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Institutional roots of international alliances: Party groupings and position similarity at global climate negotiations

Abstract

A large literature in international relations explores the domestic origin of national positions at international organizations (IOs). Less researched is the institutional assembling within IOs, and how alliances formed around negotiation groups affect countries’ positions. We explore this question in the context of the United Nations Framework Convention on Climate Change (UNFCCC), focusing on the role that institutional coalitions have on members’ statement similarity. Our baseline expectation is that similar economic development is the main determinant of coalition-building, so more common preferences emerge among members of economically similar negotiation groups. At the same time, and in line with other institutionalist views, we hold that some coalitions reflect alternative cross-cutting dimensions of interdependence and that this may increase the position similarity of their members. In the case of climate cooperation, we suggest that a high level of shared environmental vulnerability in a group may also cluster countries’ positions. We interrogate our expectations with new text-as-data measures that estimate associations of countries’ statements at the UNFCCC between 2010 and 2016. We find that states in more economically homogenous negotiation blocs share more similar national statements. Additionally, similar themes emerge among more vulnerable countries, although these are only amplified in small and uniform negotiation groups. Our evidence has implications for global cooperation based on a North–South dialogue and for the effectiveness of institutionalized coalitions at international organizations.

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Data availability

The data that support the findings of this study are available from the corresponding author upon request and the Review of International Organizations' webpage.

Notes

  1. We refer here to coalitions as groups of countries that share some similar preferences and thus voluntarily ally in order to enhance their interests in a multilateral negotiation.

  2. Other complementing scholarship has studied ‘club’ approaches to global climate governance. See, for example, Hale et al. (2013); Hovi et al. (2017).

  3. By similar statements we intend statements that touch on more similar topics, and therefore are more likely to suggest similar positions. Evidently, governments may choose to talk about the same topics in different veins, hence possibly indicating different positions. However, with the exception of extreme positions, it is often likely that supportive positions and salience are correlated (Veen, 2011). This correlation has been also documented at the UNFCCC (Bailer and Weiler, 2015; Genovese, 2014). Later in the paper, we come back to when and how our empirical measures of positions may be more likely to capture common salience rather than exact preferences.

  4. Note that we intend vulnerability as the tendency to be damaged by climate change and climate-related events, and thus not simply being exposed to potential climate risks. Resilience is the opposite of vulnerability, as it is the ability to resist or recover from damage.

  5. Evidently, we make assumptions about the data generating process behind the official statements at the heart of this paper. We assume that these statements reveal genuine information on the issues that are more pressing to the governments’ domestic audiences. So, they are not purely strategic, nor that they are pure posturing. This intuition is rooted in a growing literature that makes a similar assumption (Genovese, 2014, 2019; Tobin et al., 2018).

  6. In a similar vein, Tobin et al. (2018) make inference on Paris pledge ambition based on manually selected clusters of member states that generally conform with Annex divisions.

  7. These are: the Independent Alliance of Latin America and the Caribbean (AILAC) that includes Colombia, Costa Rica, Chile, Peru, Guatemala and Panama; the Bolivarian Alliance for the Peoples of our America (ALBA), which is made up of Venezuela, Ecuador, Bolivia, Nicaragua and Cuba; the Like-Minded Groups that represents China, India, Saudi Arabia, Malaysia and Iran; the Central Asia and the Caucuses, Albania and Moldova (CACAM); the Organization of the Petroleum Exporting Countries (OPEC) and the BASIC, which brings together the major emerging economies: Brazil, China, India and South Africa.

  8. For example, some negotiating groups have been critical to creating momentum for some issues, pushing to unexpected informal alliances, such as the small islands (AOSIS)-EU bloc.

  9. Carbon Brief. 2015. `The UNFCCC negotiating alliances'. https://www.carbonbrief.org/interactive-the-negotiating-alliances-at-the-paris-climate-conference. Last accessed: May 31 2022.

  10. This is supported by various anecdotes. For example, in the Umbrella Group, a coalition with only Annex 1 countries, Canada and Australia have had an easy time convincing coalition members on how to frame issues for the group based on similar policy demands. See https://library.fes.de/pdf-files/iez/12689.pdf.

  11. Evidently, we acknowledge that every country is out to further their own domestic interests based on a range of incentives that may not be captured by the institutional variables. It is thus reasonable to expect that conflicts at the climate negotiations extend to other broader concerns in international politics (Hovi et al., 2017). In the analyses we also incorporate covariates of speech similarity that may be systematically related to other international connections and constructions that are only indirectly feeding in the UNFCCC agenda. Shared geography (Weidmann et al. 2010) and common historical patterns (Hochstetler and Viola, 2012; Vihma, 2011) could represent sources of common interest that materialize in speech similarity.

  12. While some scholars have shed some doubt of the usefulness of the high-level statements to study political patterns at the UNFCCC, it is still the case that these statements embody a political vision, even if partial, and are potentially more useful than the many other technical papers countries share at each COP. As the United Nations indicate, the high-level statements “provide a reasonable bell weather of the priorities of different states.”.

  13. Various empirical research relies on texts to code similar positions at the UNFCCC, such as Castro et al. (2014); Tobin et al. (2018). However, many of these works deductively hand code positions from texts (see, e.g., Blaxekjaer & Nielsen, 2015). Those that are more automatized (e.g. Castro, 2020) often focus on case studies that do not comprehend a universal sample of countries.

  14. All statements were retrieved from the UNFCCC website at https://unfccc.int/submissions_and_statements.

  15. An example of a bag-of-word estimation processes is the Wordfish algorithm, which represents each text as a vector of word counts and then estimates document and word parameters by a Poisson process. This algorithm compares texts efficiently, but works well only under certain conditions – including a sufficient (but unknown) number of documents and unique words. Another simple type of bag-as-word approach is the Naive Bayes algorithm. For an application of these methods to UNFCCC documents (specifically, the National Communications), see Genovese (2014).

  16. For example, a properly trained set of word vectors can produce a representation of words where the distance between ‘man’ and ‘king’ is the same as the distance between ‘woman’ and ‘queen’. For a more detailed description of the steps undertaken by the word embedding algorithm, see the Appendix.

  17. This model, which is based on a Latent Dirichlet Allocation, is reported in the Appendix. The identified topics in large part replicate the results already shown in other studies, e.g. Bagozzi (2015); Castro (2020).

  18. Namely, we used a set of training texts in earlier years to predict a test set of held-out texts in later years, and find that older texts have high forecasting capacity especially if the institutional variables central to our paper are included in the algorithm.

  19. Evidently, as we mentioned before, coalitions just like IOs themselves can evolve over time and sometimes die or become ‘zombie’ (Gray, 2018). However, we think that in our case pooling the data over time is warranted by the fact that our time period (2010–2016) is not long, and also few coalitions discontinued their operations. We come back to this empirically below.

  20. We use the doc2vec algorithm to generate these vectors.

  21. See the Appendix for more technical notes on cosine similarity.

  22. On the Annex 1 side, these pairs involve smaller European countries such as Czech Republic, Latvia and Cyprus. On the Non-Annex 1 side, the countries whose statements ‘cross border’ are richer ones such as Israel and Saudi Arabia but also emerging economies such India and the Philippines. For example, according to our estimates India’s statements are as similar to statements by Singapore and Namibia as to the speeches by Denmark and Switzerland.

  23. We realize the overlapping of members across these groups could be interesting to explore the complementarity and consistency of behavior across coalitions. However, including these groups in the fashion of dummy variables in a regression framework would induce collinearity. Exchanging these groups for the aforementioned ones does not qualitatively change our empirical results.

  24. Our classification of ‘homogenous’ and ‘mixed’ groups is based on the listed metrics, but other studies have considered some of these official groupings in slightly different ways. For example, Castro (2020) investigates the Like Minded Group (i.e. the Like-Minded Developing Countries coalition) as a heterogenous group, for it brings together emerging economics, oil-dependent monarchies and poor developing countries. In our coding we keep this as ‘homogenous’ because it includes only Non-Annex 1/developing countries compared to other groups, which Castro (2020) does not investigate. Other groups are however interpreted consistently to other literature, see for example the discussion about AILAC in Blaxekjaer and Nielsen (2015); Watts and Depledge (2018).

  25. https://unfccc.int/process-and-meetings/parties-non-party-stakeholders/parties/party-groupings. (last checked: 26 September 2020).

  26. In Durban (2011), Canada said “our position has long been clear: we support a new international climate change agreement that includes commitments from all major emitters. That is the only way we are going to achieve real reductions and real results. We must be fair if we are to be effective.” Along the same lines, Australia said: “[our] position remains unchanged—we will be part of a second commitment period only if it is a part of a wider agreement covering all major emitters. We have this approach because we are committed to an environmentally effective outcome. The reality is that a second commitment period of the Kyoto Protocol may only cover 15 percent of global emissions.

    A more comprehensive agreement is fundamental for environmental effectiveness.”.

  27. The p-value for the interaction between CRI Difference and non-Annex 1 dyad is 0.6 and suggests that there is a somewhat substantive relationship. Note also that confidence intervals in Fig. 3 stop overlapping if calculated at that p < .10 significance threshold.

  28. A strong clustering of adaptation and climate risk issues is also consistent with what separate topic models suggest (see Appendix).

  29. Models where we estimate each single interaction for each party grouping at a time do not change the qualitative inference of the results, as reported in the Appendix.

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Acknowledgements

We thank Liam Beiser-McGrath, Patrick Bayer, Amanda Kennard, Vally Koubi, Charles Roger, Sam Rowan, Detlef Sprinz, Vegard Torstad, participants at the 2019 PEIO conference and the 2021 EPG Online workshop for useful feedback. We also thank Raghav Anand and Chetan Hebbale for research assistance, and Axel Dreher and three anonymous reviewers for constructive criticism and comments.

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Research design and conceptualization: F.G. (50%) R.M. (25%), J.U. (25%); statistical analysis: F.G. (25%), R.M. (75%); writing: F.G. (75%), R.M. (25%); The order of authors reflects the significance of the authors’ contributions.

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Correspondence to Richard J. McAlexander.

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Genovese, F., McAlexander, R.J. & Urpelainen, J. Institutional roots of international alliances: Party groupings and position similarity at global climate negotiations. Rev Int Organ (2022). https://doi.org/10.1007/s11558-022-09470-4

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Keywords

  • Climate change politics
  • United Nations
  • Institutions
  • Negotiation groups
  • Coalitions
  • Text analysis