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How and why do countries respond differently to the dilemma of pursuing global climate reform through national legislation? This dissertation project explores the socio-political foundations of national carbon price policies, which resonate with global ideals and prioritize a global challenge over national economic benefits. An investigation into carbon prices in France, the United States, and Nordic countries reveals key sites of trade-offs. In France, this project traces the formation of their carbon tax, comparatively neoliberal by design, and the backlash from the populist Yellow Vest movement. In the United States, this project investigates the demise of a proposed carbon price, revealing how economic growth models complicate effective climate reform and empower business-elites to block regulatory reforms. In Nordic countries, this project compares the socio-politics of their relatively strong policies. All in all, this project explores the conditions under which such a law can be adopted, but it also emphasizes that enactment is not the end of the story. Rather, policies, themselves, reshape continuing political controversies over climate change. Carbon pricing thus becomes a case study in the trade-offs between global norms and national interests, highlighting the importance of national growth models, business-elite power, neoliberalism, and populist movements.

Atmospheric rivers (ARs) are large and narrow filaments of poleward horizontal water vapor transport. AR carry over 90% of moisture from the tropics to higher latitudes but cover only between 2% and 10% of the earth’s surface. When ARs are forced upwards frequently lead to heavy precipitation. ARs are associated with up to half of the extreme events in the top 2% of the precipitation and wind distribution across most mid-latitude regions. ARs can lead to hydrological hazards, and a better understanding of AR can help in the study, forecasting, and communication of flooding. Because of its direct relationship with horizontal vapor transport, extreme precipitation, and overall AR impacts over land, the AR size is an important characteristic that needs to be better understood. Furthermore, most of the ARs research work focuses on midlatitudes and polar regions. It is not until recently that ARs in tropical latitudes are starting to generate interest within the scientific AR community. We develop and implement five size estimation methods independent of the AR detection algorithms and use them to characterize the size of ARs. We create North American landfalling AR composites using ERA5 reanalysis data in the 1980-2017 period. To study how AR size changes with future climate scenarios, we use data from the Coupled Model Intercomparison Project 5 and 6 (CMIP5/6) to create historical and future AR composites in the 1950-2100 period. We apply our size estimation methods to study how AR size responds to climate change. Additionally, we use data from the ERA-20C reanalysis to study the relationship between lower latitude ARs and the extreme precipitation in Central-Western Mexico (CWM) during the dry season (November-March) in the 1900-2010 period.North American landfall ARs (NALFARs) that originate in the Northwest Pacific (WP) (100◦E-180◦E) have larger sizes and are more zonally oriented than those from the Northeast Pacific (EP) (180◦E-240◦E). ARs become smaller through their life cycle, mainly due to reductions in their width. They also become more meridionally oriented towards the end of their life cycle. NALFARs become smaller through their life cycle, mainly due to reductions in their width. They also become more meridionally oriented towards the end of their life cycle. Overall, the size estimation methods developed in this work provide a range of AR areas (between 7x1011m2 and 1013 m2) that is several orders of magnitude narrower than the current estimation by the AR detectors from the Atmospheric River Tracking Method Intercomparison Project (ARTMIP).From a global AR size analysis, we show an increase between 10% and 21% in the background IVT field among CMIP5/6 models. According to our results, AR width is more sensitive to climate change and has a larger contribution than length to the change in the AR area. We find a mean AR area of 3.15x106 (2.32x106-3.98x106) km2 for historical runs, and 3.42x106 (2.73x106-4.11x106) km2 for future runs. Most size estimation methods and CMIP5/6 models show positive trends in AR area, length, and width, between historical and strong radiative forcing future simulations (CMIP5: RCP-8.5, CMIP6: SSP-858). Regardless of the individual sign in AR size change, the mean AR cross-section water vapor transport increases between 8% and 37% for future simulations. Additionally, our results suggest that NALFARs are more likely to penetrate further inland under climate change.Regarding landfalling ARs in CWM, our results suggest that more than 25% of the extreme dry-season precipitation is associated with AR-like events, with up to 75% in December and January. This AR-associated precipitation is associated with an enhanced mean vertically integrated water vapor (IWV) and horizontal vapor transport (IVT) fields (30 kg m−2 and IVT 400 kg m−1s−1, respectively). The meteorological state of the atmosphere shows “ideal” conditions for orographic precipitation due to landfalling ARs: a high plume of horizontal vapor transport perpendicular to the mountain range. These events are associated with a weakening of the westward equatorial IVT and a tropospheric wave pattern, observable in the mean sea level pressure and geopotential height anomalies.We believe that the size estimation methods developed in this work provide statistical constraints for AR size and geometry, and how they change in future climates. These results could help as a reference for tuning existing ARDTs or designing new AR detection algorithms. Furthermore, we demonstrate the relationship between ARs and winter rainfall in CWM. This relationship leaves the question open of how similar are these tropical ARs to the more studied higher latitude ARs and how they will respond in a warming world.

Never, in oil’s one and a half century of commercial extraction has the global oil industry’s future been so fraught. The renewable energy transition, an ongoing investment and volatility crisis, the decline in the quality of reserves and current production, renewed fears of geopolitical conflict, and the inherently anarchic character of capitalist oil production have all converged to cast a shadow over the future of oil, and energy as a whole. This dissertation is a combination of four distinct essays, each of which contributes to unraveling the current juncture. My argument, put simply, is that under this convergence, the capitalist renewable energy transition will be plagued by the greatest period of dysfunction the oil industry has ever seen, impacting billions of people around the world in their access to energy during this time. This, what I call, ‘Carbon Purgatory,’ deserves study as this tumultuous period of transition begins.

Global emergencies resulting from conflict, human rights violations, and natural disasters have displaced more than 90 million people worldwide, half of whom are under 18. While the United Nations’ Sustainable Development Goal 4 (SDG4) calls for sustainable (i.e. long-lasting) access to inclusive and quality education for all people by 2030, global education systems have thus far fallen short, particularly in emergency settings. In Myanmar, a country affected by multiple state-led conflicts and genocidal acts against ethnic minorities, access to quality and inclusive education is severely limited. In response to the state’s neglect of education amid war, several ethnic minority communities have created their own education systems. These community-based schools (CBS) are one type of non-state schooling (i.e. private or nongovernmental) where all financing and provision of education is owned and managed by local community actors. The research on CBS shows demonstrated benefits in the areas of culturally relevant curriculum and local ownership of organizational practices, though challenges like inconsistent quality and lack of attention to inclusivity have also been found. Few studies have been conducted on CBS operating amidst active conflict. Accordingly, this qualitative and participatory research study investigates, through an in-depth case study, the macro-level sociopolitical history of institutions, meso-level organizational practices, and micro-level curriculum development processes of CBS operating amidst emergencies in Myanmar. In my analysis, I draw from a range of academic and practitioner-based theoretical approaches to present findings on how these macro, meso, and micro level community-based education practices reflect sustainable access to quality and inclusive education in emergencies. Ultimately, I argue that a rich historical understanding of community and their sustained engagement with CBS, from visioning to implementation and refining, are necessary to best realize educational goals. I conclude with recommendations for CBS efforts in Myanmar specifically and how this case might inform and inspire practice and research surrounding other instances of community-based education in emergencies globally.

Wearable devices have seen tremendous growth in the recent decade in the consumer electronics space, which promotes research on their next-generation technologies and form factors for comprehensive physical, physiological and biochemical sensing, as well as high flexibility, conformity, and stretchability form factors toward more intimate human-machine interactions. However, the current development of wearable sensors and electronics has been hindered by the lack of efficient, autonomous economical, and practical energy systems. In particular, the power of wearable energy harvesters and the energy density of flexible energy storage devices cannot satisfy the demand of common wearable applications, which fundamentally challenges the concept of self-sustainable wearable devices. Aiming to address this challenge, in this dissertation, the concept of designing a microgrid-like wearable system was proposed, describing a new design concept for wearables that features reliable, practical, sustainable, and autonomous operation. The scenario-specific design considerations for eliminating the performance mismatch between components, minimizing individual disadvantaged characteristics, and maximizing the system’s energy reliability are discussed. Towards establishing high-performance microgrids on wearable platforms, advances in wearable bioenergy harvesters and batteries, along with implementations of the wearable microgrid concept into electronic textile and electronic skins platforms are presented. Such implementations include systematic integrations of energy harvesting, storage, and regulation modules into self-sustainable biosensing platforms, which operate independently on the human body without requiring external energy input. Separately, structural innovations to enable flexibility and stretchability in wearable electronics are introduced. Lastly, this dissertation summarizes existing challenges, theoretical limitations, and prospects of wearable microgrids for commercializing next-generation wearable electronics.

The rapidly growing number of edge devices continuously generating data with real-time response constraints coupled with the bandwidth, latency, and reliability issues of centralized cloud computing have made computing near the edge indispensable. As a result, using Field Programmable Gate Arrays (FPGAs) at the edge, due to their unique capabilities that meet the requirements of both high-performance applications and the Internet of Things (IoT) domain, is becoming prevalent. However, designs deployed on these devices suffer from efficiency gap versus custom implementations mainly due to the overhead associated with the FPGAs reconfigurability. This problem is more pronounced in the edge domain, where most devices are battery-powered. In the first part of this dissertation, we identify and overcome the challenges xvi behind the power reduction of FPGA-based applications and propose techniques to lower their energy consumption. Our approach exploits the pessimistic timing margin of the designs to tune the voltage and improves the energy consumption by 66%. An increasing number of edge applications rely on machine learning (ML) algorithms to generate useful insights from data. While modern machine learning techniques – in particular deep neural networks (DNNs) – can produce state-of-the-art results, they often entail substantial memory and compute requirements that may exceed the power and resources available on lightweight error-prone edge devices. Hyperdimensional Computing (HDC) is an emerging lightweight and robust learning paradigm suited for the edge domain that copes with the memory and compute overhead of conventional ML algorithms. The next part of the dissertation proposes efficient FPGA-based and custom hardware implementations of HDC to enable intelligence on devices with limited resources, strict energy constraints, and in noisy environments. The proposed HDC algorithms and accelerators reduce the energy consumption by more than three orders of magnitude compared to other ML solutions, with a comparable or better accuracy. The last part of the dissertation seeks to resolve the privacy concerns of HDC that stem from its reversible algorithm and pose challenges for HDC-based learning and inference. We propose hardware- and communication-efficient techniques that improve the ‘inference’ privacy of HDC by reducing the information of the transferred data while consuming less energy than the non-private baseline. We then show that HDC ‘learning’ can meet tight privacy budgets with negligible accuracy degradation. We also propose a hybrid CNN and HDC model for differentially-private training over image data, which achieves comparable or better accuracy than the state-of-the-art CNN-only methods with more than three orders of magnitude faster training.

Ecological dynamics have been altered by recent climate change in regions with pronounced warming trends. Landscape- and regional-scale ecological processes face change in both seasonal patterns and long-term trends as temperature and precipitation regimes shift. Interspecific interactions are especially sensitive to environmental change if one species responds flexibly to abiotic processes but the other does not. Herbivore movements, migrations, and distributions – each of which relate to biotic and abiotic environmental variation – are thus likely to change as effects of climate change cascade through ecosystems. Yet, multilevel relationships connecting climate with spatial dynamics of herbivores are poorly understood, in part due to the difficulties in generating consistent measurements of fine-scale ecological processes across broad geographic extents. Here, I use the Sierra Nevada mountains of California and Sierra Nevada bighorn sheep (Ovis canadensis sierrae, hereafter “Sierra bighorn”) as a model to address three questions about space and time in ungulate ecology: First, how does landscape phenology vary along a desert-alpine gradient? Second, what drives nomadic migration in Sierra bighorn? And third, how can long-term climate forecasts inform conservation of large ungulates at the continental scale? I begin by outlining a niche-centric view of altitudinal migration and its marine counterpart, bathymetric migration. I then quantify relationships among topography, geography, and weather, and their collective effects on spatial patterns in resource phenology. Next, I address movement strategy, organization, and migration timing of an alpine specialist as they relate to the resource base. Finally, I contextualize ungulate conservation efforts in future climatic conditions, identifying which species are most susceptible to loss of protected climate space toward the end of the century.

In the age of global warming, there is a crucial need to accurately assess uncertainty levels when analyzing observed changes in the climate. For many climate problems, the development of statistical methods that appropriately account for uncertainty is challenging due to the complexity of the underlying climate processes and the various sources of uncertainty involved. This thesis addresses methodological challenges in modeling uncertainty for two climate problems with important real-world applications. The first problem is concerned with quantifying the heat content of the global ocean and its change over time. Understanding the trend in ocean heat content is particularly important as it informs estimates of transient climate sensitivity, a physical parameter that largely determines the amount of warming that will be expected in the years to come. This problem is nevertheless made difficult by the challenge of representing the complex covariance structure of the ocean heat content field, as well as the challenge of quantifying the uncertainty in the estimation of this structure. The second problem is concerned with separating the influence of warming caused by human activities from natural variability in the observed climate, a problem that is often referred to as climate change ``detection and attribution''. While various sources of uncertainty in this problem have been addressed in the literature, recent results have suggested that commonly-used methods under-estimate uncertainty in their conclusions. Producing reliable detection and attribution confidence intervals is difficult in part due to the challenge of modeling the uncertainty in the estimation of the natural variability covariance structure from limited climate model simulations.This thesis proposes methods for addressing statistical challenges in these two problems with respect to three overarching themes. The first theme is the use of spatially-coherent statistical models to represent the covariance structures of the underlying physical processes. For the ocean heat content problem, a novel cylindrical kernel-convolution Gaussian process model is developed to flexibly represent the complex spatial correlation patterns of the global ocean heat content field. For the detection and attribution problem, a Laplacian basis vector parameterization of the covariance matrix is proposed to enforce spatially-coherent correlation patterns. This parameterization is also able to avoid the uncertainty in the traditional approach of estimating principal component vectors from limited numbers of climate model runs. The second theme is the use of hierarchical Bayesian models to propagate the uncertainty in estimating the covariance structure to the final results. In the ocean heat content problem, the spatially-varying parameter fields describing the kernel-convolution Gaussian process are themselves modeled as Gaussian processes in a hierarchical framework. This allows for the uncertainty in estimating these parameters to be propagated to the final posterior distribution for the ocean heat content trend. In the detection and attribution problem, the parameters of the Laplacian parameterization of the covariance matrix, as well as the number of Laplacians to use, are both represented in a Bayesian hierarchical framework that prioritizes the accurate modeling of uncertainty. Finally, the third theme concerns the evaluation of the statistical properties of the Bayesian posterior distributions. For the ocean heat content problem, this is done using cross-validation on the observations with respect to a metric for evaluating both the mean and uncertainty implied by the posterior predictive distributions. For the detection and attribution problem, climate model simulations are used to evaluate the accuracy of the posterior means and credible intervals produced by the proposed methods in the context where the true value can be assumed to be known.Chapter 1 begins by introducing the broader context and implications of the two climate problems and proceeds to give a brief overview of the three statistical themes. Chapter 2 develops the proposed methodology for the ocean heat content problem in a restricted context focusing on spatial variability. A cross-validation study is presented showing that the proposed framework achieves higher accuracy in the predictive posterior distributions than a commonly-used previous method as well as simpler Bayesian approaches. This framework is then extended to the full spatio-temporal context in Chapter 3 and is applied to the quantification of the trend in ocean heat content from 2007 to 2021. The detection and attribution problem is addressed in Chapter 5, where a climate model validation study shows that the proposed approach achieves higher accuracy in the posterior mean and more accurate credible intervals than a traditional approach. While the validation results for each of these proposed methods show quantitative improvements over previous approaches, the results suggest several promising opportunities for additional improvements and extensions. Several of these potential avenues for future research are discussed in Chapter 6.

Heavy-duty electric and hybrid electric vehicles are potential means to reduce the emissions of the transportation sector. However, the lithium batteries needed to power these vehicles can be cost and weight prohibitive, and battery degradation adds to the lifetime cost of these vehicles. Buses in particular are considered throughout this work—their frequent stopping and starting makes them prime candidates for electrification or hybridization, yet that same stopping and starting can be a source of significant battery wear. This work explores methods to improve battery lifespan and improve the overall economic feasibility of heavy-duty alternative powertrain vehicles.Four studies are carried out to this effect; simulation is used in all cases due to the slow rate of battery degradation and the expense associated with destructive testing. First, an electric bus is fitted with an on-board photovoltaic system. A full model for on-board photovoltaics is developed and it is shown that the power provided by the modules reduce the battery discharge depth to a sufficient degree to improve battery lifespan. Bus rooftop photovoltaics are shown to have a positive return on investment. Next, aging-aware control of a hybrid energy storage system is considered. Hybrid energy storage pairs an ultracapacitor with the conventional lithium battery to reduce large current spike and improve battery aging. A new energy management strategy that incorporates ultracapacitor aging is shown to be a more effective means of control than existing literature. The third and fourth studies concern robust energy management. The third considers the robustness of aging-aware energy management to aging model variations and methods of improving the robustness of aging-aware strategies are proposed. The fourth study introduces a new energy management concept that incorporates elements of minimax dynamic programming. This new strategy is first shown to improve robustness of a series hybrid bus to driving condition uncertainty, then second it is shown to improve the performance of aging-aware control of an electric vehicle with hybrid energy storage.

Precipitation characteristics have a great influence on tropical ecosystems under a changing climate. It has been widely suggested that precipitation changes are expected to impact the populations and communities of tropical birds. Here I investigated the changes of the precipitation regime, length of the dry season (defined as months with precipitation lower than the annual mean), and the vapor pressure deficit in the tropical South America projected for 2080 – 2100 under emission scenario SSP5-8.5. It has been found that most of the studied area will experience decreasing annual precipitation (up to 37%) and increasing vapor pressure deficit (up to 190%) compared to 1970 – 2000, with seasonal variations. Furthermore, dry seasons are expected to extend over most regions and the monthly averaged precipitation within dry seasons is projected to decrease especially in Central Amazon. The protection areas identified to experience lower impacts concentrate along the east side of the Andes and northeastern Amazon.