Executive Summary

January 08, 2026

Analysis and prediction of greenhouse gas emissions in family food consumption

Author: Sergio Silvestri Silva — Advisor: Gabrielle Maria Romeiro Lombardi

Summary prepared by the ResumeAI tool, an artificial intelligence solution developed by Instituto Pecege focused on synthesis and writing.

This study applied data science methodologies to assess and predict the annual per capita greenhouse gas (GHG) emissions from the food consumption of a family of four Brazilian immigrants in the Czech Republic. The objectives included comparing the results with the averages of the European Union (EU-27) and the Czech Republic, using the European Platform on Life Cycle Assessment (EPLCA) model as a basis, and identifying the best time series model to project the family’s emissions for the next 12 months, providing a quantitative tool for carbon footprint management.

The growing concentration of GHGs in the atmosphere, driven by human activity, represents an urgent challenge. The Intergovernmental Panel on Climate Change (IPCC, 2023) reports that, between 2011 and 2020, the average global surface temperature increased by 1.1 °C compared to pre-industrial levels (1850-1900), approaching the 1.5 °C limit set by the Paris Agreement. This scenario is exacerbated by a development model based on unsustainable energy use, land-use changes, and unequal consumption patterns.

The European Union has focused on mitigating climate change by analyzing Consumption-Based Emissions (CBE), which consider the environmental impact of goods and services throughout their entire life cycle. As highlighted by Axelsson et al. (2024), the CBE approach is crucial for understanding a region’s true carbon footprint, as it internalizes emissions associated with imported products. The European Commission, through the EPLCA platform, has developed a Life Cycle Assessment (LCA) framework to monitor the impacts of consumption in key sectors such as food, transportation, and housing, providing data for public policies (European Commission [EU], 2021).

The food sector is a significant contributor to GHG emissions. Studies such as Aleksandrowicz et al. (2016) demonstrate that the transition to diets with lower content of animal products, especially red meat, can lead to significant reductions in emissions. Therefore, the analysis of consumption behavior at the family level, as proposed in this work, serves as a microcosm for understanding how individual actions can collectively contribute to achieving climate goals.

By quantifying the impact of their choices, families can make more informed decisions. This research uses data science tools to translate everyday consumption data into tangible environmental metrics, promoting micro-level agency while engaging with the macro-trends and challenges faced by the European Union on its journey towards climate neutrality.

The methodology adopted was an exploratory analysis of quantitative data, following the conceptual hierarchy that transforms data into applicable knowledge (Favero and Belfiore, 2024). The study was based on secondary data, divided into internal (family purchase records) and external (scientific and governmental databases), according to Malhotra’s (2004) classification. The computational tools used included the Python language and its libraries (Pandas, NumPy, Matplotlib), as well as Microsoft Excel software for consolidation.

The main internal dataset, nota_fiscal, was built from the extraction of information from supermarket invoices in PDF between 2018 and 2024. Python scripts automated the reading and applied Data Wrangling techniques to clean and standardize the data, resulting in an initial dataset with 14,118 observations. The analysis period was focused between 2021 and 2024, comprising a sample of 12,301 observations. From this, the item set was created to categorize each product by unit of measure, consumption area, and country of origin.

For the quantification of emissions, two external datasets were constructed. The first, co2eproduct, contained the GHG emission factors (in kg of CO2eq per kg or liter) for 125 food types, mainly extracted from the Poore and Nemecek (2018) database and supplemented with data from the Carbon Cloud platform (2019). The second, co2etransport, was created to estimate the emissions associated with product logistics, using data from the CO2 Everything website (2019) and distances estimated by Google Maps for different transport modes.

The consolidation of all sets resulted in the final dataset, consumption, with 10,909 observations and 13 variables. For time series modeling, emission data were aggregated monthly and normalized per capita. The forecasting methodology, based on Hyndman and Athanasopoulos (2021), involved series decomposition, splitting the data into training (75%) and testing (25%) sets, and evaluating multiple models (Mean, Naïve, Drift, SES, Holt, Holt-Winters, ARIMA). The selection of the best model was based on the Mean Absolute Percentage Error (MAPE), and its statistical validation was confirmed by residual analysis with the Ljung-Box and Shapiro-Wilk tests, ensuring model adequacy, as recommended by Oliveira et al. (2024).

The analysis of data from 2021 to 2024 revealed a progressive increase in food consumption, with the year 2024 accounting for 36% of all observations. This growth can be attributed to factors such as the children entering adolescence, a phase of higher energy demand (Story et al., 2002), the adoption of a physical activity routine, and the transition to remote learning, which increased meals at home. The analysis showed that only six products — bread, beef, cheese, chocolate, yogurt, and chicken — represented 37% of all items purchased.

Beef emerged as the main driver of GHG emissions, accounting for 51.81% of the total food emissions of the household, a finding consistent with the literature (Poore and Nemecek, 2018). Together, animal products (beef, chicken, cheese, yogurt, and lasagna) were responsible for over 71% of the food carbon footprint. The annual analysis showed consistency in this pattern, with a notable jump in emissions in 2024, driven by increased consumption of meat and dairy products.

The comparison with EPLCA data for the EU-27 and the Czech Republic revealed significant cultural differences. From 2021 to 2023, per capita household emissions (1.85 tons of CO2eq in 2023) remained below regional averages. However, in 2024, household emissions increased by 68% to 3.01 tons of CO2eq per capita, surpassing estimates for the EU-27 (3.0 tons) and the Czech Republic (2.5 tons). While the European consumption pattern showed a balance between emissions from pork, beef, poultry, and cheese, the Brazilian household profile showed a marked dependence on beef, whose emissions were more than double the regional average.

This behavior suggests a strong resilience of cultural eating habits, even after eight years of residence in a new country, explained by factors such as food identity and traditional meal preparation (Lillekroken et al., 2024; Parasecoli, 2014). The time series analysis also revealed a seasonal pattern, with drops during the spring and summer months, possibly associated with holidays, and peaks in autumn and winter, when the climate encourages domestic consumption (Zarei et al., 2019).

In predictive modeling, the Drift model stood out as the most suitable for projecting future emissions, presenting the lowest Mean Absolute Percentage Error (MAPE) of 9.7%. The ARIMA model was deemed unsuitable, as the Augmented Dickey-Fuller test indicated that the training series was already stationary. The validation of the Drift model was performed through residual analysis, where the Ljung-Box test confirmed the absence of autocorrelation (p-value > 0.05) and the Shapiro-Wilk test attested to the normality of the residuals (p-value > 0.05), conferring statistical reliability to the predictions.

The Drift model, which extrapolates a linear trend, projected a continuous increase in emissions. The model’s slope indicated an average growth of 0.0007 tons of GHG per capita each month. The forecast for the next 12 months points to a total of 3.62 tons of GHG per capita, which would represent a 20% increase compared to the 2024 level. This result reinforces the growth trend observed in the descriptive analysis and underscores the urgency of interventions to mitigate this trajectory.

However, it is fundamental to recognize the limitations of the Drift model. Its premise of a constant linear trend makes it vulnerable to structural breaks or abrupt changes in the series’ behavior (Hyndman and Athanasopoulos, 2021). Therefore, although it is a useful tool for short-term forecasts, its application must be accompanied by continuous monitoring.

The research successfully demonstrated the application of Data Science methodologies to quantify and predict GHG emissions associated with food consumption, revealing that food identity, linked to beef consumption, is the main determinant of the family carbon footprint. The comparison with EU-27 and Czech Republic standards highlighted the profound differences in consumption habits and cultural resilience. The Drift model proved to be the most effective for short-term forecasting, confirming a growth trend in emissions that requires attention.

The continuity of this study is recommended through the annual incorporation of new observations, allowing for the re-evaluation of analyses and the exploration of more sophisticated predictive models. The research underscores the need to seek consumption alternatives with lower environmental impact, such as the gradual substitution of beef with other protein sources, in order to align family habits with the European Union’s sustainability goals. It is concluded that the objective was achieved: it was demonstrated that the application of data science methodologies allowed for the evaluation, comparison, and prediction of the family’s GHG emissions, identifying the Drift model as the most suitable for projection and beef as the main emission vector.

References:
Aleksandrowicz L.; Green R.; Joy E. J. M.; Smith P.; Haines A. 2016. The Impacts of Dietary Change on Greenhouse Gas Emissions, Land Use, Water Use, and Health: A Systematic Review. PLoS ONE 11(11): e0165797.
Axelsson, K.; Gong, J.; Dugast, C.; Lambe, F.; Maquet, P; Suljada, T. 2024. Consumption-based emissions: a new frontier for EU climate policy. SEI Report, June 2024. Stockholm Environment Institute, Stockholm, Sweden.
Carbon Cloud. 2019. ClimateHub is an open climate footprint database that can be used for internal purposes without cost.
CO2 Everything. 2019. The Carbon Footprint of everyday products and activities.
European Commission [EU]. 2013. European Platform on LCA | EPLCA.
European Commission [EU]. 2021. Consumption Footprint.
Favero, L. P.; Belfiore, P. 2024. Manual de Análise de Dados: estatística e Machine Learning com EXCEL®, SPSS®, STATA®, R® e Python®. 2ed. LTC, Rio de Janeiro, RJ, Brazil.
Hyndman, R. J.; Athanasopoulos, G. 2021. Forecasting: principles and practice, 3rd edition. OTexts, Melbourne, Australia.
Intergovernmental Panel on Climate Change [IPCC]. 2023. Sections. In: Climate Change 2023: Synthesis Report. Contribution of Working Groups I, II and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team, H. Lee and J. Romero (eds.)]. IPCC, Geneva, Switzerland, pp. 35-115, doi: 10.59327/IPCC/AR6-9789291691647.
Lillekroken, D.; Bye, A.; Halvorsrud, L.;Terragni, L.; Debesay, J. 2024. Food for Soul—Older Immigrants’ Food Habits and Meal Preferences After Immigration: A Systematic Literature Review. J Immigrant Minority Health 26, 775–805.
Malhotra, N. K. 2004. Pesquisa de Marketing: uma aplicação orientada. Bookman, Porto Alegre, RS, Brazil.
Oliveira, R.; Albarracin, O. Y. E.; Silva, G. R. 2024. Introdução às séries temporais: uma abordagem prática em Python. Mackenzie, São Paulo, SP, Brazil.
Parasecoli, F. 2014. Food, Identity, and Cultural Reproduction in Immigrant Communities. Social Research: An International Quarterly 81(2): 415-439.
Poore, J.; Nemecek T. 2018. Reducing food’s environmental impacts through producers and consumers.
Story, M.; Neumark-Sztainer, D.; & French, S. 2002. Individual and environmental influences on adolescent eating behaviors.
Zarei, M. M.; Agudo-Peregrina, a. F.; Ponce-Cueto, E. 2019. Choosing the delivery and return method in purchases: the effect of situational factors in omni-channel contexts.

Summary prepared by the ResumeAI tool, an artificial intelligence solution developed by Instituto Pecege focused on synthesis and writing.


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