0502673 20240103221741.920190312235959.9 85062963820 000563054500008 10.1007/s10479-019-03192-4 17 s. P cav_un_epca*02508070254-53302922 (2020)735-751Springer Multiperiod CVaR Multi-stage model Stochastic programming Emission allowance Steel company cav_un_auth*0324365 33 Zapletal F. CZ cav_un_auth*0101206 33 Šmíd Martin UTIA-B Ekonometrie Department of Econometrics E E Department of Econometrics Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0289084 34 Kopa M. CZ K http://library.utia.cas.cz/separaty/2019/E/smid-0502673.pdf https://link.springer.com/article/10.1007/s10479-019-03192-4 cav_un_auth*0341139 GA16-01298S GA ČR CZ 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. WOS BB 10000 10100 10103 2021 3 RVO:67985556 http://hdl.handle.net/11104/0295686 cav_un_auth*0336435 VŠB–Technical University of Ostrava VŠB-TUO CZ cav_un_auth*0329926 MFF UK CZ S 3+4 Article Operations Research Management Science C OPERATIONSRESEARCH&MANAGEMENTSCIENCE 4.161 1.247 5.4 0.01217 0.835 12669 132 1.068 43.88 3.33 3295 Q1 3.816 651 Q1 39.133 75.6 Q3 Q2 0.98 Q2 75.595 2020 85062963820 SCOPUS PUBMED 000563054500008 WOS cav_un_epca*0250807 Annals of Operations Research 0254-5330 1572-9338 Roč. 292 č. 2 2020 735 751 Springer