Water, a fundamental human right, impacts human health through its quantity (i.e., physical amount and ability to access it) and quality. Consumption of poor-quality water can lead to a variety of waterborne illnesses, often manifested as diarrhoea. Millions of individuals worldwide lack access to drinking water that is free from contaminants and is available and accessible when needed. In areas where water is not piped to homes, several physical, demographic, socio-economic and health factors affect access to potable water. These factors may also influence which water point an individual fetches water (i.e. their waterpoint choice) from in the presence of multiple alternative waterpoints. Through this study, effects of various physical, health, demographic and socio-economic factors on waterpoint choice were explored. This study, based on datasets from a rural Maasai community in Kenya, implements a multinomial logit model to explore effects of various physical (travel time and water quality), health (aggregate frequency of self-reported diarrhoea stratified by age groups), demographic (average household age, household population, number of children under 5, number of women between 8-45 years of age and ratio of household population to number of women between 8-45) and socio-economic factors (education and income) on waterpoint choice. Travel time to the most probable waterpoint as predicted by the model was compared with the travel time to a household’s chosen waterpoint. Both travel times were calculated using the least-resistance path function incorporating slope and landcover. Results from model optimization showed that combinations of travel time, average household age, diarrhoea among adult women, income, education and number of women between 8-45 years were significant contributors to the three waterpoint choice models. The expected travel time to the most probable waterpoint predicted by these models and actual travel time to chosen waterpoint fit well, showing that the models explain waterpoint choice well. / Thesis / Master of Public Health (MPH)

Lighting accounts for nearly 20% of overall U.S. electricity consumption, 14% of U.S. residential electricity consumption, and 6% of total U.S. carbon dioxide equivalent (CO2e) emissions. A transition to alternative energy-efficient technologies could reduce this energy consumption considerably. We studied three questions related to energy efficiency lighting choices and consequences, which are: • Question 1: How large is the system-wide effect of a residential lighting retrofit with more efficient lighting technologies? • Question 2: Based on stated preference (SP) data, which factors influence consumer choices for general service light bulbs? What is the effect of the new lighting efficiency label mandated by the Federal Trade Commission? • Question 3: What can we learn about market trends and consumer choices from consumer panel data (i.e. revealed preference (RP) data) for general service light bulbs between 2004 and 2009? How can we compare the findings from SP and RP data, and which findings are robust across the two? In Chapter 2, we focus on the issue of lighting heat replacement effects. The issue is as follows: lighting efficiency goals have been emphasized in various U.S. energy efficiency policies. However, incandescent bulbs release up to 95% of input energy as heat, and it has been argued that replacing them with more efficient alternatives has a side effect in the overall building energy consumption: it increases the heating service that needs to be provided by the heating systems and decreases the cooling service that needs to be provided by the cooling systems. We investigate the net energy consumption, CO2e emissions, and saving in energy bills for single family detached houses across the U.S. as one moves towards more efficient lighting systems. In some regions, these heating and cooling effects from more efficient lighting can undermine up to 40% of originally intended primary energy savings, erode anticipated carbon savings completely, and lead to 30% less household monetary savings than intended. However, this overall effect is at most one percent of total emissions or energy consumption by a house. The size of the effect depends on various regional factors such as climate, electricity fuel mix, differences in emission factors of main energy sources used for heating and cooling, and electricity prices. Other tested factors such as building orientation, insulation level, occupancy scenario, or day length do not significantly affect the results. Then, in Chapter 3, we focus on factors that drive consumer choices for light bulbs. We collected stated preference data from a choice-based conjoint field experiment with 183 participants. We estimate discrete choice models from the data and find that politically liberal consumers have a stronger preference for compact fluorescent lighting technology and for low energy consumption. Greater willingness-to-pay for lower energy consumption and longer life is observed in conditions where estimated operating cost information was provided. Providing estimated annual cost information to consumers reduces their implicit discount rate by a factor of five, lowering barriers to adoption of energy efficient alternatives with higher up-front costs; however, even with cost information provided, consumers continue to use implicit discount rates of around 100%, which is larger than that estimated for other energy technologies. Finally, we complemented the stated preference study with a revealed preference study. This is because stated preference data alone have limitations in explaining consumer choices, as purchases are affected by many other factors that are outside of the experimenter control. We investigate consumer preferences for lighting technology based on revealed preference data between 2004 and 2009. We assess the trends in lighting sales for different lighting technologies across the country, and by store type. We find that, across the period between 2004 and 2009, sales of all general service light bulbs are almost monotonically decreasing, while CFL sales peaked in 2007. Thanks to increasing adoption of CFLs during the period, newly purchased light bulbs contributed to lowering carbon emissions and electricity consumption, while not sacrificing total produced lumens as much. We study consumer preferences for real light bulbs by estimating choice models, from which we estimate willingness-to-pay (WTP) for light bulb attributes (watt and type) and implicit discount rates (IDR) consumers adopt for their purchases. We find that the campaign for efficient bulbs in Wal-Mart in 2007 is potentially related to the peak in CFL adoption in 2007 in addition to the effects of the EISA or other factors/programs around the same period. Consumers are willing to pay, $1.84 more for a change from an incandescent bulb to a CFL and -$0.06 for 10W increase, the values which also include willingness-to-pays for corresponding changes in unobserved variables such as life and color. IDRs for four representative states range between around 230% and 330%, which is in a similar range we estimate from the choice experiment. Overall, even with energy efficiency labels, nationwide promotion of CFLs by retailers, or better availability of CFLs in the transforming residential lighting market, we see the barriers to energy efficient residential lighting are still persistent, which are reflected in high implicit discount rates observed from the models. While we can expect the EISA to be effective in lowering the barriers through regulation, it alone will not close energy efficiency gap in the residential lighting sector.

This dissertation aims to address issues related to supply and demand of organic wheat either as a commodity or contained in the final consumer products. Objectives for the first essay are to evaluate organic wheat price and premium risk, how it affects the profitability of organic wheat production, and examine whether hedging and forecasting can be used to manage the organic wheat price risk. A side objective is to apply and evaluate several data imputation methods to recover missing organic wheat price observations. Objectives for the second essay are to identify “very likely,” “likely,” and “unlikely” consumers of organic wheat products, examine the differences across the consumer groups to understand which sociodemographic characteristics and other factors drive demand for organic wheat products, and which product characteristics and labels are important to consumers. Objectives for the third essay are to obtain willingness to pay values for organic label alone, examine whether combining organic label with other labels (non-GMO, gluten-free, sugar-free or low-carb) is beneficial for consumers, and evaluate whether knowledge and familiarity with organic, wheat or gluten intolerance or avoidance, and other sociodemographic characteristics affect how consumers value the organic label alone and in combination with other labels. The analyses in the second and third essay are performed using two wheat product categories (bread and cookies) to examine how findings differ across different product categories. This dissertation provides several societal benefits. The findings provide insights that may play an important role in supporting growth of the organic wheat production through reduction of uncertainty associated with wheat commodity prices and final consumer demand. Understanding the dynamics of organic wheat prices, how they can affect profitability of organic wheat production and what can be done to reduce the uncertainty is critical to organic wheat growers and food manufacturers when they make production decisions. The findings in the second and third essay will assist food manufacturers and marketers as they develop new products and marketing strategies and make labelling decisions. The findings in this dissertation may allow them to match consumers’ needs better, and thus use the limited organic wheat supply more efficiently.

Increased focus on sustainability and climate change has made reduction in CO2 emissions a priority across all industries. The construction equipment (CE) industry sees alternate powertrain technologies like battery electric and hydrogen fuel-cells as the solution to the climate problem and has therefore started investing in this domain. However, with their niche application, the demand on construction equipment is unique and this indicates that it is important to understand the customer preferences when introducing new technologies in this market. This presents the research question answered in thisthesis - do customers prefer non-fossil based alternate powertrain in construction equipment?. In order to answer this question, extensive literature review is carried out and parallelsare drawn with passenger vehicle and commercial truck industry. Based on previous workin this area, the first step is to calculate the total cost of ownership of the four different alternatives - diesel, bio-diesel, battery electric and fuel-cell electric. This information is then used to establish a discrete choice experiment, which is designed to gauge thes tated preference of the customers. A survey was conducted to this effect, where CE customers were provided with multiple choice scenarios and asked to choose between fouralternatives, differentiated by varying attributes such as total investment cost, total variablecost, operation range, refill duration and technology maturity. The survey responsewas then used to build a random utility model using logistical models like MultinomialLogit, Mixed Logit and Nested Logit. The utility coefficients were estimated in these three models and compared against each other and the most significant attributes were identified. Out of 54 respondents, even though majority 62% preferred a conventional diesel machine in the stated preference choices, an overwhelming 38% had opted for the sustainable alternatives which was more than expected, specially since such machines are not available in the mainstream market. The mixed multinomial logit model showed the best fit among the model alternatives tested and attributes like fixed cost, variable cost and market share showed statistical significance with this model. The results indicate that the market preferenceis still in favour of diesel machinery but the perception towards sustainable poweredmachinery looks positive and better than expected. Original Equipment Manufacturers(OEMs) need to target smaller fleet owners for adoption of sustainable alternatives like Battery Electric and Fuel Cell electric machines. Even though Sustainable powered machines show a lot of promise, they are still not competitive in terms of cost as the discrete choice analysis shows that variable cost significantly influence purchase decision.

In Sweden, three principles prescribed by law compose the ethical platform aiming to ensure a fair distribution of healthcare resources. The goals of each of the three principles are to 1: ensure equal care, regardless of personal characteristics or social function; 2: give priority to patients of bad health; 3: base priority decisions on cost-effectiveness. The weights given to the last two principles yield different implications on which pharmaceuticals and medical procedures to subsidize and can be seen as an equity-efficiency trade-off. Knowledge of the Swedish public's views on this balance has been stated to be of large value to decision-makers, to be used as a basis for priority decisions. However, no such information exists to date. A large share of studies from other countries has moreover provided counter-intuitive estimates, possibly indicating a need for methodological development. In this thesis, I provide a suggestion on a discrete choice approach to quantify opinions on how to weight disease severity and cost-effectiveness in healthcare prioritization. In addition, I present a novel method to use the estimates to rank treatments. The design is furthermore tested in a pilot study, being the first to investigate this question in a Swedish setting. The results indicate that the population values both the amount and distribution of health created, favoring individuals suffering from severe conditions, which is seen as in line with a priori expectations. The thesis contributes to the literature aiming to quantify opinions on healthcare prioritization.

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Background: The increasing consumption of dietary supplements (DS) has drawn the attention of regulatory agencies, researchers and healthcare professionals. The US Food and Drug Administration (FDA) does not require premarketing assessment of DS considering them safe unless proven otherwise. However, the reporting rate of DS adverse events (DS-AE) is low. Objective: To describe pharmacists’ attitudes and knowledge of DS and DS information resources, and to determine the importance of selected attributes in pharmacists’ decisions to report a DS-AE. Methods: A convenience sample of practicing pharmacists in Virginia was surveyed using a web-based self-administered questionnaire. A conjoint analysis exercise was developed using several scenarios based on a set of five attributes: patient’s age, initiation of DS, last modification in drug therapy, evidence supporting the AE, and outcome of the AE. Participants were asked to indicate their decision to report the AE in each scenario to prescriber, drug manufacturer, DS manufacturer and FDA on a 6-point ordered scale. Participants’ attitude, knowledge of DS, demographic information, and DS information resources were also requested. Linear regression models were used to determine the relative importance of the profile attributes on a pharmacist’s decision to report the AE. The effects of other characteristics on the importance of the attributes were assessed. Results: Participants’ overall attitudes were relatively positive for the clinical use of DS but negative for safe of DS. Formal training on DS was associated with better knowledge of DS regulation. The average knowledge score of DS identification was relatively good but was low for DS regulation. Lexi-Comp® was the most widely used and available information resource and the Natural Medicines Comprehensive Database was the most useful once. The most important attribute that a pharmacist considered in the decision to report a DS-AE to DS manufacturer, drug manufacturer and FDA was the outcome of the AE followed by the evidence supporting the AE. Ranking of these two factors was the reversed in reporting to prescriber. Conclusions: Outcome and evidence of the AE are the most important factors participants considered when reporting. Other characteristics do not have an impact on the relative importance of the attributes.