Does renewable energy consumption drive economic growth: Evidence from granger-causality techniques
Hlalefang Khobai and
Pierre Le Roux ()
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Pierre Le Roux: Department of Economics, Nelson Mandela University
No 1708, Working Papers from Department of Economics, Nelson Mandela University
This study investigates the causal relationship between renewable energy consumption and economic growth in South Africa. It incorporates carbon dioxide emissions, capital formation and trade openness as additional variables to form a multivariate framework. Quarterly data is used for the period 1990 – 2014 and is tested for stationarity using the Augmented Dickey Fuller (ADF), Dickey Fuller Generalised Least Squares (DF-GLS) and Phillips and Perron (PP) unit root tests. The study employs the Autoregressive distributed lag (ARDL) model to examine the long run relationship among the variables. Lastly, the study determines the direction of causality between the variables using the Vector Error Correction Model (VECM). The results validated an existence of a long run relationship between the variables. Moreover, a unidirectional causality flowing from renewable energy consumption to economic growth was established in the long run. The short run results suggested a unidirectional causality flowing from economic growth to renewable energy consumption. The findings of the study suggest that an appropriate and effective public policy is required in the long run, while considering sustainable economic growth and development.
Keywords: Renewable energy consumption; Economic growth; Causality; South Africa. (search for similar items in EconPapers)
JEL-codes: C22 C23 Q43 (search for similar items in EconPapers)
Pages: 19 pages
Date: 2017-08, Revised 2017-08
New Economics Papers: this item is included in nep-ene, nep-env and nep-reg
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Journal Article: Does Renewable Energy Consumption Drive Economic Growth: Evidence from Granger-Causality Technique (2018)
Working Paper: Does renewable energy consumption drive economic growth: Evidence from Granger-causality technique (2017)
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