{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/122188"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/122188","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Assessing the long-term attractiveness of mining a commodity based on the structure of its industry","abstract":"Throughout this thesis, we sought to determine which forces drive commodity attractiveness, and how a general framework could assess the attractiveness of mining commodities. Attractiveness can be defined from multiple perspectives (investor, company, policy-maker, mine workers, etc.), which lead to varying measures of attractiveness. The scope of this thesis is limited to assessing attractiveness from an investor's perspectives, wherein the key performance indicators (KPIs) for success are risk and return on investments (ROI). To this end, we have studied the structure of a mining industry with two concurrent approaches. Both approaches aggregate 18 key drivers of ROI and risks, like demand growth, the size of the reserves pool, or the share of state-owned enterprise. The first approach was based on a microeconomics model of cost curve updates and led us to developing the Cost curve model. The second one is based on industry expertise and on intuitive, logical and transparent ways to account for the effects of different industry forces like barriers to entry, market power or spikes likelihood on industry attractiveness. It led us to developing the Decision tree model. These two models are complementary: while the first is a rigorous framework that relies on simplifying assumptions, the second relies on intuition and logic. Through this complementarity the two models provide clear and aligned insights about (i) which key combinations of key drivers are preeminent for attractiveness, (ii) how key drivers interact to mitigate or enhance attractiveness, and (iii) how commodities can be screened at a high-level in order to prioritize commodity investigation.","abstract_html":"Throughout this thesis, we sought to determine which forces drive commodity attractiveness, and how a general framework could assess the attractiveness of mining commodities. Attractiveness can be defined from multiple perspectives (investor, company, policy-maker, mine workers, etc.), which lead to varying measures of attractiveness. The scope of this thesis is limited to assessing attractiveness from an investor's perspectives, wherein the key performance indicators (KPIs) for success are risk and return on investments (ROI). To this end, we have studied the structure of a mining industry with two concurrent approaches. Both approaches aggregate 18 key drivers of ROI and risks, like demand growth, the size of the reserves pool, or the share of state-owned enterprise. The first approach was based on a microeconomics model of cost curve updates and led us to developing the Cost curve model. The second one is based on industry expertise and on intuitive, logical and transparent ways to account for the effects of different industry forces like barriers to entry, market power or spikes likelihood on industry attractiveness. It led us to developing the Decision tree model. These two models are complementary: while the first is a rigorous framework that relies on simplifying assumptions, the second relies on intuition and logic. Through this complementarity the two models provide clear and aligned insights about (i) which key combinations of key drivers are preeminent for attractiveness, (ii) how key drivers interact to mitigate or enhance attractiveness, and (iii) how commodities can be screened at a high-level in order to prioritize commodity investigation.","abstract_has_math":false,"creators":["Marion, Tanguy(Tanguy Marie Philippe)"],"institution":"Massachusetts Institute of Technology","degree_name":"Master","degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Institute for Data, Systems, and Society","school":null,"contributors":[],"advisors":["Richard Roth."],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019","date_published":"2019","updated_at":"2026-07-22T22:22:08Z","subjects":["Institute for Data, Systems, and Society.","Technology and Policy Program."],"languages":["eng"],"rights":["MIT theses are protected by copyright. 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It led us to developing the Decision tree model. These two models are complementary: while the first is a rigorous framework that relies on simplifying assumptions, the second relies on intuition and logic. Through this complementarity the two models provide clear and aligned insights about (i) which key combinations of key drivers are preeminent for attractiveness, (ii) how key drivers interact to mitigate or enhance attractiveness, and (iii) how commodities can be screened at a high-level in order to prioritize commodity investigation."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["S.M. in Technology and Policy"]},{"key":"dc:title","label":"Title","values":["Assessing the long-term attractiveness of mining a commodity based on the structure of its industry"]}]}],"canonical_facts":{"dc:contributor.advisor":["Richard Roth."],"dc:contributor.department":["Massachusetts Institute of Technology. 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The scope of this thesis is limited to assessing attractiveness from an investor's perspectives, wherein the key performance indicators (KPIs) for success are risk and return on investments (ROI). To this end, we have studied the structure of a mining industry with two concurrent approaches. Both approaches aggregate 18 key drivers of ROI and risks, like demand growth, the size of the reserves pool, or the share of state-owned enterprise. The first approach was based on a microeconomics model of cost curve updates and led us to developing the Cost curve model. The second one is based on industry expertise and on intuitive, logical and transparent ways to account for the effects of different industry forces like barriers to entry, market power or spikes likelihood on industry attractiveness. It led us to developing the Decision tree model. 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