq/1000 kcal; the medium CF
group had a median of 1.901,88 gCO
2
eq/1000 kcal; and the high CF group had a median
of 2777.67 gCO
2
eq/1000 kcal. The low WF group had a median of 1436.58 L/1000 kcal;
the medium WF group had a median of 1834.03 L/1000 kcal; and the high WF group had
a median of 2430.40 L/1000 kcal. Finally, the low EF group had a median of 9.80 m
2
/
1000 kcal; the medium EF group had a median of 14.29 m
2
/1000 kcal; and the high EF
group had a median of 21.01 m2/1000 kcal.
3.2. Environmental Footprints and Populatin Characteristics
Table 3shows the characteristics of the population studied and their respective envi-
ronmental footprints.
Regarding socioeconomic characteristics, adults (p= 0.00), male individuals (
p= 0.05
),
and monthly per capita family income of BRL 1560.00 or more (p= 0.00) had higher
CF values
. For WF, male individuals (p= 0.02), adults (p= 0.01), and monthly per capita
family income of BRL
≥
1560.00 (p= 0.00) had the highest values. No statistically significant
differences were seen for EF.
Such results match the findings of other studies. In Brazil, males and adults had the
highest contributions to CF, WF, and EF in the study by Travassos, Cunha, and Coelho [
25
],
as well as to CF in the study by Garzillo et al. [
28
]. In Sweden [
46
], India [
14
], China [
50
],
and the United States [
18
], males also had the highest contributions to dietary CF and
WF values.
Foods 2022,11, 3842 9 of 20
Table 3.
Socioeconomic characteristics, food purchase practices, and environmental footprints (CF,
WF, and EF) per 1000 kcal of adults and elderlies living in the municipality of Natal/RN, Brazil,
BRAZUCA Natal Study.
Variables n% CI 95% CF * WF * EF *
1000 kcal p-Value 1000 kcal p-Value 1000 kcal p-Value
Sex
Male
173 42.1
37.2–46.7 1957.29 a0.05 1915.43 a0.02 14.28 a0.85
Female
238 57.9
53.3–62.8 1828.36 b1745.90 b14.36 a
Age group
Adults
220 53.5
48.7–58.9 2106.32 a0.00 1889.73 a0.01 14.82 a0.06
Elderly
191 46.5
41.1–51.3 1797.76 b1765.35 b13.71 a
Ethnicity
White
145 35.3
30.7–40.1 2001.98 a0.36 1864.03 a0.28 14.33 a0.31
Non-white
266 64.7
59.9–69.3 1975.56 a1813.65 a14.20 a
Schooling (years of study)1
0–5
146 35.8
31.1–40.4 1812.52 a
0.06
1757.70 a
0.21
14.45 a
0.47
6–9 62
15.2
11.8–18.6 1717.28 a1731.63 a13.36 a
10–13
126 30.9
26.5–35.8 1968.77 a1835.12 a14.00 a
≥14 74
18.1
14.5–22.1 2168.33 a1951.36 a14.88 a
Monthly per capita family
income (BRL)2
<249.50 (∼
=USD 47.22) 84
20.9
17.0–24.9 1710.69 b
0.00
1715.54 b
0.00
13.60 a
0.95
249.50–449.45 (∼
=USD
47.22–85.07) 76
19.0
15.2–22.9
1816.02
a,b
1885.33
a,b 13.66 a
449.46–762.67 (∼
=USD
85.07–144.35) 81
20.2
16.2–23.9
1900.64
a,b 1771.22 b14.82 a
762.68–1559.99 (∼
=USD
144.36–295.08) 80
20.0
16.2–23.7
2095.09
a,b
1866.13
a,b 14.16 a
≥1560.00 (∼
=USD 295.27) 80
20.0
16.5–23.9 2186.92 a2092.83 a14.78 a
Frequency of fast food
purchase 3,†
Never
262 65.7
61.2–69.9 1797.51 b
0.00
1737.21 b
0.00
13.68 a
0.34
Sometimes
117 29.3
25.1–33.8 2112.25 a2007.87 a14.81 a
Often 20 5.0 3.0–7.3 2761.45 a2097.04 a16.74 a
Frequency of lunch or dinner
at snack bars 4
Never
230 57.5
52.5–62.2 1813.81 b
0.00
1784.65 b
0.02
14.31 a
0.39
Hardly ever
119 29.8
25.3–34.0 1987.81 b1853.54 b13.10 a
At least once a week 51
12.8
9.3–16.3 2347.80 a2065.91 a15.09 a
Frequency of food purchase
at street fairs 3
Never
116 29.1
34.8–33.6 2007.63 a
0.41
1872.45 a
0.39
13.97 a
0.26
Sometimes
154 38.6
33.8–43.4 1947.70 a1831.42 a15.00 a
Often
129 32.3
28.1–36.8 1830.99 a1788.39 a13.71 a
Frequency of use of food
delivery services 3
Never
247 61.9
57.4–66.4 1825.72 a
0.03
1783.62 a
0.12
14.32 a
0,33
Sometimes
123 30.8
26.6–35.3
2057.07
a,b 1919.23 a13.93 a
Often 29 7.3 4.8–10.0 2213.07 b1828.81 a14.77 a
1
Three values answered as “didn’t answer/didn’t know” (DA/DK) were ignored;
2
ten values answered as
DA/DK were ignored;
3
twelve values answered as DA/DK were ignored;
4
eleven values answered as DA/DK
were ignored; †fast food was considered as hamburger, pizza, pastries, and other highly processed foods, while
snack bars serve quick meals that include sandwiches, natural juices, smoothies, coffee, cakes, sweets, and other
products that may or may not be considered fast food; * different letters in the columns indicate a statistical
difference between values according to Mann–Whitney U or Kruskal–Wallis tests (p< 0.05). The same letters do
not significantly differ.
Foods 2022,11, 3842 10 of 20
Garzillo et al. [
28
] also found that the CF of the Brazilian diet increase with income
and education. In the study by Song et al. [
50
], Chinese family income had a strong impact
on the consumption of foods of animal origin, which are the foods that have the highest
EnF, and less impact on the consumption of foods of plant origin. In this sense, it is worth
pointing out the difference in dietary EnF analysis in developed and developing countries.
The lower environmental footprints in developing countries such as Brazil would be
associated with the low purchasing power of the population and consequent lower access
to foods with higher environmental impact, such as foods of animal origin [
28
,
51
,
52
]. We
emphasize that 64.5% of the population in this study is below the poverty line proposed by
the World Bank [
53
], i.e., they survive with less than USD 5.50 per day. This scenario refers
to the conditions before the COVID-19 pandemic and has worsened after the pandemic.
Data from 2021 and 2022 show that 125.2 million Brazilian households were in a food
insecurity situation, while 33 million were in severe food insecurity [54].
It is also worth highlighting the difference in protein sources among the groups
presented in Table 3. We observed a higher consumption of fish by the groups with
lower per capita family income, lower schooling, and by seniors. Such a difference in fish
consumption may have impacted the EF values, explaining the higher EF value observed,
for example, in the group with up to five years of schooling. The high fish consumption
and its impact on EF values were observed in a previous study performed in Natal [
55
],
which points out the high consumption of cheaper meat cuts, processed meats, and higher
consumption of fish and seafood by Natal residents when compared with the average
Brazilian, which would increase the demand for fishing grounds, one of the ecological
resources considered in the EF analysis [55].
Studies in other countries also show the impact of fish consumption on EF values. In
Portugal, it was observed that the high consumption of animal protein, particularly fish and
red meat, negatively impacted the EF. The consumption of fish and seafood contributed to
approximately 26% of the total EF in Portugal, which is even higher than the consumption
of meats (23%) [56].
Regarding food purchase practices, those who bought fast food “sometimes” or “often”
had higher CFs (p= 0.00) and WFs (p= 0.00) than those who never did. It was also observed
that those who had lunch or dinner at snack bars at least one day a week had higher CFs
(p= 0.00) and WFs (p= 0.02). No statistical difference was observed in the environmental
footprint results considering the frequency of food purchases at street fairs. Individuals
who never used food delivery services had lower CF values when compared with those
who used them often (p= 0.03).
We point out that the higher CF and WF values associated with those practices may
be related to the higher consumption of ultra-processed foods and meat. A Brazilian study
reported that 70% of all food supplied in food delivery applications are ultra-processed or
ready-to-eat preparations [
57
]. Another study stated that the most often-bought foods at
Brazilian snack bars were savory snacks and fast food [
58
]. Some studies have explored the
relationship between the higher consumption of ultra-processed foods and their impacts
on EnF values. A study found that ultra-processed foods significantly contributed to the
increase in dietary CFs, WFs, and EFs of Brazilians between 1987 and 2018, accounting
for an increase by 245% in CFs, 233% in WFs, and 183% in EFs over the years, whereas
no statistically significant difference was found for the contribution of in natura or min-
imally processed food during that period [
9
]. The study by Vale et al. [
37
] reported a
progressive increase in WF values in the diets of Brazilian adolescents as the number of
days they ate at fast food restaurants increased, which are businesses that normally sell
ultra-processed foods.
It is noteworthy that despite the increase in the consumption of ultra-processed foods
being associated with higher EnF values, the profile of the ultra-processed foods consumed
is important. A larger amount of meat products within the group of ultra-processed foods
contributes to the increase in EnF values [
27
]. In addition, we also point out that the
analysis of environmental impacts still requires further research. The current analyses
Foods 2022,11, 3842 11 of 20
based on LCA, such as footprints, does not consider the industrial process or the use of
several compounds, such as chemical additives, and the use of large amounts of packaging,
which may mask an even higher impact [59].
3.3. Association among Environmental Footprints, Socioeconomic Characteristics, and Food
Purchase Practices
Figure 2shows the graphical representation of the associations of EnF tertiles (ellipses)
with the remaining variables (dots). The association was considered significant (p< 0.05)
(Table S3) for CF and WF (Figures 2and 3).
Foods2022,11,xFORPEERREVIEW11of20
ready‐to‐eatpreparations[57].Anotherstudystatedthatthemostoften‐boughtfoodsat
Braziliansnackbarsweresavorysnacksandfastfood[58].Somestudieshaveexplored
therelationshipbetweenthehigherconsumptionofultra‐processedfoodsandtheir
impactsonEnFvalues.Astudyfoundthatultra‐processedfoodssignificantlycontributed
totheincreaseindietaryCFs,WFs,andEFsofBraziliansbetween1987and2018,
accountingforanincreaseby245%inCFs,233%inWFs,and183%inEFsovertheyears,
whereasnostatisticallysignificantdifferencewasfoundforthecontributionofinnatura
orminimallyprocessedfoodduringthatperiod[9].ThestudybyValeetal.[37]reported
aprogressiveincreaseinWFvaluesinthedietsofBrazilianadolescentsasthenumberof
daystheyateatfastfoodrestaurantsincreased,whicharebusinessesthatnormallysell
ultra‐processedfoods.
Itisnoteworthythatdespitetheincreaseintheconsumptionofultra‐processedfoods
beingassociatedwithhigherEnFvalues,theprofileoftheultra‐processedfoods
consumedisimportant.Alargeramountofmeatproductswithinthegroupofultra‐
processedfoodscontributestotheincreaseinEnFvalues[27].Inaddition,wealsopoint
outthattheanalysisofenvironmentalimpactsstillrequiresfurtherresearch.Thecurrent
analysesbasedonLCA,suchasfootprints,doesnotconsidertheindustrialprocessorthe
useofseveralcompounds,suchaschemicaladditives,andtheuseoflargeamountsof
packaging,whichmaymaskanevenhigherimpact[59].
3.3.AssociationamongEnvironmentalFootprints,SocioeconomicCharacteristics,andFood
PurchasePractices
Figure2showsthegraphicalrepresentationoftheassociationsofEnFtertiles
(ellipses)withtheremainingvariables(dots).Theassociationwasconsideredsignificant
(p<0.05)(TableS3)forCFandWF(Figures2and3).
Figure2.Correspondenceanalysisbetweencarbonfootprintandsocioeconomicvariablesandfood
purchasepracticesofadultsandelderlypeopletakingpartintheBRAZUCANatalstudy(n=411).
0–5YS=0–5yearsofschooling;6–9YS=6–9yearsofschooling;10–13YS=10–13yearsofschooling;
Figure 2.
Correspondence analysis between carbon footprint and socioeconomic variables and food
purchase practices of adults and elderly people taking part in the BRAZUCA Natal study (n= 411).
0–5 YS = 0–5 years of schooling; 6–9 YS = 6–9 years of schooling; 10–13 YS = 10–13 years of schooling;
14+ YS = 14 or more years of schooling; Income_Q1
≤
BRL 249.50; Income_Q2 = BRL 249.50–449.45;
Income_Q3 = BRL 449.46–762.67; Income_Q4 = BRL 762.68–1559.99; Income_Q5 ≥BRL 1560.00.
The variables associated (p= 0.00) with the lowest CF values (Low CF) (Figure 2)
were non-white, having zero to five years of schooling, having a monthly per capita family
income of up to BRL 249.50, never buying fast food, never using food delivery services at
home, never buying food at street fairs, and never having lunch or dinner at snack bars.
The populational values associated with the highest CF values (High CF) were male, adult,
white, having 14 or more years of schooling, monthly per capita family income equal to
or above BRL 1559.99, sometimes buying fast food, and sometimes or often using food
delivery services at home.
Foods 2022,11, 3842 12 of 20
Foods2022,11,xFORPEERREVIEW12of20
14+YS=14ormoreyearsofschooling;Income_Q1=<≤BRL249.50;Income_Q2=BRL249.50–449.45;
Income_Q3=BRL449.46–762.67;Income_Q4=BRL762.68–1559.99;Income_Q5=≥≥BRL1560.00.
Figure3.Correspondenceanalysisbetweenthewaterfootprintandsocioeconomicvariablesand
foodpurchasepracticesofadultsandelderlypeopletakingpartintheBRAZUCANatalstudy(n=
411).0–5YS=0–5yearsofschooling;6–9YS=6–9yearsofschooling;10–13YS=10–13yearsof
schooling;14+YS=14ormoreyearsofschooling;Income_Q1≤=<BRL249.50;Income_Q2=BRL
249.50–449.45;Income_Q3=BRL449.46–762.67;Income_Q4=BRL762.68–1559.99;Income_Q5≥=
≥BRL1560.00.
Thevariablesassociated(p=0.00)withthelowestCFvalues(LowCF)(Figure2)were
non‐white,havingzerotofiveyearsofschooling,havingamonthlypercapitafamily
incomeofuptoBRL249.50,neverbuyingfastfood,neverusingfooddeliveryservicesat
home,neverbuyingfoodatstreetfairs,andneverhavinglunchordinneratsnackbars.
ThepopulationalvaluesassociatedwiththehighestCFvalues(HighCF)weremale,
adult,white,having14ormoreyearsofschooling,monthlypercapitafamilyincome
equaltooraboveBRL1559.99,sometimesbuyingfastfood,andsometimesoroftenusing
fooddeliveryservicesathome.
Thevariablesassociated(p=0.00)withthelowestWFvalues(LowCF)(Figure3)
werefemale,elderly,non‐white,havingzerotofiveyearsofschooling,neverbuyingfood
atfastfoodrestaurants,neverusingfooddeliveryservicesathome,andneverhaving
lunchordinneratsnackbars.ThehighestWFvalues(HighWF)wereassociatedwith
male,adult,white,having14ormoreyearsofschooling,sometimesoroftenbuyingfast
food,sometimesoroftenusingfooddeliveryservicesathome,andhavinglunchordinner
atsnackbarsatleastonceaweek.ForEFvalues(Figure4),nosignificantassociationwas
observed(p=0.21).
Figure 3.
Correspondence analysis between the water footprint and socioeconomic variables and food
purchase practices of adults and elderly people taking part in the BRAZUCA Natal study (
n= 411
).
0–5 YS = 0–5 years of schooling; 6–9 YS = 6–9 years of schooling;
10–13 YS = 10–13 years of schooling
;
14+ YS = 14 or more years of schooling; Income_Q1
≤
BRL 249.50; Income_Q2 = BRL 249.50–449.45;
Income_Q3 = BRL 449.46–762.67; Income_Q4 = BRL 762.68–1559.99; Income_Q5 ≥BRL 1560.00.
The variables associated (p= 0.00) with the lowest WF values (Low CF) (Figure 3) were
female, elderly, non-white, having zero to five years of schooling, never buying food at
fast food restaurants, never using food delivery services at home, and never having lunch
or dinner at snack bars. The highest WF values (High WF) were associated with male,
adult, white, having 14 or more years of schooling, sometimes or often buying fast food,
sometimes or often using food delivery services at home, and having lunch or dinner at
snack bars at least once a week. For EF values (Figure 4), no significant association was
observed (p= 0.21).
It was found that the lowest CF and WF values were associated with values related to
social vulnerability, such as being female, non-white, having a lower schooling, and having
a lower per capita family income. Meanwhile, the highest CF and WF are associated with
variables related to better living conditions, such as being male, white, having a higher
schooling, and having a higher per capita family income.
The highest footprints were also associated with a greater frequency of purchasing
food at fast food restaurants and snack bars, more frequent use of food delivery services,
and lower frequency of food purchases at street fairs. In this sense, although the variables
of the groups with high footprint are associated with better quality of life, that does not
necessarily suggest good dietary quality.
Foods 2022,11, 3842 13 of 20
Foods2022,11,xFORPEERREVIEW13of20
Figure4.Correspondenceanalysisbetweentheecologicalfootprintandsocioeconomicvariables
andfoodpurchasepracticesofadultsandelderlypersonstakingpartintheBRAZUCANatalstudy
(n=411).0–5YS=0–5yearsofschooling;6–9YS=6–9yearsofschooling;10–13YS=10–13yearsof
schooling;14+YS=14ormoreyearsofschooling;Income_Q1≤=<BRL249.50;Income_Q2=BRL
249.50–449.45;Income_Q3=BRL449.46–762.67;Income_Q4=BRL762.68–1559.99;Income_Q5≥=
≥BRL1560.00.
ItwasfoundthatthelowestCFandWFvalueswereassociatedwithvaluesrelated
tosocialvulnerability,suchasbeingfemale,non‐white,havingalowerschooling,and
havingalowerpercapitafamilyincome.Meanwhile,thehighestCFandWFare
associatedwithvariablesrelatedtobetterlivingconditions,suchasbeingmale,white,
havingahigherschooling,andhavingahigherpercapitafamilyincome.
Thehighestfootprintswerealsoassociatedwithagreaterfrequencyofpurchasing
foodatfastfoodrestaurantsandsnackbars,morefrequentuseoffooddeliveryservices,
andlowerfrequencyoffoodpurchasesatstreetfairs.Inthissense,althoughthevariables
ofthegroupswithhighfootprintareassociatedwithbetterqualityoflife,thatdoesnot
necessarilysuggestgooddietaryquality.
Figure5presentsthefoodsthatmostcontributedtothevalueofthethreefootprints
analyzed.
Figure 4.
Correspondence analysis between the ecological footprint and socioeconomic vari-
ables and food purchase practices of adults and elderly persons taking part in the BRAZUCA
Natal study (n= 411). 0–5 YS = 0–5 years of schooling; 6–9 YS = 6–9 years of school-
ing; 10–13 YS = 10–13 years of schooling; 14+ YS = 14 or more years of schooling; In-
come_Q1
≤
BRL 249.50; Income_Q2 = BRL 249.50–449.45; Income_Q3 = BRL 449.46–762.67;
Income_Q4 = BRL 762.68–1559.99; Income_Q5 ≥BRL 1560.00.
Figure 5presents the foods that most contributed to the value of the three
footprints analyzed.
Foods of animal origin had the largest contribution to all three footprints, especially
meats, particularly beef, chicken, and fish. Red meat stood out in CF and WF, contributing
48% and 37% to the data, respectively, considering both in natura and jerked beef. For EF,
fish stood out with a 26% contribution, followed by beef with an 18% contribution. Several
studies have also reported the impact of meat consumption, especially beef, on dietary
EnF values, as well as the impacts associated with the production of these foods [
1
,
3
,
16
,
18
,
25
,
60
,
61
]. In Brazil, meat production, in addition to being associated with environmental
impacts such as deforestation and GHG emissions, was also associated with slave labor
situations. The livestock sector had the highest number of such cases between 1995 and
2020, accounting for 51% of notified cases over that period [62].
Figure 6shows the foods that most contributed to the environmental footprints, which
were divided into low-, medium-, and high-footprint groups.
Foods 2022,11, 3842 14 of 20
Foods2022,11,xFORPEERREVIEW14of20
Figure 5.
Contribution of foods to the values of the carbon, water, and ecological footprints based
on the answers to the propensity questionnaire by the participants of the BRAZUCA Natal study
(n= 411).
Foods 2022,11, 3842 15 of 20
Foods2022,11,xFORPEERREVIEW15of20
Figure5.Contributionoffoodstothevaluesofthecarbon,water,andecologicalfootprintsbased
ontheanswerstothepropensityquestionnairebytheparticipantsoftheBRAZUCANatalstudy(n
=411).
Foodsofanimaloriginhadthelargestcontributiontoallthreefootprints,especially
meats,particularlybeef,chicken,andfish.RedmeatstoodoutinCFandWF,contributing
48%and37%tothedata,respectively,consideringbothinnaturaandjerkedbeef.ForEF,
fishstoodoutwitha26%contribution,followedbybeefwithan18%contribution.Several
studieshavealsoreportedtheimpactofmeatconsumption,especiallybeef,ondietary
EnFvalues,aswellastheimpactsassociatedwiththeproductionofthesefoods
[1,3,16,18,25,60,61].InBrazil,meatproduction,inadditiontobeingassociatedwith
environmentalimpactssuchasdeforestationandGHGemissions,wasalsoassociated
withslavelaborsituations.Thelivestocksectorhadthehighestnumberofsuchcases
between1995and2020,accountingfor51%ofnotifiedcasesoverthatperiod[62].
Figure6showsthefoodsthatmostcontributedtotheenvironmentalfootprints,
whichweredividedintolow‐,medium‐,andhigh‐footprintgroups.
Figure6.Contributionoffoodstothevaluesofcarbon,water,andecologicalfootprintssplitinto
low,medium,andhighfootprintsbasedontheanswerstothepropensityquestionnairebythe
participantsoftheBRAZUCANatalstudy(n=411).
Meatswerethemaincontributorstothefootprintvalues,especiallyinthetertile
concerningthehighestEnFvalues.ForCF,themeatgroupaccountedfor88.0%ofthe
totalvalueinthegroupwiththehighestCF,whereasitsvaluewasaround63.1%inthe
groupwiththelowestCF.ForWFandEF,thevalueswere73.4%and79.3%inthegroup
withthehighestfootprintand50.3%and60.80%inthegroupwiththelowestfootprint,
respectively.Althoughthehighercontributionofthemeatgroup,especiallyredmeat,is
seeninthethirdtertile(highfootprintvalues),meatsalsohadasizeablecontributionto
EnFvaluesinalltertiles,differingonlyinthetype.Participantsinthelow‐footprintgroup,
forexample,consumedmorechickenandfish.Thisobservationcanbeinterpretedasa
possiblereplacementofredmeatinthefaceofdifficultiesinitspurchase,whichismainly
associatedwithaffordability.
Furthermore,weobservedthatindividualsinthelow‐footprintgroupconsumed
morebread,sugar,couscous,andtapioca,whicharemoreaffordablefoods.Inturn,those
Figure 6.
Contribution of foods to the values of carbon, water, and ecological footprints split into low,
medium, and high footprints based on the answers to the propensity questionnaire by the participants
of the BRAZUCA Natal study (n= 411).
Meats were the main contributors to the footprint values, especially in the tertile
concerning the highest EnF values. For CF, the meat group accounted for 88.0% of the
total value in the group with the highest CF, whereas its value was around 63.1% in the
group with the lowest CF. For WF and EF, the values were 73.4% and 79.3% in the group
with the highest footprint and 50.3% and 60.80% in the group with the lowest footprint,
respectively. Although the higher contribution of the meat group, especially red meat, is
seen in the third tertile (high footprint values), meats also had a sizeable contribution to
EnF values in all tertiles, differing only in the type. Participants in the low-footprint group,
for example, consumed more chicken and fish. This observation can be interpreted as a
possible replacement of red meat in the face of difficulties in its purchase, which is mainly
associated with affordability.
Furthermore, we observed that individuals in the low-footprint group consumed more
bread, sugar, couscous, and tapioca, which are more affordable foods. In turn, those in
the high-footprint group consumed more dairy products (milk, cheese, and yogurt) when
compared with the low-footprint group.
The higher consumption of affordable foods such as sugar and bread, associated with
lower consumption of dairy and meats may be related to the socioeconomic characteristics
associated with the group and its consequent influence on purchasing more expensive
foods. Such a result was also found in previous studies [17,18,25,46].
The difficulty in purchasing more expensive foods, such as those of animal origin, and
the higher consumption of more affordable foods also is reflected in food purchase patterns.
Those with high EnF values were the ones who bought more fast food, ate more often at
snack bars, and used food delivery services more often when compared with individuals
with lower EnF values. The 2017–2018 Household Budget Survey (Pesquisa de Orçamentos
Familiares—POF) showed that the higher the income, the greater the expenses of eating
away from home [63].
Several studies around the world have also explored the hypothesis of the influence
of individual characteristics on food purchases and the consequent impacts on footprint
values. In China, individuals in the upper classes had a higher environmental impact due to
Foods 2022,11, 3842 16 of 20
the consumption of foods of animal origin when compared with those in the lower classes.
Meat consumption has also been considered a major contributor to the increase in CF, WF,
and land use [
64
]. In the United States, individuals with better socioeconomic status, which
accounted for greater environmental impacts (GHG emissions, water use, land use, and
energy consumption) and was especially related to the higher consumption of livestock
foods, such as meats, milk, and dairy products [
65
]. In a Swedish study, the authors argued
that higher schooling, associated with higher income, enables more frequent consumption
of foods that contribute to footprint values such as meat and dairy [
46
]. In Brazil, it was
found that the CF increased along with income and schooling [28].
It is, therefore, important to discuss not only the need to change dietary standards but
also the widespread access to quality diets. The study by He et al. [
65
] reported that 38%
of Blacks and Hispanics in lower classes with lower schooling were unable to adhere to a
healthy and sustainable dietary standard, as such diets can cost more and would become a
hindrance to behavior change. In this sense, non-white female individuals with a lower
family income and lower schooling would possibly require greater effort to change diets
than white males with a higher family income and higher schooling.
Nevertheless, the relationship can be paradoxical. Access to quality diets can result
in an increased consumption of food of animal origin, thus increasing the environmental
impact. In this regard, it is necessary to work on actions at the individual and popula-
tion levels. Scientific research is essential to work on change; however, as described by
Willet et al. [
1
], the full range of policy levers is likely required. It can start initially with
soft policy interventions, such as consumer advice, information, education, and labeling.
However, it is not possible to expect that the whole food system and the impacts associated
with it will be modified by only soft policy interventions. More incisive actions that involve
all stakeholders are necessary so that real change can be observed, such as laws, fiscal
measures, subsidies, and penalties.
In view of this, it is important to highlight the need to restructure the food system
at all levels by changing the way foods have been produced, rethinking agricultural and
livestock models, attributing responsibilities to the food and beverage industry, and revising
consumer behavior, which are seen as aspects that directly relate to environmental, social,
economic, health, and cultural issues.
3.4. Limitations and Strengths of the Study
As limitations, we highlight that the EnF database employed, despite considering the
different forms of preparation, was based on a review of published studies, which may not
precisely reflect the footprint values of the population in the present study, as many EnF
estimates were performed in other countries. We also emphasize that the size sample may
not be adequate to draw a profile of the environmental impact of the food consumption
of people residing in Natal/RN. For this reason, some statistical analyses could not be
performed due to the non-normal distribution, which may have compromised the strength
and precision of the analyses.
One major strength is the originality of the research. As far as we know, there are
no studies on the dietary environmental impact and its association with socioeconomic
characteristics and food purchase practices of adults and elderly people in the Northeast
region of Brazil. The importance of the results herein presented is also noteworthy, as they
can be used to guide future studies and public policies towards the promotion of behavior
change among the population for the purpose of healthier and more sustainable diets.
4. Conclusions
This study estimated the EnF of adults and elderlies living in the city of Natal, RN,
Brazil, and investigated its association with socioeconomic characteristics and food con-
sumption and purchase practices. We conclude that individual characteristics such as sex,
ethnicity, schooling, and per capita family income directly impact access to foods and,
consequently, the environmental footprint values. We also point out that food purchase
Foods 2022,11, 3842 17 of 20
behaviors, such as a greater frequency of the use of food delivery services, consumption
of fast food, and having meals at snack bars are also associated with higher CF and WF
values. Therefore, it is important to emphasize that lower footprint values do not necessar-
ily indicate a healthier and more sustainable diet. In this sense, it is important to develop
a broader outlook over issues involving diet, which are seen as dietary choices that are
influenced by the social and economic context of individuals. Studies with a larger sample,
intervention studies, and government actions on the food system are necessary to move
towards more sustainable and healthy diets.
Supplementary Materials:
The following supporting information can be downloaded at: https:
//www.mdpi.com/article/10.3390/foods11233842/s1, Table S1: Per capita consumption and EnF
values estimated for each food group; Table S2: Adaptation of foods with no environmental footprints;
Table S3: Statistical result of the correspondence analysis.
Author Contributions:
Conceptualization, L.M.J.S., P.M.R., C.d.O.L., S.C.V.C.L. and D.M.L.M.;
methodology, L.M.J.S., P.M.R., C.d.O.L., S.C.V.C.L. and D.M.L.M.; formal analysis, M.H., C.V.S.d.S.,
D.V., N.M.D. and Y.B.B.; investigation, M.H., C.V.S.d.S. and L.M.J.S.; resources, C.d.O.L., S.C.V.C.L.,
D.M.L.M., M.H. and C.V.S.d.S.; data curation, M.H., C.V.S.d.S. and L.M.J.S.; writing—original draft
preparation, M.H., L.M.J.S., C.V.S.d.S. and D.V.; writing—review and editing, M.H., L.M.J.S., P.M.R.,
C.V.S.d.S., D.V., N.M.D., C.d.O.L., S.C.V.C.L. and D.M.L.M.; supervision, L.M.J.S., C.d.O.L. and
S.C.V.C.L.; project administration, C.d.O.L.; funding acquisition, C.d.O.L., S.C.V.C.L., D.M.L.M. and
L.M.J.S. All authors have read and agreed to the published version of the manuscript.
Funding:
This work was funded by the Coordenação de Aperfeiçoamento de Pessoal de Nível
Superior (Financial Code 001) and by the Conselho Nacional de Desenvolvimento Científico e
Tecnológico (431053/2016-2 and 405837/2016-0).
Data Availability Statement:
The data presented in this study are available on request from the corre-
sponding author. The data are not made public due to Brazilian law (CNS Resolution 466/12), which
enforces the confidentiality and privacy of participants and data during all phases of research. The en-
vironmental footprint database used in this study is openly available at http://www.livrosabertos.sibi.
usp.br/portaldelivrosUSP/catalog/view/442/394/1603 (https://doi.org/10.11606/9788588848405).
Acknowledgments:
The authors would like to thank: Ângelo Giuseppe Roncalli da Costa Oliveira
for calculating the sample size and performing further analyses regarding the final sample; Adélia
da Costa Pereira de Arruda Neta, Gustavo Rosa Gentil Andrade, Ana Gabriella Costa Lemos da
Silva, and Mariana Silva Bezerra for their valuable work on food consumption data organization
and analysis; allgraduate and undergraduate students that participated in data collection and data
wrangling; the Department of Nutrition/UFRN for providing the space and materials necessary
for this study; and the editors and reviewers for their valuable comments and suggestions for
the manuscript.
Conflicts of Interest:
The authors declare no conflict of interest. The funders had no role in the design
of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or
in the decision to publish the results.
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