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Does trade matter for carbon emissions in OECD countries? Evidence from a new trade openness measure

Abstract

This paper analyzes the impacts of the per capita income, the per capita energy consumption, and the trade openness on the level of per capita carbon emissions in the panel dataset of 35 Organization for Economic Cooperation and Development (OECD) countries over the period 1960–2013. Along with the nominal trade openness, the paper uses a different trade openness measure, so called as the “trade potential index” (TPI). To the best of our knowledge, this is the first paper that uses the TPI in the empirical environmental Kuznets curve (EKC) hypothesis literature. The paper finds that the EKC hypothesis is valid and there is an “inverted-U” relationship between the income and the carbon emissions. In addition, the paper observes that there is a positive effect of the energy consumption on the carbon emissions. Furthermore, the results indicate that both trade openness measures are negatively associated with the carbon emissions in the OECD countries in the long run.

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Notes

  1. 1.

    In addition, carbon emission policy can also affect the market price (see e.g., Apergis and Lau 2015).

  2. 2.

    In the first phase of development, the ultimate goal of the countries is to increase the level of development; therefore, environmental quality has a secondary priority in this process.

  3. 3.

    Achieving a high per capita income alone does not result in a reduction of carbon emissions. In other words, policymakers need to implement policies to reduce environmental pollution (Dinda 2004).

  4. 4.

    It should be noted that besides the trade openness, many new variables are used in the empirical literature in the EKC hypothesis to analyze the effect of international trade on carbon emissions (e.g., the economic complexity indicator (Can and Gozgor 2017), the export diversification (Gozgor and Can 2016), and the export quality (Gozgor and Can 2017).

  5. 5.

    At this stage, a technological process can be defined as a process that decreases production costs or causes new products to appear.

  6. 6.

    In terms of developing countries, the effect of trade openness on carbon emissions may be negative or positive. This depends on the production structure of the exported and the imported products.

  7. 7.

    In this group, there are also the papers to use the time-series techniques for analyzing the direct effect of income on carbon emissions both in the developing countries and the developed economies (see e.g., Acaravci and Ozturk 2010).

  8. 8.

    As we discussed in the introduction, there are also several studies to neglect the square per capita GDP in the empirical analysis due to a possible multicollinearity between the per capita GDP and the square of per capita GDP (e.g., Narayan et al. 2016). This “new” approach is also considered in our empirical analysis.

  9. 9.

    In this group of studies, the explanatory variables mostly consist of the per capita GDP, the square per capita GDP, and the per capita energy consumption. Similarly, following the new approach of Narayan and Narayan (2010), there are also several papers to neglect the square per capita GDP in the empirical analysis with controlling the energy consumption (see e.g., Arouri et al. 2012). See also Al-Mulali and Ozturk (2016) for a review of the related literature.

  10. 10.

    There are also second group of studies to use the time-series techniques for analyzing the dynamic relationships among carbon emissions, energy consumption, and per capita GDP (see e.g., Ang 2007).

  11. 11.

    It is worthy of note that according to another branch of the empirical EKC literature, different data measurements in an extra control variable(s) can create biased results in favor of the validity of the EKC hypothesis (see Kaika and Zervas 2013).

  12. 12.

    Most papers in this group consider the time-series techniques for analyzing the dynamic relationships among carbon emissions, energy consumption, per capita GDP (income), and trade openness both in developing countries and developed economies (see e.g., Dogan and Turkekul 2016 in the United States (US), Farhani and Ozturk (2015) in Tunisia; Halicioglu (2009) in Turkey; Jayanthakumaran et al. (2012) in China and India; Kohler (2013) in South Africa; Ling et al. (2015) in Malaysia; Nasir and Rehman (2011) in Pakistan; Onafowora and Owoye (2014) in Brazil, China, Egypt, Japan, Korea Republic, Mexico, Nigeria, and South Africa; Shahbaz et al. (2013) in Indonesia; Shahbaz et al. (2014) in Tunisia; and Zerbo (2017) in 14 Sub-Saharan African countries). For the details of these papers, for example, see Al-Mulali and Ozturk (2016).

  13. 13.

    As a robustness check, we control the square of log per capita GDP both in the constant and the current prices.

  14. 14.

    This trade openness measure will create a biased result if the share of imports in GDP is > 1. Therefore, following Waugh and Ravikumar (2016), we exclude the data in which the share of imports in GDP is > 1.

  15. 15.

    According to Waugh (2010), the output-based measure of GDP is more appropriate than the expenditure-based measure for calculating the trade openness. See Waugh (2010) and Waugh and Ravikumar (2016) for a detailed explanation.

  16. 16.

    Simonovska and Waugh (2014) review the literature on the plausible value of the trade elasticity and observe that trade elasticity ranges from 3 to 5. See Simonovska and Waugh (2014) for a detailed explanation of the trade elasticity.

  17. 17.

    Since the TPI is basically based on the imports, we interpret the EKC hypothesis with the import side. However, the nominal trade openness measure considers both exports and imports. According to the exporting interpretation of the EKC hypothesis, an export basket contains of pollution-intensive products at the first stage of economic development; however, as a country’s per capita income increases, it begins to reduce the share of such products. Therefore, we should expect a suppressing effect of international trade on environmental degradation in the OECD countries.

  18. 18.

    Note that 4.81% = 100 × (e 0.047 − 1).

  19. 19.

    Note that 0.577 = 0.387/0.67.

  20. 20.

    Note that 2.77% = 4.81% × 0.577.

  21. 21.

    Note that 8.01% = 100 × (e 0.077 − 1).

  22. 22.

    Note that 0.309 = 0.043/0.139.

  23. 23.

    Note that 2.47% = 8.01% × 0.309.

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Acknowledgements

We would like to express our gratitude to three anonymous reviewers for their valuable comments and suggestions, which substantially improved the paper.

Author information

Correspondence to Giray Gozgor.

Additional information

Responsible editor: Philippe Garrigues

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Gozgor, G. Does trade matter for carbon emissions in OECD countries? Evidence from a new trade openness measure. Environ Sci Pollut Res 24, 27813–27821 (2017). https://doi.org/10.1007/s11356-017-0361-z

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Keywords

  • Carbon emissions
  • EKC hypothesis
  • Trade openness
  • International trade
  • Trade potential index
  • Panel data estimation techniques

JEL classifications

  • F18
  • O13
  • C33