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Trade Openness and Environmental Emissions: Evidence from a Meta-Analysis

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Abstract

How trade affects environmental emissions has generated heterogeneous results over the years. This is due to empirical ambiguities that are endemic in the literature. In order to evaluate and explain the discrepancy in the literature, this paper conducts a meta-analysis of 88 empirical studies published until 2018. Our results show that trade contributes to environmental emissions after controlling for publication bias and heterogeneity. In explaining the heterogeneous results across the primary studies, our findings largely suggest the estimated elasticities depend systematically on the estimation characteristics, the choice of pollutants and the publication characteristics of the primary studies. Accounting for heterogeneity, the result remains robust only for CO2 emissions compared to SO2. Overall, the trade elasticity of emissions effect remains robust when we decompose the analyses for different groups of countries, however, the emissions-content of trade is more pronounced for developed compared to developing countries.

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Notes

  1. According to the World Bank, world trade as a percentage of GDP has more than doubled over five decades, rising from 24 percent in 1960 to about 50 percent in 2016. Similarly, trends in global greenhouse gas (GhG) emissions indicate that GhG emissions are about 55% higher than in 1990 and 40% higher than in 2000 (Olivier et al. 2017)

  2. https://www.theatlantic.com/business/archive/2014/01/the-dark-side-of-globalization-why-seattles-1999-protesters-were-right/282831/, accessed on 1 January 2020

  3. Although not reported, our results remains robust when we decompose the results according to these different variants

  4. So we excluded four studies on the basis of the Hadi (1994) approach namely; Dogan and Seker (2016), Kearsley and Riddel (2010), Onoja et al. (2014) and Opoku et al. (2014).

  5. We extend Eqn. 4 to account for data dependence using a two-level model: \(t_{ij}= \beta _{1}+ \beta _0\frac{1}{SE_{ij}}+ \frac{\alpha _{kij} X_{kij}}{SE_{ij}} +\varsigma _j+ \epsilon _{ij}\), where the subscript i is the estimate from the j study while k refers to the group for each category of the moderator variable presented in Table 2. \(\varsigma _{j}\) refers to the study-level random effects (random intercepts). This modeling represents individual reported estimates, which are level 1, and are clustered and nested within studies, which are level 2. This representation follows closely the work of Doucouliagos and Stanley (2009).

  6. The third category is the pool of both developing and developing countries in most case representing a global sample/cross country analysis.

  7. This approach is labeled as the “best practice” in Stanley and Doucouliagos (2012).

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Authors and Affiliations

  1. Department of Food, Agricultural and Resource Economics, University of Guelph, Guelph, Canada

    Sylvanus Kwaku Afesorgbor

  2. Environment and Natural Resource Research Initiative, ENRRI-EfD Ghana Center, Accra, Ghana

    Sylvanus Kwaku Afesorgbor

  3. International Institute of Social Studies, Erasmus University Rotterdam, The Hague, The Netherlands

    Binyam Afewerk Demena

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We thank Peter van Bergeijk, Liam Kelly and all participants in the Agricultural & Applied Economics Association (AAEA) Annual Meeting in Atlanta, Georgia (2019) for their useful comments. We are grateful to John Cranfield and the department of Food, Agricultural and Resource Economics, University of Guelph for the financial assistance and Mikayla Del Medico for her research assistantship. The usual disclaimer applies.

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Table 9.

Table 9 Appendix A: List of studies
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Afesorgbor, S.K., Demena, B.A. Trade Openness and Environmental Emissions: Evidence from a Meta-Analysis. Environ Resource Econ 81, 287–321 (2022). https://doi.org/10.1007/s10640-021-00627-0

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