Thesis: Ph. D. in Engineering Systems, Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, February, 2020 / Cataloged from student-submitted PDF version of thesis. / Includes bibliographical references (pages 295-328). / This dissertation studies how physical and non-physical features of low-carbon technologies evolve and influence performance evolution. This fundamental question about the role of hardware- and non-hardware ('soft') innovations in technological progress remains largely unanswered despite the societal importance of improved technology. Multiple low-carbon technologies exhibit rising shares of soft costs, and understanding their determinants is thus critical to support climate mitigation. However, building this understanding is challenging. Technologies evolve through multi-faceted knowledge-generating processes, in which both endogenous factors, such as a technology's design, and exogenous factors, such as policies and research, play roles. / To capture this complexity, a new conceptual and quantitative model of technology performance evolution is developed, where performance change (e.g., cost change) is the outcome of changes in physical and non-physical ('soft') features ('variables'), both of which can affect the performance of hardware and processes needed to deploy technologies. While physical variables -- material usage ratios, efficiencies -- / describe the tangible aspects of technologies, soft variables (e.g., task durations, wages) characterize the performance of intangibles, including deployment processes and services. In contrast to physical variables, soft variables can change after the factory gate due to locational differences in technology management or labor costs. By defining hardware and soft performance as functions of both hardware and soft variables, and separating their contributions to cost change when multiple variables change, this framework disentangles the effects of physical and non-physical forms of improvement at multiple conceptual levels -- / from changes in hardware or soft features, to the specific physical and non-physical innovations that drive these changes, to the higher-order improvement processes in which many innovations originate (e.g., research and development). This approach addresses shortcomings in current methods to analyze and track cost change in technologies, which often treat the performance of hardware (e.g., equipment costs) and of deployment processes (e.g., soft costs) separately. However, features of hardware not only affect the cost of equipment, but also the cost of deploying this equipment, and accounting for such interdependencies can change assessments of the sources of past and future technology improvement ... / by Magdalena Maria Klemun. / Ph. D. in Engineering Systems / Ph.D.inEngineeringSystems Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society
| Identifer | oai:union.ndltd.org:MIT/oai:dspace.mit.edu:1721.1/128640 |
| Date | January 2020 |
| Creators | Klemun, Magdalena Maria. |
| Contributors | Jessika E. Trancik., Massachusetts Institute of Technology. Institute for Data, Systems, and Society., Massachusetts Institute of Technology. Institute for Data, Systems, and Society |
| Publisher | Massachusetts Institute of Technology |
| Source Sets | M.I.T. Theses and Dissertation |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | 328 pages, application/pdf |
| Rights | MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided., http://dspace.mit.edu/handle/1721.1/7582 |
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