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Ecological footprint, urbanization, and energy consumption in South Africa: including the excluded

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TLDR
The study explores the relationship between ecological footprint, urbanization, and energy consumption by applying the ARDL estimation technique on data spanning 1965–2014 for South Africa to support the energy-led growth hypothesis and confirms the long-run findings are robust.
Abstract
The study explores the relationship between ecological footprint, urbanization, and energy consumption by applying the ARDL estimation technique on data spanning 1965–2014 for South Africa. After applying the unit root test that accounts for a break in the data, the Bayer and Hanck (J Time Ser Anal 34:83–95, 2013) combined cointegration test affirms cointegrating relationship among the variables. Findings further reveal that economic growth and financial development exact a deteriorating impact on the environment in the short run. However, the same was not true for both energy use and urbanization. While urbanization and energy use promote environmental quality in the long run, financial development and economic growth degrade it further. The long-run findings of our study are confirmed to be robust as reported by the fully modified OLS (FMOLS), dynamic OLS (DOLS), and the canonical cointegrating regression (CCR) estimates. The direction of causality supports the energy-led growth hypothesis for South Africa. Policy outcomes and directions, and the possibility of promoting sustainable growth without degrading the environment are discussed.

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Consumption-based carbon emissions and International trade in G7 countries: The role of Environmental innovation and Renewable energy

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Age-Structure, Urbanization, and Climate Change in Developed Countries: Revisiting STIRPAT for Disaggregated Population and Consumption-Related Environmental Impacts

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Modelling the dynamic linkages between eco-innovation, urbanization, economic growth and ecological footprints for G7 countries: Does financial globalization matter?

TL;DR: In this paper, the effects of financial globalization, urbanization, eco-innovation, and economic growth on the ecological footprints of the G7 countries using annual frequency data spanning from 1980 to 2016, several latest econometric methods, that are robust to handling cross-sectionally dependent panel datasets, are employed to ascertain the environmental impacts of these variables.
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Dynamic relationship between technological innovations, financial development, renewable energy, and ecological footprint: fresh insights based on the STIRPAT model for Asia Pacific Economic Cooperation countries.

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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.
Journal ArticleDOI

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.
Journal ArticleDOI

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.

Estimation and hypothesis testing of cointegration vectors in Gaussian vector autoregressive models / Søren Johansen

S Johansen
TL;DR: In this paper, the authors present the likelihood methods for the analysis of cointegration in VAR models with Gaussian errors, seasonal dummies, and constant terms, and show that the asymptotic distribution of the maximum likelihood estimator is mixed Gausssian.
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