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Integrating emergy theory and food security: An examination of the emergy exchange ratio

Author

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  • Nacimento, Rafael Araújo
  • Canever, Mario Duarte
  • Ortega, Fábio José Muneratti
  • dos Santos, Luiz Carlos Terra
  • Ely, Regis Augusto
  • Gameiro, Augusto Hauber
  • Agostinho, Feni
  • Almeida, Cecília
  • Giannetti, Biagio Fernando

Abstract

Food security is often linked to economic exchange, but it involves broader dynamics and can also be achieved through non-market pathways. Recognizing this complexity, the Emergy Exchange Ratio (EER) was proposed—a ratio between the emergy of food acquired and that of money spent—as a complementary indicator to assess food security. In this study, we examined the relationship between the EER and the four dimensions of food security using indicators from the Global Food Security Index (GFSI). Machine learning models, particularly Ridge Regression, were employed to predict EER values for Brazil from 2012 to 2022. The Ridge model showed strong performance (R² = 0.89; MAE = 0.035), indicating good explanatory power. Among 25 GFSI indicators, “food security and access policy commitments” (∼16 %) and “sufficiency of supply” (∼14 %) were the top predictors of EER variation. These results suggest that the EER is sensitive to public policies and food supply dynamics, indicating its relationship with food security. Limitations include the focus on Brazilian data and data quality constraints. Still, EER offers a systemic, biophysical lens on food access grounded in Odum’s emergy theory.

Suggested Citation

  • Nacimento, Rafael Araújo & Canever, Mario Duarte & Ortega, Fábio José Muneratti & dos Santos, Luiz Carlos Terra & Ely, Regis Augusto & Gameiro, Augusto Hauber & Agostinho, Feni & Almeida, Cecília & Gi, 2026. "Integrating emergy theory and food security: An examination of the emergy exchange ratio," Ecological Modelling, Elsevier, vol. 513(C).
    • Handle: RePEc:eee:ecomod:v:513:y:2026:i:c:s0304380025004156
    DOI: 10.1016/j.ecolmodel.2025.111429
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    References listed on IDEAS

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    1. Alexis H. Villacis & Syed Badruddoza & Ashok K. Mishra, 2024. "A machine learning‐based exploration of resilience and food security," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 46(4), pages 1479-1505, December.
    2. repec:ags:aaea22:343542 is not listed on IDEAS
    3. Maricic, Milica & Bulajic, Milica & Dobrota, Marina & Jeremic, Veljko, 2016. "Redesigning The Global Food Security Index: A Multivariate Composite I-Distance Indicator Approach," International Journal of Food and Agricultural Economics (IJFAEC), Alanya Alaaddin Keykubat University, Department of Economics and Finance, vol. 4(01), pages 1-18, January.
    4. Andrew Allee & Lee R. Lynd & Vikrant Vaze, 2021. "Cross-national analysis of food security drivers: comparing results based on the Food Insecurity Experience Scale and Global Food Security Index," Food Security: The Science, Sociology and Economics of Food Production and Access to Food, Springer;The International Society for Plant Pathology, vol. 13(5), pages 1245-1261, October.
    5. Sepideh Fahimifar & Khadijeh Mousavi & Fatemeh Mozaffari & Marcel Ausloos, 2023. "Identification of the most important external features of highly cited scholarly papers through 3 (i.e., Ridge, Lasso, and Boruta) feature selection data mining methods," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(4), pages 3685-3712, August.
    6. Mwambo, Francis Molua & Fürst, Christine & Nyarko, Benjamin K. & Borgemeister, Christian & Martius, Christopher, 2020. "Maize production and environmental costs: Resource evaluation and strategic land use planning for food security in northern Ghana by means of coupled emergy and data envelopment analysis," Land Use Policy, Elsevier, vol. 95(C).
    7. Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i01).
    8. Villacis, Alexis H. & Badruddoza, Syed & Mishra, Ashok K., 2024. "A Machine Learning-based Exploration of Resilience through the Lens of Food Security," 2024 Annual Meeting, July 28-30, New Orleans, LA 343542, Agricultural and Applied Economics Association.
    9. Rafael Araujo Nacimento & Mario Duarte Canever & Cecilia Almeida & Feni Agostinho & Augusto Hauber Gameiro & Biagio Fernando Giannetti, 2023. "Hidden Costs Associated with Smallholder Family-Based Broiler Production: Accounting for the Intangibles," Sustainability, MDPI, vol. 15(22), pages 1-21, November.
    10. Headey, Derek D. & Ecker, Olivier, 2012. "Improving the measurement of food security," IFPRI discussion papers 1225, International Food Policy Research Institute (IFPRI).
    11. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
    12. Meital Izraelov & Jacques Silber, 2019. "An assessment of the global food security index," Food Security: The Science, Sociology and Economics of Food Production and Access to Food, Springer;The International Society for Plant Pathology, vol. 11(5), pages 1135-1152, October.
    13. Gasparatos, Alexandros, 2011. "Resource consumption in Japanese agriculture and its link to food security," Energy Policy, Elsevier, vol. 39(3), pages 1101-1112, March.
    14. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
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