京都大学 / 0048 / 新制・課程博士 / 博士(農学) / 甲第19031号 / 農博第2109号 / 新制||農||1031(附属図書館) / 学位論文||H27||N4913(農学部図書室) / 31982 / 京都大学大学院農学研究科生物資源経済学専攻 / (主査)教授 栗山 浩一, 教授 福井 清一, 准教授 秋津 元輝 / 学位規則第4条第1項該当 / Doctor of Agricultural Science / Kyoto University / DGAM

The primary objective of this thesis is to assess the different statistical methods used forcalibrating the Samgods model, which is a transportation planning tool employed in Sweden toforecast the demand for freight transport. By focusing on the specific context of national logisticsmodels, this research aims to enhance the accuracy and reliability of the Samgods model throughproposed improvements.In addition to evaluating the calibration techniques for the Samgods model, this thesisexplores the broader application of statistical estimation methods in national logistics models.It examines their potential benefits and limitations in order to shed light on their significance.The findings of this research highlight the crucial role of statistical estimations in improvingthe accuracy of national logistics models, thus enabling better-informed decision-making intransport planning and logistics management.By estimating the cost sensitivity parameter in the Swedish national logistics model, Samgods,this thesis contributes to a deeper understanding of the role of statistical estimations in optimizingsuch models. It underscores the importance of reliable and accurate data analysis in transportationplanning and logistics management. Ultimately, the aim is to provide valuable insights into howstatistical estimations can enhance the effectiveness of national logistics models.

<p>Blood products are used for transfusion in many routine procedures as well as emergency medical care. The balance between the supply and demand of blood products in Canada is being threatened by an increasing aging population, a growing immigrant population, and advances in medical technology which places additional strain on the blood supply. The objective of this research is to investigate the effects of demographic determinants and clinic accessibility on the frequency of blood donation in Canada excluding the province of Québec, providing a national assessment of blood donor correlates at the individual level. Exploration of these demographic factors in addition to clinic accessibility may help to explain why a limited number of repeat donors are currently contributing, with many donors giving blood only once a year. Repeat donors are vital to maintain a safe and secure blood supply, therefore it is important to retain existing donors in addition to recruiting new volunteers. In this study, individual donor and clinic information is obtained from the Canadian Blood Services 2008 national dataset, with contextual data from the 2006 Canadian Census. Discrete choice models are used to assess the effects of these variables on the frequency of blood donation across the country, highlighting the importance of clinic accessibility. The analysis is prepared for major Census Metropolitan Areas in Canada. Results may contribute to service optimization and targeted advertising, ultimately aiming to encourage the eligible population to donate.</p> / Master of Arts (MA)

Spatial and social dependence shape human activity-travel pattern decisions and their antecedent choices. Although the transportation literature has long recognized the importance of considering spatial and social dependencies in modeling individuals’ choice behavior, there has been less research on techniques to accommodate these dependencies in discrete choice models, mainly because of the modeling complexities introduced by such interdependencies. The main goal of this dissertation, therefore, is to propose new modeling approaches for accommodating flexible spatial and social dependency structures in discrete choice models within the broader context of activity-based travel demand modeling. The primary objectives of this dissertation research are three-fold. The first objective is to develop a discrete choice modeling methodology that explicitly incorporates spatial dependency (or correlation) across location choice alternatives (whether the choice alternatives are contiguous or non-contiguous). This is achieved by incorporating flexible spatial correlations and patterns using a closed-form Generalized Extreme Value (GEV) structure. The second objective is to propose new approaches to accommodate spatial dependency (or correlation) across observational units for different aspatial discrete choice models, including binary choice and ordered-response choice models. This is achieved by adopting different copula-based methodologies, which offer flexible dependency structures to test for different forms of dependencies. Further, simple and practical approaches are proposed, obviating the need for any kind of simulation machinery and methods for estimation. Finally, the third objective is to formulate an enhanced methodology to capture the social dependency (or correlation) across observational units. In particular, a clustered copula-based approach is formulated to recognize the potential dependence due to cluster effects (such as family-related effects) in an ordered-response context. The proposed approaches are empirically applied in the context of both spatial and aspatial choice situations, including residential location and activity participation choices. In particular, the results show that ignoring spatial and social dependencies, when present, can lead to inconsistent and inefficient parameter estimates that, in turn, can result in misinformed policy actions and recommendations. The approaches proposed in this research are simple, flexible and easy-to-implement, applicable to data sets of any size, do not require any simulation machinery, and do not impose any restrictive assumptions on the dependency structure. / text

<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>

The High Occupancy Vehicle (HOV) lanes on Atlanta, Georgia’s radial I-85 had long been providing sub-optimal throughput in the peak traffic hours, as the two-person occupancy requirement allowed the lanes to become heavily congested. The Georgia Department of Transportation converted 15.5 miles of HOV 2+ lanes to High Occupancy Toll (HOT) lanes, one in each direction on I-85. The lanes use dynamic value pricing to set toll levels based on the volume and average speed of traffic in the lanes. The goal of this research was to investigate the responses to toll lane pricing and the factors that appear to inform lane choice decisions, as well as examining values of travel time savings and toll price elasticity for users of the Express Lanes. This study of the metropolitan Atlanta I-85 Express Lanes operates at the microscopic level to examine the impact of demographic characteristics, congestion levels, and pricing on users’ decisions to use or not use the I-85 Express Lanes. The dissertation examined the value of travel time savings distributions across income segments. The differences in these distributions among lower, medium, and higher income households were marginal at best. The results did not indicate that higher income households had the highest value of travel time savings results, as may have been expected. The modeling work performed here provided a number of insights into toll lane use. The determinants of lane choice decision-making in the morning peak had notable differences from the determinants of the afternoon peak. The initial analysis involved models which were estimated across three different income segments to examine differences in decision making between low, medium, and higher income households. The results indicated that the parameters were largely consistent across the three segments. Further segmenting the households showed that lane choice determinants varied more within the ‘Higher’ income segment than across the original three-segment structure. In particular, the five-segment models illustrated lower elasticities with regard to corridor segment counts and toll levels for the highest-income households in the sample, as well as higher household income level elasticities for afternoon trips by that same cohort. The research was among the first in the available literature to use revealed preference lane use data for both the toll lane users and the unpriced general purpose lane users. The use of household level marketing data, rather than census or survey data, was another unique characteristic of this research. The analysis of value of travel time savings with a demographic component that looks at household income has not yet been seen in the literature; similarly, the findings regarding differing behavior among very high income households appear to be unseen in the existing literature. The results from this analysis, such as willingness-to-pay values for different population segments, will be useful inputs to the decisions surrounding future HOT implementations in the Atlanta region. The use of new data sources, the evaluation of those types of data sources, and the application of methods that have previously been unused in this field make up the primary contributions of this dissertation.

Natural disasters are increasing in frequency and intensity and have devastating impacts on individuals, both humanitarian and economic, particularly in developing countries. Microinsurance is seen as one promising instrument of disaster risk management, however the level of demand for respective projects remains low. Using behavioural games and a discrete choice experiment, this paper analyses the demand for hypothetical microinsurance products in rural Cambodia and contributes significant household level evidence to the current research. A general preference for microinsurance can be found, with demand significantly affected by price, provider, requirements for prevention and combinations with credit. Furthermore, financial literacy, risk aversion, levels of trust and previous disaster experience impact the individual demand for flood insurance in rural Cambodia.

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 examines organic food marketing from three aspects: household demand for organic food, household choice of retail formats accounting for preference organic food preference, and farmers’ joint adoption of organic farming and direct marketing methods. In Chapter Two, given the fast growth of private label milk and organic milk in the U.S., we estimate a censored demand system to study the demand relations among types of milk differentiated by brand types and organic status, using recent Nielsen Homescan data. We find that sociodemographic factors still play important roles in a household choice of milk types, and fluid milk is an inferior good. Moreover, as income increases, households are more likely to shift from buying conventional milk to organic milk and from private label conventional milk to branded conventional milk, as indicated by the asymmetric cross price elasticities. In Chapter Three, we examine whether households’ preference for organic food can affect their retail format choices for their grocery shopping trips. We model households’ choices of five major retail format with a conditional logit model, also using the Nielsen Homescan data. Our main findings are that regular organic user households are more likely to patronage organic specialty stores and discount stores, but less likely to shop in warehouse clubs. Price, consumer loyalty, and household shopping behavior also affects household retail format choice. In Chapter Four, we examine the relation between farmers’ adoption of organic farming and direct marketing, given their similar objectives in satisfying consumer demand and increasing farm income. We model farmers’ adoption of the two practices with a bivariate simultaneous linear probability model using data from USDA Agricultural Resource Management Survey. Our main finding is that the farmers’ adoption of organic farming decreases their probability of adopting direct marketing, whereas the reverse effect is insignificant. Also, organic farming is found to improve gross farm income.

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