The South Nation watershed is located in Eastern Ontario, Canada and managed under the authority of the South Nation Conservation (SNC). The watershed covers an area of 400,000 hectares with four dominant categories of land-use classes (60% agriculture, 34% forest, 5% mixed urban, and 1% other). Water quality is a great concern for the SNC as many anthropogenic activities generate harmful pollutants (such as heavy metals, nitrogen, phosphorus, and pesticides) that are discharged to the river through surface and groundwater flow. The discharge patterns of these pollutants are mainly driven by land-use distribution within the watershed which has been constantly evolving with urbanization and intensification of agriculture. Major changes in land-uses can potentially offset current SNC efforts to mitigate water pollution. The objective of the current study is to predict land-use series of maps for the South Nation watershed starting from 1991 to 2020. The prediction is carried out using the land-use allocation algorithm of the Dyna-CLUE (Dynamic Conversion of Land-Use and its Effects) model which is implemented for local regions. Dyna-CLUE is a spatially explicit hybrid land-use allocation model that combines estimation and simulation models, and its allocation procedures predict future trends of land-use surface (estimated from historical trends). The binary logistic regression is used to link preferences of land-use classes and potential demographic and geographic driving factors. Expert judgment was used to select a set of spatial driving factors believed to be responsible for changes in land-use distribution in the South Nation watershed. Three different scenarios for future development of the region were considered, with different initial conditions and conversion restrictions. The simulation results were evaluated using visual and statistical validation techniques to assess the performance of the model in generating maps similar to reality. The Dyna-CLUE model was successfully applied to the South Nation watershed. It was observed that the simulated maps generated from the model were in good agreement with the reality maps. This was confirmed through statistical validation via map pair analysis (error matrix) used to assess the overall accuracy of the model predictions. Results showed that the model was sensitive to land-use restrictions. Such type of modeling can be valuable for assessing the land-use changes at the local level, and setting up a decision support system for the South Nation Conservation towards sustainable land-use management in the watershed. Better results are expected to be achieved with more reliable datasets (i.e., accurate classification of land-use types in reality maps). Data availability and quality were the main obstacles that faced this research work. Our work has the merit to be the first application of CLUE model in Eastern Ontario.

The South Nation watershed is located in Eastern Ontario, Canada and managed under the authority of the South Nation Conservation (SNC). The watershed covers an area of 400,000 hectares with four dominant categories of land-use classes (60% agriculture, 34% forest, 5% mixed urban, and 1% other). Water quality is a great concern for the SNC as many anthropogenic activities generate harmful pollutants (such as heavy metals, nitrogen, phosphorus, and pesticides) that are discharged to the river through surface and groundwater flow. The discharge patterns of these pollutants are mainly driven by land-use distribution within the watershed which has been constantly evolving with urbanization and intensification of agriculture. Major changes in land-uses can potentially offset current SNC efforts to mitigate water pollution. The objective of the current study is to predict land-use series of maps for the South Nation watershed starting from 1991 to 2020. The prediction is carried out using the land-use allocation algorithm of the Dyna-CLUE (Dynamic Conversion of Land-Use and its Effects) model which is implemented for local regions. Dyna-CLUE is a spatially explicit hybrid land-use allocation model that combines estimation and simulation models, and its allocation procedures predict future trends of land-use surface (estimated from historical trends). The binary logistic regression is used to link preferences of land-use classes and potential demographic and geographic driving factors. Expert judgment was used to select a set of spatial driving factors believed to be responsible for changes in land-use distribution in the South Nation watershed. Three different scenarios for future development of the region were considered, with different initial conditions and conversion restrictions. The simulation results were evaluated using visual and statistical validation techniques to assess the performance of the model in generating maps similar to reality. The Dyna-CLUE model was successfully applied to the South Nation watershed. It was observed that the simulated maps generated from the model were in good agreement with the reality maps. This was confirmed through statistical validation via map pair analysis (error matrix) used to assess the overall accuracy of the model predictions. Results showed that the model was sensitive to land-use restrictions. Such type of modeling can be valuable for assessing the land-use changes at the local level, and setting up a decision support system for the South Nation Conservation towards sustainable land-use management in the watershed. Better results are expected to be achieved with more reliable datasets (i.e., accurate classification of land-use types in reality maps). Data availability and quality were the main obstacles that faced this research work. Our work has the merit to be the first application of CLUE model in Eastern Ontario.

The South Nation watershed is located in Eastern Ontario, Canada and managed under the authority of the South Nation Conservation (SNC). The watershed covers an area of 400,000 hectares with four dominant categories of land-use classes (60% agriculture, 34% forest, 5% mixed urban, and 1% other). Water quality is a great concern for the SNC as many anthropogenic activities generate harmful pollutants (such as heavy metals, nitrogen, phosphorus, and pesticides) that are discharged to the river through surface and groundwater flow. The discharge patterns of these pollutants are mainly driven by land-use distribution within the watershed which has been constantly evolving with urbanization and intensification of agriculture. Major changes in land-uses can potentially offset current SNC efforts to mitigate water pollution. The objective of the current study is to predict land-use series of maps for the South Nation watershed starting from 1991 to 2020. The prediction is carried out using the land-use allocation algorithm of the Dyna-CLUE (Dynamic Conversion of Land-Use and its Effects) model which is implemented for local regions. Dyna-CLUE is a spatially explicit hybrid land-use allocation model that combines estimation and simulation models, and its allocation procedures predict future trends of land-use surface (estimated from historical trends). The binary logistic regression is used to link preferences of land-use classes and potential demographic and geographic driving factors. Expert judgment was used to select a set of spatial driving factors believed to be responsible for changes in land-use distribution in the South Nation watershed. Three different scenarios for future development of the region were considered, with different initial conditions and conversion restrictions. The simulation results were evaluated using visual and statistical validation techniques to assess the performance of the model in generating maps similar to reality. The Dyna-CLUE model was successfully applied to the South Nation watershed. It was observed that the simulated maps generated from the model were in good agreement with the reality maps. This was confirmed through statistical validation via map pair analysis (error matrix) used to assess the overall accuracy of the model predictions. Results showed that the model was sensitive to land-use restrictions. Such type of modeling can be valuable for assessing the land-use changes at the local level, and setting up a decision support system for the South Nation Conservation towards sustainable land-use management in the watershed. Better results are expected to be achieved with more reliable datasets (i.e., accurate classification of land-use types in reality maps). Data availability and quality were the main obstacles that faced this research work. Our work has the merit to be the first application of CLUE model in Eastern Ontario.

Using data from government sources (FRED, BEA, BLS), the thesis explores the underlying reasons for declining U.S. economic growth. A long standing trend of annual 3% growth no longer seems to hold true for the economy. The paper summarizes current theory as to why the growth has slowed and finds new explanations by analyzing the various major industries which make up GDP. The results show that sectoral shifts in employment from high paying industries to low paying industries help to explain a significant portion of the decline in national growth rates. The decline in growth is primarily driven by about ten poor performing states.

One of the most visible impacts of climate change in Arctic environments is declining sea ice. Due to an absence of spatially coarse quantitative data, there is a lack of understanding on declining sea ice on a community scale. This study seeks to document historical trends in air temperature, sea ice thickness (SIT), break-up dates (BUDs) and freeze-up dates (FUDs), correlate sea ice behaviour to air temperatures, and document the socio-economic impacts of sea ice change in Resolute Bay Nunavut, using traditional Inuit knowledge (TIK) and scientific methods. During the scientific portion of this study linear regression, statistical significance, anomaly analysis, and change point detection were used on time series of sea ice concentration (SIC), SIT, and air temperature. Two SIC datasets were accessed to characterize BUDs and FUDs, Canadian Ice Services archived sea ice charts from 1982-2022 and Copernicus Climate Change Services (C3S) gridded satellite derived SIC from 1979-2015. The BUD was defined when SIC fell and stayed below 20%, and the FUD was when SIC returned and stayed above 50%. By applying a statistically significant linear regression to both datasets, the BUD was shown to occur 37.5 days earlier and the FUD occurred 23.4 days later in 2022 than in 1979. This study accessed two SIT datasets, Environment and Climate Change Canada fast ice measurements from 1947-2022 and C3S satellite derived sea ice freeboard measurements from 2002-2020 at four locations in the Barrow Strait. After applying change point detection algorithms, this study found annual maximum fast ice thicknesses increased 32.5 cm from 1948-1981 and decreased 33.2 cm from 1981-2021. Fast ice decreased most substantially in the months of April and May. Sea ice freeboard decreased by 260.8 cm on average from 2002-2020 in the Barrow Strait. Freezing degree days (FDDs) were used to correlate sea ice behaviour to air temperature. As FDDs decrease, sea ice freeboard was the most rapidly changing sea ice parameter and fast ice thickness was most strongly correlated to FDDs. Both these results indicate that air temperature has a greater effect on SIT than the BUD and FUD. During the TIK investigation of this study, seventeen community members from Resolute Bay, Nunavut, ranging from age 19-81 were interviewed about their perception of changes in SIT, BUD, FUD, and seasonal weather patterns. Participants were interviewed about the socio-economic impacts of sea ice change, traditional Inuit methods of determining thickness, and asked to indicate typical areas of thin and thick ice, areas that break-up and freeze-up first, and hunting and travel routes on printed maps. The interviewees described a decline in sea ice thickness, areas of thin ice in the Barrow Strait, north of Cornwallis Island, and between Bathurst and Devon Island, less frequent use of the sea ice, less traditional food available in the community, increased vessel traffic, a decrease in seal population, new species and birds in the area, and detailed traditional methods of determining ice thickness through observation of color and use of the harpoon. Agreement between traditional knowledge and the scientific data was present in typical break-up and freeze-up patterns, and annual maximum thickness decreasing over time. While most respondents indicated FUD was later and BUD earlier, more participants responded there have been changes to the FUD than the BUD, whereas the scientific data showed more severe changes to the BUD than FUD. During interviews, there was consensus that summer temperatures are getting warmer but there was variability in responses when asked about winter temperatures. The scientific results showed less warming in the summer (Jun-Aug) than winter months (Jan-Mar) with the most warming in the fall (Sept-Nov). The lack of agreement between these results could be attributed to local perceived changes to winter weather referring to storminess rather than strictly temperature. TIK provided small scale information about the sea ice that the current state of scientific observation can not. In conclusion, a more holistic understanding of sea ice behaviour can be achieved by including Inuit traditional knowledge in partnership with scientific methods.

Le 1er chapitre présente une série de 50 ans sur les hauts revenus chiliens basée sur des données fiscales et comptes nationaux. L’étude contredit les enquêtes, selon lesquelles les inégalités diminuent les 25 dernières années. Au contraire, elles changent de direction à partir de 2000. Le Chili est parmi les pays les plus inégalitaires de l’OCDE et l’Amérique latine. Le 2ème chapitre mesure la sous-estimation des revenus factoriels dans les données distributives. Les ménages ne reçoivent que 50% des revenus du capital brut, par opposition aux firmes. L’hétérogénéité des taux de réponse et autres problèmes font que les enquêtes ne capturent que 20% de ceux-ci, contre 70% du revenu du travail. Cela sous-estime l’inégalité,dont les estimations deviennent insensibles à la "capital share" et sa distribution. Je formalise à partir d’identités comptables pour ensuite calculer des effets marginaux et contributions aux variations d’inégalité. Le 3ème chapitre présente une méthode pour ajuster les enquêtes. Celles-ci capturent souvent mal le sommet de la distribution. La méthode présente plusieurs avantages par rapport aux options précédentes : elle est compatible avec les méthodes de calibration standard ; elle a des fondements probabilistes explicites et préserve la continuité des fonctions de densité ; elle offre une option pour surmonter les limites des supports d’enquête bornées; et elle préserve la structure de micro données en préservant la représentativité des variables sociodémographiques. Notre procédure est illustrée par des applications dans cinq pays, couvrant à la fois des contextes développés et moins développés. / The 1st chapter presents historical series of Chilean top income shares over a period of half a century, mostly using data from tax statistics and national accounts. The study contradicts evidence based on survey data, according to which inequality has fallen constantly over the past 25 years. Rather, it changes direction, increasing from around the year 2000. Chile ranks as one of the most unequal countries among both OECD and Latin American countries over the whole period of study. The 2nd chapter measures the underestimation of factor income in distributive data. I find that households receive only half of national gross capital income,as opposed to corporations. Due to heterogeneous non-response and misreporting, Surveys only capture 20% of it, vs. 70% of labor income. This understates inequality estimates, which become insensitive to the capital share and its distribution. I formalize this system based on accounting identities. I then compute marginal effects and contributions to changes in fractile shares. The 3rd chapter, presents a method to adjust surveys. These generally fail to capturethe top of the income distribution. It has several advantages over previous ones: it is consistent with standard survey calibration methods; it has explicit probabilistic foundations and preserves the continuity of density functions; it provides an option to overcome the limitations of bounded survey-supports; and it preserves the microdata structure of the survey.