A panel data heterogeneous Bayesian estimation of environmental Kuznets curves for CO<sub>2</sub>emissions

Musolesi, Antonio; Mazzanti, Massimiliano; Zoboli, Roberto

doi:10.1080/00036840701858034

Cited by 92 publications

(49 citation statements)

References 43 publications

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“…The results therefore do not confirm an EKC for the OECD countries as a whole, although such an outcome does not mean that countries can not have individual EKCs, such as in France (Iwata et al,2010). Hence these outcomes counter the findings of an EKC for CO2 in such studies as Cole (2005), Galeotti et al (2006), and Schmalensee et al (1998) but verify Galeotti (2007 as well as Musolesi and Mazzanti (2010). The implication is that people and governments become more willing to accept higherCO2emissions at very high income levels particularly because they desire an elevated standard of living or they want to emphasize continual economic growth above the environment, as seen by the refusal of the United States to sign the Kyoto Accord.…”

Section: Empirical Modelcontrasting

confidence: 52%

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An Analysis of the Environmental Kuznets Curve for Carbon Dioxide Emissions: Evidence for OECD and Non-OECD Countries

2015

EJSD

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Over the past two decades, researchers have sought to establish empirical evidence for an Environmental Kuznets Curve (EKC) for carbon dioxide (CO2), with varied results. This study builds on that research to re-evaluate whether the EKC exists for CO2 emissions, using an improved dataset and the enhanced econometric technique Arellano-Bover/Blundell-Bond Generalized Methods of Moments (GMM) estimator. We directly compare OECD countries with countries of the non-OECD regions of Latin America, Asia, and Africa to determine how various factors like economic growth, population, trade, urbanization, and energy use influence CO2 emissions. We find that the OECD countries have an N-shaped curve with income growth whereas the regions of Asia and Africa experience an income-based EKC pattern. The results further reveal that population growth has a mixed impact on CO2emissions,increased trade and urbanization contribute to CO2 emissions for most areas, and increased energy use actually helps to decrease CO2 emissions.

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Section: Empirical Modelcontrasting

confidence: 52%

“…Other research revealed that CO2 emissions occur with an N-shaped curve, with emissions declining after a country reaches high economic development but then increasing again at even higher income levels (Galeotti, 2007;Musolesi and Mazzanti, 2010).…”

Section: Selected Literature Reviewmentioning

confidence: 99%

An Analysis of the Environmental Kuznets Curve for Carbon Dioxide Emissions: Evidence for OECD and Non-OECD Countries

2015

EJSD

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“…This constitutes their famous "relinking hypothesis": in the long run, environmental pressure and economic growth perform an N-shape curve, not an inverted U-shape curve. A large number of empirical studies have subsequently verified this hypothesis [54,55]. In relation to the present study, we can provide a dual explanation for the way in which the relationship between CO 2 emission and economic level takes the form of an N-shape curve in China in the period 1995-2011.…”

Section: Factors Influencing Co 2 Emissionsmentioning

confidence: 86%

“…This study therefore introduces a traditional cubic term into the STIRPAT model, called the "CKC relink effect." A number of previous studies have in fact found the cubic term to be more effective portraying the relationship between economic growth and CO 2 emissions [53][54][55]. The extended STIRPAT model can thus be established as follows:…”

Section: Stirpat Modelmentioning

confidence: 99%

Spatiotemporal variations of energy-related CO 2 emissions in China and its influencing factors: An empirical analysis based on provincial panel data

Wang

Fang

Wang

2016

Renewable and Sustainable Energy Reviews

223

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This paper examines carbon dioxide (CO 2) emissions from the perspective of energy consumption, detailing an empirical investigation into the spatiotemporal variations and impact factors of energy-related CO 2 emissions in China. The study, which is based on a provincial panel data set for the period 1995-2011, used an extended STIRPAT model, which was in turn examined using System-Generalized Method of Moments (Sys-GMM) regression. Results indicate that while per capita CO 2 emissions in China were characterized by conspicuous regional imbalances during the period studied, regional inequality and spatial autocorrelation (agglomeration) both decreased gradually between 1995 and 2011, and the pattern evolutions of emissions evidenced a clear path dependency effect. The urbanization level was found to be the most important driving impact factor of CO 2 emissions, followed by economic level and industry proportion. Conversely, tertiary industry proportion constituted the main inhibiting factor among the negative influencing factors, which also included technology level, energy consumption structure, energy intensity, and tertiary industry proportion. Importantly, the study revealed that the CO 2 Kuznets Curve (CKC), which describes the relation between CO 2 emissions and economic growth, in fact took the form of N-shape in the medium-and long-term, rather than the classical inverted-U shape of the environmental Kuznets Curve (EKC). Specifically, an additional inflection appeared after the U-shape relationship between economic growth and CO 2 emissions, indicating the emergence of a relink phase between the two variables. The findings of this study have important implications for policy makers and urban planners: alongside steps to improve the technology level, accelerate the development of tertiary industry, and boost recycling and renewable energies, the optimization of a country's energy structure that can in fact reduce reliance on fossil energy resources and constitute an effective measure to reduce CO 2 emissions.

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“…Martínez-Zarzoso and Bengochea-Morancho (2004) applied the pooled mean group estimation method for regressing the short-term and long-term effect of CO 2 emissions on the economic growth. Musolesi et al (2010) used Bayesian method to estimate the relationship between economic growth and CO 2 emissions based on 109 countries data during 1951-2001. Aslanidis and Xepapadeas (2006) employed the panel smooth transformation model for studying the relationship between the economic growth and the pollution emissions.…”

Section: Literature Reviewmentioning

confidence: 99%

The coordination between China’s economic growth and environmental emission from the Environmental Kuznets Curve viewpoint

Xia

et al. 2016

Nat Hazards

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A panel data heterogeneous Bayesian estimation of environmental Kuznets curves for CO₂emissions

Cited by 92 publications

References 43 publications

An Analysis of the Environmental Kuznets Curve for Carbon Dioxide Emissions: Evidence for OECD and Non-OECD Countries

An Analysis of the Environmental Kuznets Curve for Carbon Dioxide Emissions: Evidence for OECD and Non-OECD Countries

Spatiotemporal variations of energy-related CO 2 emissions in China and its influencing factors: An empirical analysis based on provincial panel data

The coordination between China’s economic growth and environmental emission from the Environmental Kuznets Curve viewpoint

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A panel data heterogeneous Bayesian estimation of environmental Kuznets curves for CO2emissions

Cited by 92 publications

References 43 publications

An Analysis of the Environmental Kuznets Curve for Carbon Dioxide Emissions: Evidence for OECD and Non-OECD Countries

An Analysis of the Environmental Kuznets Curve for Carbon Dioxide Emissions: Evidence for OECD and Non-OECD Countries

Spatiotemporal variations of energy-related CO 2 emissions in China and its influencing factors: An empirical analysis based on provincial panel data

The coordination between China’s economic growth and environmental emission from the Environmental Kuznets Curve viewpoint

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A panel data heterogeneous Bayesian estimation of environmental Kuznets curves for CO₂emissions