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Long-term petroleum product supply analysis through a robust modelling approach

Author

Listed:
  • Umed Temurshoev

    (Universidad Loyola Andalucía)

  • Fréderic Lantz

    (IFP-School)

Abstract

Linear programming approach to economic modelling of petroleum refining has important shortcomings that make it less useful and less robust for the purposes of impact assessments of related policies. These have to do with its natural inability to calibrate observed data and obtaining jumpy responses of the decision variables to smooth exogenous shocks due to the large number of substitutions between the refining processes. Relying on positive mathematical programming literature, in this paper we propose a method that solves these issues. The main idea is that a refining model has to have a non-linear objective function via inclusion of an implicit total cost function that captures the aggregated impact of all other relevant factors that are not explicitly modelled. We discuss in some detail the issues relevant for practical implementation of the proposed approach for interested practitioners.

Suggested Citation

  • Umed Temurshoev & Fréderic Lantz, 2016. "Long-term petroleum product supply analysis through a robust modelling approach," Working Papers 2016-003, Universidad Loyola Andalucía, Department of Economics.
  • Handle: RePEc:loy:wpaper:2016-003
    as

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    References listed on IDEAS

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    1. Thomas Heckelei & Hendrik Wolff, 2003. "Estimation of constrained optimisation models for agricultural supply analysis based on generalised maximum entropy," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 30(1), pages 27-50, March.
    2. Heckelei, Thomas & Britz, Wolfgang & Zhang, Yinan, 2012. "Positive Mathematical Programming Approaches – Recent Developments in Literature and Applied Modelling," Bio-based and Applied Economics Journal, Italian Association of Agricultural and Applied Economics (AIEAA), vol. 1(1), pages 1-16, April.
    3. Richard E. Howitt, 1995. "Positive Mathematical Programming," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 77(2), pages 329-342.
    4. Heckelei, Thomas & Britz, Wolfgang, 2005. "Models Based on Positive Mathematical Programming: State of the Art and Further Extensions," 89th Seminar, February 2-5, 2005, Parma, Italy 234607, European Association of Agricultural Economists.
    5. Bruno Henry Frahan & Jeroen Buysse & Philippe Polomé & Bruno Fernagut & Olivier Harmignie & Ludwig Lauwers & Guido Huylenbroeck & Jef Meensel, 2007. "Positive Mathematical Programming for Agricultural and Environmental Policy Analysis: Review and Practice," International Series in Operations Research & Management Science, in: Andres Weintraub & Carlos Romero & Trond Bjørndal & Rafael Epstein & Jaime Miranda (ed.), Handbook Of Operations Research In Natural Resources, chapter 0, pages 129-154, Springer.
    6. Umed Temurshoev & Marian Mraz & Luis Delgado Sancho & Peter Eder, 2015. "EU Petroleum Refining Fitness Check: OURSE Modelling and Results," JRC Research Reports JRC96207, Joint Research Centre.
    7. David Hummels, 2007. "Transportation Costs and International Trade in the Second Era of Globalization," Journal of Economic Perspectives, American Economic Association, vol. 21(3), pages 131-154, Summer.
    8. Frédéric Lantz & Valérie Saint-Antonin & Jean-François Gruson & Wojciech Suwala, 2012. "The OURSE model: Simulating the World Refining Sector to 2030," JRC Research Reports JRC68853, Joint Research Centre.
    9. Pierre Mérel & Richard Howitt, 2014. "Theory and Application of Positive Mathematical Programming in Agriculture and the Environment," Annual Review of Resource Economics, Annual Reviews, vol. 6(1), pages 451-470, October.
    10. Richard E. Howitt, 1995. "A Calibration Method For Agricultural Economic Production Models," Journal of Agricultural Economics, Wiley Blackwell, vol. 46(2), pages 147-159, May.
    11. Umed Temurshoev & Ronald E. Miller & Maaike C. Bouwmeester, 2013. "A Note On The Gras Method," Economic Systems Research, Taylor & Francis Journals, vol. 25(3), pages 361-367, September.
    12. Sami Bensassi & Inmaculada Martinez-Zarzoso & Celestino Suárez, 2014. "The effect of maritime transport costs on the extensive and intensive margins: Evidence from the Europe–Asia trade," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 16(3), pages 276-297, September.
    13. John F. Helliwell, 1997. "National Borders, Trade and Migration," NBER Working Papers 6027, National Bureau of Economic Research, Inc.
    14. Pierre Mérel & Santiago Bucaram, 2010. "Exact calibration of programming models of agricultural supply against exogenous supply elasticities," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 37(3), pages 395-418, September.
    15. Edward J. Balistreri & Ayed Al-Qahtani & Carol A. Dahl, 2010. "Oil and Petroleum Product Armington Elasticities: A New-Geography-of-Trade Approach to Estimation," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3), pages 167-180.
    16. Tehrani Nejad Moghaddam, Alireza & Michelot, Christian, 2009. "A contribution to the linear programming approach to joint cost allocation: Methodology and application," European Journal of Operational Research, Elsevier, vol. 197(3), pages 999-1011, September.
    17. Matthieu Bussière & Giovanni Callegari & Fabio Ghironi & Giulia Sestieri & Norihiko Yamano, 2013. "Estimating Trade Elasticities: Demand Composition and the Trade Collapse of 2008-2009," American Economic Journal: Macroeconomics, American Economic Association, vol. 5(3), pages 118-151, July.
    18. Pierre Mérel & Leo K. Simon & Fujin Yi, 2011. "A Fully Calibrated Generalized Constant-Elasticity-of-Substitution Programming Model of Agricultural Supply," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 93(4), pages 936-948.
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    More about this item

    Keywords

    Petroleum refining industry; perfect calibration; positive mathematical programming; robust analysis;
    All these keywords.

    JEL classification:

    • C60 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - General
    • L71 - Industrial Organization - - Industry Studies: Primary Products and Construction - - - Mining, Extraction, and Refining: Hydrocarbon Fuels

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