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Do deep and comprehensive regional trade agreements help in reducing air pollution?

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Abstract

Environmental concerns are increasingly being incorporated into regional trade agreements (RTAs) to promote environmental quality and ultimately ensure compatibility between trade and environmental policies. This occurs in a context where air pollution and its effects on human health are of major concern. This paper investigates whether the proliferation and depth of environmental provisions (EPs) in RTAs are associated with lower concentration levels of particulate matter. We present an index of EPs in RTAs that measures the breadth and depth of the provisions and use it to estimate the effect of ratifying RTAs with different levels of EPs on changes in PM2.5 concentration levels in a panel of OECD countries over the 1999–2011 period. Using an instrumental variables strategy, we find that countries that have ratified RTAs with EPs show lower levels of PM2.5 concentrations when we control for scale, composition and technique effects and for national environmental regulations. Moreover, the PM2.5 concentration levels in the pairs of countries that belong to an RTA with EPs tend to converge for the country sample. The results also hold for a longer period of time (1990–2011) and a broader sample of 173 countries as well as for other pollutants, namely CO2 and NO2.

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Fig. 1

Source: OECD Green Growth Headline Indicators (OECD 2017)

Fig. 2

Source: Botta and Kozluk (2014)

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Notes

  1. There are, however, some exceptions. Some environmental issues are being discussed under Doha and the WTO Dispute Settlement Body (DSB) and the Appellate Body have ruled on several trade and environmental disputes since the WTO’s inception, creating an interesting precedent.

  2. An excellent survey is presented in Mitchell (2003). Although numerous studies have investigated the effectiveness of MEAs (e.g. Breitmeier et al. 2011; Helm and Sprinz; 2000; Michell 2006), no clear correlation has been established between the operation of the MEAs and the state of the environment.

  3. Since 2007, the OECD has undertaken regular reviews of how environmental issues are treated in trade agreements (OECD 2007) and providing and updating an inventory of RTAs with environmental provisions (EPs) (Gallagher and Serret 2010, 2011; George 2013, 2014a, b). The OECD reports refer to some ex-post-assessments of environmental impacts [e.g. EU-Chile and the US for the RTAs recently signed (George 2013)] and mention the difficulty in isolating the impact of the RTAs on environmental outcomes from other factors.

  4. Morin and Jinnah (2018) assess climate-related provisions in preferential trade agreements (PTAs), but only along four dimensions.

  5. PM2.5 refers to atmospheric particulate matters (PM) that have a diameter of less than 2.5 µm.

  6. Calculations based on existent studies suggest that ambient (outdoor) PM2.5 caused about 3 million deaths worldwide in 2012 (Cohen et al. 2017; World Health Organization 2016).

  7. The ASEAN–China Free Trade Area (ACFTA), also known as China–ASEAN Free Trade Area is a free trade area among the ten member states of the Association of Southeast Asian Nations (ASEAN) and the People's Republic of China.

  8. Ghosh and Yamarik (2006) just mention that regional trade agreements address environmental issues and give the examples of NAFTA and the EU (page 20, second paragraph: “Whatever the route through which trading blocs impact the environment, regional trading arrangements are addressing environmental issues…”).

  9. We use PM2.5 instead of SPM in this study. SPM refers to particles in the air of all sizes, whereas PM2.5, usually called fine particles, are not visible to the eye and are more harmful for health.

  10. Australia, Austria, Belgium, Canada, Denmark, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Japan, the Netherlands, Norway, Poland, Portugal, Slovakia, Spain, Sweden, Switzerland, the United Kingdom and United States.

  11. Brazil, Russia, India, Indonesia, China and South Africa.

  12. Data on PM2.5 are elaborated on by the OECD using datafrom the Atmospheric Composition Analysis Group (Boys et al. 2014). Available at: http://fizz.phys.dal.ca/~atmos/martin/?page_id=140.

  13. See Botta and Kozluk (2014).

  14. PWT-8.1:http://www.rug.nl/research/ggdc/data/pwt/pwt-8.1.

  15. Data on the breadth and depth indicators is available from the authors upon request.

  16. The provision on a legal precedence of MEAs roots from NAFTA in 1994, and presumably under the influence of NAFTA, most RTAs signed by Canada provide this provision.

  17. Our model considers the main factors affecting emissions in line with Frankel and Rose (2005) and Baghdadi et al. (2013). Moreover, as in Frankel and Rose (2005) and Ghosh and Yamarik (2006), a Kuznets curve term, namely the square term of the log of income per capita, is added in Model 1.

  18. A small part of PM2.5 can be considered as transboundary air pollution. However, there is no data available allowing the distinction between what has been emitted in a country and what goes through the borders.

  19. Alternatively, we used bilateral trade lagged 2 years as weights in Eq. (1), the results remained similar in direction and statistical significance. Equal weights are selected to facilitate the interpretation of the results.

  20. We also experimented with specific time trends for different groups of countries and the results concerning our target variable remained unchanged. Results are available upon request.

  21. In a panel data framework, the inclusion of country- and time-fixed effects as regressors, together with the policy variable that identifies the before and after policy intervention (in the case when an RTA has begun to be enforced), is equivalent to a difference in differences strategy. See for example Galiani et al. (2005).

  22. The model is estimated with the Stata command xtregar with fixed effects. Similar results were obtained with alternative specifications (e.g xtreg, fe and time dummies). The Hausman test suggested that the error term is correlated with time-invariant country heterogeneity, which suggests that only the within estimator is consistent. The model was also estimated using group specific time dummies for OECD and non-OECD countries and no significant differences in the results were observed.

  23. The BRIICS countries used in this study hold some interesting policy insight into whether or not their membership in RTAs that have EPs leads to lower pollution.  Since India and China are two countries with cities that have some of the worst PM2.5 emission concentrations in the world, their membership in RTAs (whether in breadth or depth), which may potentially lower air pollution, can provide some interesting policy insight. Of course, given that these are only 6 countries, data constraints prevent us from showing the empirical results for these 6 countries in a similar format as Tables 2 and 4. However our main results in our extended sample remain unchanged even after removing the 2 outlier countries of China and India. We thank an anonymous referee for this suggestion.

  24. To investigate the effect that excluding ESPI has on the results for the target variables, we estimated the model for the sample of 29 countries without ESPI. The estimated coefficients for rtaenv, w_score, breadth and depth are − 0.0033, − 0.0166, − 0.0171 and − 0.0362, respectively. Hence, these effects are shown as an upward bias in the coefficients of the variables (compare with − 0.00295, − 0.0108, − 0.0158 and − 0.0342 in Table 2).

  25. We would like to thank one anonymous referee for raising this issue. The Stata command xtscc has been used, which is appropriate when the time dimension becomes large. The results are available upon request from the authors.

  26. The model with country-fixed effect is preferred to a random effects model because the error term is correlated with the unobserved heterogeneity and hence does not provide consistent estimates.

  27. The indirect effect of trade and RTAs on emissions through income per capita could also be obtained in a separate exercise.

  28. \(Remote_{ij} = 0.5 D_{ij}^{CC} \left\{ {\left[ {{ \ln }\left( {\mathop \sum \limits_{k = 1,k \ne j}^{N} Dist_{ik} /\left( {N - 1} \right)} \right)} \right] + \left[ {ln\left( {\mathop \sum \limits_{k = 1,k \ne i}^{N} Dist_{kj} /\left( {N - 1} \right)} \right)} \right]} \right\}\).

    where \(D_{ij}^{CC}\) is a common continent dummy. This variable will then be equal to zero if countries are on the same continent. Remote is then the log of the average value of the mean distances of countries i and j from all other countries.

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Acknowledgements

The funding was provided by OECD, University Jaume I (UJI-B2017-33) and Ministerio de Economía y Competitividad (Grant Number ECO2017-83255-C3-3-P). We would like to thank the editor and two anonymous reviewers for their comments and suggesting and Shunta Yamaguchi for his very helpful comments on an early draft. We also thank the participants at the INTECO and ETSG conferences for their insightful comments.

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Authors and Affiliations

  1. Department of Economics, University of Göttingen, Platz der Göttinger Sieben 3, 37073, Göttingen, Germany

    Inmaculada Martínez-Zarzoso

  2. University Jaume I, Castellón de la Plana, Spain

    Inmaculada Martínez-Zarzoso

  3. Environment Directorate, Organisation for Economic Co-operation and Development, 2, rue André Pascal, 75775, Paris Cedex 16, France

    Walid Oueslati

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  1. Inmaculada Martínez-Zarzoso
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  2. Walid Oueslati
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Correspondence to Inmaculada Martínez-Zarzoso.

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The opinions expressed in this paper are those of the authors and do not necessarily reflect the official views of the OECD or of the governments of its member countries. An earlier version of this paper appeared in the OECD Environment Working Paper Series (see Martínez-Zarzoso 2018).

Appendix

Appendix

1.1 Growth empirics and gravity model estimations

1.1.1 Growth empirics

As emphasised by Frankel and Rose (2005), trade flows, regional agreements and pollutant’s emissions and environmental regulations may affect income. Therefore, we predict real income with a number of variables, namely lagged income per capita (GDPcapi,t-1), conditional convergence hypothesis, population (pop), investment per income (I/GDP) and human capital formation. The latter is approximated by the rate of school enrolment (in primary school, School1, and secondary school, School2). The predicted values (linear projection) of this equation are used to calculate GDPcap-it and GDPcap-jt.

$$\ln \left( {GDPcap} \right)_{it} = \alpha_{i} + \delta_{1} \ln \left( {Pop} \right)_{it} + \delta_{2} \ln \left( {GDPcap} \right)_{i,t - 1} + \delta_{3} \ln \left( {\frac{I}{GDP}} \right)_{i,t} + \delta_{4} n_{it} + \delta_{5} \ln \left( {School1} \right)_{it} + \delta_{6} \ln \left( {School2} \right)_{it} + u_{it}$$
(A.1)

where nit is the growth rate of population and uit is a random term that is assumed to be independently and identically distributed and with a constant variance. Model (A.1) is estimated using panel data estimation techniques, mainly assuming that the country-unobserved heterogeneity (time-invariant factors that determine GDP per capita and differ by country) is modelled using fixed effects (a different intercept for each country).Footnote 26

The income equation is taken from Baghdadi et al. (2013). The main difference between the model specified in (A.1) and the income equation in Frankel and Rose (2005) and Ghosh and Yamarik (2006) is that the Frankel and Rose (2005) also include trade openness as an explanatory variable and Ghosh and Yamarik (2006) include an RTA variable in addition to trade openness. We relegate trade openness and trade policy factors to the error term (unexplained part of the income model), since we are interested in predicting changes in GDP per capita that are explained by factors different from trade and trade policy. In this way, we obtain a ‘pure’ scale effect that does not include the effect of trade in income.Footnote 27

1.1.2 Gravity model with geographical determinants

The predicted multilateral openness and the bilateral trade variables used in models (1) and (2) above are obtained from a gravity model of trade, which is estimated using a large panel data set on pair-wise trade flows. The standard gravity model states that trade between countries is positively determined by their size (GDP, population and land area) and negatively determined by geographical and cultural distance. The geographical variables are exogenously determined and hence are suitable instruments for trade (Frankel and Romer 1999). We follow Badinger’s (2008) specification of the gravity model, in which bilateral trade openness is regressed on countries’ populations (Popit, Popjt), land area (Areaij = Areai*Areaj), distance (Dij), a common border dummy (Adjij), a common language dummy (Langij) and a landlocked variable (Landlok = sum of a landlocked dummy of countries i and j). Two other variables are included in order to be consistent with the theoretical model: a measure of similarity of country size (Landcapit/Landcapjt) and remoteness from the rest of the world (Remote).Footnote 28

$$\ln \left( {Trade_{ijt} /GDP_{it} } \right)_{{}} = \gamma_{i} + \tau_{j} + \varphi_{t} + \beta_{1} \ln \left( {Pop} \right)_{it} + \beta_{2} \ln \left( {Pop} \right)_{jt} + \beta_{3} \ln Dist_{ij} + \beta_{4} Area_{ij} + \beta_{5} Lang_{ij} + \beta_{6} Adj_{ij} + \beta_{7} Landlok_{ij} + \beta_{8} \ln (Landcap_{it} /Landcap_{jt} ) + \beta_{6} \text{Re} mote_{ij} + \mu_{ijt}$$
(A.2)

Finally, from equation (A.2) the exponent of the fitted values across bilateral trading partners \(ln\widehat{Bilopen} = \ln (\widehat{{trade_{ijt} /GDP_{it} }})\) is aggregated to obtain a prediction of total trade for each country and year.

$$\widehat{Open}_{it} = \mathop \sum \limits_{j} { \exp }\left[ {\ln \widehat{Bilopen}_{ijt} } \right]$$
(A.3)

Both, the bilateral prediction and the aggregated bilateral prediction are used as regressors in the environment-damage model (2) and the later is also used in model (1). By using these predicted values, we are able to isolate the part of trade that is explained exclusively by geographical, cultural and time-invariant country-specific factors. Other policy changes that could also explain trade variations are relegated to the unexplained part of the model (error term).

1.2 Commitment index of EPs in RTAs and list of RTAs with EPs

See Tables 5 and 6.

Table 5 Indicators and criteria of Environmental commitment in RTAs
Full size table
Table 6 List of RTAs with environmental provisions
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1.3 Results for NOx and CO2 and results for 48 countries and Dif-GMM for PM2.5

See Tables 7, 8 and 9.

Table 7 Results for NOx and CO2
Full size table
Table 8 Results for PM2.5 (48 countries sample)
Full size table
Table 9 Results for PM2.5. Dif-GMM Estimations (48 countries sample)
Full size table

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Martínez-Zarzoso, I., Oueslati, W. Do deep and comprehensive regional trade agreements help in reducing air pollution?. Int Environ Agreements 18, 743–777 (2018). https://doi.org/10.1007/s10784-018-9414-0

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