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Renewable and non-renewable energy consumption-growth nexus: Evidence from a panel error correction model

TLDR
In this paper, the authors examined the relationship between renewable and non-renewable energy consumption and economic growth for 80 countries within a multivariate panel framework over the period 1990-2007.
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This article is published in Energy Economics.The article was published on 2012-05-01. It has received 661 citations till now. The article focuses on the topics: Energy consumption & Energy source.

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

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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How economic growth, renewable electricity and natural resources contribute to CO2 emissions?

TL;DR: This article explored the relationship between economic growth and CO2 emissions in the so-called European Union 5 (EU-5) countries (Germany, France, Italy, Spain, and the United Kingdom) for the 1985-2016 period.
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Determinants of CO2 emissions in the European Union: The role of renewable and non-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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The influence of real output, renewable and non-renewable energy, trade and financial development on carbon emissions in the top renewable energy countries

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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Renewable, non-renewable energy consumption, economic growth, trade openness and ecological footprint: Evidence from organisation for economic Co-operation and development countries

TL;DR: In this paper, the authors examined the validity of Environmental Kuznets Curve hypothesis for ecological footprint with the role of renewable energy use, non-renewable energy use and trade openness in 24 Organisation for Economic Co-operation and Development countries.
References
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Journal ArticleDOI

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

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

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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A Comparative Study of Unit Root Tests with Panel Data and a New Simple Test

TL;DR: The Im-Pesaran-Shin (IPS) test as discussed by the authors relaxes the restrictive assumption of the LL test and is best viewed as a test for summarizing the evidence from independent tests of the sample hypothesis.
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Pooled Mean Group Estimation of Dynamic Heterogeneous Panels

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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Critical Values for Cointegration Tests in Heterogeneous Panels with Multiple Regressors

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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