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Unemployment Insurance serves as a vital economic stabiliser, offering financial assistance and promoting workforce reintegration. In Sweden, occupation-specific unemployment funds, known as "Arbetslöshetskassan" (A-KASSAN), manage these claims. New complex challenges pertaining to A-KASSAN's decision-making process and unemployment insurance claims necessitate a holistic data analytics framework, innovative modelling approaches, and effective implementation strategies.  This study aims to establish a comprehensive approach to data analytics for unemployment insurance claims to provide a more accurate prediction model to aid A-KASSAN's decision-making. It accomplishes this through three main objectives: the development of a thorough framework employing management data analytics for claim analysis; advancement in modelling approaches to predict unemployment trends; and deliberation on effective strategies to visualise the developed solutions.  Drawing on Data Science, Computer Science, and Economics and Management Science, this study has crafted a four-tiered comprehensive framework encompassing descriptive, diagnostic, predictive, and prescriptive analytics. It has explored novel methodologies, formulated a model library, devised rules for result integration, and validated these through case studies. The model library showcases diverse models from Economic modelling, Statistical modelling, Big Data analytics with Machine Learning and Deep Learning, alongside hybrid modelling strategies. This study primarily concentrates on developing visualisation tools as an implementation strategy. In a summary, this study provides A-KASSAN with an approach to overcome two central issues: the lack of a comprehensive data analytics approach for unemployment insurance claims, including a framework and predictive modelling, and a dearth of visualisation solutions for management data analytics pertinent to these claims.

River ice breakup is a vital process to the morphology and hydrology of many rivers in Canada, often governing peak flows of the river. These events can occur through multiple mechanisms, with the potential for volatile or early breakup events that can have severe impacts to the river. Ice jam flooding can be a potentially devastating result of river ice breakup while early breakup of ice cover in a mid-winter breakup can be unpredictable and greatly alter the remaining ice season. These events are growing increasingly common as a result of climate change, and as a result there is a need to develop prediction tools for these events to aid in decision making support. Past investigations into developing such tools, especially from a data-driven modelling perspective, are challenged by the availability and complexity of the data related to these rare and dangerous to measure events. Therefore, the goal of this dissertation was to develop and apply methods to address the historical challenges and shortcomings in predicting these events through the use of data-driven modelling techniques. This includes: i) development of a stacking ensemble modelling framework for the prediction of ice jam presence during the spring breakup season of a river, utilising variable selection and rare-event forecasting techniques in combination with a comprehensive selection of machine-learning algorithms; ii) return period and trend analysis of mid-winter breakups in conjunction with comprehensive input analysis techniques to identify the key drivers of these events’ severity and develop a means of classifying the flood risk based on hydroclimatic traits; iii) the development of a two-level modelling system for the prediction of the occurrence and timing of mid-winter breakups on a national scale utilising rare event forecasting techniques and imbalanced learning; and iv) development of a novel hybrid semantic and machine learning modelling system in which an ontology is used in conjunction with network analysis techniques to select variables for machine learning models, which is used on a national case study of the prediction of spring breakup timing in Canada. The results of each study in application to their respective case studies demonstrate the effectiveness of the proposed techniques, which are shown to be easily adaptable to other regions or locations. These techniques can form the backbone of decision-making support for communities on rivers that are affected by the unpredictable and oftentimes volatile nature of river ice breakup. / Thesis / Candidate in Philosophy / River ice breakup is a key event to the hydrology of rivers throughout Canada, playing a major role in their physical and ecological characteristics. The timing and mechanism of these events can, however, be unpredictable and volatile, with the effects of climate change only exacerbating these risks. This dissertation focuses on addressing these potential issues through the application of machine learning and hybrid modeling in the prediction of river ice breakup events. Advanced data driven techniques coupled with novel applications of other analytical methods are used to: i) predict the presence of ice jams through the application of stacking ensemble modelling; ii) predict the severity of mid-winter breakups through application of trend and variable analysis; iii) predict the occurrence and timing of mid-winter breakups using rare-event forecasting techniques; and iv) develop a novel hybrid modelling scheme coupling ontology-based semantic modelling and machine learning to predict spring breakup timing. Detailed case studies for each application are provided demonstrating the effectiveness of the discussed techniques.

Cette thèse aborde les enjeux de l'hybridation des données pour la modélisation énergie-économie-environnement, et ses implications pour la politique climatique dans le cas de la France.Le travail met l'accent sur l'importance de construire une représentation hybride de l'économie, articulant de façon cohérente le cadre économique de la comptabilité nationale et les flux physiques, fournis par des bilans de matières (ex: bilan énergétique). Partant du principe qu’il est possible de réduire les incertitudes dans la recomposition des données grâce à des contraintes d’équilibres de flux, cette thèse met d’abord en place une méthode permettant de dépasser les problèmes de nomenclatures non cohérentes, de données disparates, ou simplement manquantes. Nous montrons que l’hybridation permet de décrire plus précisément le poids de l’énergie dans l’appareil productif français, ainsi que celui de certains secteurs de l’économie (ciment, acier).Le cadre hybride sert alors de base au modèle d’équilibre général IMACLIM. Ce modèle sert à explorer dans quelle mesure la comptabilité hybride permet de renouveler la discussion sur l’introduction d’une taxe carbone unilatérale en France.Nous mesurons d’abord l’importance de la procédure d’hybridation dans l’évaluation de l’impact macroéconomique de la politique climatique. La désagrégation sectorielle nous permet, dans un second temps, de conduire une discussion autour de paramètres centraux mais controversés de la modélisation : les élasticités-prix du commerce international, et la courbe salaire-chômage interprétée comme un indicateur du pouvoir de négociation des salaires. La thèse montre en particulier qu’il est possible, grâce au progrès sur la description sectorielle, de prendre en compte une hétérogénéité des régimes de formations salariales entre secteurs tout en les reliant à leur niveau d’exposition au commerce extérieur.Enfin, la thèse propose une méthode pour évaluer différents inventaires des émissions de CO2, tels que les émissions liées à la consommation, ou les émissions incorporées dans les importations, tout en s’appuyant sur le cadre hybride. Ainsi, nous fournissons des informations originales sur les moteurs des émissions en France qui permettront de prolonger l’analyse à d’autres mesures tels que l'ajustement d’une taxe carbone aux frontières / This thesis addresses the issue of data hybridisation for energy-economy-environment modelling and its implications for climate policy in the case of France.The work emphasises the importance of building a hybrid representation of the economy, articulating coherently the economic framework of national accounts and the physical flows, provided by sectoral database (energy balance, industrial statistics). Assuming that it is possible to reduce the uncertainties of data construction, thanks to the equilibrium constraints of flows, this thesis first introduces a method which overcomes the problems of non-coherent nomenclatures, disparate data, or simply missing ones. We show that this hybridisation procedure allows to better describe the weight of both the energy in the French productive system and key sectors of the economy (cement, steel).The hybrid framework then serves to feed the IMACLIM general equilibrium model. The model is used to explore to what extent the hybrid accounts give an opportunity to renew discussion on the introduction of a unilateral carbon tax in France.We first measure the importance of the hybridisation procedure for assessing the macroeconomic impact of climate policy.Then, the sectoral disaggregation allows us to conduct a discussion around central but controversial parameters of modelling: the international trade elasticity and the wage curve interpreted as an indicator of the wage bargaining power. The thesis shows in particular that it is possible, thanks to the progress on the sectoral description, to take into account heterogeneous representation of wage formation between sectors while linking them to their level of exposure to external trade.Finally, the thesis proposes a methodology to evaluate different emission inventories of CO2, such as "consumption-based" emissions, and emissions embodied in imports while relying on the hybrid framework. We thus provide original insights on the drivers of emissions in France which could extend the analyses to other policies such as the adjustment of a carbon tax at the borders

Die Einführung neuartiger Energietechnologien wird allgemein als der Schlüssel zur Senkung klimaschädlicher Treibhausgase angesehen. Allerdings ist die Abbildung derartiger Technologien in numerischen Modellen zur Simulation und ökonomischen Analyse von energie- und klimaschutzpolitischen Maßnahmen vielfach noch rudimentär. Die Dissertation entwickelt neue Ansätze zur Einbindung von technologischen Innovationen in energie-ökonomische allgemeine Gleichgewichtsmodelle, mit dem Ziel den Energiesektor realitätsnäher abzubilden. Die Dissertation adressiert einige der Hauptkritikpunkte an allgemeinen Gleichgewichtsmodellen zur Analyse von Energie- und Klimapolitik: Die fehlende sektorale und technologische Disaggregation, die beschränkte Darstellung von technologischem Fortschritt, und das Fehlen von einem weiten Spektrum an Treibhausgasminderungsoptionen. Die Dissertation widmet sich zwei Hauptfragen: (1) Wie können technologische Innovationen in allgemeine Gleichgewichtsmodelle eingebettet werden? (2) Welche zusätzlichen und politikrelevanten Informationen lassen sich durch diese methodischen Erweiterungen gewinnen? Die Verwendung eines sogenannten Hybrid-Ansatzes, in dem neuartige Technologien für Stromerzeugung und Eisen- und Stahlherstellung in ein dynamisch multi-sektorales CGE Modell eingebettet werden, zeigt, dass technologiespezifische Effekte von großer Bedeutung sind für die ökonomische Analyse von Klimaschutzmaßnahmen, insbesondere die Effekte hinsichtlich von Technologiewechsel und dadurch bedingten Änderungen der Input- und Emissionsstrukturen. Darüber hinaus zeigt die Dissertation, dass Lerneffekte auf verschiedenen Stufen der Produktionskette abgebildet werden müssen: Für regenerative Energien, zum Beispiel, nicht nur bei der Anwendung von Stromerzeugungsanlagen, sondern ebenso auf der vorgelagerten Produktionsstufe bei der Herstellung dieser Anlagen. Die differenzierte Abbildung von Lerneffekten in Exportsektoren, wie zum Beispiel Windanlagen, verändert die Wirtschaftlichkeit und die Wettbewerbsfähigkeit und hat wichtige Implikationen für die ökonomische Analyse von Klimapolitik. / Energy technologies and innovation are considered to play a crucial role in climate change mitigation. Yet, the representation of technologies in energy-economy models, which are used extensively to analyze the economic, energy and environmental impacts of alternative energy and climate policies, is rather limited. This dissertation presents advanced techniques of including technological innovations in energy-economy computable general equilibrium (CGE) models. New methods are explored and applied for improving the realism of energy production and consumption in such top-down models. The dissertation addresses some of the main criticism of general equilibrium models in the field of energy and climate policy analysis: The lack of detailed sectoral and technical disaggregation, the restricted view on innovation and technological change, and the lack of extended greenhouse gas mitigation options. The dissertation reflects on the questions of (1) how to introduce innovation and technological change in a computable general equilibrium model as well as (2) what additional and policy relevant information is gained from using these methodologies. Employing a new hybrid approach of incorporating technology-specific information for electricity generation and iron and steel production in a dynamic multi-sector computable equilibrium model it can be concluded that technology-specific effects are crucial for the economic assessment of climate policy, in particular the effects relating to process shifts and fuel input structure. Additionally, the dissertation shows that learning-by-doing in renewable energy takes place in the renewable electricity sector but is equally important in upstream sectors that produce technologies, i.e. machinery and equipment, for renewable electricity generation. The differentiation of learning effects in export sectors, such as renewable energy technologies, matters for the economic assessment of climate policies because of effects on international competitiveness and economic output.