This paper deals with the global energy consumption to forecast future projections based on primary energy, global oil, coal and natural gas consumption using a hybrid Cuckoo optimization algorithm and information of British Petroleum Company plc and BP Amoco plc. The Artificial Neural Network (ANN) has some significant disadvantages, such as training slowly, easiness to fall into local optimal point, and sensitivity of the initial weights and bias. To overcome the shortcomings, an improved ANN structure, that is optimized by the Cuckoo Optimization Algorithm (COA), is proposed in this paper (COANN). The performance of the COANN is evaluated with Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Correlation Coefficient (CC) between the output of the model and the actual dataset. Finally, CO 2 emission in the world by 2050 is forecasted using COANN. The findings showed that COANN is a helpful and reliable tool for monitoring global warming. This proposed method will assist experts, policy planners and researchers who study greenhouse gases. The method can be used as a potential tool for policymakers and governments to make policy on global warming monitoring and control. The proposed method can play a key role in the global climate changes policies and can have a significant impact on the efficiency or inefficiency of government's intervention policies.
Money demand is one of the most important economic variables which are a critical component in appointing and choosing appropriate monetary policy, because it determines the transmission of policy-driven change in monetary aggregates to the real sector. In this paper, the data of economic indicators in Iran are presented for estimating the money demand using biogeography-based optimization (BBO) algorithm, particle swarm optimization (PSO) algorithm, and a new hybrid metaheuristic method based on biogeography-based optimization and particle swarm optimization algorithm (BBPSO). The data are used in two forms (i.e. linear and exponential) to estimate money demand values based on true liquidity, Consumer price index, GDP, lending interest rate, Inflation, and official exchange rate. The available data are partly used for finding optimal or near-optimal values of weighting parameters (1974–2013) and partly for testing the models (2014–2018). The performance of methods is evaluated using mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE). According to the simulation results, the proposed method (i.e. BBPSO) outperformed the other models. The findings proved that the recommended method was an appropriate tool for effective money demand prediction in Iran. These data were the result of a comprehensive look at the most influential factors for money market demand. With this method, the demand side of this market was clearly defined. Along with other markets, the consequences of economic policy could be analyzed and predicted. • The article provides a method for observing the effect of economic scenarios on the money market and the analysis obtained by this proposed method allows experts, public sector economics, and monetary economist to see a clearer explanation of the country's liquidity plan. • The method presented in this article can be beneficial for the policy makers and monetary authorities during their decision-making process.
Coal as a fossil and non-renewable fuel is one of the most valuable energy minerals in the world with the largest volume reserves. Artificial neural networks (ANN), despite being one of the highest breakthroughs in the field of computational intelligence, has some significant disadvantages, such as slow training, susceptibility to falling into a local optimal points, sensitivity of initial weights, and bias. To overcome these shortcomings, this study presents an improved ANN structure, that is optimized by a proposed hybrid method. The aim of this study is to propose a novel hybrid method for predicting coal consumption in Iran based on socio-economic variables using the bat and grey wolf optimization algorithm with an artificial neural network (BGWAN). For this purpose, data from 1981 to 2019 have been used for modelling and testing the method. The available data are partly used to find the optimal or near-optimal values of the weighting parameters (1980–2014) and partly to test the model (2015–2019). The performance of the BGWAN is evaluated by mean squared error (MSE), mean absolute error (MAE), root mean squared error (RMSE), standard deviation error (STD), and correlation coefficient (R^2) between the output of the method and the actual dataset. The result of this study showed that BGWAN performance was excellent and proved its efficiency as a useful and reliable tool for monitoring coal consumption or energy demand in Iran.
Structural alterations has made essential changes in the development of countries' economy, international trading and the expansion of urban centers. Structural changes are defined as an alteration in proportional weight of important parts in the major indices such as the output of governments' expenditure, taxes, etc. Since change in traditional structures of economical factors to new structures is one of the most effective reasons of growth and development in the economy of governments, the attention of governments to influencially important economical factors, and studying the process of changes in important parts of economy such as tax has been considered. So considering the important role of tax in the countries' economy the investigation of changes in the tax structure is of paramount importance, hence in the present study we have examined the structure changes of the types of taxes in America by using the Bai and Perron sequential procedure for the period 1971-2012. The results show that only the corporate tax has faced with the structural changes.
Introduction: Poverty reduction is one of the important macroeconomic goals of any country, but achieving this important issue requires examining the factors affecting it. Changing the age structure of the population is one of the effective factors in reducing poverty in countries. Therefore, governments can make the most of their population, given the capacity of countries and providing the necessary conditions, and as a result, achieve high growth and development among countries. The purpose of this study is to investigate and analyze the effect of population age structure on poverty in Iran. Method: The method of this research is analytical-descriptive. First, theoretical topics and experimental studies and research data are collected, and the appropriate analysis model is selected. Then, with the Generalized Method of Momen for Iran since 1975 until 2017, the desired research models have been estimated, and finally, using statistical and econometric inferences, the research results have been studied. Findings: In general, according to the results, it can be concluded that the dependent population of the child and years of schooling have a negative and significant relationship with the dependent variable, the elderly dependent population has a negative and significant relationship with the dependent variable and also the percentage of trade in GDP and Physical capital inventories have a negative relationship with the dependent variable. Discussion: In this study, the years of schooling, the percentage of trade in GDP, as well as capital stock are based on a coefficient that agrees with the theory. The positive effect of the elderly population on poverty reduction indicates the existence of high savings in old age. Paying attention to the growth of highly educated employees as well as employing specialized personnel increases the productivity of the labor force and also creates the possibility of creating new production methods that have significant effects on poverty reduction.
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