Renewable energy, as an environmentally friendly and sustainable source of energy, is key to realizing the nationally determined contributions of the United States (US) to the December 2015 Paris agreement. Policymakers in the US rely on energy forecasts to draft and implement cost-minimizing, efficient and realistic renewable and sustainable energy policies but the inaccuracies in past projections are considerably high. The inaccuracies and inconsistencies in forecasts are due to the numerous factors considered, massive assumptions and modeling flaws in the underlying model. Here, we propose and apply a machine learning forecasting algorithm devoid of massive independent variables and assumptions to model and forecast renewable energy consumption (REC) in the US. We employ the forecasting technique to make projections on REC from biomass (REC-BMs) and hydroelectric (HE-EC) sources for the 2009-2016 period. We find that, relative to reference case projections in Energy Information Administration's Annual Energy Outlook 2008, projections based on our proposed technique present an enormous improvement up to~138.26-fold on REC-BMs and 24.67-fold on HE-EC; and that applying our technique saves the US~2692.62 PJ petajoules (PJ) on HE-EC and~9695.09 PJ on REC-BMs for the 8-year forecast period. The achieved high-accuracy is also replicable to other regions.
Revenues generated from taxes constitute a major source of income for governments. However, the epic display of tax evasion by individuals and firms in most countries has induced researches on the factors accounting for tax evasion in developing countries. Therefore, this study is conducted to investigate the determinants of the informal sector compliance issues and the causality nexus between tax evasion and Gross Domestic Product (GDP). This research solely adopts the theory of planned behavior in analyzing tax compliance issues. The research work is divided into two parts. In analyzing the informal sector compliance issues, questionnaires were submitted to 600 respondents comprising informal sector taxpayers in all the ten regions in Ghana. Regression analysis was employed in our study to depict the results of the informal sector compliance issues. The result revealed that attitudes, subjective norm and perceived behavioral control are the main determinants of the informal sector compliance issues. The second part of this research examined the causality between taxes and GDP in Ghana's economy over the period of 1980-2015. The data were analyzed by employing the Augmented Dickey Fuller Unit Root test, the variables were found to be integrated of the order one and the Johansen test showed the presence of co-integration between the variables. The Granger causality test for the study indicated a unidirectional causality from taxation to GDP. Therefore, the study recommends that efforts should be geared towards the improvement of tax systems in order to augment the GDP of the country.
Electricity plays a crucial role in the economic development of most economies. The causality nexus between electricity consumption and economic growth is important in enacting energy consumption policy and environmental policy. Many researchers have studied the causality between energy consumption and economic growth yet no consensus has emerged. Irrespective of the numerous researches conducted between these two variables, less evidence has been recorded in Ghana. Studies establishing the direction of causality between economic growth and energy consumption have concluded mixed result posing stern threat to Ghana's energy policy. It is therefore viable to investigate the direction of causality between electricity consumption and economic growth in Ghana. This study uses the Cobb-Douglas growth model covering time series data from 1970 to 2014. Vector Error Correction Model was also conducted in order to empirically ascertain the error correction adjustment. Granger Causality test was used to determine the direction of causality between electricity consumption and economic growth and the empirical findings obtained herein reveals that there exists a unidirectional causality running from GDP to electricity consumption. This line of causality obtained from the data supports Growth-LedEnergy Hypothesis. Therefore, it is evident that Ghana is a less energy-dependent economy.
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