The causal relationship between renewable electricity generation and GDP growth in OECD and OPEC selected countries

Khalaj Amir Hosseini, Amir Bahador; Mohseni, Reza

The causal relationship between renewable electricity generation and GDP growth in OECD and OPEC selected countries

Document Type : Original Article

Authors

¹ MA Student

² Faculty Member of Economics and Political Science

Abstract

By the reason of increasing environmental worries and also the limits of fossil fuel sources in the world, including Iran, all the developers and developing countries are changing their technology from fossil fuel sources to renewable energy sources.
This research is reviewing the relation between producing electricity from types of renewable energy sources and GDP growth rate.
According to this, first, existing of short-run and long-run relation between electricity production from types of renewable energy sources and GDP growth rate has been studied with serial correlation panel model (FMOLS - Fully modified least squares) and panel error correction model (ECM).
Results show that in producing electricity from renewable and non-renewable energy, capital and labor variables have a positive and meaningful effect on GDP in short-run and long-run. For example, increasing 1% of electricity production by renewable energy, can raise GDP to 0/66% in the long - run. While electricity production of non-renewable energy can raise GDP to 0/45% in the long - run.
Also results in estimating each energy source show that biomass, solar, waste and wind have a positive and meaningful effect on GDP. That means increasing 1% in the amount of biomass (solar, waste, wind and Geothermal) can increase GDP in order of mentioned to 0/01%, 0/72%, 0/33%, 0/11% and 0/61%. While hydroelectric source has elasticity with 0/11 that it has a positive effect on GDP but this is not a meaningful effect.

Keywords

References

Apergis, N., Payne, J.E. )2010a(. Renewable energy consumption and economic growth: evidence from a panel of OECD countries. Energy Policy 38. 656–660

Apergis, N., Payne, J.E.) 2010b(. Renewable energy consumption and growth in Eurasia. Energy Econ 32. 1392–1397.

Apergis, N., Payne, J.E., 2012b. Renewable and non-renewable energy consumption– growth nexus: evidence from a panel error correction model. Energy Econ 34.733–738.

Asia Pacific Energy Research Center (APERC), A Quest for energy security in the 21th century. Institute of Energy Economics2007. http://www.ieej.or.jp/aperc2007.pdf.

Atems, Bebonchu, Hotaling, Chelsea, 2018, The effect of renewable and nonrenewable electricity generation on economic growth, Energy Policy. Volume 112. January 2018. Pages 111-118.

Badr M., Benjamin R.,. Comparative cost of California central station electricity generation technologies, California Energy Commission, Gray Davis. Governor. 100-03-001F. 2003. Refer information in: www.energy.ca.gov/reports/2003-06-06_100-03-001F.PDF.

Best, Rohan,.Burke, Paul J,2018, Electricity availability: A precondition for faster economic growth?, Energy Economics. Volume 74, August 2018, Pages 321-329.

Dalians D., Petassis D., Santamouris M., Argirou A., Cartalis C., Social cost of electricity generation in Greece, Journal Renewable Energy, 85, 2002, (3), 409–434.

Ewing, B.T., Sari, R., Soytas, U., 2008. The relationship between disaggregate energy consumption and industrial production in the United States: an ARDL approach.Energy Econ. 30, 2302–2313.

Fisher, R.A.," Statistical Methods for Research Workers", Oliver & Boyd, Edinburgh, 4th Edition. 1932.

Grassi G., Potential employment impacts of bioenergy activity on employment, Proceedings of the 9thEuropean Bioenergy conference, 1996, Vol. I, pp. 419-423.

Im, K. S., Pesaran, M. H., and Shin, Y." Testing for Unit Roots in Heterogeneous Panels",University of Cambridge, Deparment of Applied Economics. 1997.

International Renewable Energy Agancy (IRENA 2017), Rethinking Enrgy 2017

Kao, C., Chiang, M.H., 2000. On the estimation and inference of a cointegrated regression in panel data. Center for Policy Research, Paper 145.

Levin, A., and Lin, C. F.,. Unit Root Tests in Panel Data: Asymptotic and Finite Sample Properties, University of California, San Diego, Discussion Paper No: 92-93. 1992.

Maddala, G.S., and Wu, S., (). A Comparative Study of Unit Root Tests with Panel Data and a New. Simple Test, Oxford Bulletin of Economics and Statistics, Special Issue, 61:631-652. 1999.

Pedroni, P,. Panel Cointegration, Asymptotic and Finite Sample Properties of Pooled Time Series Tests with an Application to the PPP Hypothesis, New Results, Indiana University. 1997.

Pesaran,H,M, " A Simple Panel Unit Root Test in Presence of Cross Section Dependence", Mimeo, University of Southern California. 2003.

Phillips, P.C.B, and Sul,D." Dynamic Panel Estimation and Homogeneity Testing Under Cross Section Dependence",Econometrics Journal, 6(1),217-259. 2003.

Quah, D." Exploiting Cross-Section Variation for Unit Root Inference in Dynamic Data",Economics Letters, 44:9-19. 1994.

RENEWABLES 5102, GLOBAL STATUS REPORT, online ren21.net/gsr-2017-producs/

Stern, D.I., Cleveland, C.J., (2004).Energy and Economic Growth, Rensselaer Working Papers in Economics No. 0410,Rensselaer Polytechnic Institute, USA, 1-42

Vine E. Breaking down the silos: the integration of energy efficiency, renewable energy, demand response and climate change. Energy Efficiency 2008;1:49–63.
1. Ayres and I. Nair, ‘Thermodynamics and economics’, Physics Today, Vol 35, 1984, pp 62-71
Stern, D.I. & C. J. Cleveland (2004), "Energy and Economic Growth, Rensselaer", Working Papers in Economics 0410.

Brendt, E.R. & Wood, D.O. (1975), “Technology, Prices and the Derived Demand for Energy”. Review of economics and Statistics, No.57, PP.259- 268.

Noor, S., Siddiqi, M.W., (2010). “Energy consumption and economic growth in south asian countries: A co-integrated panel analysis”. Int. J. Hum. Soc. Sci. 5 (14)

Bowden, N., Payne, J.E., (2010). “Sectoral analysis of the causal relationship between renewable and non-renewable energy consumption and real output in the US”. Energy Sources Part B 400–408.

Ozcan B., Ozturk I., (2019). “Renewable energy consumption-economic growth nexus in emerging countries”: A bootstrap panel causality test Renew. Sustain. Energy Rev., 104, pp. 30-37.

KabiruMajia I, Sulaimana C, Abdul-Rahim A.S.(2019). “Renewable energy consumption and economic growth nexus: A fresh evidence from West Africa”. Energy Reports, Volume 5, November 2019, Pages 384-392.

Aydin M, (2019). “Renewable and non-renewable electricity consumption–economic growth nexus: Evidence from OECD countries”. Renewable Energy Volume 136, June 2019, Pages 599-606.

Breituing, j. and Meyer, W., (1994), “Testing for Unit Roots in Panel Data: Are Wages on Different Bargaining Levels Cointegrated?”, Applied Economics, Vol.26, pp.353-361.

Pedroni, P., 1999. “Critical values for cointegration tests in heterogeneous panels with multiple regressors.” Oxf. Bull. Econ. Stat. 61 (S1), 653–670. Pedroni, P., 2001. Fully modified OLS for heterogeneous cointegrated panels. Adv. Econ. 15, 93–130.

Stock, J. H., & Watson, M. W. (1993); “ A Simple Estimator of Cointegrating Vectors in Higher Order Integrated Systems”, Econometrica: Journal of the Econometric Society, no. 61 (4), pp. 783-820.

Pesaran, H. and Shin, M,Y. (1999) .“Auto regressive distributed lag modeling approach to cointegratin analysis,In: Storm, S.(ED)”.

Lutkepohl, H., 1982. Non-causality due to omitted variables. J. Econ. 19, 267–378. Marques, A.C., Fuinhas, J.A., 2012. Is renewable energy effective in promoting growth? Energy Policy 46, 434–442.

Ohler, A. and Fetters I. (2014). “The causal relationship between renewable electricity generation and GDP growth: A study of energy sources”, Energy Economics Volume 43, May 2014, Pages 125-139.

Menegaki, A.N., 2011. "Growth and renewable energy in Europe: a random effect model with evidence for neutrality hypothesis". Energy Econ. 33, 257–263.