IDEAS home Printed from https://ideas.repec.org/p/ags/eaae05/24751.html
   My bibliography  Save this paper

Energy Crop Supply in France: A Min-Max Regret Approach

Author

Listed:
  • Kazakci, Akin
  • Rozakis, Stelios

Abstract

This paper attempts to estimate energy crop supply using an LP model comprising hundreds of representative farms of the arable cropping sector in France. In order to enhance the predictive ability of such a model and to provide an analytical tool useful to policy makers, interval linear programming (ILP) is used to formalise bounded rationality conditions. In the presence of uncertainty related to yields and prices it is assumed that the farmer minimises the distance from optimality once uncertainty resolves introducing an alternative criterion to the classic profit maximisation rationale. Model validation based on observed activity levels suggests that about 40% of the farms adopt the min-max regret criterion. Then energy crop supply curves, generated by the min-max regret model, are proved to be upward sloped alike classic LP supply curves.

Suggested Citation

  • Kazakci, Akin & Rozakis, Stelios, 2005. "Energy Crop Supply in France: A Min-Max Regret Approach," 2005 International Congress, August 23-27, 2005, Copenhagen, Denmark 24751, European Association of Agricultural Economists.
    • Handle: RePEc:ags:eaae05:24751
    DOI: 10.22004/ag.econ.24751
    as

    Download full text from publisher

    File URL: https://ageconsearch.umn.edu/record/24751/files/cp05ka02.pdf
    Download Restriction: no
    File URL: https://libkey.io/10.22004/ag.econ.24751?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Ishibuchi, Hisao & Tanaka, Hideo, 1990. "Multiobjective programming in optimization of the interval objective function," European Journal of Operational Research, Elsevier, vol. 48(2), pages 219-225, September.
    2. J W Chinneck & K Ramadan, 2000. "Linear programming with interval coefficients," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 51(2), pages 209-220, February.
    3. Gabriel R. Bitran, 1980. "Linear Multiple Objective Problems with Interval Coefficients," Management Science, INFORMS, vol. 26(7), pages 694-706, July.
    4. Mausser, Helmut E. & Laguna, Manuel, 1999. "A heuristic to minimax absolute regret for linear programs with interval objective function coefficients," European Journal of Operational Research, Elsevier, vol. 117(1), pages 157-174, August.
    5. Loomes, Graham & Sugden, Robert, 1982. "Regret Theory: An Alternative Theory of Rational Choice under Uncertainty," Economic Journal, Royal Economic Society, vol. 92(368), pages 805-824, December.
    6. Simon, Herbert A, 1979. "Rational Decision Making in Business Organizations," American Economic Review, American Economic Association, vol. 69(4), pages 493-513, September.
    7. Inuiguchi, Masahiro & Sakawa, Masatoshi, 1995. "Minimax regret solution to linear programming problems with an interval objective function," European Journal of Operational Research, Elsevier, vol. 86(3), pages 526-536, November.
    8. Ralph E. Steuer, 1981. "Algorithms for Linear Programming Problems with Interval Objective Function Coefficients," Mathematics of Operations Research, INFORMS, vol. 6(3), pages 333-348, August.
    9. H E Mausser & M Laguna, 1999. "Minimising the maximum relative regret for linear programmes with interval objective function coefficients," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 50(10), pages 1063-1070, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jungho Park & Hadi El-Amine & Nevin Mutlu, 2021. "An Exact Algorithm for Large-Scale Continuous Nonlinear Resource Allocation Problems with Minimax Regret Objectives," INFORMS Journal on Computing, INFORMS, vol. 33(3), pages 1213-1228, July.
    2. Stelios Rozakis, 2010. "Hybrid linear programming to estimate CAP impacts of flatter rates and environmental top-ups," Working Papers 2010-03, Agricultural University of Athens, Department Of Agricultural Economics.
    3. Carlo Giupponi & Francesco Bosello & Andrea Povellato, 2007. "A Review of Recent Studies on Cost Effectiveness of GHG Mitigation Measures in the European Agro-Forestry Sector," Working Papers 2007.14, Fondazione Eni Enrico Mattei.
    4. Bartoli, A. & Cavicchioli, D. & Kremmydas, D. & Rozakis, S. & Olper, A., 2016. "The impact of different energy policy options on feedstock price and land demand for maize silage: The case of biogas in Lombardy," Energy Policy, Elsevier, vol. 96(C), pages 351-363.
    5. Rozakis, Stelios, 2011. "Impacts of flatter rates and environmental top-ups in Greece: A novel mathematical modeling approach," Agricultural Economics Review, Greek Association of Agricultural Economists, vol. 12(2).
    6. Mavrotas, George & Diakoulaki, Danae & Florios, Kostas & Georgiou, Paraskevas, 2008. "A mathematical programming framework for energy planning in services' sector buildings under uncertainty in load demand: The case of a hospital in Athens," Energy Policy, Elsevier, vol. 36(7), pages 2415-2429, July.
    7. Reidsma, Pytrik & Janssen, Sander & Jansen, Jacques & van Ittersum, Martin K., 2018. "On the development and use of farm models for policy impact assessment in the European Union – A review," Agricultural Systems, Elsevier, vol. 159(C), pages 111-125.
    8. Fabio Furini & Manuel Iori & Silvano Martello & Mutsunori Yagiura, 2015. "Heuristic and Exact Algorithms for the Interval Min–Max Regret Knapsack Problem," INFORMS Journal on Computing, INFORMS, vol. 27(2), pages 392-405, May.
    9. Roy, Bernard, 2010. "Robustness in operational research and decision aiding: A multi-faceted issue," European Journal of Operational Research, Elsevier, vol. 200(3), pages 629-638, February.
    10. Stelios Rozakis, 2010. "Positive multi-criteria models in agriculture for energy and environmental policy analysis," Working Papers 2010-04, Agricultural University of Athens, Department Of Agricultural Economics.
    11. P. Mathiou & Stelios Rozakis & Rafal Pudelko & A. Faber & A. Petsakos, 2014. "Utility maximising supply response: the case of perennial biomass plantations in Poland," Working Papers 2014-3, Agricultural University of Athens, Department Of Agricultural Economics.
    12. Bartoli, Andrea & Hamelin, Lorie & Rozakis, Stelios & Borzęcka, Magdalena & Brandão, Miguel, 2019. "Coupling economic and GHG emission accounting models to evaluate the sustainability of biogas policies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 106(C), pages 133-148.
    13. Ram, Camelia, 2020. "Scenario presentation and scenario generation in multi-criteria assessments: An exploratory study," Technological Forecasting and Social Change, Elsevier, vol. 151(C).
    14. Aissi, Hassene & Bazgan, Cristina & Vanderpooten, Daniel, 2009. "Min-max and min-max regret versions of combinatorial optimization problems: A survey," European Journal of Operational Research, Elsevier, vol. 197(2), pages 427-438, September.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wu, Hsien-Chung, 2009. "The Karush-Kuhn-Tucker optimality conditions in multiobjective programming problems with interval-valued objective functions," European Journal of Operational Research, Elsevier, vol. 196(1), pages 49-60, July.
    2. Zhou, Feng & Huang, Gordon H. & Chen, Guo-Xian & Guo, Huai-Cheng, 2009. "Enhanced-interval linear programming," European Journal of Operational Research, Elsevier, vol. 199(2), pages 323-333, December.
    3. Henriques, C.O. & Inuiguchi, M. & Luque, M. & Figueira, J.R., 2020. "New conditions for testing necessarily/possibly efficiency of non-degenerate basic solutions based on the tolerance approach," European Journal of Operational Research, Elsevier, vol. 283(1), pages 341-355.
    4. Oliveira, Carla & Antunes, Carlos Henggeler, 2007. "Multiple objective linear programming models with interval coefficients - an illustrated overview," European Journal of Operational Research, Elsevier, vol. 181(3), pages 1434-1463, September.
    5. S. Rivaz & M. A. Yaghoobi & M. Hladík, 2016. "Using modified maximum regret for finding a necessarily efficient solution in an interval MOLP problem," Fuzzy Optimization and Decision Making, Springer, vol. 15(3), pages 237-253, September.
    6. Luo, Chunling & Tan, Chin Hon & Liu, Xiao, 2020. "Maximum excess dominance: Identifying impractical solutions in linear problems with interval coefficients," European Journal of Operational Research, Elsevier, vol. 282(2), pages 660-676.
    7. Liu, Yong-Jun & Zhang, Wei-Guo & Zhang, Pu, 2013. "A multi-period portfolio selection optimization model by using interval analysis," Economic Modelling, Elsevier, vol. 33(C), pages 113-119.
    8. V Gabrel & C Murat, 2010. "Robustness and duality in linear programming," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(8), pages 1288-1296, August.
    9. Jianjian Wang & Feng He & Xin Shi, 2019. "Numerical solution of a general interval quadratic programming model for portfolio selection," PLOS ONE, Public Library of Science, vol. 14(3), pages 1-16, March.
    10. Amadeu Almeida Coco & João Carlos Abreu Júnior & Thiago F. Noronha & Andréa Cynthia Santos, 2017. "Erratum to: An integer linear programming formulation and heuristics for the minmax relative regret robust shortest path problem," Journal of Global Optimization, Springer, vol. 68(2), pages 463-466, June.
    11. Michaël Lainé, 2014. "Vers une alternative au paradigme de la rationalité ? Victoires et déboires du programme spinoziste en économie," Post-Print hal-01335618, HAL.
    12. Rozakis, Stelios, 2011. "Impacts of flatter rates and environmental top-ups in Greece: A novel mathematical modeling approach," Agricultural Economics Review, Greek Association of Agricultural Economists, vol. 12(2).
    13. Masahiro Inuiguchi & Zhenzhong Gao & Carla Oliveira Henriques, 2023. "Robust optimality analysis of non-degenerate basic feasible solutions in linear programming problems with fuzzy objective coefficients," Fuzzy Optimization and Decision Making, Springer, vol. 22(1), pages 51-79, March.
    14. Mavrotas, George & Diakoulaki, Danae & Florios, Kostas & Georgiou, Paraskevas, 2008. "A mathematical programming framework for energy planning in services' sector buildings under uncertainty in load demand: The case of a hospital in Athens," Energy Policy, Elsevier, vol. 36(7), pages 2415-2429, July.
    15. Giove, Silvio & Funari, Stefania & Nardelli, Carla, 2006. "An interval portfolio selection problem based on regret function," European Journal of Operational Research, Elsevier, vol. 170(1), pages 253-264, April.
    16. B. Luo & I. Maqsood & G. Huang, 2007. "Planning water resources systems with interval stochastic dynamic programming," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 21(6), pages 997-1014, June.
    17. Mrinal Jana & Geetanjali Panda, 2018. "$$\chi$$ χ -Optimal solution of single objective nonlinear optimization problem with uncertain parameters," OPSEARCH, Springer;Operational Research Society of India, vol. 55(1), pages 165-186, March.
    18. Averbakh, Igor & Lebedev, Vasilij, 2005. "On the complexity of minmax regret linear programming," European Journal of Operational Research, Elsevier, vol. 160(1), pages 227-231, January.
    19. Mausser, Helmut E. & Laguna, Manuel, 1999. "A heuristic to minimax absolute regret for linear programs with interval objective function coefficients," European Journal of Operational Research, Elsevier, vol. 117(1), pages 157-174, August.
    20. P. Kumar & A. K. Bhurjee, 2022. "Multi-objective enhanced interval optimization problem," Annals of Operations Research, Springer, vol. 311(2), pages 1035-1050, April.

    More about this item

    Keywords

    Crop Production/Industries; Resource /Energy Economics and Policy;

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • Q18 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Agricultural Policy; Food Policy; Animal Welfare Policy

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ags:eaae05:24751. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: AgEcon Search (email available below). General contact details of provider: https://edirc.repec.org/data/eaaeeea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.