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Impacts of urbanization and industrialization on energy consumption/CO2 emissions: Does the level of development matter?

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TLDR
In this article, the authors adopted the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) framework as a starting point and re-estimated the relationship using different panel date models.
Abstract
Urbanization and industrialization have significant impacts on energy consumption and CO2 emissions, but their relationship varies at different stages of economic development. Taking cognizance of heterogeneity and the “ratchet effect,” this paper adopts the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) framework as a starting point and re-estimates the relationship using different panel date models. The main results are obtained by dynamic panel threshold regression models, which divide a balanced panel dataset of 73 countries over the period of 1971–2010 into four groups according to their annual income levels. The key results are: (1) in the low-income group, urbanization decreases energy consumption but increases CO2 emissions; (2) in the middle-/low-income and high-income groups, industrialization decreases energy consumption but increases CO2 emissions, while urbanization significantly increases both energy consumption and CO2 emissions; (3) for the middle-/high-income group, urbanization does not significantly affect energy consumption, but does hinder the growth of emissions; while industrialization was found to have an insignificant impact on energy consumption and CO2 emissions; (4) from the population perspective, it produces positive effects on energy consumption, and also increases emissions except for the high-income group. These novel methodology and findings reveal that different development strategies of urbanization and industrialization should be pursued depending on the levels of income in a bid to conserve energy and reduce emissions.

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References
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Book

Econometric Analysis of Cross Section and Panel Data

TL;DR: This is the essential companion to Jeffrey Wooldridge's widely-used graduate text Econometric Analysis of Cross Section and Panel Data (MIT Press, 2001).
Journal ArticleDOI

Another look at the instrumental variable estimation of error-components models

TL;DR: In this paper, a framework for efficient IV estimators of random effects models with information in levels which can accommodate predetermined variables is presented. But the authors do not consider models with predetermined variables that have constant correlation with the effects.
Journal ArticleDOI

Testing for unit roots in heterogeneous panels

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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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.
Journal ArticleDOI

A Simple Panel Unit Root Test in the Presence of Cross Section Dependence

TL;DR: In this paper, a simple alternative test where the standard unit root regressions are augmented with the cross section averages of lagged levels and first-differences of the individual series is also considered.
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