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Energy demand management has very importance in economic security and planning. To identify the energy demand affecting factors and energy demand prediction can help Policy makers and activists in the energy market to improve market performance and better economic decisions and high fuel security. Recently, new techniques have been developed to economic variables prediction and modeling. Among these techniques Genetic algorithm and Particle Swarm Optimization are the best known and most widely used in literature including economy. Therefore, in this study the genetic algorithm and particle Swarm Optimization is used for energy demand estimation and prediction in the form of linear and exponential and then their performance in each of the models evaluated. The results indicate that accuracy and efficiency of the particle swarm optimization in both of exponential and linear forms is better than genetic algorithm. In addition, among the different forms the exponential form estimated with the particle swarm optimization is the best way to predict the future energy demand.

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As regards carbon dioxide emissions are the major cause of climate change for numerous, study on the factors affecting its emissions have a high significance. This study focuses on the relationship between co₂ emissions and GDP, population growth, openness and energy consumption, which inclusive a wider range of factors affecting on co₂ emissions will be examined. For this purpose, long-term and short-term relationship between co₂ emissions and other variables is estimated and analyzed with utilizing ARDL model for annual data from 1972 to 2011.The results of this study indicate that all variables except for openness, have a positive impact on co₂ emissions, while openness has a negative effect on co₂ emissions.