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Optimizing School Food Supply: Integrating Environmental, Health, Economic, and Cultural Dimensions of Diet Sustainability with Linear Programming

Author

Listed:
  • Patricia Eustachio Colombo

    (Department of Public Health Sciences, Karolinska Institutet, 171 77 Stockholm, Sweden)

  • Emma Patterson

    (Department of Public Health Sciences, Karolinska Institutet, 171 77 Stockholm, Sweden
    Centre for Epidemiology and Social Medicine, Stockholm County Council, 112 21 Stockholm, Sweden)

  • Liselotte Schäfer Elinder

    (Department of Public Health Sciences, Karolinska Institutet, 171 77 Stockholm, Sweden
    Centre for Epidemiology and Social Medicine, Stockholm County Council, 112 21 Stockholm, Sweden)

  • Anna Karin Lindroos

    (The National Food Agency, Uppsala, Sweden and Department of Internal Medicine and Clinical Nutrition, Institute of Medicine, Gothenburg University, 405 30 Gothenburg, Sweden)

  • Ulf Sonesson

    (RISE Research Institutes of Sweden, 420 29 Gothenburg, Sweden)

  • Nicole Darmon

    (MOISA, INRA, Univ Montpellier, CIHEAM-IAMM, CIRAD, Montpellier SupAgro, 34060 Montpellier, France)

  • Alexandr Parlesak

    (Global Nutrition and Health, University College Copenhagen, 2200 Copenhagen, Denmark)

Abstract

There is great potential for reducing greenhouse gas emissions (GHGE) from public-sector meals. This paper aimed to develop a strategy for reducing GHGE in the Swedish school food supply while ensuring nutritional adequacy, affordability, and cultural acceptability. Amounts, prices and GHGE-values for all foods and drinks supplied to three schools over one year were gathered. The amounts were optimized by linear programming. Four nutritionally adequate models were developed: Model 1 minimized GHGE while constraining the relative deviation (RD) from the observed food supply, Model 2 minimized total RD while imposing stepwise GHGE reductions, Model 3 additionally constrained RD for individual foods to an upper and lower limit, and Model 4 further controlled how pair-wise ratios of 15 food groups could deviate. Models 1 and 2 reduced GHGE by up to 95% but omitted entire food categories or increased the supply of some individual foods by more than 800% and were deemed unfeasible. Model 3 reduced GHGE by up to 60%, excluded no foods, avoided high RDs of individual foods, but resulted in large changes in food-group ratios. Model 4 limited the changes in food-group ratios but resulted in a higher number of foods deviating from the observed supply and limited the potential of reducing GHGE in one school to 20%. Cost was reduced in almost all solutions. An omnivorous, nutritionally adequate, and affordable school food supply with considerably lower GHGE is achievable with moderate changes to the observed food supply; i.e., with Models 3 and 4. Trade-offs will always have to be made between achieving GHGE reductions and preserving similarity to the current supply.

Suggested Citation

  • Patricia Eustachio Colombo & Emma Patterson & Liselotte Schäfer Elinder & Anna Karin Lindroos & Ulf Sonesson & Nicole Darmon & Alexandr Parlesak, 2019. "Optimizing School Food Supply: Integrating Environmental, Health, Economic, and Cultural Dimensions of Diet Sustainability with Linear Programming," IJERPH, MDPI, vol. 16(17), pages 1-18, August.
  • Handle: RePEc:gam:jijerp:v:16:y:2019:i:17:p:3019-:d:259508
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    References listed on IDEAS

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    5. United Nations UN, 2015. "Transforming our World: the 2030 Agenda for Sustainable Development," Working Papers id:7559, eSocialSciences.
    6. Victor E. Smith, 1959. "Linear Programming Models for the Determination of Palatable Human Diets," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 41(2), pages 272-283.
    7. Janet Ranganathan & Daniel Vennard, 2016. "Shifting Diets for a Sustainable Food Future," Working Papers id:10890, eSocialSciences.
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    1. García-Leal, Javiera & Espinoza Pérez, Andrea Teresa & Vásquez, Óscar C., 2023. "Towards the sustainable massive food services: An optimization approach," Socio-Economic Planning Sciences, Elsevier, vol. 87(PA).

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