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<p>  </p> <p>Kentucky bluegrass is the most common cool-season turfgrass grown in the northern US. <br> The fact that Kentucky bluegrass requires s high quantity of fertilizers, pesticides, and irrigation to produce high quality turf has led to environmental concerns among policymakers, researchers, and consumers. To address this concern, turfgrass breeders have developed improved cultivars of low-input turfgrass species that aim to improve the sustainability of US lawns (Ghimire et al., 2019). For instance, tall fescue [(<em>Festuca arundinacea </em>Schreb.; syn. <em>Schedonorus arundinaceus</em> (Schreb.) Dumort., nom. cons.] and fine fescues (<em>Festuca </em>spp.) may represent viable options for residential and commercial buildings due to their outstanding performance under lower amounts of inputs such as irrigation, pesticides, and fertilizers (Watkins et al., 2011). Thus, adopting improved cultivars of low-input species may be a step towards reducing the use of inputs in landscapes (Simmons et al., 2011; Pooya et al., 2013). Yet, the production of low-input turfgrasses in the northern US is slow and limited, which leads to marketing and education obstacles that support their adoption. Thus, understanding factors that influence sod buyers to purchase low-input turfgrasses is imperative to increase the market share and the adoption of these turfgrasses. </p> <p>This study investigated the factors influencing sod buyers to purchase low-input turfgrass in the northern US, using tall fescue and fine fescue as low-input sod species. Using a logistic regression model, we assessed the determinants of low-input turfgrass purchase among sod buyers (i.e., athletic facilities, landscape contractors, garden centers, general contractors, lawn care, golf courses, and municipal parks). The logit model assumed the adoption decision to be driven by the buyers’ perception of the utility of buying low-input turfgrass species. Thus, the dependent binary variable Y equals 1 if the firm purchased tall or fine fescue in 2020, and 0 otherwise. The adoption is then expressed as a function of determinants, including the firm’s characteristics, supplier characteristics, sod attributes, and buyer’s perceptions. </p> <p>Data for this study came from a 2021 web-based survey of sod buyers located in 19 states of the Northern US. A total of 200 buyers completed the survey, including landscape contractors, golf courses, general contractors, lawn care services, and landscape maintenance firms who have purchased sod in 2020. The significant mean comparisons between adopters and non-adopters showed that adopters of low-input turfgrasses purchased most of their sod through contract agreements. The main suppliers of adopters were located at a closer distance to on-site delivery than the non-adopters. The logit regression results showed that low-input turfgrass adoption was positively influenced by the number of sod suppliers and managerial experience of the sod buyer. Landscapers were more likely to purchase tall fescue and fine fescue compared to golf courses and municipal parks. We found that distance from sod supplier to on-site delivery negatively impacted the purchase of low-input turfgrasses. Similarly, Kentucky bluegrass buyers were less likely to purchase low-input turfgrass species.</p>

This thesis examines the existence of a dominant route between a hub pair and factors that influence bike share cyclists route choices. This research collects 132,396 hub to-hub global positioning system (GPS) trajectories over a 12-month period between April 1, 2015 and March 31, 2016 from 750 bicycles provided by a bike share program (BSP) called SoBi (Social Bicycles) Hamilton. Then, a GIS-based map-matching toolkit is used to convert GPS points to map-matched trips and generate a series of route attributes. In order to create choice sets, unique routes between the same hub pair are extracted from all corresponding repeated trips using a link signature tool. The results from t statistics and Path-size logit models indicate that bike share cyclists are willing to detour for some positive features, such as bicycle facilities and low traffic volumes, but they also try to avoid too circuitous routes, turns, and steep slopes over 4% though detouring may come with a slight increase in turns. This research not only helps us understand BSP cyclist route preferences but also presents a GIS-based approach to determine potential road segments for additional bike facilities on the basis of such preferences. / Thesis / Master of Science (MSc)

La formación dual es una modalidad educativa de enseñanza y aprendizaje que se desarrolla en dos lugares distintos y complementarios; en el aula y la empresa, caracterizada principalmente por un enlace cooperativo. Así, la visión de la formación del talento humano es alcanzar un nivel sagaz de desarrollo en un puesto de trabajo que le permite competir como un profesional altamente calificado por sus cualidades intelectuales, prácticas y actitudinales, en el mercado laboral. Con esta perspectiva, el objetivo de esta investigación es evidenciar el aporte de la formación dual de los egresados de la Universidad de Cuenca durante los años 2010 a 2016, con el fin de medir los impactos en el salario que poseen, el tiempo que se toman en insertarse al mercado laboral, su condición de estar empleado y su trayectoria laboral de ser promovido en la empresa donde labora; frente a la modalidad Tradicional. Por lo tanto, esta investigación aplica la metodología de evaluación de impacto basado en el Propensity Score Matching y los Modelos Logit y Probit, en el cual los indicadores laborales demuestran que la principal rama de las actividades económicas en donde se ocupan los egresados de la Dual es en el sector de servicios, especialmente en empresas grandes y medianas; además se observa que los que recibieron el programa Dual mejoran sus salarios en $ 380.75 USD promedio respecto a los que no recibieron el programa, siendo una diferencia salarial positiva y significativa. En lo que respecta a la inserción de los egresados al mercado laboral, se aprecia que éste tiene un impacto positivo y significativo, evidenciando que aquellos que recibieron el programa Dual les toman entre 1 mes 24 días insertarse al mercado laboral mientras que a los que no recibieron el programa les toma alrededor de 4 meses. De igual manera, la probabilidad de que un estudiante que recibe el programa Dual sea promovido durante su trayectoria laboral, se incrementa en promedio entre 53.08% y 54.34% en referencia a un egresado que no recibió el programa, considerando un impacto positivo y también significativo. Por otro lado, la probabilidad de formar parte del mercado laboral se incrementa, en promedio, entre 1.56% y 2.02% con respecto a un egresado que no recibió el programa, dando como resultado un impacto positivo, pero no significativo. Finalmente, se observan egresados satisfechos con la formación recibida, reconocidos en el medio no sólo por el prestigio y efecto marca de la Institución sino también por su excelente desempeño en ámbitos sociales y laborales. / The Dual training is an educational modality of teaching and learning that takes place in two different and complementary places; both in the classroom and in the company, characterized mainly by a cooperative bond. Thus, the vision of human talent training is to reach a shrewd level of development in a job that allows you to compete as a highly qualified professional for your intellectual, practical and attitudinal qualities in the labor market. With this perspective, the objective of this research is to show the contribution of the dual training of the graduates of the University of Cuenca during the years 2010 to 2016, in order to measure the impacts on the salary they have, the time taken in entering the labor market, their condition of being employed and their career of being promoted in the company where they works; versus the traditional modality. Therefore, this research applies the impact assessment methodology based on the Propensity Score Matching and the Logit and Probit Models, in which labor indicators demonstrate that the main branch of economic activities where graduates of Dual are engaged it is in the service sector, especially in large and medium-sized companies. In addition, it is observed that those who received the Dual program improve their salaries by an average of $ 380.75 USD compared to those who did not receive the program, being a positive and significant salary difference. Regarding the insertion of graduates to the labor market, it can be seen that it has positive and significant impact, showing that those who received the Dual program take between 1 month and 24 days to enter the labor market while those who do not take around 4 months. Likewise, the probability of a student who received the Dual program being promoted during their work trajectory will increase, on average, between 53.08% and 54.34% in reference to a graduate who did not receive the program, considering it a positive and also significant impact. On the other hand, the probability of being part of the labor market, increases on average, between 1.56% and 2.02% with respect to a graduate who did not receive the program, resulting in a positive but not significant impact. Finally, graduates are satisfied with the training received, and they are also recognized in the media not only for the prestige and brand effect of the Institution but also for their excellent performance in social and labor fields.

The primary objective of this study is to develop an integrated public transportation planning framework to evaluate and plan Advanced Public Transportation Systems (APTS). With this purpose, a systems approach point of view is adopted to study the influence of new APTS technology in supply and demand transit variables. In this project the Systems Dynamics methodology is adopted to track the dynamic behavior of model variables and feedback loops forming among them. The proposed framework is illustrated in a case study involving automated vehicle location systems (AVL) applied to a small transit community. The proposed approach follows the same steps of the Systems Dynamics method; First, identify some key variables which are not only susceptive to AVL technology but also affect the supply-demand relationship of a bus transit environment. Second, trace and simplify the causal relationships of the variables considering impacts of facility supply changes to passenger demand responses and vice versa. To accomplish this, four detailed sub-models representing parts of the transit system are developed and combined under the Systems Dynamics methodology point of view. Theses Sub-models are: 1) demography, 2) urban transportation planning, 3) bus operations, and 4) evaluation. Finally, to validate the model procedure, the model is applied to a case study. This study attempts to encompass as many as possible factors around a bus transit system environment which can be impacted by new APTS technology to illustrate the use of the proposed framework. Some of these factors include: 1) Demographic characteristics; 2) urban or social activity of the study area and 3) changes to transportation facilities. The case study illustrates how the physical characteristics of the transit systems such as traffic demand, traffic conditions along the transit route, route layout, and bus performance can be affected by the new technology. Since APTS impacts are time dependent a continuous multi-loop simulation technique is adopted to track dynamic changes of all model variables. The analysis of the transit system is carried over a 20-year life cycle to illustrate the long term dynamics of the feedback structures inherent in the model. <i>[Vita removed Aug. 2, 2010. GMc]</i> / Ph. D.

This study examines the impact of the COVID-19 pandemic on residents' commuting patterns in the United States. Using the MNL (Multinomial Logit) and binary logit models, we analyze the factors influencing the choice of commuting modes before and during the pandemic. Our findings indicate that various personal, travel-related, and COVID-19-related factors significantly affect commuting choices. For commuting methods other than driving, factors such as gender, age, possession of a driver's license, bicycle ownership, car ownership, family size, working days per week, COVID-19 testing, and mask restrictions play a significant role. The decision to walk to work is influenced by gender, vehicle ownership, travel time, travel distance, working days per week, family income, COVID-19-related relocation, and level of COVID-19 anxiety. Public transportation choices are influenced by factors such as age, race, possession of a driver's license, car ownership, travel time, travel distance, COVID-19-related migration, and COVID-19 testing of cohabitants. Furthermore, the binary logit model reveals that personal factors (e.g., gender, driver's license) and COVID-19-related factors (e.g., mask restrictions, level of concern about the coronavirus) significantly impact the consistency of travel modes before and during the pandemic. This study contributes to our understanding of the changes in commuting patterns during the COVID-19 pandemic and provides insights into the factors that shape residents' commuting choices. The findings can inform transportation planning and policy-making to promote sustainable and resilient transportation systems in the face of future disruptions.

The primary objective of this thesis is to examine the preferences local-level economic development directors possess for different firm characteristics when deciding whether to offer incentives. The thesis examines the different incentives that exist in Virginia and finds that incentive activity has been steadily increasing since 1990. The historical rates of business activity reveal that more non-manufacturing firms locate and expand in metropolitan areas, but manufacturing firms in non-metropolitan areas hire more people per firm. The results indicate that this is not due to an explicit strategy of Virginia's economic development directors. A comparison is made between community economic development goals and important firm characteristics as perceived by local-level economic development directors. A rank-ordered logit model is then used to measure the willingness to pay for various firm characteristics. The results indicate that economic developers are willing to pay for increases in firm investment, increases in wages per employee, and decreases in the probability of a firm closing or moving. Economic developers in Virginia are not willing to pay directly for increases in firm employment, but firm employment is important in its indirect effect on the willingness to pay for wages. The linkages of a firm with a community (community (measured by sales impact, the employment multiplier, and overall employment impact) were insignificant variables for all economic developers. / Master of Science

Smallholder households in Haiti face many natural resource management challenges. Agricultural production occurs on deforested hillsides prone to erosion. Charcoal is in an important source of income, and woodfuel stocks are often over-exploited. Donor-funded projects and non- governmental organizations have made large investments in programs that promote soil conservation practices and reforestation. Despite the magnitude of the problems and the amount invested, there are relatively few economic analyses of the long-term adoption of soil conservation practices and woodfuel management. This dissertation uses an economics approach to examine the adoption of conservation practices and the management of woodfuel resources in Central Haiti using cross-sectional data covering 600 households. The results show that plot and household characteristics have different effects on adoption across different classes of soil practices, particularly with regard to perceived soil quality, market access, and household health status. The results also provide evidence of the management of charcoal woodfuel stocks on private land. These findings inform the design and targeting of new programs related to soil conservation and reforestation in Haiti and other developing countries. / Ph. D.

本論文主要目地是在研究財務比率對上市公司發生下市事件之預測。我們運用歷史事件研究法和Cox迴歸模型去研究上市公司發生下市事件之原因。同時，我們也針對Cox迴歸模型和Logit模型在發現對下市事件有顯著影響的財務比率作比較。 / This study applies the event history analysis and the Cox regression model to examine the causes of firm delisting, and also compares the performance of the Cox regression model with that of the logit model in detecting factors that have a statistically significant impact on the delisting event. The empirical results show that the hazard rate of firm delisting increases with the ratio of current liabilities to current assets, a binary variable indicating if the total liabilities of a firm is greater than its total assets, and a binary variable indicating if the net income of a firm was negative for the last two quarters, while the hazard rate of firm delisting decreases with increases in the firm size and the ratio of funds provided by operations to total liabilities.

Le problème de tarification qui nous intéresse ici consiste à maximiser le revenu généré par les usagers d'un réseau de transport. Pour se rendre à leurs destinations, les usagers font un choix de route et utilisent des arcs sur lesquels nous imposons des tarifs. Chaque route est caractérisée (aux yeux de l'usager) par sa "désutilité", une mesure de longueur généralisée tenant compte à la fois des tarifs et des autres coûts associés à son utilisation. Ce problème a surtout été abordé sous une modélisation déterministe de la demande selon laquelle seules des routes de désutilité minimale se voient attribuer une mesure positive de flot. Le modèle déterministe se prête bien à une résolution globale, mais pèche par manque de réalisme. Nous considérons ici une extension probabiliste de ce modèle, selon laquelle les usagers d'un réseau sont alloués aux routes d'après un modèle de choix discret logit. Bien que le problème de tarification qui en résulte est non linéaire et non convexe, il conserve néanmoins une forte composante combinatoire que nous exploitons à des fins algorithmiques. Notre contribution se répartit en trois articles. Dans le premier, nous abordons le problème d'un point de vue théorique pour le cas avec une paire origine-destination. Nous développons une analyse de premier ordre qui exploite les propriétés analytiques de l'affectation logit et démontrons la validité de règles de simplification de la topologie du réseau qui permettent de réduire la dimension du problème sans en modifier la solution. Nous établissons ensuite l'unimodalité du problème pour une vaste gamme de topologies et nous généralisons certains de nos résultats au problème de la tarification d'une ligne de produits. Dans le deuxième article, nous abordons le problème d'un point de vue numérique pour le cas avec plusieurs paires origine-destination. Nous développons des algorithmes qui exploitent l'information locale et la parenté des formulations probabilistes et déterministes. Un des résultats de notre analyse est l'obtention de bornes sur l'erreur commise par les modèles combinatoires dans l'approximation du revenu logit. Nos essais numériques montrent qu'une approximation combinatoire rudimentaire permet souvent d'identifier des solutions quasi-optimales. Dans le troisième article, nous considérons l'extension du problème à une demande hétérogène. L'affectation de la demande y est donnée par un modèle de choix discret logit mixte où la sensibilité au prix d'un usager est aléatoire. Sous cette modélisation, l'expression du revenu n'est pas analytique et ne peut être évaluée de façon exacte. Cependant, nous démontrons que l'utilisation d'approximations non linéaires et combinatoires permet d'identifier des solutions quasi-optimales. Finalement, nous en profitons pour illustrer la richesse du modèle, par le biais d'une interprétation économique, et examinons plus particulièrement la contribution au revenu des différents groupes d'usagers. / The network pricing problem consists in finding tolls to set on a subset of a network's arcs, so to maximize a revenue expression. A fixed demand of commuters, going from their origins to their destinations, is assumed. Each commuter chooses a path of minimal "disutility", a measure of discomfort associated with the use of a path and which takes into account fixed costs and tolls. A deterministic modelling of commuter behaviour is mostly found in the literature, according to which positive flow is only assigned to \og shortest\fg\: paths. Even though the determinist pricing model is amenable to global optimization by the use of enumeration techniques, it has often been criticized for its lack of realism. In this thesis, we consider a probabilistic extension of this model involving a logit dicrete choice model. This more realistic model is non-linear and non-concave, but still possesses strong combinatorial features. Our analysis spans three separate articles. In the first we tackle the problem from a theoretical perspective for the case of a single origin-destination pair and develop a first order analysis that exploits the logit assignment analytical properties. We show the validity of simplification rules to the network topology which yield a reduction in the problem dimensionality. This enables us to establish the problem's unimodality for a wide class of topologies. We also establish a parallel with the product-line pricing problem, for which we generalize some of our results. In our second article, we address the problem from a numerical point of view for the case where multiple origin-destination pairs are present. We work out algorithms that exploit both local information and the pricing problem specific combinatorial features. We provide theoretical results which put in perspective the deterministic and probabilistic models, as well as numerical evidence according to which a very simple combinatorial approximation can lead to the best solutions. Also, our experiments clearly indicate that under any reasonable setting, the logit pricing problem is much smoother, and admits less optima then its deterministic counterpart. The third article is concerned with an extension to an heterogeneous demand resulting from a mixed-logit discrete choice model. Commuter price sensitivity is assumed random and the corresponding revenue expression admits no closed form expression. We devise nonlinear and combinatorial approximation schemes for its evaluation and optimization, which allow us to obtain quasi-optimal solutions. Numerical experiments here indicate that the most realistic model yields the best solution, independently of how well the model can actually be solved. We finally illustrate how the output of the model can be used for economic purposes by evaluating the contributions to the revenue of various commuter groups.