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dc.contributor.advisorRandolph E. Kirchain and Joel P. Clark.en_US
dc.contributor.authorRampuria, Abhisheken_US
dc.contributor.otherMassachusetts Institute of Technology. Dept. of Materials Science and Engineering.en_US
dc.date.accessioned2013-01-07T19:06:29Z
dc.date.available2013-01-07T19:06:29Z
dc.date.copyright2012en_US
dc.date.issued2012en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/75852
dc.descriptionThesis (S.B.)--Massachusetts Institute of Technology, Dept. of Materials Science and Engineering, 2012.en_US
dc.descriptionThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.en_US
dc.descriptionCataloged from student submitted PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (p. 49-50).en_US
dc.description.abstractAs environmental footprint considerations for companies gain greater importance, the need for quantitative impact assessment tools such as life cycle assessment (LCA) has become a higher priority. Currently, the cost and time burden associated with LCA has prevented it from becoming more prevalent. While several streamlining approaches have been suggested, questions regarding the effectiveness and efficiency of the streamlined results are still of concern. The streamlining method of probabilistic underspecification has shown initial success in its ability to reduce LCA efforts while simultaneously increasing certainty in the final impact assessment. Probabilistic underspecification streamlines LCA by prioritizing targets of more refined data collection and by implementing the use of underspecified surrogate data within LCI analysis. This thesis concentrates on further developing and improving the streamlining methodology of probabilistic underspecification through refinement of the materials classification systems for polymers and minerals and through additional case study analysis. The classification system allows for a better understanding of the relationship between the degree of materials specificity and the uncertainty in the resulting impact values. Additionally, the resulting polymer and mineral classifications were combined with existing materials classifications to conduct an alkaline battery case study in order to test the effectiveness of the streamlining method. The material classifications created through this research provide a logical and practical approach to underspecification while maintaining consistent and reasonable levels of uncertainty. Furthermore, the case study analysis showed that the streamlining methodology significantly lowered LCA burden by systematically reducing the number of product components requiring full specification. This research provides further evidence that probabilistic underspecification may provide a promising LCA streamlining method among a set of such strategies that can significantly reduce LCA efforts while maintaining the accuracy of the overall impact assessment.en_US
dc.description.statementofresponsibilityby Abhishek Rampuria.en_US
dc.format.extent58 p.en_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsM.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectMaterials Science and Engineering.en_US
dc.titleEnabling streamlined life cycle assessment : materials-classification derived structured underspecificationen_US
dc.typeThesisen_US
dc.description.degreeS.B.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Materials Science and Engineering
dc.identifier.oclc821214138en_US


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