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A Modified Model for Prediction of India's Future Energy Requirement

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  • L. Suganthi
  • T.R. Jagadeesan

Abstract

A modified model has been projected in this paper which links environmental quality and technological efficiency with energy and economic factors. A comparison is made between the results obtained from the modified model with a time series and an econometric model using Ordinary Least Square Error (OLSE), square of the correlation coefficient R 2 and Durbin Watson statistic. The time series model is built using seven regression equations and the best fit is selected from among them. The econometric model is built with consumption, price and gross national product. In the modified model, two more variables, technological efficiency and carbon dioxide emission are included, which help to determine the impact of these variables on energy consumption. It is found that the modified model gives least squared error and higher correlation coefficient for coal, oil and electricity. Also the Durbin Watson statistic ‘DW’ is found to be higher in two out of the three cases - coal and oil. The requirement of coal, oil and electricity in the year 1995–96 and 2000–01 is determined using the data for the period 70–89.

Suggested Citation

  • L. Suganthi & T.R. Jagadeesan, 1992. "A Modified Model for Prediction of India's Future Energy Requirement," Energy & Environment, , vol. 3(4), pages 371-386, June.
    • Handle: RePEc:sae:engenv:v:3:y:1992:i:4:p:371-386
    DOI: 10.1177/0958305X9200300403
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    References listed on IDEAS

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    1. Ang, B.W., 1987. "Structural changes and energy-demand forecasting in industry with applications to two newly industrialized countries," Energy, Elsevier, vol. 12(2), pages 101-111.
    2. Harel, P. & Baguant, J., 1991. "A growth prediction for electrical energy consumption in Mauritius," Energy, Elsevier, vol. 16(4), pages 707-711.
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    Cited by:

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    2. Hiremath, R.B. & Shikha, S. & Ravindranath, N.H., 2007. "Decentralized energy planning; modeling and application--a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 11(5), pages 729-752, June.

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