Compared to similar studies, the results obtained in this research evidence that individuals in Brasilia contribute significantly to GHG emissions. For instance, GHG emissions due to food consumption in Japan have been estimated at 3.84 kg CO
2e/person/day [
43] while, in Finland, estimates range between 3.84 kg CO
2e/person/day [
44] and 4.79 kg CO
2e/person/day [
43]. Brasilia’s food-related GHG emissions were also more significant than other countries in Latin America. In Chile, daily emissions ranged from 2.42 to 4.74 kg of CO
2e/person/day [
25] whereas, in Mexico, the food carbon footprint implies 3.9 kg of CO
2e/person/day [
26]. However, some places still show higher GHG emissions per capita than Brasilia. For example, while in Argentina the diets averaged 5.48 kg CO
2e/person/day [
45] of GHG emissions, Toronto contributes 6.03 kg of CO
2e/person/day [
24]. In the United States, individuals with a standard diet could release 8.14 kilograms of CO
2e daily [
46]. Previous research also indicates that average daily GHG emissions from food consumption in Brazil range from 4.49 [
30] to 6.67 [
28] kilograms of CO
2e per person.
The high intensity of GHG emissions related to meat consumption is due to beef, whose production involves 24.47 kg CO2e/kg. Furthermore, pork contributes 6.9 kg CO2e/kg, and poultry with 2.10 kg CO2e/kg—combined with beef and other items within the group—results in an average intensity of emissions of 12.43 kg CO2e/kg of meat.
Fish have low GHG emissions within the animal proteins (meat). The consumption of fresh, canned, and salted fish results in 76.22 t CO2e (0.69%), 7.42 t CO2e (0.07%), and 9.232 t CO2e (0.08%), respectively, with intensities of 2.793 kg CO2e/kg, 4.892 kg CO2e/kg, and 2.472 kg CO2e/kg, respectively.
Subsequently, when it comes to reducing the impact of food consumption on climate change, replacing beef with low-carbon proteins can be an option. Just for comparison purposes, if beef (178.21 t) was replaced by 50% of poultry (GHG emissions intensity 2.01 kg CO2e/kg) and 50% of fresh fish (2.79 kg CO2e/kg), the total GHG emissions of household food consumption in Brasilia would be reduced from 11,062.39 t CO2e to 7138.45 t CO2e, a 35.47% reduction.
4.3.1. GHG Emissions by Sociodemographic Groups
Exploratory statistical analyses were performed on the results of GHG emissions. These results were compared regarding family income, per capita income, household situation, type of household, age group, and gender of the respondents. The average emissions by group are presented in
Table 5.
Our findings identified key groups for future climate change mitigation actions by focusing on multiple comparisons between clusters of individuals with similar socioeconomic variables and food consumption patterns.
First, there were significant differences in GHG emissions between individuals according to age and gender variables. Regarding age, the most meaningful average emissions were found in individuals between 45 and 54 years old, followed by those between 35 and 44. The lowest mean emission was registered for individuals older than 65. However, despite the differences in the means between the age groups up to 24 years, 25–34 years, 35–44 years, and 45–54 years, an application of Tukey’s test with a 95% confidence interval did not show differences between these groups. Only the age groups between 45–54 years and ≥65 years and between 55–64 years and ≥65 years showed sufficient statistical differences. Hence, it can be inferred that GHG emissions associated with food consumption in Brasilia decrease as individuals advance beyond 65. Furthermore, the highest level of food emissions occurs in individuals between 45 and 54 years.
As for gender, the average for respondents identified as “male” (7.06 kg CO2e/individual) was about 27% higher than that of individuals identified as “female” (5.15 kg CO2e/individual). This difference can be explained by the higher consumption of food by men, especially of meat (0.30 kg/person by men, 0.22 kg/person by women), cereals (0.30 kg/person by men, 0.21 kg/person by women), and legumes (0.30 kg/person by men, 0.19 kg/person by women).
Regarding income, no sufficient evidence was found to reject the hypothesis of equality between total GHG emissions for family income and per capita income variables. This result probably occurred because the range of GHG emissions is comprehensive for all income groups.
We subsequently analyzed the average individual GHG emissions by food group according to previously used sociodemographic variables.
Table 6 contains the key results of the Two-Way Analysis of Variance (ANOVA) between the groups formed according to household income, per capita income, household status, type of residence, age, and gender for each food category.
For household and per capita income, the cereal and legume groups have higher emissions for the lowest income bracket (up to one minimum wage). This outcome is due to the diet of individuals with lower family income, primarily made up of staple foods such as rice and beans.
On the other hand, dairy-related emissions are lower for this group of individuals (income < 1 MW), and the median increases as family income increases, indicating increased consumption of these items. Dairy products contribute, on average, 2.21 kg CO
2e/kg of food (
Table 3). However, while milk has an average intensity of GHG emissions of 1.41 kg CO
2e/kg, items such as cheese are more impactful, with 10.74 kg CO
2e/kg. Consequently, dairy-related emissions might be even more considerable in higher-income groups due to differences in the quantities and items consumed.
Observing the results of GHG emissions by food group according to household status, five of them showed statistically different averages between rural and urban areas: dairy products, vegetables, eggs, beverages, and pulses.
Among the food categories mentioned above, the average GHG emissions were higher for people domiciled in urban areas, but only for dairy products and eggs. For the others, the contribution of rural households to climate change was more notable.
Considering the average food consumption data by food category (
Table 2), the categories beverages, vegetables, and legumes were consumed more by individuals living in rural areas, which explains the higher emissions of those for these categories. Similarly, dairy products and egg consumption are more significant in urban areas, generating higher emissions for this type of household.
Regarding the household type, individuals grouped into houses, apartments, and other types showed different averages for meat, cereals, dairy products, and snacks. For meat, although the median emissions are higher for households classified as other, the average consumption for the houses is higher (
Table 4). Therefore, the emissions values related to meat consumption are more distributed to single-family dwellings. The differences in GHG emissions related to meat consumption between individuals living in houses and apartments can be explained by the higher meat consumption by individuals residing in houses, as shown in
Table 2 and
Table 3. For cereals, as for meats, the median GHG emissions are higher for houses, although the median is higher for the “other” group. In parallel, consumption in this category is higher for households, and the pairwise comparison did not allow a comparison of houses and apartments with other types of households. On the other hand, for dairy products and snacks, the average GHG emissions are higher for individuals living in apartments due to the higher consumption of items in this category.
As for the age group of the POF respondents, the GHG emissions showed differences between the averages of the food groups for two of the most impacting categories: meat and beverages. For beverages, age group pairs ≤ 24 and 45–54 (
p-value < 0.00001) and <24 and 55–64 (
p-value < 0.00001) showed statistically different GHG emissions averages from each other. Of these, the 45–54-year-old group had the highest mean, with 1.32 kg CO
2e/person, followed by 55–64-year-old individuals, with 1.25 kg CO
2e/person. The lowest GHG emissions are associated with the lowest age group (up to 24 years), accounting for 0.68 CO
2e/individual. Variations in consumption can explain the differences between these groups. Therefore, the groups that produce fewer emissions consume less, and vice versa (see
Table 2 and
Table 3), although some variations in beverage consumption were not reflected in GHG emissions.
For example, individuals between the ages of 35 and 44 are responsible for the highest beverage consumption. However, the group with the highest GHG emissions is between 45 and 54 years old. Therefore, these divergent fluctuations might be explained by differences in consumption patterns between these age groups. The groups with higher total category consumption and lower emissions consume, on average, foods with less intense climate change impacts. The same analysis can be employed for meat, where statistical evidence of differences between averages was found exclusively for groups over 65 and between 45 and 54 years old. Although, in absolute terms, the group with the highest consumption (45–54 years old) showed higher average emissions, the difference between the average consumed between the two groups was 24.17% (compared to the group between 45 and 54 years old), the difference between the average emissions was 49.03%. Therefore, it can be concluded that there was a variation in the total consumed between the age groups. On the other hand, there was a change in the foods consumed so that the 45–54 age group chose items with a higher pollution intensity, such as beef.
In conclusion, considering the gender of the respondents, men and women had different average GHG emissions in the beverages, meat, cereals, sweets, fruits, legumes, and eggs groups, all due to the proportional increase in consumption in the respective categories compared to females.
4.3.2. The Effect of Dietary Patterns on GHG Emissions
The purpose of this research is to characterize the effect of different dietary patterns (omnivorous, no beef, no pork, no red meat, pescatarian, vegetarian, and vegan) on the contribution to GHG emissions. The results of this analysis can be observed in
Figure 2. Individuals with a self-reported vegan diet are responsible for the lowest average GHG emissions (3.05 kg CO
2e/day), followed by no red meat (3.07 kg CO
2e/day), pescatarian (4.48 kg CO
2e/day), and vegetarian diets (4.57 kg CO
2e/day). On the other hand, people with omnivorous (7.44 kg CO
2e/day), no pork (7.01 kg CO
2e/day), and no beef (4.61 kg CO
2e/day) diets have the higher food-related contribution to climate change. The ANOVA results confirmed significant differences between the dietary groups (
p-value < 0.0001). An application of Tukey’s test with a 95% confidence interval showed statistical evidence that the average emissions of omnivorous and no pork diets are higher than vegan, vegetarian, no beef, and no pork diets (
p-value < 0.05).
When analyzing the results associated with the dietary pattern, meat, and dairy consumption play a significant role in carbon footprint. Vegan and no red meat diets have a lower average emission once there is no consumption of carbon-intense food, i.e., animal-based or red meat. Surprisingly, a vegetarian diet without meat averaged higher GHG emissions than no red meat and pescatarian diets in Brasília. In this case, consuming carbon-intense dairy products such as milk and cheese is responsible for increasing the vegetarian carbon footprint. While a pescatarian diet promotes a transition towards sustainable food habits, this diet does not consider meat consumption different from fish and sea products. Therefore, its GHG emissions are higher than the no red meat diet. The results can be explained by substituting red and poultry meat for fish and, primarily, by dairy products, implying a higher carbon intensity. Therefore, due to consumer choices in Brasília, an effective policy aiming to reduce food contribution to climate change should focus on a transition to the reduction of red meat through promoting the shift to the no red meat diet, a transition which could be less impactful for consumers.