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Investigating the non-linearity between national income and environmental pollution: international evidence of Kuznets curve

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Abstract

This paper analyzes how national income (per capita real GDP) influences the environmental pollution (per capita CO2 emissions) using a very heterogenous sample composed by 120 countries during the 2000–2009 period. We first apply a panel unit root test suggested by Im et al. (J Econometr 115(1):53–74, 2003) to examine the stationarity properties of CO2 emissions and GDP and then a two-step generalized method of moments (GMM) estimator, paying particular attention to the non-linearity of the national income–environmental pollution relationship, to investigate the existence of a Kuznets curve for CO2 emissions. Preliminary evidence showing the existence of an inverted U-shaped relationship between national income and environmental pollution, validating the Kuznets’s hypothesis, turned out to be measleading once the issue of (non) stationarity has been taken into account. Results also show that as population and industrial output expand, more pressure will be put forth the environment, leading to more emissions, calling for more strict environmental and energy conservation policies.

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Notes

  1. Kuznets (1955) predicted that the changing relationship between per capita income and income inequality is an inverted U-shaped curve. As per capita income increases, income inequality also increases at first and then starts declining after a turning point. This relationship between per capita income and income inequality can be represented by a bell-shaped curve. This observed phenomenon is described as the Kuznets curve (KC). In the 1990s and onwards, the KC took on a new existence. There is evidence that the level of environmental degradation and income per capita follows the same inverted U-shaped relationship as does income inequality and income per capita in the original KC. As a result, the Kuznets curve has become a tool for describing the relationship between the measured levels of environmental quality indicators such as CO2 and income per capita.

  2. Although many studies attempt to test the EKC hypothesis, the mixed empirical evidence is also due to the fact that the EKC concept is open to criticism in various directions such as the normal distribution of income (Stern et al. 1996), the fact that causality may run from income to environmental degradation (Arrow et al. 1995), the presence of different outcomes depending on the pollutant in question (Arrow et al. 1995; Lieb 2003), the methodology used in empirical EKC studies, like the use of panel data instead of time-series data (List and Gallet 1999) and various econometric problems in estimation (Müller-Fürstenberger and Wagner 2007). For a complete review of the major features and critiques behind the EKC concept, see Kaika and Zervas (2013a, b).

  3. Electric power consumption measures the production of power plants and combined heat and power plants less transmission, distribution, and transformation losses and own use by heat and power plants. The trade freedom score is based on two inputs: The trade-weighted average tariff rate; non-tariff barriers (NTBs). Weighted average tariffs is a purely quantitative measure and accounts for the basic calculation of the score. The presence of NTBs in a country affects its trade freedom score by incurring a penalty of up to 20% points, or one-fifth of the maximum score. The country's trade freedom ranges between 0 and 100, where 100 represents the maximum degree of trade freedom. Urban population (Source: World Bank) refers to people living in urban areas as defined by national statistical offices. It is calculated using World Bank population estimates and urban ratios from the United Nations World Urbanization Prospects.

  4. The GMM technique allows to overcome an important econometric issue due to the omitted variable bias (Stern 2004) which concerns three sub-subjects: differences between the parameters of the random-effects and fixed-effects models (using a Hausman test), differences between the estimated coefficients in different sub-samples, and the tests for serial correlation.

  5. The early EKC estimations involve potentially non-stationary variables which must satisfy the cointegration property; otherwise, regressions may be “spurious” (Aslanidis and Iranzo 2009). In fact, the GDP series alone, is a non-stationary variable (I(1) process). The presence of non-stationary series invalidates the use of standard unit root tests and cointegration techniques in a time-series or a panel context, so any findings obtained in such studies are highly questionable (Müller-Fürstenberger and Wagner 2007). If unit root tests indicate a unit root in each series, then all series should be integrated of the same order. Lee and Lee (2009) estimate that the series of real GDP and CO2 emissions are a mixture of stationary and non-stationary series, so panel root tests can lead to misleading inferences, while cointegration analysis is, perhaps, an inappropriate method (Lee and Lee 2009).

  6. All the empirical tests and estimations have been performed using the STATA 13 software.

  7. The sub-samples are constructed following the OECD definition.

  8. Lind and Mehlum (2010) test for the following hypotheses: H1: inverse U shape; H0: monotone or U shape.

  9. Results without the use of square of per capita GDP are available on request.

  10. For the sake of brevity, the results are not reported and are availble on request.

  11. Among others, the studies by Lindmark (2002), Cole (2004), Jalil and Mahmud (2009) and Narayan and Narayan (2010) support the EKC hypothesis. Mixed evidence has been found by Cole et al. (1997), Coondoo and Dinda (2008), Lee et al. (2009) and Dutt (2009).

  12. Another option would have been running the analysis either on each region (i.e., only on REG1, REG2, and so on) or on each income cluster (i.e., only on INC1, ONC2, and so on) to better explore differences among countries located in different areas and among high, middle, and low income countries, but the observations were not enough to perform the GMM estimation correctly.

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  1. Department of Economics and Statistics, University of Salerno, Via Giovanni Paolo II, 132, 84084, Fisciano, SA, Italy

    Cristian Barra & Roberto Zotti

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  1. Cristian Barra
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  2. Roberto Zotti
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Correspondence to Cristian Barra.

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Barra, C., Zotti, R. Investigating the non-linearity between national income and environmental pollution: international evidence of Kuznets curve. Environ Econ Policy Stud 20, 179–210 (2018). https://doi.org/10.1007/s10018-017-0189-2

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