L. Suganthi, S. Iniyan, and A.A. Samuel (India)
Forecasting, energy modeling, neural networks and environmental tax
Energy is a vital input for the social and economic development of any country. India has been witnessing an exponential growth over the years. Large scale consumption of the non renewable commercial energy sources has resulted in environmental degradation. Hence the situation calls for proper energy planning so as to effectively leverage on the available commercial energy sources in an environment friendly manner. In this paper, an attempt has been made to study the rate of growth of the various commercial energy sources namely coal, oil, natural gas and electricity. Also, the growth rate of GNP, population and price has been explored. Time series, neural network and econometric models are deployed to find the best fit to predict the future requirement of commercial energy sources. Two stage least square error technique is used. Squared error and regression coefficient are used to determine the best fit. The coal requirement in 2030-31 is found to be 4.211 10^15 kJ, oil 10.035 10^15 kJ, natural gas 1.944 10^15 kJ and electricity 3.107 10^15 kJ. A study is also made to find the impact of environmental tax on the energy demand using three scenarios Most Probable, High Tax and Low Tax.
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