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Factors Affecting Carbon Emissons in the G7 and BRICS Countries: Evidence from Quantile Regression

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Integrated Uncertainty in Knowledge Modelling and Decision Making (IUKM 2019)

Abstract

Over the last three decades, the relationship between growth, foreign direct investment and carbon emissions has become an important issue among environmental economists. The target of this study is to investigate the impact of economic growth and foreign direct investment on carbon emissions in order to provide environmental improvement suggestions. This study tests the validity of the Environmental Kuznets Curve (EKC) hypothesis, including G7 countries (Canada, United State, United Kingdom, Japan, France, Germany, Italy) and BRICS countries (Brazil, Russia, India, China and South Africa) for the period of 1992–2014. We adopt a panel quantile regression model that takes unobserved individual heterogeneity and distributional heterogeneity into consideration. Moreover, to avoid an omitted variable bias, certain related control variables are included in our model. Firstly, our empirical results show that the effect of the independent variables on carbon emissions is heterogeneous across quantiles. Secondly, regarding the impact of FDI on carbon emissions, we find that these results support the pollution halo theory in G7 countries and support the pollution haven hypothesis in BRICS countries. Thirdly, the empirical findings are in support of inverted U-shaped curve of EKC in G7 countries. Finally, the results of the study also provide policymakers with important policy recommendations. In addition, our findings suggest carbon emissions control measures should be tailored differently across low-emissions and high-emissions nations.

Supported by Puey Ungphakorn Center of Excellence in Econometrics, Faculty of Economics, Chiang Mai University.

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Correspondence to Jirakom Sirisrisakulchai .

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Zhou, Y., Sirisrisakulchai, J., Liu, J., Sriboonchitta, S. (2019). Factors Affecting Carbon Emissons in the G7 and BRICS Countries: Evidence from Quantile Regression. In: Seki, H., Nguyen, C., Huynh, VN., Inuiguchi, M. (eds) Integrated Uncertainty in Knowledge Modelling and Decision Making. IUKM 2019. Lecture Notes in Computer Science(), vol 11471. Springer, Cham. https://doi.org/10.1007/978-3-030-14815-7_34

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  • DOI: https://doi.org/10.1007/978-3-030-14815-7_34

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-14814-0

  • Online ISBN: 978-3-030-14815-7

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