In recent years environmental life-cycle assessments (LCA) have been increasingly used to support planning and development of sustainable infrastructure. This study demonstrates the application of LCA to estimate embedded energy use and greenhouse gas (GHG) emissions related to materials manufacturing and construction processes for low and high density single-family neighborhoods typically found in the Southwest. The LCA analysis presented in this study includes the assessment of more than 8,500 single family detached units, and 130 miles of related roadway infrastructure. The study estimates embedded and GHG emissions as a function of building size (1,500 - 3000 square feet), number of stories (1 or 2), and exterior wall material composition (stucco, brick, block, wood), roof material composition (clay tile, cement tile, asphalt shingles, built up), and as a function of roadway typology per mile (asphalt local residential roads, collectors, arterials). While a hybrid economic input-out life-cycle assessment is applied to estimate the energy and GHG emissions impacts of the residential units, the PaLATE tool is applied to determine the environmental effects of pavements and roads. The results indicate that low density single family neighborhoods are 2 - 2.5 X more energy and GHG intensive, per residential dwelling (unit) built, than high density residential neighborhoods. This relationship holds regardless of whether the functional unit is per acre or per capita. The results also indicate that a typical low density neighborhood (less than 2 dwellings per acre) requires 78 percent more energy and resource in roadway infrastructure per residential unit than a traditional small lot high density (more than 6 dwelling per acre). Also, this study shows that new master planned communities tend to be more energy intensive than traditional non master planned residential developments.

Restaurants have a cumulative impact on the environment, economy, and society. The majority of restaurants are small-to-medium enterprises (SMEs). Review of sustainability and industry literature revealed that considering restaurants as businesses with sustainable development options is the most appropriate way to evaluate their sustainable practices or lack thereof. Sustainable development is the means by which a company progresses towards achieving an identified set of sustainability goals and harnesses competitive advantage. The purpose of this thesis is to identify barriers to implementing sustainable practices in restaurants, and explore ways that restaurateurs can incorporate sustainable business practices. Energy consumption, water use, waste production, and food throughput are the four sustainability indicators addressed in this thesis. Interviews were conducted with five Tempe, Arizona restaurants, two of which consider their operations to be sustainable, and three of which are traditional restaurants. Results show that for traditional restaurants, the primary barriers to implementing sustainable business practices are cost, lack of awareness, and space. For sustainability-marketed restaurants, the barriers included a lack of knowledge or legal concerns. The sustainability-marketed restaurants have energy-efficient equipment and locally source a majority of their food purchases. There is a marked difference between the two types of restaurants in perception of barriers to sustainable business practices. I created a matrix to identify whether each indicator metric was applicable and present at a particular restaurant, and the potential barriers to implementing sustainable practices in each of the four indicator areas. Restaurants can use the assessment matrix to compare their current practices with sustainable practices and find ways to implement new or enhance existing sustainable practices. Identifying the barriers from within restaurants increases our understanding of the reasons why sustainable practices are not automatically adopted by SMEs. The assessment matrix can help restaurants overcome barriers to achieving sustainability by highlighting how to incorporate sustainable business practices.

Many expect renewable energy technologies to play a leading role in a sustainable energy supply system and to aid the shift away from an over-reliance on traditional hydrocarbon resources in the next few decades. This dissertation develops environmental, policy and social models to help understand various aspects of photovoltaic (PV) technologies. The first part of this dissertation advances the life cycle assessment (LCA) of PV systems by expanding the boundary of included processes using hybrid LCA and accounting for the technology-driven dynamics of environmental impacts. Hybrid LCA extends the traditional method combining bottom-up process-sum and top-down economic input-output (EIO) approaches. The embodied energy and carbon of multi-crystalline silicon photovoltaic systems are assessed using hybrid LCA. From 2001 to 2010, the embodied energy and carbon fell substantially, indicating that technological progress is realizing reductions in environmental impacts in addition to lower module price. A variety of policies support renewable energy adoption, and it is critical to make them function cooperatively. To reveal the interrelationships among these policies, the second part of this dissertation proposes three tiers of policy architecture. This study develops a model to determine the specific subsidies required to support a Renewable Portfolio Standard (RPS) goal. The financial requirements are calculated (in two scenarios) and compared with predictable funds from public sources. A main result is that the expected investments to achieve the RPS goal far exceed the economic allocation for subsidy of distributed PV. Even with subsidies there are often challenges with social acceptance. The third part of this dissertation originally develops a fuzzy logic inference model to relate consumers' attitudes about the technology such as perceived cost, maintenance, and environmental concern to their adoption intention. Fuzzy logic inference model is a type of soft computing models. It has the advantage of dealing with imprecise and insufficient information and mimicking reasoning processes of human brains. This model is implemented in a case study of residential PV adoption using data through a survey of homeowners in Arizona. The output of this model is the purchasing probability of PV.