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Can the management school explain noncompliance with international environmental agreements?

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Abstract

Although the management school has been highly influential in the international cooperation literature, the explanatory power of Chayes and Chayes’ three explanations of noncompliance with international environmental treaties remain understudied. Having developed a framework for examining the explanatory power of treaty ambiguity, lack of state capacity, and unexpected social or economic developments, this paper conducts a rigorous empirical test in the context of a well-suited case—the 1999 Gothenburg Protocol. A careful reading shows that the language of the protocol is clear and unambiguous; indeed, there has been no disagreement over the treaty’s content. Furthermore, statistical analyses show no positive effect of political capacity on compliance. Finally, parties had adequate time to meet their obligations, and unexpected developments explain only a small part of the observed noncompliance. These findings pose a serious challenge to Chayes and Chayes’ three explanations of noncompliance—at least as far as the Gothenburg Protocol is concerned.

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Notes

  1. As of February 2018, Google Scholar counts 1328 citations of Chayes and Chayes’ 1993 article.

  2. Scholars such as Young (1979) and Mitchell (1994, 2010) have formulated positions sharing several similarities with those of Chayes and Chayes (1993).

  3. Empirical studies of IEAshave grown in number, but scholars have focused more on effectiveness (for instance Miles et al. 2002; Victor et al. 1998) than on compliance.

  4. Granted, we may also conceive of compliance as a dichotomous concept. Hence, I also use a binary compliance variable.

  5. I refer to the Gothenburg Protocol of 1999, not the amended protocol of 2012.

  6. Acidification is largely caused by sulphur and NOx, and affects life in water and soil (Miljødirektoratet 2015).

  7. Eutrophication, which increases algae growth and thereby harms other organisms, often stems from ammonia emissions.

  8. NOx reacting with non-methane volatile organic compounds (NMVOCs) causes harmful ground-level ozone.

  9. In accordance with Young’s (1979) definition.

  10. Hence, Table 1 does not engage directly with the highly challenging task of distinguishing between Mitchell’s (2010: 147) two kinds of noncompliant behaviour (“good-faith” and “intentional”) or between his two kinds of compliant behaviour (“coincidental” and “treaty-induced” compliance).

  11. In contrast, Bernauer et al. (2013) find no support for the enforcement school’s hypothesis of a trade-off between depth and participation.

  12. However, an assessment of previous CLRTAP protocols concluded that negotiation positions, implementation, and compliance (operationalized as emissions reductions) were reasonably well predicted by a model of states as unitary rational actors (Underdal 2000: 351–353).

  13. This anthology includes studies of eight states’ (and the EU’s) compliance with five international environmental treaties.

  14. See, however, Kokkvoll Tveit’s (2018) recent in-depth case study.

  15. The original text is as follows: “Seit dem Jahr 2010 dürfen 1.081 Tausend Tonnen NOx nicht mehr überschritten werden.”.

  16. In their study of compliance with EU law, Börzel et al. (2010) operationalize state capacity as GDP per capita and scores on a government effectiveness index.

  17. WGI scores are based on surveyed views of experts, citizens and enterprise respondents.

  18. I log-transform GDP per capita because its relationship to political capacity is likely nonlinear.

  19. Unless I state otherwise, all emissions are in metric tonnes, and as reported to UNECE in 2015.

  20. Using the binary compliance variable is also warranted by the considerable over-compliance by several parties shown in Table 1. Such over-compliance may suggest that the emissions levels were not primarily a result of deliberate efforts to reach the target. Regressions using the binary compliance variable do not estimate on that potentially irrelevant information.

  21. A multilevel model is infeasible because of few (4) units on the state level. Likewise, estimating causal effects by using instrumental variables (Angrist and Pischke 2009) is infeasible since it is highly doubtful that any valid instrument Z exists for my variables (see Angrist and Pischke’s (2009: 117) discussion of criteria for valid instrumental variables). Bratberg et al. (2005) estimate the effect of participation in CLRTAP agreements on emissions by employing the difference-in-differences (DID) estimator, thus comparing participants to non-participants. The DID technique is, however, less feasible when compliance is the dependent variable, since only states that participate in the agreement may comply (or defect).

  22. This report was written by scientists at the International Institute for Applied Systems Analysis (IIASA) to make the scientific background for Gothenburg’s commitments available to the wider public. Projections and other analyses from IIASA are considered as important inputs in the process deciding emissions targets (Castells and Ravetz 2001; Rensvik 2017; Tuinstra 2008).

  23. Since emission coefficients vary considerably among sources of energy, aggregate energy consumption is not my first-best data. However, this is the only projection concerning energy consumption included by Amann et al. (1999).

  24. Based on data from Eurostat (2017).

  25. Except for Spain, all parties that did not reach their NOx targets were represented by national experts at the meeting.

  26. These vehicles often use diesel fuel.

  27. I include only states that were noncompliant with their 2010 NOx Gothenburg target.

  28. Actual emissions are here defined as the estimates reported in 2015. Obviously, there is an artificiality to this classification, since even recent emissions estimates may subject to change because of new scientific evidence. However, since estimates from 2015 are derived from the presently best available scientific knowledge, I use 2015 estimates as baseline.

  29. Yet another analysis shows that the estimate of capacity in Model 16 is not sensitive to operationalizing capacity as Government Effectiveness (see Tables 2 and 3). I have also run this full model using the dichotomous compliance variable, and the effect of capacity remains negative (not reported here, on file with author).

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Authors and Affiliations

  1. Department of Political Science/Institutt for Statsvitenskap, University of Oslo, Boks 1097 Blindern, 0317, Oslo, Norway

    Andreas Kokkvoll Tveit

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  1. Andreas Kokkvoll Tveit
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Correspondence to Andreas Kokkvoll Tveit.

Additional information

I am most grateful to Jon Hovi, two anonymous reviewers, participants at a panel at the ISA Annual Conference in Baltimore, February 2017, and participants at CICEP’s annual research conference, Oslo, September 2016, for constructive comments and suggestions. My research has benefited greatly from my stay at the International Institute for Applied Systems Analysis (IIASA) in May 2016. Parts of my research were carried out while at the Fridtjof Nansen Institute, Lysaker, Norway. Any remaining errors are the author’s responsibility.

Appendix

Appendix

1.1 Sensitivity check: statistical assessments of the effect of capacity on compliance

Table 9 shows the results of additional OLS regressions using another measure from Worldwide Government Indicators (WGI) to operationalize capacity. According to the World Bank’s description, Regulatory Quality “reflects perceptions of the ability of the government to formulate and implement sound policies and regulations that permit and promote private sector development”.

Table 9 OLS regressions, alternative capacity operationalization. Dependent: compliance
Full size table

Once again, I find no positive relationship between capacity and compliance.

Table 10 shows the results of a final robustness check (Model 16). Here, I have included dummies for each substance that Gothenburg regulates. Again, the effect of capacity is negative and statistically significant. Since Model 16 includes dummies for all regulated substances except sulphur, the substance dummy estimates can be interpreted as the difference in compliance between the substance concerned and sulphur. As all Gothenburg parties complied with their sulphur targets (see Table 1 in the main document), it comes as no surprise that all substance dummy estimates shown in Model 16 are negative.Footnote 29

Table 10 OLS regressions, incl. substance dummies. Dependent: compliance
Full size table

1.2 Additional comparisons of emissions estimates over time

See Tables 11, 12, 13 and 14.

Table 11 Estimates of 2003 NOX emissions of noncompliant parties (thousand tonnes)
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Table 12 Estimates of 2006 NOX emissions (thousand tonnes)
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Table 13 Estimations of 2008 NOX emissions (thousand tonnes)
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Table 14 Estimates of 2010 NOX emissions (thousand tonnes)
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Kokkvoll Tveit, A. Can the management school explain noncompliance with international environmental agreements?. Int Environ Agreements 18, 491–512 (2018). https://doi.org/10.1007/s10784-018-9400-6

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