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.
Similar content being viewed by others
Notes
Even if the prices started at the prices from the time of submission of this paper, i.e., 20 EUR, the production still would not be affected.
References
Artzner, P., Delbaen, F., Eber, J.-M., & Heath, D. (1999). Coherent measures of risk. Mathematical Finance, 9(3), 203–228.
Council of European Union. (2003). Eu directive no 2003/87/ec.
Dupačová, J., & Kopa, M. (2012). Robustness in stochastic programs with risk constraints. Annals of Operations Research, 200(1), 55–74.
Dupačová, J., & Kopa, M. (2014). Robustness of optimal portfolios under risk and stochastic dominance constraints. European Journal of Operational Research, 234(2), 434–441.
Gong, X., & Zhou, S. X. (2013). Optimal production planning with emissions trading. Operations Research, 61(4), 908–924.
Kopa, M., Moriggia, V., & Vitali, S. (2018). Individual optimal pension allocation under stochastic dominance constraints. Annals of Operations Research, 260(1), 255–291.
Kovacevic, R., & Pflug, G. C. (2009). Time consistency and information monotonicity of multiperiod acceptability functionals. Advanced Financial Modelling, 8, 347.
Luo, C., & Desheng, W. (2016). Environment and economic risk: An analysis of carbon emission market and portfolio management. Environmental Research, 149, 297–301.
Miller, R. E., & Blair, P. D. (2009). Input–output analysis: Foundations and extensions. Cambridge: Cambridge University Press.
Moriggia, V., Kopa, M., & Vitali, S. (2019). Pension fund management with hedging derivatives, stochastic dominance and nodal contamination. Omega. https://doi.org/10.1016/j.omega.2018.08.011.
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, 53, 203–211.
Rockafellar, R. T., & Uryasev, S. (2002). Conditional value-at-risk for general loss distributions. Journal of Banking & Finance, 26(7), 1443–1471.
Rong, A., & Lahdelma, R. (2007). CO2 emissions trading planning in combined heat and power production via multi-period stochastic optimization. European Journal of Operational Research, 176(3), 1874–1895.
Shapiro, A., & Ugurlu, K. (2016). Decomposability and time consistency of risk averse multistage programs. Operations Research Letters, 44(5), 663–665.
Šmíd, M. (2009). The expected loss in the discretization of multistage stochastic programming problemsestimation and convergence rate. Annals of Operations Research, 165(1), 29–45.
Šmíd, M., Zapletal, F., & Hančlová, J. (2017). Which Carbon derivatives are appicable in practise? A case study of a European steel industry. Kybernetika, 53(6), 1071–1085.
Tang, H., & Song, G. (2013). Optimization of enterprise production based on carbon emissions credits trading. In Proceedings of the 2nd international conference on green communications and networks 2012 (GCN 2012) (Vol. 5, pp. 325–330). Springer.
Zapletal, F., & Šmíd, M. (2016). Mean-risk optimal decision of a steel company under emission control. Central European Journal of Operations Research, 24(2), 435–454.
Acknowledgements
This work was supported by Grant No. GA 16-01298S of the Czech Science Foundation. The support is gratefully acknowledged. The authors would also like to thank three anonymous referees for valuable comments and suggestions.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Zapletal, F., Šmíd, M. & Kopa, M. Multi-stage emissions management of a steel company. Ann Oper Res 292, 735–751 (2020). https://doi.org/10.1007/s10479-019-03192-4
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10479-019-03192-4