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<bibitem type="J">   <ARLID>0557302</ARLID> <utime>20230323164438.4</utime><mtime>20220512235959.9</mtime>   <SCOPUS>85128573521</SCOPUS> <WOS>000830169300004</WOS>  <DOI>10.1016/j.eswa.2022.117021</DOI>           <title language="eng" primary="1">Multi-stage stochastic optimization of carbon risk management</title>  <specification> <page_count>12 s.</page_count> <media_type>P</media_type> </specification>   <serial><ARLID>cav_un_epca*0252943</ARLID><ISSN>0957-4174</ISSN><title>Expert Systems With Applications</title><part_num/><part_title/><volume_id>201</volume_id><volume/><publisher><place/><name>Elsevier</name><year/></publisher></serial>    <keyword>Stochastic programming</keyword>   <keyword>Emissions trading</keyword>   <keyword>Multi-stage</keyword>   <keyword>SDDP</keyword>   <keyword>Dominance</keyword>    <author primary="1"> <ARLID>cav_un_auth*0324365</ARLID> <name1>Zapletal</name1> <name2>F.</name2> <country>CZ</country> </author> <author primary="0"> <ARLID>cav_un_auth*0101206</ARLID> <name1>Šmíd</name1> <name2>Martin</name2> <institution>UTIA-B</institution> <full_dept language="cz">Ekonometrie</full_dept> <full_dept>Department of Econometrics</full_dept> <department language="cz">E</department> <department>E</department> <full_dept>Department of Econometrics</full_dept> <fullinstit>Ústav teorie informace a automatizace AV ČR, v. v. i.</fullinstit> </author> <author primary="0"> <ARLID>cav_un_auth*0363894</ARLID> <name1>Kozmík</name1> <name2>Václav</name2> <institution>UTIA-B</institution> <full_dept language="cz">Ekonometrie</full_dept> <full_dept>Department of Econometrics</full_dept> <department language="cz">E</department> <department>E</department> <country>CZ</country> <fullinstit>Ústav teorie informace a automatizace AV ČR, v. v. i.</fullinstit> </author>   <source> <url>http://library.utia.cas.cz/separaty/2022/E/smid-0557302.pdf</url> </source> <source> <url>https://www.sciencedirect.com/science/article/pii/S0957417422004389?via%3Dihub</url>  </source>        <cas_special> <project> <project_id>GA21-07494S</project_id> <agency>GA ČR</agency> <country>CZ</country> <ARLID>cav_un_auth*0430801</ARLID> </project>  <abstract language="eng" primary="1">Emissions trading within the Emissions Trading Scheme of the European Union (EU ETS) strongly influences European industrial companies. The companies must choose their strategy of reduction the costs of emissions allowances as possible. The changing system’s conditions and volatile prices of allowances make this decision challenging. The main aim of this study is to compare different ways of risk management: banking (i.e., buying the allowances in forward) and using derivatives: futures and options. Despite several studies devoted to the relationship between the EU ETS and companies have already been published, there is still a gap in this field. Namely, the published studies have been substantially simplified so far by ignoring the risk of driving parameters. We construct a realistic large-scale stochastic optimization model, which avoids the mentioned simplifications. We use the Markov Stochastic Dual Dynamic Programming algorithm (MSDDP) to find the optimal solution. We apply the model to the data of a real-life industrial company. We find that banking is the most costly way of risk reduction, while using derivatives is efficient in risk reduction. Surprisingly, out of the derivatives, it is always optimal to use futures and not to use options. These results are confirmed by a thorough sensitivity analysis. The preference of the futures over options is mainly due to the less price of futures in comparison to options reducing risk equivalently.</abstract>     <result_subspec>WOS</result_subspec> <RIV>AH</RIV> <FORD0>50000</FORD0> <FORD1>50200</FORD1> <FORD2>50204</FORD2>    <reportyear>2023</reportyear>      <num_of_auth>3</num_of_auth>  <inst_support> RVO:67985556 </inst_support>  <permalink>http://hdl.handle.net/11104/0331507</permalink>   <confidential>S</confidential>  <article_num> 117021 </article_num> <unknown tag="mrcbC86"> 3+4 Article Computer Science Artificial Intelligence|Engineering Electrical Electronic|Operations Research Management Science </unknown> <unknown tag="mrcbC91"> C </unknown>         <unknown tag="mrcbT16-e">ENGINEERING.ELECTRICAL&amp;ELECTRONIC|OPERATIONSRESEARCH&amp;MANAGEMENTSCIENCE|COMPUTERSCIENCE.ARTIFICIALINTELLIGENCE</unknown> <unknown tag="mrcbT16-f">8.3</unknown> <unknown tag="mrcbT16-g">1.5</unknown> <unknown tag="mrcbT16-h">5.3</unknown> <unknown tag="mrcbT16-i">0.05131</unknown> <unknown tag="mrcbT16-j">1.277</unknown> <unknown tag="mrcbT16-k">76302</unknown> <unknown tag="mrcbT16-s">1.873</unknown> <unknown tag="mrcbT16-5">7.600</unknown> <unknown tag="mrcbT16-6">2165</unknown> <unknown tag="mrcbT16-7">Q1</unknown> <unknown tag="mrcbT16-C">90.2</unknown> <unknown tag="mrcbT16-D">Q2</unknown> <unknown tag="mrcbT16-E">Q1</unknown> <unknown tag="mrcbT16-M">1.73</unknown> <unknown tag="mrcbT16-N">Q1</unknown> <unknown tag="mrcbT16-P">93.6</unknown> <arlyear>2022</arlyear>       <unknown tag="mrcbU14"> 85128573521 SCOPUS </unknown> <unknown tag="mrcbU24"> PUBMED </unknown> <unknown tag="mrcbU34"> 000830169300004 WOS </unknown> <unknown tag="mrcbU63"> cav_un_epca*0252943 Expert Systems With Applications 0957-4174 1873-6793 Roč. 201 č. 1 2022 Elsevier </unknown> </cas_special> </bibitem>