Various adaptation and mitigation strategies have been explored to cope with changes in the climate. Estimating these strategies impacts on the local economy is one of the growing and pressing issues for the management of natural resources. This thesis consists of three parts and aims to contribute to regional economic studies by analyzing: (1) the economic impact of solar energy facilities, (2) the level of virtual water flow and the effectiveness of scenarios to mitigate water resource shortage, and (3) the impact of climate change on agriculture through a Ricardian approach weighted by stream flow connectivity. As an increasingly adopted renewable energy resource, solar power has a high potential for carbon emission reduction and economic development. In the first essay the impacts on jobs, income, and economic output of a new solar power plant are calculated in an input-output framework. The contribution is twofold. First, we compare the multipliers generated by the construction and operation/maintenance of a plant located in California with those that would pertain had it been built in Arizona. Second, we point out the differences in the results obtained with the popular IMPLAN software from those obtained with the solar photovoltaic model of JEDI. The second essay focuses on water use in Arizona. As much as 73% of the state's scarce water is used by a single sector: crop production. Because 79% of Arizona's crop production is consumed outside the state, this means that, 67% of the water available in the state is being exported to the rest of the country and abroad. This should be of major concern for a state expected to see its population grow and its climate get drier. Using input-output techniques we explore three scenarios aimed at saving 19% of the water available. This figure is based on the results of the first of the scenarios that explores how much can be saved through improving the efficiency of the current irrigation system. The second scenario shows that equivalent water savings could be reached by a twenty-seven-fold increase in the price of water. The third scenario shows that a 19.5% reduction in crop exports could conserve an equal amount of water. The model results suggest that the least costly solution is a more efficient irrigation system, while export reduction is the second best choice. The third and final essay offers an extension of the well-known Ricardian model of agrarian economic rent. In spite of its popularity among studies of the impact of climate change on agriculture, there has been few attempts to examine the role of interregional spillovers in this framework. We remedy this gap by focusing on the spatial externalities of surface water flow used for irrigation purposes and demonstrate that farmland value—the usual dependent variable used in the Ricardian framework—is a function of the climate variables experienced locally and in upstream locations. This novel approach is tested empirically on a spatial panel model estimated across the counties of the Southwest USA for every five-year period from 1997 to 2012. This region is one of the driest in the country, hence its agriculture relies heavily on irrigation with the preponderance of the sources being surface water transported over long distances. The results highlight the significant role of irrigation spillovers and indicate that the actual impact of climate change on agriculture and subsequent adaptation policies can no longer overlook the streamflow network.

La littérature économique attribuant des rôles endogènes à l'évolution technologique et à lagéographie pour expliquer la croissance économique suggère que la compréhension des fluxde connaissances dans l'espace et de leurs conséquences sur les activités d'innovation est aucoeur de l'explication des disparités dans la croissance économique. À cet égard, cette thèsemet l'accent sur les réseaux de connaissances comme un mécanisme permettant la circulationdes connaissances dans l'espace et le temps. Parmi les différents types de réseaux deconnaissances, elle étudie les réseaux de collaboration de R&D et explore deux questionsprincipales.Tout d'abord, elle examine l'effet de la géographie sur la formation du réseau pour savoir si lesflux de connaissances par les réseaux de collaboration en R&D sont limités dans l'espace oupas. Elle s'enquiert de cette question à la fois pour le réseau multilatéral entre lesorganisations et le réseau inter-régional entre les régions européennes dans le domaine desbiotechnologies en utilisant les données sur les collaborations de R&D promues par lesProgrammes-Cadres Européens.Deuxièmement, elle explore comment un réseau évolutif de collaborations de R&D entre lesrégions affecte les performances des régions en matière d'innovation. Elle utilise l'économétriespatiale pour quantifier les effets statiques et dynamiques des flux de connaissances desvoisins spatiaux et d'un ensemble évolutif de partenaires de collaboration sur l'activitéinventive des régions dans le domaine des TIC au cours de la période 2003-2009. / The economic literature attributing endogenous roles to technological change and geography inexplaining economie growth suggests that understanding knowledge flows in space and theirconsequences on innovative activities is central to explaining disparities in economie growth. Inthis regard, this PhD thesis focuses on knowledge networks as a mechanism that enablesknowledge flows in space and time. Among different types of knowledge networks, it studiesR&D collaboration networks and addresses two main issues.First, it investigates the effect of geography on network formation to figure out whetherknowledge flows through R&D collaboration networks are constrained in space or not. It inquiresthis question both for multilateral R&D collaboration network among organizations and theresulting inter-regional network among European regions in the field of biotechnology using dataon R&D collaborations promoted via European Framework Programmes.Second, it addresses how an evolving network of R&D collaborations among regions affectsregional innovation performances in time. It employs a spatial econometric approach to quantifythe static and dynamic effects of knowledge flows from spatial neighbors and an evolving set ofcollaboration partners on inventive activity of regions in the field of ICT durin 2003-2009.

<p>Over the last century, developed countries have been successful in enhancing maternal health and reducing Maternal Mortality Ratio (MMR). By 2018, MMR across OECD countries and World Bank Group Regions have converged towards very low levels, averaging more than 5 deaths per 100,000 live births. The United States has become an outlier among the developed countries in maternal deaths and compares unfavorably to a number of poorer countries where the ratio has declined. In 2017, the US ranked worst in MMR among the 39 industrialized nations. United States has experienced almost a 142 percent increase in MMR from 1987-2018. According to the Centers for Disease Control and Prevention (CDC), every year in the US, more than 700 women die due to the pregnancy or childbirth-related complications, with 60 percent of these deaths being preventable. Within the US, MMR varies considerably, leaving large disparities across states as well as between all racial groups. This research study aims to understand the interplay of spatial and racial impacts on the variation of maternal mortality ratios within the US. The paper estimates Ordinary Least Squares (OLS) and Spatial Lag Models for MMR using cross-sectional US state data for 2012-2017, taken from CDC. The results show that the dominant root causes of high maternal mortality differ between black and white women. </p> <p> </p>

The last couple of decades have seen a renewed interest among urban transportation planners in light rail transit (LRT) systems in large cities across the United States (US) as a possible means of addressing negative transportation externalities such as congestion and greenhouse gas emissions while encouraging the use of public transit [1]. LRT infrastructure investments have also gained traction as a means of revitalizing decayed urban centres because transportation infrastructure developments are highly correlated with economic growth in surrounding areas [2]. <div>The primary objective of this dissertation is to examine the externalities associated with LRTs during its the construction and operations phases. In particular, three areas of concern are addressed: (1) The effect that proximity to LRT stations </div><div>have on nearby single family residences (SFRs) throughout the LRT life-cycle; (2) the effect that directional heterogeneity between LRT stations, the central business district (CBD), and the SFR; and (3) the longer term effects on nearby populations due to LRT operations. To answer the first two research objectives, quasi-experimental spatial econometric models are used; to address the last objective, a-spatial fixed effects panel models are developed. The analyses primarily relies on SFR sales data from 2001-2019, publicly available geographical information systems data, as well as demographic data from eight 5-year American Communities Surveys (ACS). Charlotte, NC, a medium-sized US city, is chosen as the site of analysis, both due to the relative novelty factor of its LRT in the region and data availability.</div><div>The results show that SFR values are positively associated with proximity to LRT stations in the announcement and construction phases but negatively associated with proximity to stations once the LRT is operational. Additionally, potential homeowners with prior experience with LRT do not behave any differently than potential homeowners with no prior experience with LRT in terms of willingness to pay to live a certain distance from LRT stations. Further, directional heterogeneity is shown to be a statistically significant source factor in deciding the extent to which house-buyers are willing to pay to be near LRT stations. Lastly, distance from LRT stations are found to have no statistically significant effect on changes in the racial composition of nearby areas but have significant positive effects on educational attainment and average median incomes of residents living in nearby areas over time. </div><div>The contributions of this research are twofold. First, in addition to highlighting the need to use spatial econometric methods when analyzing the effect that LRTs have on surrounding real estate markets, this research provides a framework by which directional heterogeneity can be incorporated into these analyses. Second, this research adds to the existing pool of knowledge on land use externalities of LRT through incorporating the life-cycle of LRT from announcement to operations. Furthermore, this research examines the effects that LRT have on surrounding populations in transit adjacent areas to provide a look at the broader effects of LRT over time. </div><div>A major challenge in the analyses conducted in this dissertation is its reliance on SFR sales data. Urban areas near LRT may contain additional land uses. In order to fully determine LRT’s effects on its surrounding area, one should examine the proximity effects on all land use types. Furthermore, LRT stations and rail lines are assumed exogenous, which may not be the case as public hearings and town halls during the planning phase may influence stations’ locations. Future research should seek to understand how the circumstances surrounding the planning process could indirectly affect the socio-demographic characteristics in transit adjacent areas over time. Finally, additional research is needed to better understand the extent to which LRT affects urban intra- and inter-migration. Knowing the population repulsion and attraction of LRT can help planners design facilities to better serve the public.</div>

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The big-box retail stores such as Wal-Mart and Target have become the focus of many studies researching their impacts on local economic outcomes. This dissertation studies three related topics: (i) the dynamic interrelationship among the presence of the big-box stores, retail wage, and employment, (ii) the impact of the big-box retailers on personal income growth, and (iii) the dynamic interrelationship between the presence of big-box retailers and personal income growth. The research draws important insights with potential implications for regional developers and policy makers. The first essay analyzes the dynamic relationship among the presence of the big-box retailers, retail wage, and employment at the county level for 1986-2005. A vector autoregression model is applied on panel data. Impulse response functions and variance decompositions are also presented. Results suggest that the presence of big-box stores decreases retail wages and increases retail employment. Retail employment has a higher impact on the retailers’ location decision than retail wage. The results also show that the presence of Wal-Mart drives the above-mentioned effects, while the presence of Target is insignificant. The second essay investigates the impact from the presence of big-box retailers on personal income growth in U.S. counties between 2000 and 2005 - based on neoclassical growth models of cross-country income convergence. Results suggest that counties having both Wal-Mart and Target stores experienced slower growth in personal income. After controlling for spatial autocorrelation, similar to the first essay, the effect of Wal-Mart’s presence on personal income growth is dominant in terms of statistical significance relative to Target’s. The third essay expands the second essay and investigates the dynamic interaction between the presence of big-box retailers and personal income growth over time at the county level for the period 1987-2005, using a panel vector autoregression model. For this analysis, the earning shares of natural resources and manufacturing sectors are included - assuming that all the variables are endogenous to one another. The findings indicate that big-box retailers negatively affect personal income growth, which is consistent with the second essay. However, personal income growth has an insignificant effect on the big-box retailers’ location decision.

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Hedonic property value analysis is one of the leading methods of environmental valuation. This non-market technique uses variation in home sales to infer the values of amenities or disamenities. While there have been numerous studies about air quality and hazardous waste, the number of papers focusing on water quality is much smaller. Consequently, there are still many unanswered questions about the proper handling of water quality through hedonic methods. Furthermore, estimates from hedonic property price analyses are rarely used in government cost benefit analyses. This dissertation investigates several important hedonic issues in a large analysis of water quality in central Florida. The first chapter of this paper explores the extent of water quality benefits. Almost all past studies have focused exclusively on waterfront homes. The present paper includes non-waterfront homes and investigates three hypotheses about the marginal impact of water quality. The first hypothesis is that non-waterfront homes are positively affected by water quality, but by a smaller amount than waterfront homes. The second hypothesis is about the effect of lake distance on the relationship between water quality and property prices: this relationship should be negative. The third hypothesis states that properties near larger lakes have a higher implicit price for water quality than homes around smaller lakes, all else constant. These three hypotheses are investigated in each chapter of the dissertation, and provide a unifying theme to the paper. Results from Chapter 1 support all three hypotheses. Most importantly, the empirical estimates indicate that water quality benefits extend beyond the waterfront in a declining gradient. Excluding non-lakefront homes from the analysis can therefore substantially underestimate the total benefits of a water quality improvement. Estimates of the total property price benefits from a one foot increase in water quality were found to double with the addition of non-waterfront homes. The second chapter examines the sensitivity of results to several spatial specifications. Spatial issues can be a problem in analyses of real estate data because of spatially correlated variables, unobservable neighborhood codes and covenants, identical or similar builders, and property appraisal valuation techniques. The focus of the chapter is on the spatial weights matrix (SWM). Six different SWM's are constructed, which are based on popular specifications encountered in the current spatial hedonic literature. An out-of-sample forecasting exercise is used to compare multiple spatial specifications. Results indicate that certain spatial models may be sensitive to the specification of the weights matrix. Furthermore, many popular models currently used in the literature could be improved by allowing more non-zero elements in the SWM. The third chapter investigates the definition of "water quality" and uses several additional quality indicators. Choosing the proper pollution indicator is an issue that has plagued many areas of the valuation literature. While clarity indicators have become popular in hedonic property price analysis, they are not used for the purposes of regulation by many state environmental departments. This chapter uses several indicators that are used by the state of Florida to classify lakes and implement policy. Implicit prices are computed for all of the indicators and issues of benefit extent and total benefits are explored. Instead of finding an optimal indicator for all situations, results indicate that the use of at least two types of indicators may capture a larger range of the true total benefits. The final chapter uses a repeat sales model to address potential problems with omitted variable bias. Due to the size of the data set in this paper, there are a substantial number of homes that have sold more than once. The repeat sales model analyzes differences in property sales prices for the same home over time. The three hypotheses of the first chapter are explored in this alternative model. The implicit price obtained from the repeat sales model is much larger than the regular hedonic model. However, there are some concerns with the smaller population of repeat sales.

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