Thesis: Ph. D. in Engineering Systems, Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, 2018. / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 168-171). / Space is one of the most scarce, expensive, and difficult to manage resources in urban retail establishments. A typical retail space broadly consists of two areas, the customer facing frontroom area and the backroom area, which is used for inventory storage and other support activities. While frontrooms have received considerable amount of attention from both academics and practitioners, backrooms are an often neglected area of retail space management and design. However, the allocation of space to the backroom and its management impact multiple operational aspects of retail establishments. These include in-store labor utilization, delivery schedules, product packaging, and inventory management. Therefore, the backroom area directly affects the performance of the store because it impacts stock-outs, customer service levels, and labor productivity. Moreover, extant literature suggests that backroom related operations contribute to a large fraction of the total retail supply chain costs. Thus, optimizing the management of backroom spaces is an important lever for store performance improvement. We address the gap in the extant literature related to space management of retail backrooms by investigating the following three questions: First, what is the effect of pack size on inventory levels and space needs in the backroom? Second, how can a given backroom space be efficiently utilized through optimal inventory control? Third, what is the optimal amount of space that should be allocated to the backroom in a given retail establishment? To address the first question, we evaluate the effect of two discrete pack sizes, order pack size (OPS) and storable pack size (SPS), on inventory levels and storage space requirements in the backrooms. While SPS drives the space needs for a given inventory level, OPS drives the amount of excess inventory and therefore, the space needs. Using inventory theory and probability theory, we quantify the amount of excess inventory and the expected stock-out probability for a given OPS in the case of a normally distributed demand. To address the second question, we discuss an inventory-theoretic approach to efficiently manage a given backroom space within a limited service restaurant. Specifically, we formulate a mathematical optimization model using mixed-integer linear programing with the objective of maximizing store profit. Applying this optimization model to real store data in collaboration with a major US retailer reveals cost implications related to constrained backroom space and the sensitivity of backroom space requirements to changes in OPS and SPS. The proposed model can serve as a decision support tool for various real-world use cases. For instance, the tool can help the retailers to identify (i) items whose contribution to the store profit does not justify their space needs in the backroom, and (ii) stores that are constrained in their profitability growth by backroom space limitations. To address the third question, we introduce the notion of interdependency between the frontroom and the backroom of a retail establishment. Such interdependencies yield nontrivial trade-offs inherent to the optimal retail space allocation. Demand can be lost due to unavailability of inventory (or inventory stock-out), which is a result of scarce amount of backroom space, or due to unavailability of sufficient frontroom space (or space stock-out). Furthermore, constrained backroom spaces increase in-store labor cost and the ordering costs incurred per unit of revenue generated in a retail establishment. The strategic decision model formulated in this chapter accounts for revenue, inventory cost, labor cost and ordering cost to determine the optimal amount of backroom space that should be allocated within a retail establishment. Sensitivity analyses with respect to the change in input parameters is used to connect the backroom space allocation and its impact on store profit to the different supply chain levers that can be managed by the retailers. / by Lita Das. / Ph. D. in Engineering Systems

Thesis: Ph. D. in Engineering Systems, Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, 2018. / Cataloged from PDF version of thesis. "Some pages in the original document contain text that runs off the edge of the page"--Disclaimer Notice page. / Includes bibliographical references. / China's rapid economic growth in the twenty-first century has driven, and been driven by, concomitant motorization and growth of passenger and freight mobility, leading to greater energy demand and environmental impacts. In this dissertation I develop methods to characterize the evolution of passenger transport demand in a rapidly-developing country, in order to support projection and policy assessment. In Essay #1, I study the role that vehicle tailpipe and fuel quality standards ("emissions standards") can play vis-à-vis economy-wide carbon pricing in reducing emissions of pollutants that lead to poor air quality. I extend a global, computable general equilibrium (CGE) model resolving 30 Chinese provinces by separating freight and passenger transport subsectors, road and non-road modes, and household-owned vehicles; and then linking energy demand in these subsectors to a province-level inventory of primary pollutant emissions and future policy targets. While climate policy yields an air quality co-benefit by inducing shifts away from dirtier fuels, this effect is weak within the transport sector. Current emissions standards can drastically reduce transportation emissions, but their overall impact is limited by transport's share in total emissions, which varies across provinces. I conclude that the two categories of measures examined are complementary, and the effectiveness of emissions standards relies on enforcement in removing older, higher-polluting vehicles from the roads. In Essay #2, I characterize Chinese households' demand for transport by estimating the recently-developed, Exact affine Stone index (EASI) demand system on publicly-available data from non-governmental, social surveys. Flexible, EASI demands are particularly useful in China's rapidly-changing economy and transport system, because they capture ways that income elasticities of demand, and household transport budgets, vary with incomes; with population and road network densities; and with the supply of alternative transport modes. I find transport demand to be highly elastic ([epsilon][subscript x] = 1.46) at low incomes, and that income-elasticity of demand declines but remains greater than unity as incomes rise, so that the share of transport in households' spending rises monotonically from 1.6 % to 7.5 %; a wider, yet lower range than in some previous estimates. While no strong effects of city-level factors are identified, these and other non-income effects account for a larger portion of budget share changes than rising incomes. Finally, in Essay #3, I evaluate the predictive performance of the EASI demand system, by testing the sensitivity of model fit to the data available for estimation, in comparison with the less flexible, but widely used, Almost Ideal demand system (AIDS). In rapidly-evolving countries such as China, survey data without nationwide coverage can be used to characterize transport systems, but the omission of cities and provinces could bias results. To examine this possibility, I estimate demand systems on data subsets and test their predictions against observations for the withheld fraction. I find that simple EASI specifications slightly outperform AIDS under cross-validation; these offer a ready replacement in standalone and CGE applications. However, a trade-off exists between accuracy and the inclusion of policy-relevant covariates when data omit areas with high values of these variables. Also, while province-level fixed-effects control for unobserved heterogeneity across units that may bias parameter estimates, they increase prediction error in out-of-sample applications-revealing that the influence of local conditions on household transport expenditure varies significantly across China's provinces. The results motivate targeted transport data collection that better spans variation on city types and attributes; and the validation technique aids transport modelers in designing and validating demand specifications for projection and assessment. / by Paul Natsuo Kishimoto. / Ph. D. in Engineering Systems

Thesis: Ph. D. Engineering Systems: Human-Systems Engineering, Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, 2018 / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 146-152). / Current approaches to nuclear security can produce elegantly designed physical protection systems (PPS) that may be limited by untenable assumptions or well stated-albeit vague and imprecise-descriptions of how to improve nuclear security culture itself. According to one nuclear security culture expert: While the International Atomic Energy Agency has released methodologies on evaluating vulnerabilities and physical protection, it has not yet introduced guidelines on assessing the human-factor in detection, delay, and response. (Khripunov, 2014, pp. 39-40) (Emphasis added) This dissertation argues that such a link lies in understanding how organizational influences affect the completion of tasks required for PPS to meet expected nuclear security performance goals. In this dissertation, I propose the System-Theoretic Framework for Security (the STFS) for evaluating system-level interactions between PPS and human/organizational behaviors to describe overall security performance. / Invoking key tenets of systems theory and organization science, the STFS uses the concept of "security task completion" to explain how the interactions between PPS and human/organizational behaviors result in security performance at nuclear facilities. Yet, empirical data is needed to explore the efficacy of this approach for incorporating organizational influences into security performance. As such, my research objectives were to: 1. Improve the understanding of how PPS and human/organizational behaviors interact to produce security performance at nuclear facilities, 2. Identify a manageable (but not exhaustive) set of organizational influences on this interaction, and 3. Develop a framework for assessing these interactions and organizational influences on security performance at nuclear facilities. I used a mixed methods research design to develop the STFS. / My first study consisted of 18 narrative interviews across different areas of nuclear security expertise and my second study examined the case of the 2012 security incident at the Y-12 National Security Complex. These two studies provided evidence for the security task completion construct (as a new causal mechanism), behavioral performance requirements (assumptions on which the causal mechanism is based), a set of organizational influences and quality indicators related to nuclear security performance. While this framework does not address every aspect of achieving high security performance, the STFS offers a structured thought process and direction for further development regarding how technologies and organizations interact to affect individual behaviors that contribute to security at nuclear facilities. / by Adam D. Williams. / Ph. D. Engineering Systems: Human-Systems Engineering / Ph.D.EngineeringSystems:Human-SystemsEngineering Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society

No description available.

Thesis: Ph. D. in Engineering Systems, Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, 2018 / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 125-135). / Rapidly falling wind and solar energy costs over the past four decades have led to exponential growth in installation of these technologies. However, these intermittent renewables do not reliably produce power on demand. One possible mitigation strategy is the addition of energy storage technologies, which are able to shift generation to later periods of higher demand or price. In competitive markets, storage adoption to facilitate renewables penetration will depend on how much value storage can bring to a wind or solar power plant. Which of the diverse energy storage technologies are best suited to profitably perform this function? How do price and resource variability determine the preferred technologies? This thesis develops two novel methods of comparing storage technologies in hybrid wind-storage or solar-storage power plants. In the first, we evaluate technologies based on the increased value of a marginal hybrid plant under today's conditions. We further explain these results by finding the determinants of storage value under uncertainty. In the second, we find the least-cost hybrid plants able to meet predefined demand profiles. Through simulation, optimization, and statistical analysis, we address the following questions: 1) How can one compare candidate storage technologies? 2) What price and resource features determine storage value? 3) What are the cost targets for storage under different market conditions? To address question 1, we optimize storage operation and size for grid-scale energy arbitrage, and study the value of hybrid plants using different storage technologies. The value of the hybrid plant is found by comparing benefits to costs, and is estimated across locations and technologies. We show that at today's wind and solar generation costs, some storage technologies can provide value, but further cost improvement is needed, especially for electrochemical technologies, to facilitate widespread adoption. Finally, we determine both cost targets and the optimal direction of cost improvement for diverse storage technologies and locations. In order to answer question 2, we identify features of the electricity market and the renewables resource availability that determine value. Through simulations of an artificial price time series in which features of electricity price spikes are varied, we find that storage value is driven by the frequency and amplitude of price spikes and the availability of the energy resource. The durations of price spikes determine the relative value of one storage technology to another, because of differing technology cost structures. We demonstrate these results in historical data and explain the differences in storage value across locations. We also explore how uncertainty in future prices impacts storage value. We determine a new heuristic for storage operation and sizing absent perfect foresight. This approach is able to capture at least 80% of the expected value under perfect foresight and improves upon existing heuristics. In answering question 3, we determine the least-cost combination of wind and solar with storage that provides reliable, dispatchable, pre-determined outputs. This approach allows for the evaluation of storage technologies for a possible future with higher renewables penetration. Preferred technologies for this use context have very low energy capacity costs (< $50/kWh), enabling inexpensive installation of long duration storage. Long periods of low wind or solar availability determine storage requirements and can be mitigated by including both wind and solar in the generation portfolio. New cost targets are derived for storage development that would help enable higher levels of renewables adoption. / by Joshua Michael Mueller. / Ph. D. in Engineering Systems / Ph. D. in Engineering Systems 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, 2018 / Cataloged from PDF version of thesis. "Doctor of Philosophy in Healthcare Systems: Management and Policy Research." / Includes bibliographical references. / For my PhD thesis, I conducted behavioral science research and wrote three first- author journal format papers, of which one paper has been published and the other two will be submitted to healthcare management journals after completion of my degree. All three papers introduce new information about either the cost or the behaviors of patients in local clinics, filling a gap in the healthcare system's management and policy literature. The first paper studies patients with diabetes who are non-adherent to scheduled appointments with physicians in a specialized diabetes clinic setting in Boston. I developed and introduced new and interesting ''technology comfort" measures and a "Smartphone usage" scale, to evaluate if patients would be able to use smart technologies for their disease self-management. This paper not only suggests that patients with diabetes could potentially benefit from using existing advanced technologies, but that new policies can be introduced to reduce the rate of diabetes patients' appointment-related non-adherence. The second paper examines the system of adherence or self-management in five areas ( diet, exercise, medications, doctor's appointments and regular glucose monitoring), revealing how it is correlated to emergency visits and patient lifestyle satisfaction. I analyze predictors of emergency room visits and propose potential policies to reduce these ER visits through the use of advanced smart technologies. The third paper identifies the incidence and consequences of not practicing non- pharmaceutical interventions, during the time of a pandemic, in a student population at a local university clinic. / by Sahar Hashmi, MD. / Ph. D. in Engineering Systems / Ph.D.inEngineeringSystems Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society

This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. / Thesis: Ph. D. in Engineering Systems, Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, 2019 / Cataloged from student-submitted PDF version of thesis. / Includes bibliographical references (pages 241-257). / Electricity tariffs typically charge residential users a volumetric (that is, per-unit of electricity consumed) price that recovers the bulk of the costs of generating, transmitting, and distributing electrical energy. These tariffs also often include taxes and recover other costs associated with regulatory or policy measures. The resulting prices do not reflect the true social marginal costs of generating, transmitting, and distributing energy, capturing little or none of the temporal and geographic variability of marginal electricity costs. These inefficient rates incentivize customers to over-consume power during periods of peak system stress and under-consume power during periods of relatively low demand; this dynamic drives up power system costs, costing Americans and Europeans tens of billions of dollars annually. Critically, it leads to investments in long-lived and low-utilization infrastructure needed to meet peak demands. / Economists have long argued for reforming rates, but progress has historically been slow. Today, less than one quarter of one percent of residential electricity customers in the United States pay a tariff that reflects the real-time price of energy. The emergence of distributed energy resources -- such as solar photovoltaics and battery energy storage -- / has sparked renewed interest among regulators and utilities in reforming electricity tariffs. Efficient rates hold the potential to improve the economic efficiency of distributed energy resource installation and operation decisions. However, the economic pressure to redesign electricity rates is countered by concerns of how more efficient rate structures might impact different socioeconomic groups. In particular, regulators have been dubious of efforts to reform how the costs of network infrastructure (that is, transmission and distribution networks) are recovered, rejecting more than 75% of such efforts in the U.S. in 2017. Focusing on developed power systems in contexts like the U.S. and Europe, this Thesis examines the distributional impacts of rate reform and proposes methods to improve the economic efficiency of rates without creating undesirable distributional impacts. / This Thesis also explores the distributional impacts of rooftop solar photovoltaics adoption under alternative rate designs. This Thesis leverages data on electricity consumption measured half-hourly for more than 100,000 customers in the Chicago, Illinois area, paired with Census data to gain unprecedented insight into the impacts of reforming electricity pricing across customers of varying socioeconomic statuses. This Thesis then builds a simple model of the local utility's -- Commonwealth Edison's -- / cost of service, and simulates solar PV adoption under alternative rate designs, measuring the impacts on customers of differing income levels. This Thesis demonstrates that low-income customers would face increases in expenditures on average in a transition to rates that recover residual network and policy costs through economically efficient fixed charges. However, this Thesis demonstrates that simple changes to fixed charge designs can mitigate these disparities while preserving all, or the vast majority, of the efficiency gains. These designs rely exclusively on observable information and could be replicated by utilities in many geographies across the U.S. / Rooftop solar PV adoption under tariffs with inefficient, volumetric residual cost recovery are shown to create substantial distributional challenges: PV adoption under such tariffs increases expenditures substantially for non-adopters, which tend to be predominately lower income customers; efficient tariffs prevent this regressive cost shifting. In short, failing to reform rates may lead to worse distributional outcomes than reforming rates, even if reforms are implemented naively. Collectively, the findings in this Thesis underscore the need for regulatory reform around electricity pricing, and chart a path forward for balancing economic efficiency and distributional equity in public utility pricing. / by Scott P. Burger. / Ph. D. in Engineering Systems / Ph.D.inEngineeringSystems Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society

This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. / Thesis: Ph. D. in Engineering Systems, Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, 2019 / Cataloged from student-submitted PDF version of thesis. / Includes bibliographical references (pages 139-150). / Networks are essential modeling tools in engineering, business, and public policy research. They can represent physical connections, such as manufacturing processes. They can be relationships among people, such as patient treatment in healthcare. They can also represent abstract interactions, such as the biological reaction between a certain vaccine and a certain virus. In this work, we bring several seemingly disparate problems under the same modeling framework, and show their thematic coherence via the angle of dynamic optimization on networks. Our research problems are drawn from business risk management, public health security, and public policy on vaccine selection. A common theme is the integrative design of (1) strategic resource placement on a network, and (2) operational deployment of such resources. We outline the research questions, challenges, and contributions as follows. / Modern automotive manufacturing networks are complex and global, comprising tens of thousands of parts and thousands of plants and suppliers. Such interconnection leaves the network vulnerable to disruptive events. A good risk mitigation decision support system should be data-driven, interpretable, and computational efficient. We devise such a tool via a linear optimization model, and integrate the model into the native information technology system at Ford Motor Company. In public security, policymakers face decisions regarding the placement of medical resources and training of healthcare personnel, to minimize the social and economic impact of potential large scale bio-terrorism attacks. Such decisions have to integrate the strategic positioning of medical inventories, understanding of adversary's behavior, and operational decisions that involve the deployment and dispensing of medicines. / We formulate a dynamic robust optimization model that addresses this decision question, apply a tractable solution heuristic, and prove theoretical guarantees of the heuristic's performance. Our model is calibrated with publicly available data to generate insights on how the policymakers should balance investment between medical inventory and personnel training. The World Health Organization and regional public health authorities decide on the influenza (flu) vaccine type ahead of flu season every year. Vaccine effectiveness has been limited by the long lead time of vaccine production - during the production period, flu viruses may evolve and vaccines may become less effective. New vaccine technologies, with much shorter production lead times, have gone through clinical trials in recent years. We analyze the question of optimal vaccine selection under both fast and slow production technologies. We formulate the problem as a dynamic distributionally robust optimization model. / Exploiting the network structure and using tools from discrete convex analysis, we prove some structural properties, which leads to informative comparative statics and tractable solution methods. With publicly available data, we quantify the societal benefit of current and future vaccine production technologies. We also explore the reduction in disease burden if WHO expand vaccine portfolio to include more than one vaccine strain per virus subtype. In each of the applications, our main contributions are four-fold. First, we develop mathematical models that capture the decision process. Second, we provide computational technology that can efficiently process these models and generate solutions. Third, we develop theoretical tools that guarantee the performance of these computational technology. Last, we calibrate our models with real data to generate quantitative and implementable insights. / by Peter Yun Zhang. / Ph. D. in Engineering Systems / Ph.D.inEngineeringSystems Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society

Thesis: Ph. D., Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, 2019 / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 181-187). / Authentic approaches to design education are typically defined as experiences centered on industry involvement. This industry connection is commonly either in the form of projects provided by industry partners or practicing engineers that serve as mentors to students. After exploring the goals and current practices of design education, this dissertation proposes an expanded definition of authentic design education: any design project with impact beyond the classroom environment that encourages the development of a student's self-identity as an engineer. To investigate the potential benefits afforded by an expanded definition of authentic design, a new design class was developed, taught, and evaluated across four years. The class, entitled Design for the Wilderness, was developed with a focus on projects that have impact beyond the classroom environment. Students were required to design and build products that they relied on while traveling in remote wilderness environments. / These impactful projects required students to experience the results of their design decisions. Building on our experiences implementing Design for the Wilderness, a curricular approach of Design for Use is introduced that requires students to use products developed by their peers. Design for Use helps increase students' understanding of human-centered design principles by encouraging students to confront the interplay between their intentions when designing a product and their experiences when failing to understand the intentions behind products designed by their peers. This dissertation also considers a mechanical engineering capstone design class (MIT's 2.009). An interesting outcome of this class is that some teams continue to work on commercializing their products after the semester ends. Team characteristics most strongly correlated with persisting on product development beyond the end of the class are related to healthy team dynamics and a positive social environment. / Teams that persisted spent more of their time working together, had fewer teammates that worked significantly more or less than the team average, and spent more of their time simply "hanging out" in lab. Drawing on our findings from investigating multiple approaches to authentic design education, recommendations are made for the future development of effective engineering design classes. / by Christopher R. Saulnier. / Ph. D. / Ph.D. 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 219-232). / Personal vehicles account for almost 25% of U.S. greenhouse gas emissions, and this share is increasing. The increase is due to several factors, including a growth in transportation demand and the decarbonization of electricity by 30% since 2007. Alternative technologies for road vehicles, such as battery electric, plug-in hybrid, and fuel cell powertrains have the potential to achieve significant emission reductions. Yet questions remain about the emissions and costs of these alternative technologies. This thesis evaluates the emissions reduction potential of vehicles with electrified powertrains, focusing on battery electric vehicles (BEVs). It evaluates this potential taking into account heterogeneous regional conditions and consumer behavior. Consumers help determine vehicle fleet emissions through their purchasing and driving decisions, which are guided in part by the costs of different options. / Therefore, the costs of ownership of BEVs in comparison to conventional vehicles inform the emissions reduction potential of BEVs. Here, we measure the lifecycle greenhouse gas emissions and costs of ownership of BEVs across different vehicle models as a function of travel patterns, driving styles, and properties of the natural, built, and institutional environment. We compare these costs and emissions to gasoline combustion engine vehicles (ICEVs), and then ask whether and under which condition electric vehicle adoption can play a central role in meeting emission targets for the transportation sector. The current literature does not cover all the interdependent sources of variation in the emissions and costs of BEVs compared to ICEVs. In particular, the effects of annual travel distance and fuel efficiency related to individual travel behavior and the wide variety of available vehicle models have not been assessed. / In addition, this variation in emissions and costs of personal vehicles has only been studied across regions, but not across individual vehicles within each region due to vehicle-specific driving patterns. This work addresses these gaps by developing several interlinked models. This includes the construction of a parametrized lifecycle emissions and cost of ownership model (Chapter 2), an algorithm to measure driving style linked to a vehicle energy model (Chapter 3), and a model to quantify the variability in annual travel distance and fuel consumption of different types of vehicles across regions within the United States, encoded as zipcodes, and across individual vehicles within those zipcodes (Chapter 4). Chapter 5 then ties Chapters 2 and 4 together and complements them with additional information to assess the overall heterogeneity in the emissions reduction potential of BEVs. The central results of the thesis are threefold. / First, a rapid decarbonization of electricity in conjunction with an electrification of powertrains will likely be required to meet emission targets for the U.S. transportation sector. Measures that relate to heterogeneous consumer behavior, such as improving driving style and nudging consumers towards purchasing smaller vehicles, can help to reduce greenhouse gas emissions. Second, the electrification of powertrains can come at little to no additional expense to consumers with today's technology and prices. In most parts of the country, BEVs are substantially cheaper than comparable ICEVs. Within regions, the individuals for which BEVs offer the greatest emissions savings would also tend to experience the largest cost savings, since both emissions savings and cost savings are correlated with annual travel distance. Third, emission reductions achieved by BEVs and their costs relative to ICEVs are highly heterogeneous. / The within-region variation in emissions and costs of BEVs compared to ICEVs due to individual driving patterns is at least as large as the variation across regional averages. As a result, a 10% share of BEVs in the fleet can lead to anywhere between 1% and 10% emission reductions, depending on which types of vehicles are being replaced by electric vehicles, by whom, and where. A key application of this work is to inform tools that provide localized and personalized information about the environmental and economic performance of different vehicle models. In Chapter 6, we discuss such a tool that was built as part of this work, called Carboncounter.com. Results from a survey launched on Carboncounter add to existing evidence that providing such information to consumers can help inform a transition to a cleaner light-duty vehicle fleet. These findings further confirm the importance of understanding heterogeneous human behaviors to inform decarbonization strategies for personal transport. / by Marco Miotti. / Ph. D. in Engineering Systems / Ph.D.inEngineeringSystems Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society