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In this paper, the energy demand of transport sector from 1386 to 1400 was forecasted using artificial neural networks (ANN) approach considering economic and social indicators. Feed forward supervised neural networks to forecast and back propagation algorithm to train networks were used. In order to analyze the influence of economic and social indicators on energy demand of transport sector, Gross Domestic Product (GDP), population and the total number of vehicles in 1347-1385 were taken into consideration. The obtained results as compared with the multiple regression method, revealed much less mistakes. The average absolute error percentage was decreased from 15.52% to 6.05%. .

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Iran is one of the most energy-rich countries subsidizing energy carriers, especially in the agricultural sector, to the extent that the resulting growth is at the expense of the environment. This study tries to investigate the potential impacts of energy price reform on the agro-environment, based on the Marginal Abatement Costs (MACs) of emissions. Firstly, the energy demand function of the agricultural sector and the probable reaction of inputs and outputs to the reform were estimated. Then, using an Input Distance Function (IDF), the country and provincial-wide MAC were simulated through counterfactual reform scenarios. The results indicated that energy price reform would increase the MAC of emissions and socio-environmental benefits. However, the reform adversely affected the income of farmers. Also, the results provided detailed information both at a nationwide and provincial scale. Finally, it was recommended to implement complementary policies alongside reforms to compensate for the reduction in farmers’ income.

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The aim of this paper is to develop a prediction model of energy demand of Iran’s industrial sector. For that matter a Markov Chain Grey Model (MCGM) has been proposed to forecast such energy demand. To find the effectiveness of the proposed model, it is then compared with Grey Model (GM) and regression model. The comparison reveals that the MCGM model has higher precision than those of the GM and the regression. The MCGM is then used to forecast the annual energy demand of industrial sector in Iran up to the year 2020. The results provide scientific basis for the planned development of the energy supply of industrial sector in Iran.