Renewable energy consumption and economic growth: Evidence from a panel of OECD countries

doi:10.1016/J.ENPOL.2009.09.002

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Renewable energy consumption and economic growth: Evidence from a panel of OECD countries

Journal Article•DOI•

Renewable energy consumption and economic growth: Evidence from a panel of OECD countries

Nicholas Apergis¹, James E. Payne²•Institutions (2)

University of Piraeus¹, Florida State University College of Arts and Sciences²

01 Jan 2010-Energy Policy (Elsevier)-Vol. 38, Iss: 1, pp 656-660

TL;DR: In this paper, the relationship between renewable energy consumption and economic growth for a panel of twenty OECD countries over the period 1985-2005 within a multivariate framework was examined, where a panel cointegration and error correction model was employed to infer the causal relationship.

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About: This article is published in Energy Policy.The article was published on 2010-01-01. It has received 1096 citations till now. The article focuses on the topics: Energy consumption & Cointegration.

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Journal Article•DOI•

The effect of renewable energy consumption on economic growth: Evidence from top 38 countries

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Madhumita Bhattacharya¹, Sudharshan Reddy Paramati², Ilhan Ozturk³, Sankar Bhattacharya⁴•Institutions (4)

Monash University¹, Jiangxi University of Finance and Economics², Çağ University³, Monash University, Clayton campus⁴

15 Jan 2016-Applied Energy

TL;DR: In this article, the authors investigate the effects of renewable energy consumption on the economic growth of major renewable energy consuming countries in the world and conclude that renewable energy investment has a significant positive impact on economic output for 57% of the selected countries.

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967 citations

Cites background from "Renewable energy consumption and ec..."

...Apergis and Payne [13] Panel 1985–2005 20 OECD countries GDP <> RE...
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...Study Methodology Period Country Findings Sadosky [10] Panel, FMOLS 1994–2003 18 emerging countries GDP > RE Apergis and Payne [13] Panel 1985–2005 20 OECD countries GDP <> RE Apergis and Payne [14] Panel 1992–2007 13 Eurasian countries GDP <> RE Apergis and Payne [15] Panel 1980–2006 6 Central American countries GDP <> RE Menegaki [16] Panel, random effect 1997–2007 27 European countries GDP and RE are neutral to each other Fang [17] OLS 1978–2008 China RE > GDP Tiwari [18] Structural VAR 1960–2009 India RE > GDP Apergis and Payne [19] Panel 1990–2007 80 countries GDP <> EC (RE, NRE) Salim and Rafiq [20] Panel 1980–2006 6 major emerging countries GDP <> RE in the short-run Tugcu et al. [21] ARDL approach for cointegration; Hatemi-J (2012) for causality test 1980–2009 G7 countries The relationship is different for countries and varies with specification Ai-mulali et al. [22] FMOLS 1980–2009 108 countries 79% feedback; 2% conservation; 19% neutrality Ozturk and Bilgili [12] Dynamic panel analysis 1980–2009 51 Sub-Sahara African countries Biomass has positive effect on GDP Cho et al. [23] Panel vector error correction model 1990–2010 31 OECD and 49 non-OECD countries GDP > RE fo developed and GDP <> RE for less-developed countries Bilgili and Özturk [24] Panel, DOLS 1980–2009 G7 countries Biomass has positive effect on GDP Note: EC: Energy Consumption; RE: Renewables; NRE: Non-renewables....
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Journal Article•DOI•

CO2 emissions, nuclear energy, renewable energy and economic growth in the US

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Kojo Menyah¹, Yemane Wolde-Rufael•Institutions (1)

London Metropolitan University¹

01 Jun 2010-Energy Policy

TL;DR: In this article, the causal relationship between carbon dioxide emissions, renewable and nuclear energy consumption and real GDP for the US for the period 1960-2007 was explored, using a modified version of the Granger causality test.

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819 citations

Cites background from "Renewable energy consumption and ec..."

...We include real GDP because both real GDP and CO2 emissions are found to be important drivers of renewable energy consumption (Sadorsky, 2009; Apergis and Payne, 2010)....
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...Following Apergis and Payne (2010) and Sadorsky (2009), renewable energy consumption includes net geothermal, solar, wind, and wood and waste electric power consumption (Energy Information Administration, 2009a, 2009b)....
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Journal Article•DOI•

On the causal dynamics between emissions, nuclear energy, renewable energy, and economic growth ☆

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Nicholas Apergis¹, James E. Payne², Kojo Menyah³, Yemane Wolde-Rufael•Institutions (3)

University of Piraeus¹, Illinois State University², London Metropolitan University³

15 Sep 2010-Ecological Economics

TL;DR: This paper examined the causal relationship between CO2 emissions, nuclear energy consumption, nuclear consumption, renewable energy consumption and economic growth for a group of 19 developed and developing countries for the period 1984-2007 using a panel error correction model.

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759 citations

Cites background from "Renewable energy consumption and ec..."

...…consumption and pollutant emissions on the other (Coondoo and Dinda, 2002; Dinda, 2004; Soytas et al., 2007; Chontanawat et al., 2008; Aslanidis and Iranzo, 2009; Apergis and Payne, 2009, 2010a); Sadorsky, 2009a; Sari and Soytas, 2009; Soytas and Sari, 2009; Ozturk, 2010; Payne, 2009, 2010a,b)....
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Journal Article•DOI•

Determinants of CO2 emissions in the European Union: The role of renewable and non-renewable energy

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Eyup Dogan¹, Fahri Seker•Institutions (1)

Abdullah Gül University¹

01 Aug 2016-Renewable Energy

TL;DR: In this article, the authors investigated the impacts of renewable and non-renewable energy, real income and trade openness on CO2 emissions in the Environmental Kuznets Curve (EKC) model for the European Union over the period 1980-2012 by employing panel estimation techniques robust to cross-sectional dependence.

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679 citations

Journal Article•DOI•

The influence of real output, renewable and non-renewable energy, trade and financial development on carbon emissions in the top renewable energy countries

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Eyup Dogan¹, Fahri Seker•Institutions (1)

Abdullah Gül University¹

01 Jul 2016-Renewable & Sustainable Energy Reviews

TL;DR: In this paper, the influence of real income, renewable energy consumption, non-renewable energy consumption and trade openness and financial development on CO2 emissions in the EKC model for the top countries listed in the Renewable Energy country Attractiveness Index by employing heterogeneous panel estimation techniques with cross-section dependence.

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Abstract: Due to tremendous increase in the level of carbon dioxide (CO2) emissions in the last several decades, a number of studies in the energy-growth-environment literature have attempted to identify the determinants of CO2 emissions. A major criticism related to the existing studies, we realize, is the selection of panel estimation techniques. Almost all studies use panel methods that ignore the issue of cross-sectional dependence even though countries in the panel are most likely heterogeneous and cross-sectionally dependent. In addition, the majority of existing studies use aggregate energy consumption, and thus fail to identify the impacts of energy consumption by sources on the environment. In order to fulfill the mentioned gaps in the literature, this empirical study analyzes the influence of the real income, renewable energy consumption, non-renewable energy consumption, trade openness and financial development on CO2 emissions in the EKC model for the top countries listed in the Renewable Energy Country Attractiveness Index by employing heterogeneous panel estimation techniques with cross-section dependence. We find that the analyzed variables become stationary at their first-differences by using the CADF and the CIPS unit root tests, and the analyzed variables are cointegrated by employing the LM bootstrap cointegration test. By using the FMOLS and the DOLS, we also find that increases in renewable energy consumption, trade openness and financial development decrease carbon emissions while increases in non-renewable energy consumption contribute to the level of emissions, and the EKC hypothesis is supported for the top renewable energy countries.

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674 citations

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References

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Journal Article•DOI•

Co-integration and Error Correction: Representation, Estimation and Testing

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Robert F. Engle¹, Clive W. J. Granger¹•Institutions (1)

University of California, San Diego¹

01 Mar 1987-Econometrica

TL;DR: The relationship between co-integration and error correction models, first suggested in Granger (1981), is here extended and used to develop estimation procedures, tests, and empirical examples.

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Abstract: The relationship between co-integration and error correction models, first suggested in Granger (1981), is here extended and used to develop estimation procedures, tests, and empirical examples. If each element of a vector of time series x first achieves stationarity after differencing, but a linear combination a'x is already stationary, the time series x are said to be co-integrated with co-integrating vector a. There may be several such co-integrating vectors so that a becomes a matrix. Interpreting a'x,= 0 as a long run equilibrium, co-integration implies that deviations from equilibrium are stationary, with finite variance, even though the series themselves are nonstationary and have infinite variance. The paper presents a representation theorem based on Granger (1983), which connects the moving average, autoregressive, and error correction representations for co-integrated systems. A vector autoregression in differenced variables is incompatible with these representations. Estimation of these models is discussed and a simple but asymptotically efficient two-step estimator is proposed. Testing for co-integration combines the problems of unit root tests and tests with parameters unidentified under the null. Seven statistics are formulated and analyzed. The critical values of these statistics are calculated based on a Monte Carlo simulation. Using these critical values, the power properties of the tests are examined and one test procedure is recommended for application. In a series of examples it is found that consumption and income are co-integrated, wages and prices are not, short and long interest rates are, and nominal GNP is co-integrated with M2, but not M1, M3, or aggregate liquid assets.

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27,170 citations

Journal Article•DOI•

Testing for unit roots in heterogeneous panels

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Kyung So Im¹, M. Hashem Pesaran², Yongcheol Shin³•Institutions (3)

University of Central Florida¹, University of Cambridge², University of Edinburgh³

01 Jul 2003-Journal of Econometrics

TL;DR: In this article, a unit root test for dynamic heterogeneous panels based on the mean of individual unit root statistics is proposed, which converges in probability to a standard normal variate sequentially with T (the time series dimension) →∞, followed by N (the cross sectional dimension)→∞.

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12,838 citations

Journal Article•DOI•

Unit root tests in panel data: asymptotic and finite-sample properties

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Andrew T. Levin¹, Chien-Fu Lin², Chia-Shang James Chu²•Institutions (2)

Federal Reserve Board of Governors¹, National Taiwan University²

01 May 2002-Journal of Econometrics

TL;DR: In this article, the authors consider pooling cross-section time series data for testing the unit root hypothesis, and they show that the power of the panel-based unit root test is dramatically higher, compared to performing a separate unit-root test for each individual time series.

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10,792 citations

Journal Article•DOI•

Pooled Mean Group Estimation of Dynamic Heterogeneous Panels

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M. Hashem Pesaran¹, Yongcheol Shin², Ron Smith³•Institutions (3)

University of Southern California¹, University of Edinburgh², Birkbeck, University of London³

01 Jun 1999-Journal of the American Statistical Association

TL;DR: The pooled mean group estimator (PMG) estimator as discussed by the authors constrains long-run coefficients to be identical but allows short run coefficients and error variances to differ across groups.

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Abstract: It is now quite common to have panels in which both T, the number of time series observations, and N, the number of groups, are quite large and of the same order of magnitude. The usual practice is either to estimate N separate regressions and calculate the coefficient means, which we call the mean group (MG) estimator, or to pool the data and assume that the slope coefficients and error variances are identical. In this article we propose an intermediate procedure, the pooled mean group (PMG) estimator, which constrains long-run coefficients to be identical but allows short-run coefficients and error variances to differ across groups. We consider both the case where the regressors are stationary and the case where they follow unit root processes, and for both cases derive the asymptotic distribution of the PMG estimators as T tends to infinity. We also provide two empirical applications: Aggregate consumption functions for 24 Organization for Economic Cooperation and Development economies over th...

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4,592 citations

Journal Article•DOI•

Critical Values for Cointegration Tests in Heterogeneous Panels with Multiple Regressors

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Peter Pedroni¹•Institutions (1)

Indiana University¹

01 Nov 1999-Oxford Bulletin of Economics and Statistics

TL;DR: In this paper, a method for testing the null of no cointegration in dynamic panels with multiple regressors and computing approximate critical values for these tests is presented. But the method is limited to simple bivariate examples, in large part due to the lack of critical values available for more complex multivariate regressions.

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Abstract: I. INTRODUCTION In this paper we describe a method for testing the null of no cointegration in dynamic panels with multiple regressors and compute approximate critical values for these tests. Methods for non-stationary panels, including panel unit root and panel cointegration tests, have been gaining increased acceptance in recent empirical research. To date, however, tests for the null of no cointegration in heterogeneous panels based on Pedroni (1995, 1997a) have been limited to simple bivariate examples, in large part due to the lack of critical values available for more complex multivariate regressions. The purpose of this paper is to fill this gap by describing a method to implement tests for the null of no cointegration for the case with multiple regressors and to provide appropriate critical values for these cases. The tests allow for considerable heterogeneity among individual members of the panel, including heterogeneity in both the long-run cointegrating vectors as well as heterogeneity in the dynamics associated with short-run deviations from these cointegrating vectors.

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4,221 citations