UNDERSTANDING PEOPLE'S ADOPTION OF INDIVIDUAL-USE RENEWABLE ENERGY TECHNOLOGY

This thesis attempts to understand why people adopt or reject individual-use renewable energy technologies (IURET). I used factors from Everett Rogers' Diffusion of Innovation Theory to understand how people's perceptions towards the characteristics of a given IURET (such as price, compatibility, complexity, etc.), the characteristics of the individual adopter (such as innovativeness and environmental awareness), and the communication network (inter-personal communications and mass media) can influence adoption. An online questionnaire was sent to 101randomly selected Michigan households (using random digit dialing) to ask people whether or not they had adopted at least one IURET and to assess the above-mentioned factors from Rogers' theory. Data analysis was then conducted in SPSS using Chi-squared and binary logistic regression to determine the relationship between adoption behaviors (the dependent variable) and the factors from Rogers' theory (the independent variables) while controlling for education. The results show that Rogers' factors of price and observability and the control variable of education were all significant in explaining adoption but the other factors of Rogers' theory were not. For example, if individuals perceive the price of IURET to be reasonable or if they observe their neighbors using these technologies, then they are more likely to adopt. These results indicate that, if we want to promote greater adoption of IURET, we should focus our efforts on making the price of IURET more affordable through incentives and other mechanisms. Adopters should also be given some form of reward if they provide free demonstrations of their IURET in use to their neighbors to take advantage of the observability effects.

PROBABILISTIC MODELING OF RAINFALL INDUCED LANDSLIDE HAZARD ASSESSMENT IN SAN JUAN LA LAGUNA, SOLOLÁ, GUATEMALA

The municipality of San Juan La Laguna, Guatemala is home to approximately 5,200 people and located on the western side of the Lake Atitlán caldera. Steep slopes surround all but the eastern side of San Juan. The Lake Atitlán watershed is susceptible to many natural hazards, but most predictable are the landslides that can occur annually with each rainy season, especially during high-intensity events. Hurricane Stan hit Guatemala in October 2005; the resulting flooding and landslides devastated the Atitlán region. Locations of landslide and non-landslide points were obtained from field observations and orthophotos taken following Hurricane Stan. This study used data from multiple attributes, at every landslide and non-landslide point, and applied different multivariate analyses to optimize a model for landslides prediction during high-intensity precipitation events like Hurricane Stan. The attributes considered in this study are: geology, geomorphology, distance to faults and streams, land use, slope, aspect, curvature, plan curvature, profile curvature and topographic wetness index. The attributes were pre-evaluated for their ability to predict landslides using four different attribute evaluators, all available in the open source data mining software Weka: filtered subset, information gain, gain ratio and chi-squared. Three multivariate algorithms (decision tree J48, logistic regression and BayesNet) were optimized for landslide prediction using different attributes. The following statistical parameters were used to evaluate model accuracy: precision, recall, F measure and area under the receiver operating characteristic (ROC) curve. The algorithm BayesNet yielded the most accurate model and was used to build a probability map of landslide initiation points. The probability map developed in this study was also compared to the results of a bivariate landslide susceptibility analysis conducted for the watershed, encompassing Lake Atitlán and San Juan. Landslides from Tropical Storm Agatha 2010 were used to independently validate this study’s multivariate model and the bivariate model. The ultimate aim of this study is to share the methodology and results with municipal contacts from the author's time as a U.S. Peace Corps volunteer, to facilitate more effective future landslide hazard planning and mitigation.