<p>Wildlife species generate value through their consumptive and non-consumptive uses. Consumptive uses of these species include hunting and trapping, while wildlife watching is an example of a non-consumptive use. Understanding the value of various wildlife is imperative for public agencies’ management decisions regarding different wildlife areas (e.g., nature preserves, state forests/parks, reservoirs, county/city parks). Individuals’ values for wildlife interactions on public lands can depend on the context in which these interactions occur as well as the probability of an interaction occurring. We utilize a stated preference choice experiment to estimate Indiana residents’ willingness to pay (WTP) for a marginal increase in the chance of seeing white-tailed deer and five furbearing species (bobcat, coyote, river otter, raccoon, red fox) while engaging in their favorite activities at Indiana recreational areas. Our WTP estimates are analogous to value of statistical life (VSL) calculations, and hence we refer to them as the “value of a statistical species” (VSS). We find that the VSS of a bobcat ranges from $22.73 to $41.30, the VSS of a coyote ranges from -$1.94 to $9.74, the VSS of a raccoon ranges from $5.25 to $21.69, the VSS of a red fox ranges from $43.31 to $62.52, the VSS of a white-tailed deer ranges from $22.70 to $27.00, and the VSS of a river otter ranges from $23.18 to $45.98. Our analysis suggests that individuals’ values for wildlife depend on the activity they are undertaking when they see the wildlife.  </p>

<p dir="ltr">The emergence of time-dependent data provides researchers with unparalleled opportunities to investigate disaggregated levels of safety performance on roadway infrastructures. A disaggregated crash risk analysis uses both time-dependent data (e.g., hourly traffic, speed, weather conditions and signal controls) and fixed data (e.g., geometry) to estimate hourly crash probability. Despite abundant research on crash risk analysis, coordinated signalized intersections continue to require further investigation due to both the complexity of the safety problem and the relatively small number of past studies that investigated the risk factors of coordinated signalized intersections. This dissertation aimed to develop robust crash risk prediction models to better understand the risk factors of coordinated signalized intersections and to identify practical safety countermeasures. The crashes first were categorized into three types (same-direction, opposite-direction, and right-angle) within several crash-generating scenarios. The data needed were organized in hourly observations and included the following factors: road geometric features, traffic movement volumes, speeds, weather precipitation and temperature, and signal control settings. Assembling hourly observations for modeling crash risk was achieved by synchronizing and linking data sources organized at different time resolutions. Three different non-crash sampling strategies were applied to the following three statistical models (Conditional Logit, Firth Logit, and Mixed Logit) and two machine learning models (Random Forest and Penalized Support Vector Machine). Important risk factors, such as the presence of light rain, traffic volume, speed variability, and vehicle arrival pattern of downstream, were identified. The Firth Logit model was selected for implementation to signal coordination practice. This model turned out to be most robust based on its out-of-sample prediction performance and its inclusion of important risk factors. The implementation examples of the recommended crash risk model to building daily risk profiles and to estimating the safety benefits of improved coordination plans demonstrated the model’s practicality and usefulness in improving safety at coordinated signals by practicing engineers.</p>

The global decline of pollinators, insects and animals that assist in pollination and fertilizing plants, poses a great risk to global food supply. This study investigates if people have preferences for a program to abate decline in a region along the East Coast of the United States. A survey is used to investigate these preferences and connections to the region to understand if individual connections can explain preferences. The majority of people were found to not have preferences to help increase pollinator abundance. Additionally, measures of connection to the region were found to be insignificant in explain individual preferences. / Master of Science / The global decline of pollinators, insects and animals that assist in pollination and fertilizing plants, poses a great risk to global food supply. This study investigates if people have preferences for a program to abate decline in a region along the East Coast of the United States. A survey is used to investigate these preferences and connections to the region to understand if individual connections can explain preferences. The majority of people were found to not have preferences to help increase pollinator abundance. Additionally, measures of connection to the region were found to be insignificant in explain individual preferences.

台商製造業在中國大陸的投資區位選擇行為，依個體選擇理論來說，是受到廠商本身的特性與區域條件兩者的影響，本研究的重點放在區位條件的影響上。但由於台商也是中國大陸FDI的一環，因此在參考過去其他地區的FDI區位選擇實證後，本研究將針對台商研究中較為欠缺的區位選擇行為，架構出台商投資中國大陸的個體選擇模型，並以更細化的產業別分類探討各區域條件的影響效果。最後再透過訪談的過程，針對實證的結果加以再驗證，得到完整以區位條件為觀點所架構出的台商製造業區位選擇模式。 由本研究的研究成果中可以發現，台商還是延續了過去在台灣的生產模式，凸顯了台商對於中國大陸的投資重視聚集的特性。但在勞力因素、商業服務的集中度、國家高新政策上，則因產業別的不同而產生出較大的歧異。訪談的結果也顯示出勞力條件的影響將會在未來日漸弱化，這種趨勢是和生產要素的移動性息息相關。但台商在中國大陸的投資已注意到市場潛力的需求，也成為近來台商投資中國大陸區位選擇時的重要轉變。另一方面，在基礎建設的投資與國家產業政策的投入對各產業別的影響上，顯示不見得政府的投資與廠商的投入有絕對的正向關係，這可能和投資的效率性有關，必須更進一步探討；但就整體台商而言，則驗證了人為扶植的產業政策的確可以為地方帶來廠商的聚集，且訪談結果更認為產品性質的差異，會影響到廠商在區位選擇時，考量基礎運輸設施種類的需求，這一部份也因為投審會資料的限制而無法得到量化模型證實。而稅的負擔經過模型校估後得到顯著的負相關，在訪談的結果中，發現跟台商在會計上的習慣可能也有關。台商通常會以作帳的方式產生負債經營的狀況以隱藏實際獲利情形，使實際的所得稅負擔遠低於應負擔金額。 總之，本研究雖然是以各省市所能提供的區域條件來看待台商的區位選擇。但如勞力等生產要素在在流動性逐漸增強的趨勢下，的確可能削弱了地區條件的優勢，可是由政策與聚集所創造出來的地域條件，卻是不容易被替代的。本研究的結果證實的這樣的觀點，至少在目前台商投資大陸的情形中，還是深受這種創造出來的地域條件所影響，一旦聚集形成，就傾向於自我延續下去，強化了由地域觀點的角度來看廠商區位選擇的理由。但政策的介入卻要注意效率性的問題，這也是未來的研究可以發展的方向。

The purpose of this thesis is to investigate further whether oil price has an effect on house price inflation and additionally if it has a link to house price turning points. The methodology is grounded on the previous research paper made by Breitenfellner et al. (2015). The results are based on quarterly data from the countries; Finland, Denmark, Norway and Sweden through the time span of 1990-2018. A linear fixed regression model was performed including the explanatory variables of monetary policy and credit developments, macroeconomic fundamentals, housing market variable and demographic variables. Secondly, a logit model was used to identify a relationship between oil price and house price turning points. The model used misalignment made from GDP per capita and real interest rate. The empirical analysis confirms that there is a positive relationship between oil prices and house price inflation. This evidence contradicts a major share of previous research papers (see Bernanke, 2010; Kaufmann et al., 2011). However, there are also some previous papers (see Yiqi, (2017); Antonakakis et al., 2016) and theoretical linkages in line with a positive correlation. Concerning, the oil price and house price inflation no empirical significance was found regarding their relationship. For future research, one could include regional aspects for the purpose of controlling for geographical differences.

The Water Framework Directive requires reduced environmental impacts from human activities and for the assessment of the non-market benefits of pollution remediation schemes. This policy shift has exacerbated the research problems surrounding the physical, social and economic consequences of the relationship between land use and water quality. This research seeks to quantify the major socio-economic and environmental benefits for people which may arise as riverine pollution is reduced. To achieve these aims this research integrates primary data analyses combining choice experiment techniques with geographical information system based analyses of secondary data concerning the spatial distributions of riverine pollution. Current knowledge on the microbial quality of river water, measured by faecal indicator organism (FIO) concentrations and assessed at catchment scale, is inadequate. This research develops generic regression models to predict base- and high-flow faecal coliform (FC) and enterococci (EN) concentrations, using land cover and population (human and livestock) variables. The resulting models are then used both to predict FIO concentrations in unmonitored watercourses and to evaluate the likely impacts of different land use scenarios, enabling insights into the optimal locations and cost-effective mix of implementation strategies. Valuation experiments frequently conflate respondents’ preferences for different aspects of water quality. This analysis uses stated preference techniques to disaggregate the values of recreation and ecological attributes of water quality, thereby allowing decision makers to better understand the consequences of adopting alternative investment strategies which favour either ecological, recreational or a mix of benefits. The results reveal heterogeneous preferences across society; specifically, latent class analysis identifies three distinct groups, holding significantly different preferences for water quality. From a methodological perspective this research greatly enhances the ongoing synthesis of geographic and economic social sciences and addresses important policy questions which are of interest to a variety of stakeholders, including government departments and the water industry.

As an alternative renewable source for bioenergy, forest biomass has recently drawn more attention from the U.S. government and the general public. Woody biomass policies have been adopted to encourage the new bioenergy industry. A variety of state policy incentives attempt to create a desirable legal climate and lure new firms, imposing two important questions regarding state government policies and the sustainable use of forest resources. This dissertation sheds some light on these questions. The first paper constructs a woody biomass policy index through scoring each statute and weighting different categories of policies from the vantage point of renewable energy investment. It analyzes the disparity in the strength of state government incentives in the woody biomass utilization. The second paper employs a conditional logit model (CLM) to explore the effects of woody biomass policies on the siting decisions of new bioenergy projects. In addition, significant state attributes influencing the births of new bioenergy firms are identified such as resource availability, business tax climate, delivered pulpwood price, and the average wage rate. The third paper uses the Sub-Regional Timber Supply (SRTS) model to examine the regional aggregate forest biomass feedstock potential in Tennessee and to predict the impacts of additional pulpwood demand on the regional roundwood market through 2030. The fourth paper includes the benefits of thinning and logging residues in a dynamic optimization model to analyze how bioenergy policies will impact forest stock, harvest levels, optimal rotation, and silvicultural effort. The results may have substantial implications regarding woody biomass policies, the creation of a new bioenergy industry, and sustainable forest resource management. A lucrative state woody biomass policy support and tax climate can attract new bioenergy businesses. States endowed with abundant forest resources may choose to provide strong tax incentives to spur the birth of new plants. However, overuse of forest biomass can impact roundwood markets and traditional wood processing industries. How government incentives will affect the sustainability of natural resources can be diverse. These findings offer constructive insights in the enactment and implementation of new woody biomass legislation.

The dissertation consists of four essays that treat different aspects or the evaluation of public policy. Two essays are applications of the travel cost method. In the first of these, recreational travel to the Swedish coast is studied to obtain estimates of the social benefits from reduced eutrophication of the sea. The second travel cost essay attempts at estimating how the probability that a woman will undergo mammographic screening for breast cancer is affected by the distance she has to travel to undergo such an examination. Using these estimated probabilities, the woman's valuation of the examination is obtained. The two other essays deal with automobile taxation. One essay analyzes how taxation and the Swedish eco-labeling system of automobiles have affected the sale of different car models. The last essay treats the effects of taxes and of scrappage premiums on the life length of cars. / Diss. Stockholm : Handelshögskolan, 1999

Traditionnellement, les migrants internes chinois migrent seuls et laissent derrière eux leurs époux et enfants. Depuis vingt ans, ils sont toutefois plus nombreux à migrer avec les membres de leur famille. Peu d’études ont été consacrées à ce type de migrants. Selon la théorie de la migration de Mincer, les migrants familiaux ne choisissent pas un lieu de destination de la même façon que les migrants individuels. Ils ne sont pas attirés par les mêmes facteurs que les migrants individuels. En nous basant sur la littérature sur le sujet, qui met en relief le rôle des facteurs économiques dans la décision de migrer, nous nous posons alors la question de recherche suivante : les facteurs économiques ont-ils plus d’incidence sur le choix de destination pour les individus que les familles. En contrepartie, ces derniers seraient, plus que ceux qui migrent seuls, motivés par des facteurs environnementaux ou socio-sanitaires? Nos analyses sont effectuées avec les données de l’Enquête chinoise dynamique sur les migrants de 2014, qui contient des informations sur 200 937 migrants résidant dans 346 villes, et de l’Annuaire statistique des villes chinoises. Les analyses statistiques sont réalisées avec un modèle logit conditionnel, qui permet de déduire, sachant qu’une migration a eu lieu, l’attractivité des villes liée à leurs caractéristiques géographiques, économiques, sociales ou encore environnementales. Nos résultats suggèrent que les migrants familiaux sont en effet moins motivés que les migrants individuels par des facteurs économiques favorables dans les villes de destination, tels que des revenus moyens élevés, une quantité élevée de dépenses dans l’immobilier, une proportion élevée d’entreprises privées et de travailleurs autonomes, un taux de chômage bas et une proportion élevée d’emplois dans le secteur secondaire. Nos résultats indiquent aussi que les migrants familiaux sont motivés par certaines caractéristiques socio-sanitaires telles que la quantité de dépenses dans l’éducation, les sciences et la technologie et de lits d’hôpitaux. Nous remarquons que ces effets sont plus forts chez les migrants ayant eu des enfants avant de migrer et vivant avec ceux-ci au moment de l’enquête. Nous notons par ailleurs que certains de ces résultats sont affectés par un effet d’âge, qui peut être confondu avec le cycle de vie familiale, les migrants familiaux étant en général plus âgés que ceux ou celles qui migrent seuls. / Traditionally, Chinese internal migrants migrate alone and leave behind their spouse and children. Over the past twenty years, more migrants have migrated with members of their nuclear family (Fan et al., 2011, Fan et Li, 2019, Duan et al., 2008). Few studies have been devoted to this type of migrants. According to Mincer’s migration theory (1977), family migrants do not choose a place of destination in the same way as individual migrants. They also are not attracted by the same pull factors as individual migrants. The purpose of our research is to address the following question: Do the economic factors have a greater impact on the destination choice of families when compared to individuals, the former being more motivated by environmental, social and health factors? Our analyzes are performed with data from the 2014 China Migrants Dynamic Survey, which contains information on 200,937 migrants residing in 346 cities, and the Statistical Yearbook of Chinese Cities. The statistical analyses are performed with a conditional logit model. The results indicate that family migrants are less motivated than individual migrants by favourable economic factors such as high average incomes, high proportion of private businesses and self-employed workers, high amount of spending on real estate, high proportion of high employment in the secondary sector and low unemployment rate. Our results also allow us to conclude that family migrants are motivated by certain social and health characteristics such as the quantity of hospital beds and the amount of expenditure on education, science, and technology. We note that these effects are more important among migrants who had children before migrating and living with them at the time of the survey. However, we observe that some of these results are due to an age effect.