Thesis: Ph. D. in Social and 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 117-126). / The rapid growth of renewable energy is transforming the electric power sector. Wind and solar energy are non-dispatchable: their energy output is uncertain and variable from hour-to- hour. New challenges arise in electricity markets with a large share of uncertain and variable renewable energy. We investigate some of these challenges and identify economic opportunities and policy changes to mitigate them. We study electricity markets by focusing on the preferences and strategic behavior of three different groups: producers, consumers, and load-serving entities. First, we develop a game-theoretic model to investigate energy producer strategy in electricity markets with high levels of uncertain renewable energy. We show that increased geographic dispersion of renewable generators can reduce market power and increase social welfare. We also demonstrate that high-quality public forecasting of energy production can increase welfare. Second, we model and explain the effects of retail electricity competition on producer market power and forward contracting. We show that increased retail competition could decrease forward contracting and increase electricity prices; this is a downside to the general trend of increased access to retail electricity competition. Finally, we propose new methods for improving demand response programs. A demand response program operator commonly sets customer baseline thresholds to determine compensation for individual customers. The optimal way to do this remains an open question. We create a new model that casts the demand response program as a sequential decision problem; this formulation highlights the importance of learning about individual customers over time. We develop associated algorithms using tools from online learning, and we show that they outperform the current state of practice. / by Ian Michael Schneider. / Ph. D. in Social and Engineering Systems / Ph.D.inSocialandEngineeringSystems Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society

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

Thesis: Ph. D. in Engineering Systems: Technology, Management, and Policy, 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 135-145). / In the three decades since its introduction, Evidence-Based Practice (EBP) has become standard clinical practice and the subject of targeted interventions at all levels of the health system. Despite its prevalence, EBP is frequently challenged on philosophical, practical, empirical, and normative grounds. And EBP is often underused in practice relative to the considerable investment in training and sophisticated organizational interventions to implement EBP. In this dissertation, I identify what the concept of EBP means to health system stakeholders as a partial explanation for this persistent gap in EBP use and implementation outcomes. Through interviews with clinicians and healthcare administrators, I identify how providers and organizations use EBP in practice to clinical ends and in inter-professional relationships. First, I find that in contrast to the theoretical model, stakeholders vary in how they operationalize EBP for individual-level clinical use. / Stakeholders endorse a range of what I call implicit mental models of EBP that imply different approaches to clinical decision-making. Respondents' implicit mental models of EBP each emphasize an incomplete aspect of the full EBP model: Resource-Based EBP emphasizes specific evidence artifacts, Decision-Making EBP emphasizes the decision-making process, and EBT-Based EBP emphasizes specific Evidence-Based Treatments. These implicit models represent the decision inputs, process, and outputs, respectively. Second, I describe how and why healthcare organizations conduct EBP interventions, despite its initial design as an individual-level clinical decision-making model. I document a range of different organizational EBP activities and interventions, including disseminating resources, training providers, and implementing local standards. These organizational EBP activities both support individual EBP use and address broader organizational ends, which may conflict. / Finally, EBP takes on social and inter-professional meanings beyond its intended scope as a clinical decision-making model, which emerge in context and affect how providers understand and use EBP. Specifically, providers may renounce their standing to evaluate evidence, demonstratively use EBP, and administrators claim standing to evaluate evidence. This dissertation therefore demonstrates the varied uses of EBP that emerge in practice, contributing to our understanding of the challenges and contradictions that arise in applying general knowledge to individual cases and systematizing strategies for the same at the organization level. / by Henry Alan Fingerhut. / Ph. D. in Engineering Systems: Technology, Management, and Policy / Ph.D.inEngineeringSystems:Technology,Management,andPolicy Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society

Thesis: Ph. D. in Engineering Systems, Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, 2019 / Cataloged from PDF version of thesis. / Includes bibliographical references. / This thesis presents a new empirical approach as well as a new patent-based dataset for studying disruption and technology transition cases. At the core of this approach lies a novel engineering systems framework of technological change. The framework focuses on the relationship between changes in technological competencies and changes in product designs, and encompasses both firm-level and industry-level dynamics. The new framework and dataset are applied to the study of three cases of technology transition during the 1993- 2012 period. The cases include (1) disruption in the mobile phone industry with a focus on Apple, BlackBerry, and Nokia; (2) disruption in the photography industry with a focus on Fujifilm, Canon, and Kodak; and (3) technology transition in the automotive industry with a focus on Toyota, Volkswagen, and GM. / The former two industries comprise widely discussed disruption cases, allowing me to demonstrate advantages of the presented approach and develop novel insights into these cases. The third case, on the automotive industry, generates complementary insights by considering an industry with products comprising more integrated product architectures. The case selection allows for cross-case comparisons to begin endogenizing industry-specific factors. The thesis' main contributions are methodological and theoretical: First, I present a new dataset - and corresponding data assembly methods - of comprehensive corporate patent portfolios. The portfolios take into account each firm's corporate family tree structure as well as acquisitions. As such, the dataset reflects the actual range of firms' codified technological activities more closely than previous efforts and enables a more accurate view on how technological change manifests in firms and industries. / To connect the data to theory, I develop a set of novel metrics to operationalize semantic concepts such as technological diversification and concentration of portfolios as well as firms' technological core and growth competencies. These metrics are based on a newly developed variance measure for hierarchically structured networks. I define growth competencies as competencies that undergo rapid year-to-year growth outside of a firm's core competencies. By identifying incumbents' growth competencies from historical data before major transitions, I am able to successfully hindcast future new entrants in the cases presented. Further, I introduce the concepts of technology space and product space as mappings of compositions of technological competencies and of technological competencies required by compositions of products. Second, the thesis makes theoretical contributions to resource-based view (RBV) and disruption literatures. / Specifically, it presents a dynamic extension to the RBV, endogenizing technological change as well as firm-industry interconnections with regard to the emergence of technology convergences and the evolution of product designs. My findings suggest that a firm's relative position and movement in technology space needs to be considered separately from its position and movement in product space, i.e. its changing composition of competencies and its changing composition of products. Specifically, whereas firms' movements in product space can appear abrupt and even surprising - such as the sudden entry into new markets - my analysis shows that changes in technology space tend to be slower, more continuous, and more predictable. / I find that in disruption cases such as with Apple's sudden "entry" into the mobile phone industry, the new framework reveals that it was in fact the mobile phone industry that gradually "entered" Apple's position in technology space - as the technological requirements of phone industry products became more and more similar to Apple's preexisting, and highly stable, competencies. Moreover, I extend the concept of technology-product connections, as put forth statically by RBV theorists, by adding a time-dependent dimension. I argue that incumbent failure - such as Nokia's and Kodak's - can be explained by incumbents' inability to diagnose and respond to the gradual weakening of their technology-product connections; in other words, by neglecting to either adjust their technological competencies or to adjust their product offerings in response to technological change. / In turn, a firm with greater awareness of its own composition of technological competencies relative to its competitors as well as the changing technological requirements of prevalent product designs can deliberately incorporate such insights into strategic decision-making. In the empirical cases, I observe the ability to sense dynamics in technology and product spaces relative to the firm, and the ability to time the firm's actions accordingly, to be more present in some firms than in others. I term the existence of such abilities timing and sensing capabilities and propose them to be a concrete and operationalizable subset of Dynamic Capabilities. / by Florian Metzler. / Ph. D. in Engineering Systems / Ph.D.inEngineeringSystems Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society

International Telemetering Conference Proceedings / October 20-23, 2003 / Riviera Hotel and Convention Center, Las Vegas, Nevada / This paper introduces the Reference Architecture for Space Data Systems (RASDS) that is being developed by CCSDS. RASDS uses five Views to describe architectures of space data systems. These Views are derived from the viewpoints of the Reference Model of Open Distributed Processing (RM-ODP), but they are slightly modified from the RM-ODP viewpoints so that they can better represent the concerns of space data systems.

Thesis: S.M. in Technology and Policy, Massachusetts Institute of Technology, Institute for Data, Systems, and Society, Technology and Policy Program, June, 2021 / Thesis: S.M., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, June, 2021 / Cataloged from the official PDF of thesis. / Includes bibliographical references (pages 75-79). / Due to the nature of the geosynchronous (GEO) orbital regime, where space objects orbit the Earth once per sidereal day, GEO satellites can appear fixed to a position in the sky when observed from the Earth’s surface. This unique orbital characteristic makes GEO satellites ideal for telecommunications missions that require Earth-fixed antennas to send and receive signals, such as television broadcasts or military communications. To maintain their position relative to the Earth’s surface, GEO satellites must station-keep, or regularly expend onboard propellant to counteract the natural forces in the near-Earth space environment that perturb their orbital trajectories. Less frequently, GEO satellites perform maneuvers to alter their orbital characteristics more drastically. One such maneuver is a longitudinal shift: changing a GEO satellite’s sub-satellite point from one position on the Earth’s equator to another. Such a maneuver often requires both a series of impulsive thrusts and a period of natural drift. This work describes an approach for detecting the components of longitudinal shift maneuvers—including the patterns associated with initiating and ending eastward and westward drifts—using convolutional neural networks trained on publicly available two-line element (TLE) data from the U.S. Space Command’s (SPACECOM) space object catalog. A method for converting TLE data to geographic position histories—longitude, latitude, and altitude positions over time in the Earth-centered, Earth-fixed geographic reference frame—and labeling longitudinal shift maneuvers by inspection is described. A preliminary maneuver detection algorithm is designed, trained, and tested on all GEO satellites in orbit from January 1 to December 31, 2020. Performance metrics are presented for algorithms trained on two different training data sets corresponding to five and ten years’ worth of geographic position time-histories labeled with longitudinal shift maneuvers. When detected, longitudinal shift maneuvers can be used to identify anomalous behavior in GEO. In this work, a satellite’s behavior is considered nominal if it adheres to the satellite’s pattern of life (PoL)—its previous on-orbit behavior made up of sequences of both natural and non-natural behavioral modes, including routine station-keeping, other on-orbit maneuvers, and uncontrolled motion—and anomalous if it deviates from the satellite’s PoL. Identifying anomalous satellite behavior is of critical interest to space situational awareness (SSA) system operators, who may choose to task their sensors to obtain more observations of anomalous behavior, and satellite operators themselves, who may wish to diagnose its root cause. Applications of this work for international space policymaking, including the development of on-orbit norms of behavior and the distribution of spectral and physical space in GEO, is also discussed. / This work describes an approach for detecting the components of longitudinal shift maneuvers—including the patterns associated with initiating and ending eastward and westward drifts—using convolutional neural networks trained on publicly available two-line element (TLE) data from the U.S. Space Command’s (SPACECOM) space object catalog. A method for converting TLE data to geographic position histories—longitude, latitude, and altitude positions over time in the Earth-centered, Earth-fixed geographic reference frame—and labeling longitudinal shift maneuvers by inspection is described. A preliminary maneuver detection algorithm is designed, trained, and tested on all GEO satellites in orbit from January 1 to December 31, 2020. Performance metrics are presented for algorithms trained on two different training data sets corresponding to five and ten years’ worth of geographic position time-histories labeled with longitudinal shift maneuvers. / When detected, longitudinal shift maneuvers can be used to identify anomalous behavior in GEO. In this work, a satellite’s behavior is considered nominal if it adheres to the satellite’s pattern of life (PoL)—its previous on-orbit behavior made up of sequences of both natural and non-natural behavioral modes, including routine station-keeping, other on-orbit maneuvers, and uncontrolled motion—and anomalous if it deviates from the satellite’s PoL. Identifying anomalous satellite behavior is of critical interest to space situational awareness (SSA) system operators, who may choose to task their sensors to obtain more observations of anomalous behavior, and satellite operators themselves, who may wish to diagnose its root cause. Applications of this work for international space policymaking, including the development of on-orbit norms of behavior and the distribution of spectral and physical space in GEO, is also discussed. / by Thomas González Roberts. / S.M. in Technology and Policy / S.M. Aeronautics and Astronautics / S.M. in Technology and Policy Massachusetts Institute of Technology, Institute for Data, Systems, and Society, Technology and Policy Program / S.M. Massachusetts Institute of Technology, Department of Aeronautics and Astronautics

Thesis: Ph. D. in Engineering Systems, Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, 2015. / This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. / Cataloged from student-submitted PDF version of thesis. / Includes bibliographical references (pages 165-169). / The cost of waste for products such as soft drinks, shelf stable dry food, and dairy in the consumer packaged goods industry is massive, about $15 billion annually in the U.S.A. This thesis focuses on waste associated with product expiration since this type of waste involves both manufacturers and retailers as well as different functional areas such as production, warehousing, sales, procurement, and store operations. As a result, the industry has not made much progress in reducing this type of waste. We study three problems related to product expiration. Chapter 2 presents a descriptive study examining the root causes of product expiration and their impact on expiration. Using econometrics and our collaborator's data, we find that the amount of expiration can be reduced considerably via a case size reduction. We identify the next important opportunities in the areas of inventory aging in the manufacturer's supply chain and sales incentives, and thus the remainder of this thesis focuses on these two areas. Chapter 3 examines the manufacturer's sell-or-dispose decision for aged inventory. We develop an optimization model to find the minimum remaining shelf life below which the manufacturer does not sell the product since the cost of expiration is more than the sunk cost of production. We use machine learning to approximate optimum values which can be used as a low cost alternative method. If supply chain managers are held accountable for the cost of disposed items, they will have an incentive to better manage inventory. As a result, expiration will be reduced. Chapter 4 analyses sales-force compensation schemes from the perspective of product expiration caused by overselling. We develop a game theoretic model of the decision process of the manufacturer and the sales representative. We find a compensation scheme that aligns the interests of the manufacturer and the sales representative preventing overselling while achieving full profit potential for the manufacturer. / by Arzum Akkas. / Ph. D. in Engineering Systems

Thesis: S.M. in Engineering Systems, Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, 2015. / "September 2015." Cataloged from PDF version of thesis. / Includes bibliographical references (pages 18-22). / Since 2012, more than 1300 cases of Middle East respiratory syndrome (MERS) have been diagnosed worldwide, the vast majority of which have occurred in Saudi Arabia and over 40% of which have ended in death. In Spring 2014, a large outbreak of MERS originated in the Kingdom of Saudi Arabia - concentrated in nosocomial settings in Riyadh and Jeddah - resulting in over 300 infections. We used publicly available data from the Saudi Ministry of Health and World Health Organization to examine the outbreak potential of MERS-Coronavirus and to explore possible risk factors for MERS-related mortality within the context of Saudi Arabia. We also investigated how differential case characteristics between patients reported during the Spring 2014 Saudi MERS outbreak and those reported during non-outbreak periods may provide insight into the propagation of future outbreaks. We found that the Spring 2014 Saudi MERS outbreak was likely due to a super-spreading event, in which a small fraction of cases caused the vast majority of secondary transmissions. Though most cases infected 1 or fewer other individuals, propensity for super-spreading suggests that the outbreak potential of MERS-Coronavirus is significant and that future outbreaks of similar size are expected to occur. Furthermore, we found that early administration of supportive care may be essential to survival once an individual is infected with MERS-Coronavirus; this is especially true for the elderly, who are at increased risk of death. Thus, surveillance - especially among the elderly, who are at increased risk for MERS-related death - is key to reducing fatality. Surveillance is also integral to detecting zoonotic introduction (i.e. host-to-human transmission) events that may trigger future outbreaks if left uncontained. Finally, we found that female and non-comorbid individuals were preferentially infected during the Spring 2014 outbreak, which may lend insight into the enabling conditions that are necessary for MERS outbreaks to emerge and propagate. Further exploration of the mechanisms that result in the zoonotic introduction of MERS-Coronavirus into the human population - as well as the emergence and propagation of MERS outbreaks - is crucial. As demonstrated by the steady stream of sporadic cases that have been reported since the Spring 2014 outbreak, MERS has already gained a firm foothold in the Kingdom of Saudi Arabia. Given that Saudi Arabia is a universal religious travel destination, localized outbreaks may have massive global implications. Because of this, we conclude with the recommendation that the Saudi government should immediately prioritize systematic outbreak planning, preparedness, and prevention. Developing an early warning system (EWS) for MERS in Saudi Arabia using engineering systems modeling methods - namely, system dynamics - may help achieve these ends. If successfully within the context of MERS-Coronavirus in Saudi Arabia, such a modeling framework may also be generalized to other zoonotic pathogens with similar emergent properties and global ramifications. / by Maimuna S. Majumder. / S.M. in Engineering Systems

Thesis: Ph. D. in Engineering Systems, Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, 2016. / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 211-225). / Automation has the benefit of reducing human operators' workload. By leveraging the power of computers and information technology, the work of human operators is becoming easier. However, when the workload is too low but the human is required to be present either by regulation or due to limitations of automation, human performance can be negatively affected. Negative consequences such as distraction, mind wandering, and inattention have been reported across many high risk settings including unmanned aerial vehicle operation, process control plant supervision, train engineers, and anesthesiologists. Because of the move towards more automated systems in the future, a better understanding is needed to enable intervention and mitigation of possible negative impacts. The objectives of this research are to systematically investigate the attention and performance of human operators when they interact with automated systems under low task load, build a dynamic model and use it to facilitate system design. A systems-based framework, called the Boredom Influence Diagram, was proposed to better understand the relationships between the various influences and outcomes of low task loading. A System Dynamics model, named the Performance and Attention with Low-task-loading (PAL) Model, was built based on this framework. The PAL model captures the dynamic changes of task load, attention, and performance over time in long duration low task loading automated environments. In order to evaluate the replication and prediction capability of the model, three dynamic hypotheses were proposed and tested using data from three experiments. The first hypothesis stated that attention decreases under low task load. This was supported by comparing model outputs with data from an experiment of target searching using unmanned vehicles. Building on Hypothesis 1, the second and third hypotheses examined the impact of decreased attention on performance in responding to an emergency event. Hypothesis 2 was examined by comparing model outputs with data from an experiment of accident response in nuclear power plant monitoring. Results showed that performance is worse with lower attention levels. Hypothesis 3 was tested by comparing model outputs with data from an experiment of defensive target tracking. The results showed that the impact of decreased attention on performance was larger when the task was difficult. The process of testing these three hypotheses shows that the PAL model is a generalized theory that could explain behaviors under low task load in different supervisory control settings. Finally, benefits, limitations, generalizability and applications of the PAL model were evaluated. Further research is needed to improve and extend the PAL model, investigate individual differences to facilitate personnel selection, and develop system and task designs to mitigate negative consequences. / by Fei Gao. / Ph. D. in Engineering Systems

Thesis: Ph. D. in Engineering Systems, Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, February 2017. / "February 2017." Cataloged from PDF version of thesis. / Includes bibliographical references (pages 150-169). / The use of modern bioenergy presents an opportunity to mitigate CO2 emissions contributing to anthropogenic climate change by offsetting fossil fuel use, and the work presented in this thesis contributes to the literature on bioenergy and climate change mitigation in three areas. First, this thesis quantifies the maximum potential reduction in global lifecycle greenhouse gas (GHG) emissions from the use of bioenergy to offset demand for fossil fuel-derived electricity, heat and liquid fuels in 2050. The findings indicate that bioenergy could reduce annual emissions from these end-uses by a maximum of 4.9-38.7 Gt CO2e, or 9-68%. The range of results reflects different assumptions defining potential bioenergy availability, and fossil fuel demand that could by offset by bioenergy, in 2050. In general, assumptions leading to greater calculated bioenergy availability, and fossil fuel demand, correspond to larger reductions in anthropogenic GHG emissions. In addition, offsetting fossil fuel-fired electricity and heat with bioenergy is found to be 1.6-3.9 times more effective for emissions mitigation than offsetting fossil fuel-derived liquid fuel, on average. At the same time, liquid fuels make up 18-49% of global final bioenergy in the scenarios considered for 2050, demonstrating that a mix of bioenergy end-uses maximizes lifecycle emissions reductions. The analysis also finds that GHG emissions reductions are maximized by limiting deployment of total available primary bioenergy to 29-91%, showing that lifecycle emissions including land use change (LUC) are a constraint on the usefulness of bioenergy for mitigating global climate change. Next, this thesis quantifies the environmental and economic performance of fermentation and advanced fermentation (AF) technologies for the production of renewable middle distillate (MD) fuels, including jet and diesel, in terms of lifecycle GHG emissions and minimum selling price (MSP). The attributional lifecycle GHG emissions of AF MD derived from sugarcane, corn grain and switchgrass are found to range from -27.0 to 19.7, 47.5 to 117.5, and 11.7 to 89.8 gCO2e/MJMD, respectively, compared to 90.0 gCO2e/MJMD for conventional petroleum-derived MD. These results are most sensitive to the co-product allocation method used, the efficiency and utility requirements of feedstock-to-fuel conversion, and the co-generation technology employed. The MSP of MD fuel produced from sugarcane, corn grain and switchgrass AF is also calculated as a range from 0.61 to 2.63, 0.84 to 3.65, and 1.09 to 6.30 USD20l2 /literMD. For comparison, the price of MD fuel was 0.80 USD2O2/literMD when this analysis was initially carried out in 2013, and was $0.38 USD2O2/literMD at the time of writing. This analysis demonstrates that improvements in overall feedstock-to-fuel conversion efficiency, for example from more efficient sugar extraction, enzymatic hydrolysis, or metabolic conversion processes, could lead to reductions in both the lifecycle GHG emissions and MSP of AF MD fuels. The final contribution of this thesis is a dynamic cost-benefit assessment (CBA) of a policy of large-scale alternative jet (AJ) fuel adoption, in terms of the societal climate damages and fuel production costs attributable to aviation. A system dynamics model is developed to capture time- and path-dependence of the environmental and economic performance of AJ technologies, as well as potential non-linearities and feedbacks associated with their adoption. The analysis finds that the large-scale use of AJ could result in a reduction in the net present value (NPV) of the societal costs of aviation, in terms of climate damages and fuel costs. However, even for the most promising feedstock-to-fuel production pathways considered, a net reduction in the societal costs of aviation has a probability of less than 50% if the initial societal opportunity cost of AJ feedstock exceeds 140 USD2015 /tfeedstock, or if land use change (LUC) emissions associated with incremental feedstock demand exceed 4.2 tCO2/inc. feedstock. These results highlight the potential importance of waste- and residue-derived AJ for reducing the societal costs of aviation, as these feedstocks represent a lower risk of LUC emissions and potentially lower societal opportunity costs than commodity crops. / by Mark Douglas Staples. / Ph. D. in Engineering Systems