Infrastructure are increasingly being recognized as too rigid to quickly adapt to a changing climate and a non-stationary future. This rigidness poses risks to and impacts on infrastructure service delivery and public welfare. Adaptivity in infrastructure is critical for managing uncertainties to continue providing services, yet little is known about how infrastructure can be made more agile and flexible towards improved adaptive capacity. A literature review identified approximately fifty examples of novel infrastructure and technologies which support adaptivity through one or more of ten theoretical competencies of adaptive infrastructure. From these examples emerged several infrastructure forms and possible strategies for adaptivity, including smart technologies, combined centralized/decentralized organizational structures, and renewable electricity generation. With institutional and cultural support, such novel structures and systems have the potential to transform infrastructure provision and management.

Transit-oriented developments (TODs) are a promising strategy to increase public transit use and, as a result, reduce personal car travel. By using TOD infill to increase urban population density and encourage transportation mode-shifting, the potential exists to reduce life-cycle per capita energy use and environmental impacts of the interdependent infrastructure systems. This project specifically examined the Gold Line of light rail and Orange Line of bus rapid transit in Los Angeles, CA.

ABSTRACT Water and energy resources are intrinsically linked, yet they are managed separately even in the water scarce America southwest. This study develops a spatially explicit model of water energy inter-dependencies in Arizona and assesses the potential for co beneficial conservation programs. The interdependent benefits of investments in 8 conservation strategies are assessed within the context of legislated renewable energy portfolio and energy efficiency standards. The co- benefits of conservation are found to be significant. Water conservation policies have the potential to reduce statewide electricity demand by 1.0 - 3.0 %, satisfying 3.3 -10 % of the state's mandated energy-efficiency-standard. Adoption of energy -efficiency measures and renewable generation portfolios can reduce non - agricultural water demand by 2.3 - 12 %. The conservation co- benefits are typically not included in conservation plans or benefit cost analyses. Many co-benefits offer negative costs of saved water and energy, indicating that these measures provide water and energy savings at no net cost. Because ranges of costs and savings for water energy conservation measures are somewhat uncertain, future studies should investigate the co-benefits of individual conservation strategies in detail. Although this study focuses on Arizona, the analysis can be extended elsewhere as renewable portfolio and energy efficiency standards become more common nationally and internationally.

While public transit systems are perceived to produce lower GHG emission intensities per passenger miles traveled (PMT) and per vehicle miles traveled (VMT), there is a limited understanding of emissions per PMT/VMT across cities, or of how emissions may change across modes (light, metro, commuter, and bus) and time (e.g., with changing electricity mixes in the future). In order to better understand the GHG emissions intensity of public transit systems, a comparative emissions assessment was developed utilizing the National Transit Database (NTD) which reports energy use from 1997 to 2012 of rail and bus systems across the US. By determining the GHG emission intensities (per VMT or per PMT) for each mode of transit across multiple years, the modes of transit can be better compared between one another. This comparison can help inform future goals to reduce GHG emissions as well as target reductions from the mode of transit that has the highest emissions. The proposed analysis of the NTD and comparison of modal emission intensities will be used to develop future forecasting that can guide public transit systems towards a sustainable future.

Bicyclist and pedestrian safety is a growing concern in San Francisco, CA,

especially given the increasing numbers of residents choosing to bike and walk. Sharing

the roads with automobiles, these alternative road users are particularly vulnerable to

sustain serious injuries. With this in mind, it is important to identify the factors that

influence the severity of bicyclist and pedestrian injuries in automobile collisions. This

study uses traffic collision data gathered from California Highway Patrol’s Statewide

Integrated Traffic Records System (SWITRS) to predict the most important

determinants of injury severity, given that a collision has occurred. Multivariate binomial

logistic regression models were created for both pedestrian and bicyclist collisions, with

bicyclist/pedestrian/driver characteristics and built environment characteristics used as

the independent variables. Results suggest that bicycle infrastructure is not an important

predictor of bicyclist injury severity, but instead bicyclist age, race, sobriety, and speed

played significant roles. Pedestrian injuries were influenced by pedestrian and driver age

and sobriety, crosswalk use, speed limit, and the type of vehicle at fault in the collision.

Understanding these key determinants that lead to severe and fatal injuries can help

local communities implement appropriate safety measures for their most susceptible

road users.

Photovoltaics (PV) is an environmentally promising technology to meet climate goals and transition away from greenhouse-gas (GHG) intensive sources of electricity. The dominant approach to improve the environmental gains from PV is increasing the module efficiency and, thereby, the renewable electricity generated during use. While increasing the use-phase environmental benefits, this approach doesn’t address environmentally intensive PV manufacturing and recycling processes.

Lifecycle assessment (LCA), the preferred framework to identify and address environmental hotspots in PV manufacturing and recycling, doesn’t account for time-sensitive climate impact of PV manufacturing GHG emissions and underestimates the climate benefit of manufacturing improvements. Furthermore, LCA is inherently retrospective by relying on inventory data collected from commercial-scale processes that have matured over time and this approach cannot evaluate environmentally promising pilot-scale alternatives based on lab-scale data. Also, prospective-LCAs that rely on hotspot analysis to guide future environmental improvements, (1) don’t account for stake-holder inputs to guide environmental choices in a specific decision context, and (2) may fail in a comparative context where the mutual differences in the environmental impacts of the alternatives and not the environmental hotspots of a particular alternative determine the environmentally preferable alternative

This thesis addresses the aforementioned problematic aspects by (1)using the time-sensitive radiative-forcing metric to identify PV manufacturing improvements with the highest climate benefit, (2)identifying the environmental hotspots in the incumbent CdTe-PV recycling process, and (3)applying the anticipatory-LCA framework to identify the most environmentally favorable alternative to address the recycling hotspot and significant stakeholder inputs that can impact the choice of the preferred recycling alternative.

The results show that using low-carbon electricity is the most significant PV manufacturing improvement and is equivalent to increasing the mono-Si and multi-Si module efficiency from a baseline of 17% to 21.7% and 16% to 18.7%, respectively. The elimination of the ethylene-vinyl acetate encapsulant through mechanical and chemical processes is the most significant environmental hotspot for CdTe PV recycling. Thermal delamination is the most promising environmental alternative to address this hotspot. The most significant stake-holder input to influence the choice of the environmentally preferable recycling alternative is the weight assigned to the different environmental impact categories.

Cities are, at once, a habitat for humans, a center of economic production, a direct consumer of natural resources in the local environment, and an indirect consumer of natural resources at regional, national, and global scales. These processes do not take place in isolation: rather they are nested within complex coupled natural-human (CNH) systems that have nearby and distant teleconnections. Infrastructure systems—roads, electrical grids, pipelines, damns, and aqueducts, to name a few—have been built to convey and store these resources from their point of origin to their point of consumption. Traditional hard infrastructure systems are complemented by soft infrastructure, such as governance, legal, economic, and social systems, which rely upon the conveyance of information and currency rather than a physical commodity, creating teleconnections that link multiple CNH systems. The underlying structure of these systems allows for the creation of novel network methodologies to study the interdependencies, feedbacks, and timescales between direct and indirect resource consumers and producers; to identify potential vulnerabilities within the system; and to model the configuration of ideal system states. Direct and indirect water consumption provides an ideal indicator for such study because water risk is highly location-based in terms of geography, climate, economics, and cultural norms and is manifest at multiple geographic scales. Taken together, the CNH formed by economic trade and indirect water exchange networks create hydro-economic networks. Given the importance of hydro-economic networks for human well-being and economic production, this dissertation answers the overarching research question: What information do we gain from analyzing virtual water trade at the systems level rather than the component city level? Three studies are presented with case studies pertaining to the State of Arizona. The first derives a robust methodology to disaggregate indirect water flows to subcounty geographies. The second creates city-level metrics of hydro-economic vulnerability and functional diversity. The third analyzes the physical, legal, and economic allocation of a shared river basin to identify vulnerable nodes in river basin hydro-economic networks. This dissertation contributes to the literature through the creation of novel metrics to measure hydro-economic network properties and to generate insight into potential US hydro-economic shocks.

In the American Southwest, an area which already experiences a significant number of cooling degree days, anthropogenic climate change is expected to result in higher average temperatures and the increasing frequency, duration, and severity of heat waves. Climatological forecasts predict heat waves will increase by 150-840% in Los Angeles County, California and 340-1800% in Maricopa County, Arizona. Heat exposure is known to increase both morbidity and mortality and rising temperatures represent a threat to public health. As a result there has been a significant amount of research into understanding existing socio-economic vulnerabilities to extreme heat which has identified population subgroups at greater risk of adverse health outcomes. Additionally, research has shown that man-made infrastructure can mitigate or exacerbate these health risks. However, while recent socio-economic heat vulnerability research has developed geospatially explicit results, research which links it directly with infrastructure characteristics is limited. Understanding how socio-economic vulnerabilities interact with infrastructure systems is a critical component to developing climate adaptation policies and programs which efficiently and effectively mitigate health risks associated with rising temperatures.

The availability of cooled space, whether public or private, has been shown to greatly reduce health risks associated with extreme heat. However, a lack of fine-scale knowledge of which households have access to this infrastructure results in an incomplete understanding of the health risks associated with heat. This knowledge gap could result in the misallocation of resources intended to mitigate negative health impacts associated with heat exposure. Additionally, when discussing accessibility to public cooled space there are underlying questions of mobility and mode choice. In addition to captive riders, a growing emphasis on walking, biking and public transit will likely expose additional choice riders to extreme temperatures and compound existing vulnerabilities to heat.

Given that more and more planned special events are hosted in urban areas, during which travel demand is considerably higher than usual, it is one of the most effective strategies opening public rapid transit lines and building park-and-ride facilities to allow visitors to park their cars and take buses to the event sites. In the meantime, special event workforce often needs to make balances among the limitations of construction budget, land use and targeted travel time budgets for visitors. As such, optimizing the park-and-ride locations and capacities is critical in this process of transportation management during planned special event. It is also known as park-and-ride facility design problem.

This thesis formulates and solves the park-and-ride facility design problem for special events based on space-time network models. The general network design process with park-and-ride facilities location design is first elaborated and then mathematical programming formulation is established for special events. Meanwhile with the purpose of relax some certain hard constraints in this problem, a transformed network model which the hard park-and-ride constraints are pre-built into the new network is constructed and solved with the similar solution algorithm. In doing so, the number of hard constraints and level of complexity of the studied problem can be considerable reduced in some cases. Through two case studies, it is proven that the proposed formulation and solution algorithms can provide effective decision supports in selecting the locations and capabilities of park-and-ride facilities for special events.