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                <text>Can Ghana build its economy on renewable energy? Is it possible to substitute nonrenewable for renewable energy? What is the impact of renewable energy on the economy of Ghana? These questions are relevant and worth considering as far as the renewable energy development agenda in Ghana is concern. Using the translog production function approach, this study primarily investigates whether Ghana can move away from nonrenewable electricity generation to renewable power generation. In particular, we provide estimates for the relationship between the two energy types and output. The estimates are then used to calculate the output and substitution elasticities between renewable and nonrenewable energy. Our findings show that while the impact of nonrenewable energy on Ghana's economy is negative, that of renewable energy is insignificant, with output elasticities for both energy types averaging −0 …</text>
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                <text>Considering the significance of renewable energy in the sustainable energy future discussion, it is important to understand its influencing factors in order to draw result implications for policy formulation. This study examines these influencing factors for Ghana in the context of an unimpressive renewable electricity scale-up amid the potentials and commitment. It proceeds to forecast the trend of renewable energy fifteen years after the sample period. The results, based on the application of the Vector Error Correction Model and Johansen cointegration technique, on a dataset covering 1980 to 2015, shows that Ghana’s renewable electricity is mainly driven by foreign direct investment and trade openness, with real GDP per capita being inconsequential. What’s more, both financial development and fossil fuel consumption undermine renewable energy development in Ghana. The dynamic forecast reveals an …</text>
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                <text>Students’ satisfaction in the university environment is essential to both the student (customer) and management of the university. Satisfied students are determined to succeed in their academics, and this sustains their loyalty and trust, which results in an improved image and esteem of the university. This study examined the level of students’ satisfaction with campus facilities and infrastructure, campus social life, student support services, and the quality of academics in the University of Energy and Natural Resources (UENR) in Ghana and further investigated how students’ satisfaction with the above four areas of the university environment affect each other. A questionnaire was administered to continuous students in UENR, and the collected data were analysed using structural equation modelling within the maximum likelihood and Bayesian frameworks whose results and performance were compared. Results showed that students’ satisfaction levels with available campus facilities, campus social life, and student support services were low but were fairly satisfied with the quality of academics. Both maximum likelihood and Bayesian techniques showed positive significant effects of students’ satisfaction with campus facilities and infrastructure on satisfaction with campus social life, students’ support services, and academics. Moreover, students’ satisfaction with social life was positively associated with their satisfaction with academics and student support services. Although both estimation methods obtained similar estimates and inferences, the Bayesian SEM outperformed the ML-SEM based on the recommended fit indices. Findings of the study …</text>
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                <text>In this study, we extend Codeço’s classical SI-B epidemic and endemic model from a deterministic framework into a stochastic framework. Then, we formulated it as a stochastic differential equation for the number of infectious individuals I (t) under the role of the aquatic environment. We also proved that this stochastic differential equation (SDE) exists and is unique. The reproduction number, R0, was derived for the deterministic model, and qualitative features such as the positivity and invariant region of the solution, the two equilibrium points (disease-free and endemic equilibrium), and stabilities were studied to ensure the biological meaningfulness of the model. Numerical simulations were also carried out for the stochastic differential equation (SDE) model by utilizing the Euler-Maruyama numerical method. The method was used to simulate the sample path of the SIB stochastic differential equation for the number of infectious individuals I (t), and the findings showed that the sample path or trajectory of the stochastic differential equation for the number of infectious individuals I (t) is continuous but not differentiable and that the SI-B stochastic differential equation model for the number of infectious individuals I (t) fluctuates inside the solution of the SI-B ordinary differential equation model. Another significant feature of our proposed SDE model is its simplicity.</text>
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