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The AUS Repository serves as the Institutional Repository of the American University of Sharjah, providing open access to research outputs from AUS students and faculty. By preserving these works for the long term and increasing their global visibility, the repository plays a key role in the dissemination of knowledge. See our About Us page for more information.
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Item An economic analysis of gas pipeline trade cooperation in the GCC(Elsevier, 2021-08)Natural gas plays an important role in the global energy system. Thus, optimizing trade in natural gas is a key concern for many countries. This study investigates the value of expanding the Gulf Cooperation Council's (GCC) natural gas grid. We consider the documented successes and failures of the regional gas trade in Europe and Asia and weigh them against a GCC case study. The case study uses a partial equilibrium model of energy production, trade and demand calibrated to 2018 conditions to assess regional pipeline gas trade opportunities. The model incorporates parameters that are relevant to energy policy issues, including fuel allocation and energy price reforms. We also incorporate the regional liquified natural gas (LNG) trade strategy of Qatar, a regional and global leader in LNG production and exports. We find that pipeline gas trade cooperation in the GCC can contribute up to $3.1 billion to the regional economy by reducing transportation costs. More accessible gas offers a substitute for liquid fuel consumption and can offset the opportunity costs of using domestic oil to meet domestic energy demands. We also investigate the influence of an integrated gas market and price reforms on the power trade along the GCC interconnector.Item What causes energy and transport poverty in Ireland? Analysing demographic, economic, and social dynamics, and policy implications(Elsevier, 2022-11-04)Energy and transport poverty have been postulated as conditions linked by overlapping causal factors such as structural economic inequality or housing stock and affecting overlapping demographics such as family size or income. The strength of the overlap of these conditions and their causal mechanisms has not been assessed across Ireland prior to this study. We apply and analyse existing and novel energy and transport poverty metrics in a survey of 1564 participants across Ireland and consider results from expenditure and consensual data examining causal mechanisms and correlations. We find that energy and transport poverty rates are broadly similar across Ireland at approximately 14% for energy poverty and 18% for transport poverty using the half-median metric, while participant knowledge of causal factors, such as lack of domestic energy efficiency and perceived desirability of potential poverty solutions, such as increased public transport provision, are low. Furthermore, we find that self-reported data concerning energy and transport expenditures and preferences do not correspond to expected outcomes. We thus conclude that ever refined targeting of individuals and households for support measures is not optimal for either decarbonisation or alleviation of energy and transport poverty conditions and suggest some salient policy implications.Item Green flight paths: a catalyst for net-zero aviation by 2050(Royal Society of Chemistry, 2024)Large-scale sustainable aviation fuel (SAF) production and use is essential to achieving net-zero aviation by 2050. In this perspective, we argue that catalysing SAF production from the very low level of 2022 (0.1% of the 2050 required level for net-zero) can be achieved via the establishment of ‘‘green flight paths’’ (GFPs) that kick-start SAF implementation through targeted support from key international partner countries. The development of GFPs builds on the Clydebank Declaration from COP26 for green shipping corridors, which is aimed at transforming emissions at sea. Similarly, we define here GFPs as specific aviation routes where financially viable supply chain opportunities for zero-emission air-travel are incentivised. We examine here how GFPs are likely to be spearheaded by countries, such as the UK and the UAE, which are both large international aviation markets that have the political, technical and production capabilities to be world-leaders in pursuing the earlier stages of investment (which are inherently riskier) in developing SAF commercial production capacity for the decarbonization of their aviation sectors. We further discuss how from an energy justice perspective, GFPs are ideal for catalysing SAF adoption and cost reduction in a just way by placing the burden where accountability is required.Item Reconfiguring European industry for net-zero: a qualitative review of hydrogen and carbon capture utilization and storage benefits and implementation challenges(Royal Society of Chemistry, 2023)This research study presents a dynamic discrete optimization model for the treatment of municipal solid waste (MSW) with sustainability as an essential research objective. The optimization model screens capacity selections for MSW technologies over distributed sites with consideration of pretreatment biodrying technologies for waste calorific value enhancement. The choices for these MSW technologies and the network operation are described by binary and continuous variables, respectively. The MSW network economic seeks the maximization of the net present value (NPV). Key environmental impacts of MSW technology use as well as the impacts of MSW transportation are described by a minimization optimization problem. The social target of the MSW network considers the maximization of job creation. When applying the model to a case study, sustainability objective functions showed conflict in the results. Pareto optimal solutions are found from the multi-objective optimization model and a compromised solution is suggested for the considered case study.Item A Bayesian Approach to Feature Selection in Classification Problems(2024-07)The exponential growth of data, as well as the widespread use of machine learning in daily life, demonstrate the importance of feature selection. Feature selection, defined as the process of identifying and selecting a subset of relevant features from a larger set of available features, is a crucial step in machine learning. The performance and efficiency of machine learning models are improved by focusing on the most informative features and eliminating unnecessary or redundant ones. Furthermore, model interpretability is enhanced, resulting in clearer insights and an actionable understanding of the results. The resulting models are more robust, less prone to noise, and can be efficiently trained and deployed, ultimately contributing to more effective and efficient data-driven decision-making processes. We propose a Bayesian approach using the relative belief ratio (RBR) as a filter method in this paper. The proposed method showed an excellent performance in binary and multiclass classification problems. In addition, the proposed method generates a strength value that can be used as an importance score for each feature. The numerical value of the strength of the RBR is used to rank the features. This method aims to discern the relative importance of features concerning a target variable and test for their significance. The proposed method’s performance is evaluated using both synthetic and real-world datasets, and it is compared to various popular filter methods.
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