Risk management of food health hazard by meat consumption reduction: a coopetitive game approach

  • David CarfíEmail author
  • Alessia Donato


In this paper, we face the serious problem of food health hazard, also in connection with global food production scarcity and feeding sustainability, in view of important environmental issues and the severe incumbent climate change. Specifically, our innovative risk management approach considers cooperation among producers of vegan and non-vegan food, a strong commitment more and more observed, recently, in technologically advanced western countries. The novelty of our work consists in proposing possible quantitative agreements among complementary food producers, usually non-interacting, in order to develop a sustainable healthy food production for human population—also characterized by low impact on the planet. Another new feature of our approach lies in using coopetition and game theory together; we show, quantitatively, how to conjugate human health defense, environmental defense, economic interests and less government spending, needs which usually appear in contrast with each other. Another point of our coopetitive approach is the suggestion of an easier way to entry the global market for vegan food producers. Meanwhile, our model suggests to big producers/sellers of non-vegan food a way to smoothly and rapidly transit toward more sustainable production. Technically, we propose an innovative exemplary complex agreement among global food sellers and small (but strongly sustainable and innovative) vegan food producers. Moreover, our model implies a general saving for the countries, by mitigating the health expenditures. The result of our mathematical study suggests a novel win–win solution for global economy, world environment and governments, while improving human population sustainability and climate change effects.


Sustainability of food production Environmental sustainability Game theory Coopetitive games Green economy 


Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


  1. Baglieri D, Carfì D, Dagnino G (2012) Asymmetric R&D alliances and coopetitive games. In: Advances in computational intelligence, communications in computer and information science, vol 300. Springer, Berlin, pp 607–621. zbMATHGoogle Scholar
  2. Baglieri D, Carfì D, Dagnino GB (2016) Asymmetric R&D alliances in the biopharmaceutical industry. Int Stud Manag Organ 46(2(3)):179–201. CrossRefGoogle Scholar
  3. Baumert KA, Herzog T, Pershing J, Institute WR (2005) Navigating the numbers: greenhouse gas data and international climate policy. World Resources Institute, Washington, D.CGoogle Scholar
  4. Carfì D, Donato A (2018) Coopetitive games for sustainability of global feeding and climate change: recent developments. J Environ Manag Tour 9(1(25)):200–215. CrossRefGoogle Scholar
  5. Carfì D, Donato A, Panuccio D (2017) A game theory coopetitive perspective for sustainability of global feeding: agreements among vegan and non-vegan food firms. In: Sustainable entrepreneurship and investments in the green economy, book series on advances in business strategy and competitive advantage (ABSCA). IGI Global, pp 100–143. CrossRefGoogle Scholar
  6. Carfì D, Donato A, Schilirò D (2018) Sustainability of global feeding. Coopetitive interaction among vegan and non-vegan food firms. In: Book of papers of 24th international sustainable development research society conference “action for a sustainable world: from theory to practice”, pp 592–605Google Scholar
  7. Carfì D, Donato A, Schilirò D (2019) Coopetitive solutions of environmental agreements for the global economy after COP21 in Paris. J Environ Manag. CrossRefGoogle Scholar
  8. Carfì D, Romeo A (2015) Improving welfare in Congo: Italian national hydrocarbons authority strategies and its possible coopetitive alliances with green energy producers. J Appl Econ Sci 10(4 (34)):571–592Google Scholar
  9. Carfì D, Schilirò D (2012a) A coopetitive model for the green economy. Econ Model 29(4):12151–219. CrossRefzbMATHGoogle Scholar
  10. Carfì D, Schilirò D (2012b) Global green economy and environmental sustainability: a coopetitive model. In: Advances in computational intelligence, communications in computer and information science, vol 300. Springer, Berlin, pp 593–606. zbMATHGoogle Scholar
  11. Deng W, Zhao H, Yang X, Xiong J, Sun M, Li B (2017a) Study on an improved adaptive PSO algorithm for solving multi-objective gate assignment. J Appl Soft Comput 59(C):288–302. CrossRefGoogle Scholar
  12. Deng W, Zhao H, Zou L, Yang X, Wu D (2017b) A novel collaborative optimization algorithm in solving complex optimization problems. Soft Comput 21(15):4387–4398. CrossRefGoogle Scholar
  13. Deng W, Xu J, Zhao H (2019) An improved ant colony optimization algorithm based on hybrid strategies for scheduling problem. IEEE Access 7:20281–20292. CrossRefGoogle Scholar
  14. Fiala N (2008) Meeting the demand: an estimation of potential future greenhouse gas emissions from meat production. Ecol Econ 67(3):412–419CrossRefGoogle Scholar
  15. Fields H et al (2016) Is meat killing us? J Am Osteopath Assoc 116:296–300. CrossRefGoogle Scholar
  16. Foley JA et al (2011) Solutions for a cultivated planet. Nature 478(7369):337–342CrossRefGoogle Scholar
  17. Kompas T, Pham VH, Che TN (2018) The effects of climate change on GDP by country and the global economic gains from complying with the Paris climate accord. Earth’s Future 6(8):1153–1173. CrossRefGoogle Scholar
  18. OECD (2019) Health status: cancer. Accessed May 2019
  19. OECD-FAO (2019a) Agricultural outlook 2011–2020. Cereals consumption: kilograms/capita. Accessed May 2019
  20. OECD-FAO (2019b) Agricultural outlook 2011–2020. Meat consumption (beef and veal): kilograms/capita. . Accessed May 2019
  21. Saint Louis C (2015) Meat and cancer: the W.H.O. report and what you need to know. The New York Times. Retrieved online at
  22. Tuomisto HL, Teixeira DMMJ (2011) Environmental impacts of cultured meat production. Environ Sci Technol 45(14):6117–6123CrossRefGoogle Scholar
  23. Walker P, Rhubart-Berg P, Mckenzie S, Kelling K, Lawrencw R (2005) Public health implications of meat production and consumption. Public Health Nutr 8(4):348–356CrossRefGoogle Scholar
  24. WHO (2015) Q&A on the carcinogenicity of the consumption of red meat and processed meat. Accessed May 2019
  25. Zhao H, Sun M, Deng W, Yang X (2017) A new feature extraction method based on EEMD and multi-scale fuzzy entropy for motor bearing. Entropy. CrossRefGoogle Scholar
  26. Zhao H, Yao R, Xu L, Yuan Y, Li G, Deng W (2018) Study on a novel fault damage degree identification method using high-order differential mathematical morphology gradient spectrum entropy. Entropy. CrossRefGoogle Scholar
  27. Zhao H, Zheng J, Xu J, Deng W (2019) Fault diagnosis method based on principal component analysis and broad learning system. IEEE Access 7:99263–99272. CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of MathematicsUniversity of California RiversideRiversideUSA
  2. 2.Department of EconomicsUniversity of MessinaMessinaItaly

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