<p> The sales of deer licenses, one of the most important revenue sources for wildlife management at the Indiana Department of Natural Resources (IDNR), have been declining for a decade. To increase its funds, the agency is considering launching a new lifetime deer license, which would allow hunters to harvest deer (and possibly other species) each year for the rest of their lives in exchange for a large, up-front fee. The forward-looking nature of the decision to buy a lifetime license means hunters’ choice behavior is necessarily dynamic. We estimate a dynamic discrete choice model using data from a discrete choice experiment (DCE) to capture this forward-looking choice behavior and to estimate hunters’ preferences for different lifetime license designs. We find that our dynamic model better fits our data than a standard, static choice model. We also find that hunters prefer licenses that allow (i) harvest of antlered and antlerless deer to one that only allows harvest of antlerless deer and (ii) harvest of additional species beyond just deer. We use our model to estimate the price of lifetime licenses that maximizes IDNR revenues. This is the first study to estimate the value of lifetime deer hunting licenses using a dynamic approach. This dynamic approach can help improve the IDNR’s decision-making to maximize its revenue and stabilize wildlife management funds.  </p>

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Insufficient knowledge of residential preferences represents a major obstacle to achieving residential satisfaction and quality of life. This obstacle is even greater in the case of transit-oriented developments (TODs), as their success depends, in part, on the degree to which people's preferences are consistent with their residential environments. This study employed a visual choice experiment, which combines the benefits of visual preference surveys and discrete choice experiments, to elicit residential preference for TODs in Riyadh, Saudi Arabia, before the opening of its citywide public transportation system. Using a seemingly homogeneous sample of participants, the analysis incorporated three analytical methods to elicit residential preference: a multinomial logit model, a mixed logit model, and a latent class model. The results indicated the presence of preference heterogeneity and the emergence of four lifestyle classes that could explain and predict residential preference patterns. People with similar sociodemographic characteristics may have different lifestyles based on their choice behavior, marital status, and public transit attitudes. Additionally, the results showed a strong preference for low-density housing, even among those who favor living in a TOD; however, increasing density could be mitigated through the presence of other TOD attributes. The findings of this research point to the diversity of residential preferences and suggest that providing a variety of residential environments increases the likelihood that people will find their preferred environment. Additionally, planning efforts to convert all developments near transit, particularly in suburban locations, to TODs might be unsuitable in cities where public transportation has been introduced only recently. Instead, deferring TOD conversion efforts until public transportation and its use are mature may attract people to live near transit and encourage the gradual development of transit affinity in residents who may otherwise reject TOD living completely. Lastly, the successful application of a visual choice experiment in this research opens up a variety of potential analytical methods that are used commonly in other fields and have the potential to move visual preference research into the realm of robust empirical investigation. / Ph. D. / The work of urban planners, urban designers, architects, and policy makers centers on improving the built environment and increasing the quality of people’s lives. However, their work entails making decisions that are not always in tandem with people’s preferences (e.g., increasing housing density, proposing a mix of land uses in residential neighborhoods, introducing public transportation close to where people live and work, to name a few). Due to the uncertainty surrounding people’s acceptance of modifications of the built environment, especially when it entails introducing residential attributes for the first time, this dissertation focused on 1) assessing residential preference near public transportation nodes in Riyadh, Saudi Arabia before operation of the public transportation system, and 2) assessing the extent to which advanced analytical methods are capable of providing a better understanding of residential preference differences among a seemingly homogenous sample of participants. The work in this dissertation was motivated by the increasing use of manipulated images in choice tasks, where participants are presented with multiple images, each depicting a residential scenario, as bundles to choose from, and their choice patterns then recorded and analyzed. The results showed that among the relatively homogenous sample of participants that was recruited, four significant residential preference patterns have emerged, which could be used to describe and predict residential preference and choice with great accuracy. This dissertation laid out several policy implications that could be useful in providing a built environment that matches with what people want. It also provided research implications and suggestions on the use of visual choice experiments for urban planners and designers that are well-developed in other fields of inquiry.

Globally, urbanization and urban growth have reached unprecedented rates in cities. Riyadh, the capital of Saudi Arabia, is no exception to the rapid urbanization and growth that has occurred in a sprawling and low-density pattern. Riyadh has set a controversial target of doubling its current population of 7.1 million to reach 10-15 million by 2030, emphasizing the importance of making this growth sustainable. Currently, the literature on the applications of smart growth gives little attention to public participation in decision-making regarding the future growth of cities. Specifically, there is a lack of research focused on integrating the public into the decision-making process for managing population increases over a short time frame (e.g., ten years). In Riyadh, the planning process does not provide precise mechanisms for public engagement or preferences in shaping future growth. Therefore, the purpose of this study is to manage this rapid urbanization and growth smartly by shaping future growth from the perspective of residents' preferences and how to draw that into urban policies. This study aims to shed light on residents' satisfaction with the current neighborhoods. In addition, the study aims to investigate residents' preference for the smart growth strategy as a new urban pattern in the city. The study uses the survey method to measure residents' satisfaction with the existing neighborhoods of Riyadh through an online questionnaire. Furthermore, another online questionnaire that combines both a discrete choice experiment and visual preference for the principles of smart growth is used to examine preferences for smart growth. This study adopted a number of statistical models, and the questionnaire was conducted with 3,111 respondents for the first questionnaire and 1,710 respondents for the second questionnaire as completed answers. The results of the first questionnaire were analyzed using descriptive statistics and by examining the correlations between neighborhood elements, including the physical environment and demographic data. This study also employed the multinomial logit model to analyze the results of residents' preferences, and the latent class analysis to reveal the homogeneity and heterogeneity of preferences among respondents. The results of the neighborhood satisfaction questionnaire indicated a divergence in opinions across the three sections identified in the first questionnaire. The results indicated dissatisfaction, particularly with neighborhood characteristics, transportation and accessibility elements, and urban landscape components. Satisfaction levels leaned more toward neutrality to dissatisfaction, reflecting the possibility that rapid growth may have affected the quality of services and facilities in neighborhoods. The results of the second questionnaire showed strong and explicit preferences for neighborhoods characterized by walkability and bikeability lanes, transportation availability, and parks across both 30-meter and 60-meter streets. There was also a preference, though to a lesser extent, for diverse housing options and densities represented by seven-story buildings, as well as a slight preference for mixed-use buildings. The latent class analysis further revealed six distinct groups of urban preferences in the neighborhoods. These findings highlighted the need for improvements in some urban dimensions discussed in this study, which showed low satisfaction results. Moreover, the findings enabled the creation of a set of recommended urban development policies to ensure that future growth aligns with the preferences of Riyadh residents. The results reflected the need to create vibrant, integrated, and comprehensive urban communities that enhance quality of life while providing diverse transportation options, green spaces, appropriate densities, mixed-use developments, and diverse housing options. / Doctor of Philosophy / Rapid urbanization and expansion are among the most significant urban challenges facing many major cities worldwide. Riyadh is one such city that has experienced rapid growth due to the economic boom of past decades, which led to the establishment of numerous ministries and company headquarters within the city. This growth resulted in the migration of many Saudi residents to Riyadh, prompting the rapid construction of neighborhoods and residential complexes to accommodate the increased population. However, this urban expansion occurred without community participation in shaping urban decisions, leading to scattered urban development. Today, Riyadh has set an ambitious target of accommodating about 10-15 million residents (about 7.1 million currently) by 2030. Such a decision may introduce new urban challenges for Riyadh. Accordingly, this study aims to first measure residents' satisfaction with residential neighborhoods resulting from previous growth and assess whether these neighborhoods provide all necessary services across various dimensions. Additionally, the study seeks to test one strategy for managing growth by adopting the principles of smart growth and understanding residents' preferences for this approach through proactive community involvement in urban decision-making for the upcoming growth. This study used two electronically distributed questionnaires to measure satisfaction with current neighborhoods and residents' preferences. The results indicated dissatisfaction in most dimensions of the neighborhoods. They also revealed residents' preference for many smart growth principles, with variations across the different principles.

We estimate the willingness-to-pay for conserving crop diversity in the Czech Republic. Discrete choice experiments are used to elicit preferences for the conservation of wine, hop, and fruit tree varieties, while a double-bounded dichotomous choice approach is used to elicit preferences for the conservation of unspecified, "general" crop diversity. The WTP values are derived for both of these contingent products from a sample representative of the general Czech population (n=731) and a sample of respondents living in the South Moravian region that is characterized by agriculture and wine production (n=418). We demonstrate a strong preference for conserving fruit trees over hops and wine varieties, and derive positive mean WTP of the general Czech population (ages 18-69) of 56 Kč ($2.26). Mean WTP for the conservation of general crop diversity is 167 Kč ($6.80). On average, residents of South Moravia have a greater WTP for "general" crop as well as fruit tree conservation. In total, the Czech adult population (ages 18-69) has an aggregate WTP of ~1.25 billion Kč ($50.5 million) for the conservation of general crop diversity, and ~410 million Kč ($16.8 million) for the conservation of fruit trees, revealing the previously unmeasured social welfare benefits of these activities. The estimated benefits...

This thesis analyses stated preferences for 4 different types of passenger ve- hicles (conventional, hybrid electric, plug-in hybrid, and battery electric vehi- cles). The discrete choice experiment survey was conducted in Poland in 2014. With the use of latent class model it was possible to identify and describe dis- tinct segments in the population with varying preferences for the propulsion technologies: groups with strong and weak preferences for conventional vehi- cles, segments preferring pure hybrid and plug-in hybrid electric vehicles, and a class in favour of battery electric vehicles. Even though it was found that on average consumers would require compensation up to 22,200zł (e5,311) to switch to using an electric vehicle, respective segments in the population would be willing to pay around 10,100zł (e2,417) for this change in case of pure hy- brid, around 21,400zł (e5,128) in case of plug-in hybrid, and around 92,800zł (e22,199) in case of battery electric vehicles.

We estimate the willingness to pay for electricity generated from renewable energy in the Czech Republic. Discrete choice experiment is used to elicit preferences for various attributes of renewable electricity support scheme (PM emission, GHG emission, size of RE power plant, revenue distribution, and costs). Original survey is carried with 404 respondents living in two regions - Ustecky (polluted area) and Southern Bohemia (cleaner area). We find that respondents prefer decentralized renewable electricity sources over centralized, local air quality improvements over reduction in greenhouse gas emissions. Estimated marginal willingness to pay for 1% reduction in emission of particulate matter equals to 49 CZK, respectively 3.7 % of average monthly electricity bill. In total, WTP for green electricity is larger than current compulsory contributions to renewable energy support scheme. Powered by TCPDF (www.tcpdf.org)