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Effects of the export product quality on carbon dioxide emissions: evidence from developing economies

Environmental Science and Pollution Research volume 26, pages 12181–12193 (2019)Cite this article

Abstract

Using the new measure of the export quality of the International Monetary Fund (IMF), this paper investigates the effects of the product quality of exports on the growth rate of the per capita carbon dioxide emissions. The paper focuses on the panel dataset of 82 developing economies for the period from 1970 to 2014. Along with the index of export quality, we also consider the measures of the per capita income, per capita energy consumption, natural resource rents, and trade openness. The results indicate that there is the positive impact of the quality of exports on carbon dioxide emissions. There is also the positive relationship between the per capita income and carbon dioxide emissions. In addition, we find that the trade openness measures are positively related to carbon dioxide emissions. These results are robust to consider different income measures and to divide the developing economies, according to their income levels.

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Notes

  1. In other words, the possible effects of political and social variables on environmental pollution remained in the background in the literature.

  2. In here, the first stage of economic development has been defined as exceeding the lower-middle-income level. According to the data from the World Bank (2018), the threshold of exceeding the lower-middle-income is 3985$ in per capita Gross National Income (GNI) in 2017.

  3. For instance, Shahzad et al. (2017) observe that there is a negative impact of the energy consumption on the carbon dioxide emissions in Pakistan for the period from 1971 to 2011.

  4. In here, technology can be defined as progress for decreasing the costs of production.

  5. Of course, international trade indicators (export quality) can negatively affect the environmental degradation (carbon dioxide emissions) in specific developing economies. International trade indicators should negatively relate to environmental degradation pollution (carbon dioxide emissions) in the developed economies (Gozgor 2017). The issue depends on the product composition of the exports and/or the imports baskets of a developing- or developed economy.

  6. According to Henn et al. (2017), the upgrading process of export quality has increased rapidly until the income per capita was $10,000 and then slowed down. When the level of income per capita reaches $20,000, the level quality upgrading should almost be completed.

  7. Feenstra and Romalis (2014), Hallak and Schott (2011), and Khandelwal (2010) consider the trade costs with the unit price in measuring the export product quality.

  8. The papers have usually used the classical trade openness measure (aka nominal trade openness), which is calculated as the exports plus imports divided by nominal GDP.

  9. At this stage, there is only paper in the literature (Gozgor and Can 2017) that uses the index of export quality of Henn et al. (2017). However, Gozgor and Can (2017) look at the impact of the index of export quality on the carbon dioxide emissions in China. We need to emphasize that China is the special country since it has upgraded the export basket since the early 1980s. Our paper focuses on the panel dataset of developing economies, whose characteristics are quite different than China.

  10. Refer to the recent literature review of Al-Mulali and Ozturk (2016) for the details of the papers.

  11. It is important to note that time-series analysis is also used for the determinants of carbon dioxide emissions (see, e.g., Farhani and Ozturk 2015; Shahbaz et al. 2013, 2014; Zerbo 2017). Refer to the recent literature review of Al-Mulali and Ozturk (2016) for the details of the papers. There are also recent papers, which have used the trade openness as the potential driver of the carbon dioxide emissions (see, e.g., Ozcan et al. 2018; Zhang et al. 2017; Zhang 2018).

  12. In here, the income definition is based on the World Bank’s Country and Lending Groups database in the fiscal year of 2018.

  13. The five-year non-overlapping periods are as follows: 1970–74, 75–79, 80–84, 85–89, 90–94, 95–99, 2000–04, 05–09, and 10–14. We use the five-year non-overlapping periods to model the long-run effect of the export product quality on the level of carbon dioxide emissions.

  14. We consider the per capita income at both the current US$ price and the constant US$ price (the benchmark year is 2010) to check the robustness of the findings, according to the different definitions of the income.

  15. For details of the data, visit (https://data.imf.org/?sk=3567E911-4282-4427-98F9-2B8A6F83C3B6).

  16. All of the right-side variables are based on the initial condition. We use the first year of observation in any five-year non-overlapping period.

  17. Note that 55.02% = 100∗(e0.44 − 1).

  18. See Table 1.

  19. Note that 11.66% = 55.02% * 0.212.

  20. Note that 42.1% = 100∗(e0.35 − 1).

  21. Note that 8.93% = 42.1% * 0.212.

  22. Note that 55.27% = 100∗(e0.44 − 1).

  23. Note that 99.37% = 100∗(e0.69 − 1).

  24. Note that 11.72% = 55.27% * 0.212.

  25. Note that 21.07% = 99.37% * 0.212.

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Funding

The first author was financially supported by the National Social Science Fund of China (16BJY052).

Author information

Authors and Affiliations

  1. Zhejiang University of Technology, Hangzhou, China and Chinese Academy of Social Sciences, Beijing, China

    Jianchun Fang

  2. Istanbul Medeniyet University, Istanbul, Turkey

    Giray Gozgor

  3. Tianjin University of Commerce, Tianjin, China

    Zhou Lu

  4. School of Economics, Zhejiang University of Technology, Hangzhou, China

    Wanshan Wu

  5. School of Economics, Zhejiang University of Technology, Hangzhou, China

    Wanshan Wu

Authors
  1. Jianchun Fang
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  2. Giray Gozgor
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  3. Zhou Lu
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  4. Wanshan Wu
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Corresponding authors

Correspondence to Zhou Lu or Wanshan Wu.

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Responsible editor: Philippe Garrigues

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Appendices

Appendix 1

Table 8 Summary of the literature review
Full size table

Appendix 2 Countries in the Panel Dataset

38 Lower Middle-income Economies (are those with the Gross National Income (GNI) per capita between $996 and $3895): Angola, Bangladesh,

Bolivia, Cabo Verde, Cambodia, Cameroon, Congo Republic, Cote d’Ivoire, Djibouti, Egypt, El Salvador, Georgia, Ghana, Honduras, India, Indonesia, Kenya, Kyrgyz Republic, Laos, Lesotho, Mauritania, Moldova, Mongolia, Morocco, Myanmar, Nepal, Nicaragua, Nigeria, Pakistan, the Philippines, Sao Tome and Principe, Sri Lanka, Sudan, Tunisia, Ukraine, Uzbekistan, Vietnam, and Zambia.

44 Upper Middle-income Economies (are those with the GNI per capita between $3896 and $12,055): Albania, Algeria, Armenia, Azerbaijan, Belarus, Bosnia and Herzegovina, Botswana, Brazil, Bulgaria, China, Colombia, Costa Rica, Dominica, the Dominican Republic, Ecuador, Equatorial Guinea, Fiji, Gabon, Grenada, Guatemala, Iran, Iraq, Jamaica, Jordan, Kazakhstan, Lebanon, Macedonia, Malaysia, Maldives, Mauritius, Mexico, Namibia, Paraguay, Peru, Romania, Russia, South Africa, St. Lucia, St. Vincent and the Grenadines, Suriname, Thailand, Turkey, Turkmenistan, and Venezuela.

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Fang, J., Gozgor, G., Lu, Z. et al. Effects of the export product quality on carbon dioxide emissions: evidence from developing economies. Environ Sci Pollut Res 26, 12181–12193 (2019). https://doi.org/10.1007/s11356-019-04513-7

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  • DOI: https://doi.org/10.1007/s11356-019-04513-7

Keywords

  • Carbon dioxide emissions
  • Export quality
  • Upgrading the export basket
  • Trade openness
  • International trade
  • Panel data estimation techniques

JEL classification codes

  • F18
  • F64
  • O13
  • C33