Journal ArticleDOI
Energy consumption and economic growth in China: A multivariate causality test
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TLDR
In this paper, a multivariate causality framework was proposed by incorporating capital and labor variables into the model between energy consumption and economic growth based on neo-classical aggregate production theory, and a long run equilibrium cointegration relationship was found between economic growth and the explanatory variables: energy consumption, capital and employment.About:
This article is published in Energy Policy.The article was published on 2011-07-01. It has received 167 citations till now. The article focuses on the topics: Energy consumption & Econometric model.read more
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The dynamic links between energy consumption, economic growth, financial development and trade in China: Fresh evidence from multivariate framework analysis
TL;DR: In this paper, the authors investigated the relationship between energy use and economic growth by incorporating financial development, international trade and capital as important factors of production function in case of China over the period of 1971-2011.
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The relationship between economic growth, energy consumption, and CO2 emissions: Empirical evidence from China.
TL;DR: The results of cointegration tests suggest the existence of long-run cointegrating relationship among the variables, albeit with short dynamic adjustment mechanisms, indicating that the proportion of disequilibrium errors that can be adjusted in the next period will account for only a fraction of the changes.
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CO2 emissions, energy consumption, economic growth, and financial development in GCC countries: Dynamic simultaneous equation models
TL;DR: In this article, the authors investigated the causes of carbon emissions by taking into account the role of financial development and economic growth in GCC countries, and found a long-run unidirectional causality running from carbon emissions to energy use in the case of Saudi Arabia, UAE, and Qatar.
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Literature survey on the relationships between energy, environment and economic growth
Sofien Tiba,Anis Omri,Anis Omri +2 more
TL;DR: In this paper, an exhaustive survey of the literature related to the energy-environment-growth nexus at the individual and regional scale studies covering the period from 1978 to 2014 is presented, which is based on the causality direction among energy use variables (electricity, nuclear, renewable and non-renewable) and output growth; between economic growth and environment; and between the three variables at the same time.
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The energy consumption and economic growth nexus in top ten energy-consuming countries: Fresh evidence from using the quantile-on-quantile approach
TL;DR: In this article, the authors empirically examined the interlinkages between energy consumption and economic growth in top ten energy-consuming countries i.e. China, the USA, Russia, India, Japan, Canada, Germany, Brazil, France and South Korea.
References
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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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Testing for a Unit Root in Time Series Regression
TL;DR: In this article, the authors proposed new tests for detecting the presence of a unit root in quite general time series models, which accommodate models with a fitted drift and a time trend so that they may be used to discriminate between unit root nonstationarity and stationarity about a deterministic trend.
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Statistical analysis of cointegration vectors
TL;DR: In this paper, the authors consider a nonstationary vector autoregressive process which is integrated of order 1, and generated by i.i.d. Gaussian errors, and derive the maximum likelihood estimator of the space of cointegration vectors and the likelihood ratio test of the hypothesis that it has a given number of dimensions.
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Bounds testing approaches to the analysis of level relationships
TL;DR: In this paper, the authors developed a new approach to the problem of testing the existence of a level relationship between a dependent variable and a set of regressors, when it is not known with certainty whether the underlying regressors are trend- or first-difference stationary.