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Multi-stage emissions management of a steel company

Author

Listed:
  • František Zapletal

    (VŠB - Technical University of Ostrava)

  • Martin Šmíd

    (The Institute of Information Theory and Automation of the CAS)

  • Miloš Kopa

    (Charles University in Prague)

Abstract

We present a multi-stage model for determining the optimal production and emissions coverage for an industrial company participating in the European Emissions Trading System. This model is adapted for a real-life European steel company. A mean-multiperiod CVaR is used as a decision criterion. There are two stochastic parameters—market demand for products and emissions allowance price. The aim of this paper is to explore the costs and risk of a company caused by emissions trading. The presented model is solved for various values of the risk aversion parameters and initial price of the allowance. As a result, it is found that the production is little influenced by the price of allowances and it nearly does not depend on risk-aversion. The probability of the company’s default, on the other hand, is significantly influenced by the emission prices. Futures on allowances as well as banking (i.e., transferring allowances between periods) are used to reduce the risks of the emissions trading. We further exploit the same situation under different settings, namely, given random price margins, and time-dependent, deterministic and positively contaminated distributions of demand. In all these cases, the results follow patterns similar to those given the original setting.

Suggested Citation

  • František Zapletal & Martin Šmíd & Miloš Kopa, 2020. "Multi-stage emissions management of a steel company," Annals of Operations Research, Springer, vol. 292(2), pages 735-751, September.
    • Handle: RePEc:spr:annopr:v:292:y:2020:i:2:d:10.1007_s10479-019-03192-4
    DOI: 10.1007/s10479-019-03192-4
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    References listed on IDEAS

    as
    1. Xiting Gong & Sean X. Zhou, 2013. "Optimal Production Planning with Emissions Trading," Operations Research, INFORMS, vol. 61(4), pages 908-924, August.
    2. Pisciella, P. & Vespucci, M.T. & Bertocchi, M. & Zigrino, S., 2016. "A time consistent risk averse three-stage stochastic mixed integer optimization model for power generation capacity expansion," Energy Economics, Elsevier, vol. 53(C), pages 203-211.
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    4. Miloš Kopa & Vittorio Moriggia & Sebastiano Vitali, 2018. "Individual optimal pension allocation under stochastic dominance constraints," Annals of Operations Research, Springer, vol. 260(1), pages 255-291, January.
    5. František Zapletal & Martin Šmíd, 2016. "Mean-risk optimal decision of a steel company under emission control," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 24(2), pages 435-454, June.
    6. Rong, Aiying & Lahdelma, Risto, 2007. "CO2 emissions trading planning in combined heat and power production via multi-period stochastic optimization," European Journal of Operational Research, Elsevier, vol. 176(3), pages 1874-1895, February.
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    8. Moriggia, Vittorio & Kopa, Miloš & Vitali, Sebastiano, 2019. "Pension fund management with hedging derivatives, stochastic dominance and nodal contamination," Omega, Elsevier, vol. 87(C), pages 127-141.
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    Cited by:

    1. Kopa, Miloš & Rusý, Tomáš, 2023. "Robustness of stochastic programs with endogenous randomness via contamination," European Journal of Operational Research, Elsevier, vol. 305(3), pages 1259-1272.

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