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The effectiveness of international environmental agreements

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Abstract

Many argue that international environmental agreements (IEAs) can alter states’ cost-benefit analyses by providing crucial information about the costs of environmental degradation. Thereby, IEAs may help to effectively curb environmental pollution. However, previous attempts to empirically measure institutional effectiveness found it difficult to provide credible estimates because they have missed to produce convincing counterfactuals. This study empirically estimates the effectiveness of one prominent example of an international environmental institution, the Long Range Transboundary Air Pollution agreement (LRTAP). It sets forth a transparent identification strategy in light of latest advancements in the causal inference literature and presents evidence for the non-effectiveness of the LRTAP in changing member states’ behavior in terms of anthropogenic emissions of two substances (NO x and SO2). By deriving and illustrating the use of difference-in- differences (DID) design in the context of IEAs, this study provides a general methodological tool kit to drawing causal inferences about the effectiveness of international environmental institutions.

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Notes

  1. Downs (2000), Barrett (2003), and Ringquist and Kostadinova (2005) provide excellent discussions of the existing theoretical arguments.

  2. See the CLRTAP website of the United Nations Economic Commission for Europe (UNECE) for more information: http://www.unece.org/env/lrtap/status/lrtaps.htm.

  3. Note that while protocol targets are legally binding, the membership of the agreement is voluntary. Following the framework convention concluded in Geneva in 1979, the LRTAP protocols address the following pollutants and emissions: Helsinki, 1985, SO2; Sofia, 1988, NO x ; Geneva, 1991, VOC (Volatile Organic Compounds); Oslo, 1994, SO2; Aarhus, 1998, POPs (Persistent Organic Pollutants) and Heavy Metals; and Gothenburg, 1999, SO2,NO x , VOC, NH3 and three environmental effects.

  4. To be clear, following the above definition, these studies are semi-quantitative in nature as they recur to some degree to qualitative assessments and transform them into numerical values before they apply statistical methods. Game theorists generally derive non-cooperative Nash equilibria and/or social optima from their models as benchmarks. Also note that Miles et al. (2002), Breitmeier et al. (2006), and Breitmeier et al. (2011) evaluate variance in effectiveness across different international environmental agreements. This study restricts itself to measuring the effectiveness of a single agreement. This allows for identifying its causal effect a more transparent way.

  5. The authors base their approach on Maler (1989).

  6. Econometricians usually call this the curse of dimensionality.

  7. The covariance matrix of the errors has to be known.

  8. This study outlines the procedure in detail in the next chapter.

  9. The next chapter elaborates on the importance of discussing identifying assumptions.

  10. Likewise, comparing pre- and post-treatment outcomes for the treated units most probably incorporate biases due to temporal trends in the outcome variable or to the effect of changes in other factors between periods (Abadie 2005).

  11. However, it is not clear whether this is really the most useful approach. This is because recent research claims that IEAs do influence members as well as non-members (Mitchell and Deane 2009). So, there could also be an effect of the treatment on the control countries. Thus, this might be evidence of bias in our estimates. If we let aside this concern, the quantity of interest is the δ ATET .

  12. To make this explicit, we can multiply a state-specific trend coefficient with the time trend variable (Angrist and Pischke 2008).

  13. Note that we look at actual outcomes now.

  14. Apart from controlling for compositional effects, adding covariates can enhance statistical precision.

  15. At least as long they can be linearly explained. The precision of our DID estimator is directly given by the standard error of δ

  16. However, fixed effects regression works poorly if selection into treatment depends on time-varying covariates. In the case of LRTAP, individual state-level technological innovation could be such a confounder. Still, the DID approach is the most rigorous way to estimate institution effectiveness in the case of LRTAP.

  17. Although all protocols are voluntary by nature, they differ on the calculation of emission targets. First, the framework convention from 1979 consists of several loosely formulated emission targets. Second, the 1985, 1988 and 1991 protocols formulated uniform reduction targets for all ratifying parties. Finally, the Oslo protocol from 1994 introduced the critical loads approach which applies individual and varying reduction targets to account for country differences. The Gothenburg 1999 protocol continues this approach.

  18. All test results available from the author.

  19. See Table (10) in the appendix for a detailed list of countries in the sample and their ratification behavior.

  20. http://www.emep.int. EMEP is scientifically based and policy driven, and thus widely believed to be politically independent.

  21. Note that visual inspection functions as a first check point to evaluate the parallel trends assumption. The exception of year 2000 in NO x emission growth rates might appear extreme, but note that mathematically the difference constitutes roughly 0.15 %.

  22. A tabular overview of variables, their descriptions, and data sources can be found in the appendix under (6).

  23. Matrices from 1997 onwards are available on the EMEP homepage, EMEP, earlier years from Sandnes (1993).

  24. Summary statistics for all variables can be found in the appendix on Table 7.

  25. A random effects model would have been another possibility to estimate the quantity of interest (Wooldridge 2005). A Hausman test rejects the random effects model in favor of the fixed effects model. This holds for both SO2 and NO x . Results available from the author.

  26. In a naive model without country fixed effects, the coefficient was −0.17. The selection bias in expectations thus amounts to 3 % points. The coefficients from the naive and the baseline model were not significantly different from each other, though.

  27. Plotted time series of average log emissions suggest not to add quadratic trends to the model. Adding quadratic terms to a functionally linear model could instead introduce misspecification bias.

  28. This in turn is convenient for the researcher because covariates do not need to be theoretically well justified or what is even more difficult and oftentimes quite arbitrary, be interpreted theoretically in post-estimation analysis.

  29. Ringquist and Kostadinova (2005) also find a positive, yet insignificant, correlation between forest cover and SO2 emissions for the Helsinki 1985 protocol.

  30. In a naive model without country fixed effects, the coefficient was −0.01. The selection bias in expectations thus amounts to 2 % points. The coefficients from the naive and the baseline model were not significantly different from each other, though.

  31. Exclusion of covariates did not change the results substantially.

  32. Ideally, the model specifies nonparametric country-specific time trends. This sample, though, does not contain enough observations.

  33. Sensitivity analysis does not include the estimation of first differenced equations. If idiosyncratic errors are not serially correlated, fixed effects estimation is relatively more efficient (Wooldridge 2001). As mentioned before, tests confirmed panel stationarity. I still ran the first difference models. Results are robust and can be obtained from the author.

Abbreviations

ATE:

Average treatment effect

ATET:

Average treatment effect on the treated

CLRTAP:

Convention on long range transboundary air pollution

DID:

Difference-in-differences

EMEP:

European monitoring and evaluation program

GDP p.c.:

Gross domestic product per capita

GLS:

Generalized least squares

IEA:

International environmental agreement

NO x :

Nitrogen oxide

OLS:

Ordinary least squares

SO2 :

Sulfur dioxide

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Acknowledgments

This work was supported by ‘Swiss Federal Institute of Technology (ETH) Independent Investigators Research Award’ (ETHIIRA) grant, ETH-14 09-3. This article benefited from comments of Michael M. Bechtel, Tobias Böhmelt, Beatrice Brunner, the audience at the 2011 annual meeting of the International Political Economy Society, and members of the international political economy research group at ETH Zurich. The usual disclaimer applies.

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  1. ETH Zurich, Center for Comparative and International Studies (CIS) and Institute for Environmental Decisions (IED), IFW C 43.1, Haldeneggsteig 4, 8092, Zurich, Switzerland

    Jürg Vollenweider

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Correspondence to Jürg Vollenweider.

Appendices

Appendix

Treating missing values

I deleted a set of post-sovjet countries from the data set because of missing data, namely Kyrgyzstan, Turkmenistan, and Tajikistan. Liechtenstein was dropped for the same reason. The study does not use interpolated data on the dependent variable because the data show no linear pattern. For Russia, the data appeared to be inconsistent, that is, data from the EMEP model and the receiver-emitter matrices differed so highly that they were not considered trustworthy. Serbia and Montenegro are treated as one because they only split in 2006. Canada and the USA are excluded because they are not geographically contiguous with the other countries and therefore face, from a geophysical viewpoint, not the same transboundary constraints as the other countries (see Tables 6, 7, 8, 9, 10).

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Vollenweider, J. The effectiveness of international environmental agreements. Int Environ Agreements 13, 343–367 (2013). https://doi.org/10.1007/s10784-012-9193-y

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