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Joel Clark. Zhang, Jingshu, Ph. D. Massachusetts Institute of Technology Massachusetts Institute of Technology. Institute for Data, Systems, and Society. Massachusetts Institute of Technology. Engineering Systems Division Massachusetts Institute of Technology. Institute for Data, Systems, and Society 2018-09-28T20:57:33Z 2018-09-28T20:57:33Z 2018 2018 http://hdl.handle.net/1721.1/118223 1052616993 Thesis: Ph. D. in Engineering Systems, Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, June 2018. Cataloged from PDF version of thesis. Includes bibliographical references (pages 102-115). This work aims to provide a widely applicable modeling framework that can be used to credibly investigate materials scarcity risks for various types of commodities. Different from existing literature, this work contributes to a better understanding of commodity scarcity risk, specifically copper future consumption on several fronts. Firstly, it introduces an elaborate price mechanism absent in comparable materials flow assessment. It teases out short term and long term substitution, allowing consumers to switch from one type of commodity to another based on price signals and their respective price elasticities of demand. Secondly, the model allows for individual deposit tracking, which allows the modeler to extract ore grade information as a function of consumption and reserve size. Thirdly, it models the supply side on an agent-based basis, allowing for aggregation of granular information, capturing potential emergent phenomena. We believe these three aspects, which are least addressed (none of existing work has addressed the first aspect, and few have addressed the second or the third), are important in assessing scarcity risks. Without them, scarcity assessment is likely to be biased. We hope our work may serve as some sort of foundation upon which more reliable future work on mineral scarcity evaluation can be carried out. by Jingshu Zhang. Ph.D. in Engineering Systems 115 pages eng Massachusetts Institute of Technology MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. http://dspace.mit.edu/handle/1721.1/7582 Institute for Data, Systems, and Society. Simulation based micro-founded structural market analysis : a case study of the copper industry Case study of the copper industry Thesis application/pdf false Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="e29ebaa7-013d-4325-a0b6-90e3f501a61f"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Simulation based micro-founded structural market analysis : a case study of the copper industry</Title> <Subtitle>Case study of the copper industry</Subtitle> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2018</PublicationDate> <Authors> <Author> <DisplayName>Zhang, Jingshu, Ph. D. Massachusetts Institute of Technology</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Institute for Data, Systems, and Society.</Keyword> <Abstract>This work aims to provide a widely applicable modeling framework that can be used to credibly investigate materials scarcity risks for various types of commodities. Different from existing literature, this work contributes to a better understanding of commodity scarcity risk, specifically copper future consumption on several fronts. Firstly, it introduces an elaborate price mechanism absent in comparable materials flow assessment. It teases out short term and long term substitution, allowing consumers to switch from one type of commodity to another based on price signals and their respective price elasticities of demand. Secondly, the model allows for individual deposit tracking, which allows the modeler to extract ore grade information as a function of consumption and reserve size. Thirdly, it models the supply side on an agent-based basis, allowing for aggregation of granular information, capturing potential emergent phenomena. We believe these three aspects, which are least addressed (none of existing work has addressed the first aspect, and few have addressed the second or the third), are important in assessing scarcity risks. Without them, scarcity assessment is likely to be biased. We hope our work may serve as some sort of foundation upon which more reliable future work on mineral scarcity evaluation can be carried out.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>