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Could Sri Lanka afford sustainable electricity consumption practices without harming her economic growth?

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  • Shanthini, Rajaratnam

Abstract

The existence and direction of Granger causality between electricity consumption and economic growth, proxied by gross domestic product (GDP), has been investigated in this study using annual data covering the period 1971 to 2007. The results of the augmented Dickey-Fuller, GLS-detrended Dickey-Fuller and Phillips-Perron tests show that the natural logarithms of both the times series are individually I(1). The autoregressive distributed lag bounds testing approach to cointegration used in this study reveals that the two times series are cointegrated. The estimated long-run equilibrium relationship shows that 1% growth in GDP induces 1.45% growth in electricity consumption, and any deviation from the long-run equilibrium following a short-run disturbance is corrected within 17 months. Granger causality test results reveal uni-directional causality running from economic growth to electricity consumption without any feedback effect. The outcome of such results is beneficial to Sri Lanka’s economic growth since it is not dependent on electricity consumption, and thereby production. It is therefore possible to initiate energy policies towards minimizing wasteful electricity production and consumption practices, without compromising Sri Lanka’s GDP growth, to take her on an electricity-wise sustainable economic development path.

Suggested Citation

  • Shanthini, Rajaratnam, 2010. "Could Sri Lanka afford sustainable electricity consumption practices without harming her economic growth?," MPRA Paper 29582, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:29582
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    References listed on IDEAS

    as
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    More about this item

    Keywords

    ARDL; cointegration; Granger causality; gross domestic product; sustainable electricity consumption; Sri Lanka;
    All these keywords.

    JEL classification:

    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling

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