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International Negotiations and PublicOpinion: Is the Negotiation Behavior ofCountries in the UNFCCC Negotiations

Influenced by the Public?

Florian Weiler

University of Bamberg

Abstract

In international bargaining situations such as the climate change negotiations, statesform ties to support and reinforce each other’s views when they share common nego-tiation positions, and thus generate cooperative networks. So far, traditional networkanalysis has mainly focused on various centrality measures and how the actors in thenetwork are linked to each other. In this paper I employ exponential random graphmodels (ERGMs), a new approach to model networks allowing the researcher to for-mulate hypotheses derived from theory and to test them on the network serving asthe dependent variable. At the same time, these models are able to account for de-pendency structures in the network, i.e. the (realistic) assumption that tie formationis related to the formation of other ties can explicitly be modeled. The network in-vestigated in this study was constructed from observed cooperative behavior in theUNFCCC negotiations between December 2007 and December 2009. The hypothesesinvestigated in this paper are concerned with democratic control and the public opinion.

Keywords: network, ergm, climate change, public opinion, UNFCCC

1 Introduction

In settings were states’ interests diverge, achieving jointly beneficial cooperative outcomes

is difficult in the international system, according to both neorealists and liberals, due to

the lack of a global government able to enforce rules (Keohane, 1984; Waltz, 1979). Ac-

cepting anarchy as the ordering principle of world politics1, one way to realize cooperative

outcomes, according to Oye (1986), is through international negotiations. This article an-

alyzes bargaining behavior of countries in the multilateral negotiations under the auspices

of the United Nations Framework Convention on Climate Change (UNFCCC), which aim

(amongst others) at reducing the global greenhouse gas emissions in order to limit global

warming. According to Olson (1965, p.43-52), some form of coordination which emerges

among the various participants in the climate change negotiations is necessary to achieve

cooperative outcomes.

Looking at the climate change negotiations we notice that states indeed coordinate their

positions on issues on which they share closely related interest, but also that some countries

are more willing to coordinate their positions than others.2 Countries declare their common

negotiation positions by issuing joint statements during official negotiation meetings. In this

paper I analyze the thus formed network in which each of the joint statements serves as a tie

(i.e. a link between two nodes, or in this case countries). Statements were collected over a

two year period prior to the Conference of the Parties (COP) 15 in Copenhagen (December

2009), and the data are part of a newly collected dataset covering various issues of the climate

change negotiations such as positions and bargaining behavior. Various hypotheses on how

the democratic status of a country and the public opinion influence the bargaining behavior

are proposed, and then tested using a novel network approach employing exponential random

graph models (ERGMs). In what follows the terms joint statements and (network) ties will

be used interchangeably.

Why do states form ties with some states, but not with others? I propose that certain

characteristics induce states to coordinate their positions and to make joint statements. For

example, small, relatively powerless states with only relatively limited domestic greenhouse

gas emissions might want to increase the pressure on big, powerful emitters by showing them

that they act in unity. This is clearly the case when Tuvalu bonds up with other small island

states such as Micronesia or Barbados. On the other hand powerful states are very attractive

partners, hence when interests on certain issues overlap smaller countries have an incentive to

1 For a summary and a critique of this view see Wendt (1992) and also Lumsdaine (1993, p.3-29).2 Yet we also observe that countries with similar positions in many cases do not coordinate and issue

joint statements, hence similarity regarding positioning behavior alone is not enough to form ties in thenetwork.

2

issue joint statements with these players to amplify their opinion. For this reason, countries

like the US, China, or India tend to form a greater number of ties than less important states.

Similarly, I propose that the public opinion can also influence countries’ behavior in such

bargaining settings. Since the public opinion regarding climate change is hard to measure

for all participating countries, I rely on proxy variables to capture the public mood. The

thus derived hypotheses are then tested on the network formed through joint statements.

Making use of ERGMs I explore coordination of bargaining positions within the UN-

FCCC context and examine on the one hand whether the public opinion increase a single

country’s likelihood to form ties, and on the other hand whether similar pressure of the

public in different enhance the probability of position coordination among dyads (two coun-

tries). Conventional approaches to analyze international relations and cooperation, such as

neorealism and liberalism mentioned above, focus on actors attributes to analyze cooperative

behavior, most prominently power.

Traditional network analysis, on the other hand, is concerned with relational data, i.e.

ties, connections, and structures formed among players within a policy network (see e.g.

Hafner-Burton et al., 2009, p.559-560; Jonsson et al. 1998, p.324-326). ERGMs (described

in more detail below) combine these two ways of studying international relations by allowing

the researcher to test hypotheses regarding actors’ characteristics on a policy network, in the

case of this paper the network generated through the issuance of joint statements during the

various negotiation rounds of the climate change negotiations. Thus, the network serves as

the dependent variable of the analysis. This novel approach to study international relations,

although proposed in the literature (see Hafner-Burton et al., 2009, p.568), has to the best

of my knowledge not been used before.3

The aim of the ERGMs presented below is to explain how the public and democratic

control mechanisms influence the structure of the network formed among states during UN-

FCCC negotiations. The paper does not investigate the chances of the negotiations to lead

to an agreement able to deal with the global climate crisis, but instead sheds light on the

inner workings of the bargaining process, which is crucial to gain a better understanding of

how international regimes are formed. It is therefore important to distinguish between coop-

erative outcomes and coordination. The former, according to Dillenbourg et al. (1995), “is

accomplished by the division of labor among participants, as an activity where each [party]

is responsible for a portion of the problem solving”. Thus, a cooperative outcome in the

climate change case is congruent with finding an agreement which allocates to each party

3 Although there exists a working paper by Maliniak and Plouffe (2011) which applies this approach toexplain diplomatic ties between countries, creating the network based on the existence of embassies betweencountries.

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its portion to solve the global climate crisis. Coordination, on the other hand, is an effort

by all or a subset of member countries to act in unity in the pursuit of a common goal. The

common goal of two or more countries coordinating their positions in the climate change ne-

gotiations is to achieve an agreement as close to their initial position, and thus as favorable

to them, as possible (see Weiler, 2012). Coordination with others is thus a crucial part of

a country’s diplomatic behavior. Yet not all countries with similar negotiation positions do

form coordinated ties, a clear indication that there are also strategic considerations at play.

This coordination activity between more or less likeminded players is the central topic of

this paper.

However, coordination and potential cooperative outcomes are of course closely related.

The basic problem in the climate change case, i.e. why countries have difficulties to tackle

global warming, stems from the fact that the climate system is a global public good (Stern,

2007, p.37-38). Polluters shift costs caused by their own emissions onto others, and at

the same time benefit from abatement efforts undertaken in other countries (Barrett, 2001,

p.1836-1838). From a game theoretical point of view every country has a dominant strat-

egy to continue polluting, and a disincentive to implement meaningful abatement measures

domestically. Thus, climate change constitutes a classical Prisoner’s dilemma (Hopmann,

1996, p.37-52; Ostrom 1990, p.3-5). Olson (1965) argues that coordination is required to

achieve cooperative outcomes and to overcome the Prisoner’s dilemma. Countries with simi-

lar interests should find it easiest and in their advantage to coordinate negotiation positions

in order to achieve common goals. Conversely, as coordination reduces the complexity of

the negotiations and creates reciprocal expectations, the prospects of finding a negotiated

agreement to limit global warming should increase (see Axelrod and Hamilton, 1981; Carraro

and Siniscalco, 1993; Dupont, 1994, 1996; Schelling, 1960, 2002). Studying coordination of

bargaining positions within the context of the UNFCCC negotiation is therefore important

in its own right. In this article, however, I particularly focus on whether the public opinion

plays a role for the observed coordination behavior.

2 Hypotheses

IR scholars have long established that in two player games mutuality of interests plays a

crucial role to achieve cooperative outcomes (see e.g. Axelrod, 1967; Jervis, 1978). Agree-

ment among actors is facilitated if preferences are relatively similar, because in such settings

the bargaining space is comparatively narrow and solutions are not too far away of the pre-

ferred outcome of either party (Hinich and Munger, 1997; Hopmann, 1996). Yet this insight

is difficult to transfer to situations with multiple players and widely diverging interests. In

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complex systems, as international negotiations with multiple players, countries agreeing with

each other generally still face the problem that numerous other parties oppose them. Zart-

man (1994) describes this problem of complexity of multiparty negotiations in detail and

highlights possible approaches to analyze such multifaceted bargaining situations. One way

to reduce complexity and to facilitate finding an agreement is to form coalitions among par-

ties with similar interests (Dupont, 1994, 1996). In other words, two (or more) parties with

shared interests and attitudes decide to coordinate their positions, in general or on selected

issues, and thus substantiate their views vis-a-vis the remaining negotiating parties. Coordi-

nation among likeminded negotiators is what the Advocacy Coalition Framework (ACF, see

e.g. Sabatier and Jenkins-Smith, 1993) proposes. The ACF postulates coordination through

“biased assimilation”, which assumes that actors with similar characteristics “tend to in-

terpret evidence in a way that supports their prior beliefs and values. According to the

ACF, biased assimilation is the most basic engine that drives collaborative networking and

coalition formation around shared believe systems” (Henry, 2011, p.365). Network analysts

call such a “tendency for nodes [i.e. countries] to form ties based on common attributes

... to share strength and minimize weaknesses” (Hafner-Burton et al., 2009, p.567-568) ho-

mophily. The Resource Dependency Theory (RDT), on the other hand, postulates that

actors perceived as more influential tend to form more ties and are better connected than

less powerful players (Henry, 2011; Weible, 2005). Influence, or perceived influence, accord-

ing to this theory is thus correlated with the numbers of ties formed by an actor. While the

ACF aims at explaining why ties are formed among players with similar characteristic and

interests (homophily effect), the RDT sheds light on the total number of ties or how well an

actor is connected in the network (main effect). The RDT is complementary to the ACF in

explaining the way policy networks are formed.

Democracy, for example, is another factor in the international domain inducing dyadic

coordination, most prominently expressed in the theory of democratic peace (see e.g. Maoz

and Russett, 1993). Research has shown that democracies also tend to work more closely

together than non-democratic countries in other areas, e.g. in the field of international trade

(Morrow et al., 1998), the establishment of international organizations (Russett et al., 1998),

or the formation of alliances (Bennett, 1997; Thompson and Tucker, 1997). One reason to

expect increased coordinative behavior among democracies in negotiation settings is that

political leaders of democratic countries are more accustomed to the process of negotiating

compromises than their peers from less democratic states (see e.g. Dixon, 1994). More specific

to the climate change negotiations, I argue that the public opinion is an important driver why

democracies should be expected to work more closely together. A more “ambitious”4 treaty

4 The term ambitious is used repeatedly during the negotiations and implies a treaty aiming at limiting

5

to tackle climate change and limit global warming is in the interest of the general public,

and particularly the poorer parts of a society who will disproportionate feel the burden

of a warmer climate. Suffering of the poor can more easily be ignored in non-democratic

countries. Thus, relying on the ACF, countries with the same democratic status are expected

to exhibit an increased likelihood to coordinate their positions.

For this reason, i.e. the public opinion and audience costs, I expect democracies not only

to coordinate positions and make joint statements among them, but also to be more active in

forming ties generally, as they have to signal to the home audience that they take domestic

preferences seriously. Neumayer (2002) has shown that democracies tend to exhibit more

concern for the environment than non-democratic countries, since the electorate tends to be

better informed about environmental issues than people in authoritarian states. Further-

more, citizens are also able to express their views and to put pressure on their government

to act in environmentally friendly ways. This is also confirmed by Fredriksson and Gaston

(2000), who observe that the presence of civil liberties and democratic freedom increase the

probability of signing and ratifying environmental agreements.

H1a: The likelihood to form ties increases for countries at similar democracy

levels (homophily effect).

H1b: More democratic countries are more active and form more ties during

negotiations (main effect).

Sprinz and Vaahtoranta (1994) show that ecological vulnerability is an important factor

which shapes a country’s position in environmental negotiations such as those on climate

change due to common interests. Consequently, highly vulnerable countries constitute nat-

ural allies during the negotiations. Conversely, countries less vulnerable to climate change

also share common interest, e.g. their main goal might be to limit the costs of a potential

treaty. For this reason, I argue that vulnerability to climate change can be regarded as an-

other proxy for the public opinion. In countries less susceptible to the negative consequences

of climate change the public is more concerned with other political topics, for example the

potential (economic) loss for the country when the negotiated treaty comes into force. In

highly vulnerable countries instead, the potential negative effects of climate change are likely

one of the major concerns of the public. Hence, I use vulnerability to climate change im-

pacts as an intermediary to capture the public opinion regarding the issue. Together with

a country’s democratic status, I believe to obtain a (admittedly crude) measure of how the

public influences the bargaining behavior of states.

global warming to a maximum of 2 degree Celsius.

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Relying on the ACF again, shared interests due to climate change vulnerability and,

thus, similar public opinions should lead countries to form ties. This is what Buys et al.

(2009) imply when they calculate vulnerability values for most countries of the world and

then conclude that based on these differing vulnerabilities “countries can have very different

orientations towards a global protocol” (p.303).

Negotiating parties also gain some sort of influence from higher vulnerability levels. Ac-

cording to theory, it might be difficult for other parties for whom the issue is less salient

to ignore highly vulnerable states’ concerns. The reason is again the public opinion, be-

cause domestic audiences put pressure on their governments to take concerns of weak and

vulnerable countries seriously (Fearon, 1994, 1997). These audience costs force players for

whom the issue is less salient to consider more vulnerable party’s apprehensions. In other

words, salience can help less powerful states to be taken seriously by more powerful countries.

Therefore, even small and supposedly weak parties can increase their impact on the negotia-

tions substantially.5 Vulnerability to climate change impacts thus may serve as a substitute

for (or be additive to) pure economic power during negotiations (Jonsson, 1981), although

some authors are more skeptical and state that vulnerability is a weakness as countries are

more dependent on finding a negotiated agreement and therefore more willing to make con-

cessions (Grundig et al., 2001). Yet, accepting vulnerability as a source of power, the RDT

suggests a higher likelihood of more vulnerable countries to form ties in the network. This

makes sense from a purely logical point of view. Countries highly sensitive to changes in the

climate system have a high motivation to get involved in the negotiations, to lobby others

by pressing their case, to seek out potential allies, and in general to be more active, which

ultimately causes them to form more ties.

H2a: The likelihood to form ties increases as the vulnerability levels of two coun-

tries become more similar (homophily effect).

H2b: More vulnerable parties are more active and form more ties during climate

change negotiations (main effect).

Coalition groups play a crucial role in the climate change negotiations. Coalition for-

mation is formalized within the UNFCCC context, and official negotiation groups based

on economic and/or regional characteristics appeared over time. Examples are the African

Group, the group of Least Developed Countries (LDCs), or the Alliance of Small Island

States (AOSIS). Official meeting are held at the negotiation group level during the interna-

tional negotiation rounds to coordinate negotiation positions of the coalition members for

5 An example of small countries achieving a lot more than expected is the AOSIS negotiation group, inparticular Tuvalu (see also Betzold et al., 2012).

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the general meetings. It is therefore rather straight forward to expect countries of the same

coalition group to coordinate more often with each other than with countries from other

negotiation groups, first because they tend to share similar interests, and second because

regular interactions foster personal relations between negotiators over time, which facilitates

coordination (Rubin and Swap, 1994). For this reason I include coalition membership as a

control variable in all the models proposed in the remainder of this paper.

3 Research design

In order to test the proposed hypotheses I employ exponential random graph models (ERGMS).

In such models, the observed network is regarded as a self-organizing structure. It is further

assumed that social processes, which can be modeled, generate the dyadic relations (Robins

et al., 2007, p.175-177). Thus, in ERGMs the dependent variable is the observed network.

When modeling such a network purely on nodal attributes, the assumption underlying

the model is of dyadic independence, i.e. the formation of ties does not depend on other ties

(Goodreau et al., 2009, p.109). However, in social settings such as negotiations, it is rather

unrealistic to assume dyadic independence. Therefore the specification of dyadic dependence

models (also called dependence graphs) is proposed in the literature (see Jensen and Winzen,

2012, and in particular Snijders et al. 2006 for various possible model specifications). Fol-

lowing Goodreau et al. (2009) I propose models including one parameter for triad closure

(edge-wise shared partner parameter models) and another parameter for mean nodal degree6

in order to control for the dependency structures in the network. The former measures the

magnitude of triangular relationships formed in the network, or more specifically whether

two nodes forming ties with the same partner have an increased probability of forming a tie

as well (a friend of a friend is a friend, see also Robins et al., 2007). In practice, triad closure

is investigated making use of the geometrically weighted edge-wise shared partner (GWESP)

statistic provided by the ergm-package, part of the wider network-package written for the R

environment (Butts, 2008; Goodreau et al., 2008; Hunter et al., 2008). The weighting pa-

rameters included in the GWESP statistic is fixed at a set value (again following Goodreau

et al., 2009, p.111-112) to avoid the need for curved exponential family models, additional

parameter estimation, and model degeneracy issues (see Hunter, 2007).7

6 I employ the term ‘edges’ of the ergm-package for R for mean degree, which is measuring the same asthe ‘meandeg’-term of that same package, but is easier to interpret (see Morris et al., 2008). In addition,I also included the geometrically weighted dyadwise shared partner statistic (GWDSP, see Robins et al.,2007), yet this measure was not significant in any model tested and worsened the model fit, hence I droppedit.

7 To attain a value for the weighting parameter I employed the procedure explained in Goodreau et al.(2009, p.111-112). The value used is 0.10, although an alteration does not change the results dramatically.

8

ERGMs allow the estimation of homophily and main effects, both proposed in the hy-

potheses above, simultaneously. For the benefit of the reader unfamiliar ERGMs, I provide a

reading example how to interpret these effects here. If, for example, the estimated homophily

effect of a categorical variable is 0.3 (as in the democracy case below), these log-odds must

first be transformed into odds, which gives 1.35. This simply means that two countries from

the same category have a 35% higher likelihood to form a tie than countries from different

categories. The interpretation of homophily is slightly more complicated in the continuous

case. Say the estimated effect is -0.15, in this case the odds after the transformation are

0.86. This means that as the difference between two countries with respect to that continuous

variable increases by 1, the chances of forming a tie decrease by 14%. Again, more similar

countries have an increased chance of coordination, hence the term homophily. Therefore,

for continuous variables a negative effect indicates that countries more similar to each other

are more likely to coordinate, while in the categorical case a positive sign implies homophily.

Main effects, on the other hand, are interpreted in the usual fashion. Assuming the co-

efficient of a continuous variable to be 0.5, which de-logged is 1.65, this denotes that a 1

point increase in that variable increases the chance of forming a tie (with a randomly chosen

partner) by 65%. Hence, homophily is a dyadic measure which captures the probability of

coordination among countries based on similarity. Main effects, on the other hand, capture

how variations in characteristics influence the general activity of countries in the network.

3.1 The network of UNFCCC negotiations

During the UNFCCC negotiation meetings countries have the possibility to voice their views

in various segments such as the Ad hoc Working Group on Long-term Cooperative Action

under the Convention (AWG-LCA), the Ad hoc Working Group on Further Commitments

for Annex I Parties under the Kyoto Protocol (AWG-KP), the Subsidiary Body for Scientific

and Technological Advice (SBSTA), or the High Level Segment. These statements have been

registered and published by the Earth Negotiations Bulletin (ENBs, see IISD, 2009) for a

wide variety of environmental meetings and negotiations since 1992. An issue of the ENBs

is published for every day of the UNFCCC negotiations, and includes a summary (usually

about one sentence long) of the majority of statement made by the negotiation parties in

the publicly accessible meetings.

All the ENBs on the climate change negotiations during the 2-year period from COP 13 in

Bali (December 2007) to COP 15 in Copenhagen (December 2009) were hand-coded. Thus,

in total 11 negotiation rounds and 90 negotiation days are incorporated in the dataset from

which the network is derived. For every statement reported, four properties were recorded:

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(i) who made the statement; (ii) which segment of the negotiations it was made in (e.g. COP,

AWG-KP, COP/MOP, AWG-LCA, SBI, SBSTA); (iii) the main topic of the statement (e.g.

mitigation, adaptation, finance, measuring, reporting, and verification), any subcategories,

and (if applicable) the kind of bargaining strategy used; (iv) whether the statement was

issued by a single country or as a joint statement by two or more parties, and whether it was

later supported or opposed (and if so, by whom). After the statements of each negotiation

day were coded, they were aggregated for every negotiation round, and finally combined

to obtain values for the whole 2-year period. The thus generated data not only provide

an overview of cooperation and position coordination among countries, but also provide an

indication of saliency of the different negotiation issues (how often were topics discussed by

a country).

The network serving as the dependent variable in this paper consists of all the joint

statements made over the two year period of the analysis. Hence, if two countries ever issued

a joint statement between (and including) COP 13 and COP 15, they appear in the network

as having formed a tie. The network thus constructed is represented in Figure 1.8 Statements

made by official negotiations groups were excluded from the analysis. Coalition groups are

expected to make joint statements at the outset of meetings, hence these statements merely

represent the smallest common denominator countries within a group could agree upon

and not coordinated positions. Furthermore, group statements do not allow the inclusion of

countries from outside the group, hence they cannot be interpreted as coordination as defined

above and investigated in this paper. The one exception is the European Union (EU), which

acts as a single entity in the negotiations. As the member states do not issue individual

statements during the negotiations, they are excluded from the analysis and instead the

EU’s group statements are recorded as coming from a single participant in the negotiations.9

This reflects the structure of the UNFCCC negotiations, were the EU is allowed to negotiate

as a block. Finally, negotiating parties not issuing any statements over the study period

were excluded from the analysis.

The thus generated network has 97 nodes, each representing a party to the UNFCCC. Of

the overall possible 4656 possible ties in the network 247 were realized during the two-year

8 Whether a given dyad has coordinated only once or multiple times during the period under study doesnot affect the network for the ERGMs, countries are registered as forming a tie independent of the numberof joint statements. In the network depicted in Figure 1, however, the line width of the edges depends onthe number of joint statement to give the reader a better idea of the collected data. ERGMs for valuednetworks (i.e. networks in which different values for edges representing the intensity of dyadic coordination)are currently under development at the University of Washington, Seattle (see Krivitsky, view). Futureversions of the ergm-package for R will make the implementation of such models possible for practitioners.

9 This means other attributes such as power, or democracy must be aggregated for the EU, which isdone either by averaging (e.g. democratic level) or by summating (e.g. CO2 emissions), whichever is moreappropriate.

10

Figure 1: The network of joint statements during UNFCCC negotiations

DZA

ARG

AUS

BHR

BGD

BRB

BLR

BOL

BRA

CAN

CHL

CHN

COL

HRV

CUB

EGY

EUU

ETH

GAB

GMB

GRD

GUY

ISL

INDIDN

JPN

KAZ

KWT KGZ

MWI

MYS

MHL

MEX

FSM

NZL

NOR

OMN

PAK

PAN

PER

PHL

QAT

KOR

RUSSAU

SGP

ZAF

SDNCHE

THATUR

TUV

UKR

URY

USAVEN

ZMB

African G.AOSISG77/ChinaUmbrellaLDCsOthersEUEIG

The network was constructed using the Fruchterman-Reingold algorithm. Both the size of thenodes and proximity to the center of the graph indicates network centrality. Isolate nodes, i.e.countries not forming any ties, are omitted from the figure

period of the study. Hence, the network has a density of slightly over 0.05, meaning that

about 5% of all possible connections were realized. 38 nodes in the network are isolates, i.e.

they never formed any ties with other participants in the network, although they did issue

statements over the study period. The countries forming most ties were China (36), Saudi

Arabia and Brazil (both 23), India (21), the EU (20), Argentina (19), Canada, Japan, New

Zealand, Mexico, Norway and Russia (all 17), Australia and Singapore (both 16), South

Africa (14), as well as the US and Pakistan (both 10).

What can already be seen from Figure 1 is that countries tend to form clusters around

their negotiation group affiliation, although the coalition group statements have been deleted.

Furthermore, the center of the graph is crowed by big, powerful countries such as China,

India, the US, the EU, Japan, Brazil, etc., who form on average much more ties than less

powerful players, as well as a multitude of ties amongst them. Similar inference can be drawn

from the different centrality measure depicted in Figure 2. Degree is a general measure of

how many ties an actor in the network forms, betweenness computes how often a node lies

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Figure 2: Measures of centrality

Betweenness Degree Eigenvalue

0

20

40

60

0.00 0.05 0.10 0.15 0.20 0.00 0.05 0.10 0.15 0.20 0.00 0.05 0.10 0.15 0.20value

coun

t

on a geodesic, i.e. the shortest way between two not directly connected dyads, and the

eigenvalue reveals how well connected a node is to influential (i.e. well connected) actors in

the network (see e.g. Wassermann and Faust, 1994, p.167-219 for a more detailed description

of the three measures). All three figures show highly skewed distributions and are similar

to each other. This indicates that relatively few actors play a highly central role, reaching

out to many less prominent nodes in the network but also forming ties with each other. In

Appendix 1 the three centrality measures used here are listed for all countries in the network.

3.2 Independent variables

Democracy: To capture a country’s democratic status I employ the most basic measure

reported by Freedom House (2012), rating countries either as free, partly free, or not free.

In the network of 97 countries used for the models described below, 33 are rated as free, 30

are partly free, and the remaining 34 are assessed as not free.

Vulnerability: A country’s vulnerability to climate change impacts is measured using the

Environmental Vulnerability Index (EVI) developed by the South-Pacific Applied Geoscience

Commission (SOPAC) and the United Nations Environmental Programme (UNEP). In total,

the EVI measures 50 indices, 13 of which are used to construct a sub-index for climate change

12

vulnerability (Kaly et al., 2004).10 Although the EVI has been criticized for various reasons

(Barnett et al., 2008), - for example, on the grounds that it is impossible to quantify complex

social-ecological processes - this criticism is not particular to it and applies to all indices that

measure vulnerability.

3.3 Control variables

Power: As can be seen from Figures 1 and 2, powerful countries play a crucial role in the

negotiations. For this reason, I include two measures of power in the models. First, by the

amount of greenhouse gases a country emitted in the last year of the negotiations(UN, 2011,

yet I transform the units to 100 millions of metric tons of CO2 due to model convergence

issues). Emissions capture power in the climate change negotiations in two ways. On the

one hand they are closely related to a country’s GDP and thus economic power.11 On the

other hand they are a proxy for the influence a country has on changing climate, adding

to the negotiation party’s importance in the negotiations on climate change. As a second

power measure I apply delegation size, obtained from the official UNFCCC participants

list at the Copenhagen Climate Change Conference in December 2009 (UNFCCC, 2009).

Delegation size captures a delegation’s potential to specialize, attend meetings, and to form

close relationships with a multitude of delegation members from other countries and thus

represents a softer form of power than greenhouse gas emissions.

Negotiation group: To control for coalition groups an additional variable capturing group

membership is added. Although some non-Annex 1 countries are members of more than one

negotiation group, for the purpose of this paper they are coded as belonging to only a single

coalition deemed most important for them. Almost all non-Annex 1 countries are members

of the G77/China, however, when they are also part of another group, they are coded as

members of that coalition. The same procedure is repeated for Africa, where all countries

are part of the African Group, but some in addition are members of the LDCs and are always

coded as such. All AOSIS countries are furthermore coded to belong to that group. Table 1

provides summary statistics for the independent variables.

10 These are indices capturing climatic changes such as precipitation, droughts, etc. CO2 emissions assuch are not included, only consequences of increased global temperatures. Thus, collinearity between thevulnerability index and the greenhouse gas emissions capturing power is not problematic.

11 When using GDP instead of emissions to capture power the results are very similar although slightlyless significant (at the 1% instead of the 0.1% level)

13

Table 1: Descriptive statistics of the independent variables

Variable name Obs. Mean s.d. Min. Max.Vulnerability 97 3.28 0.71 1.85 4.90Emissions 97 2.95 10.00 0 70.32Delegation size 97 68.7 80.64 6 450

4 Results and discussion

The results of the models described in this section are summarized below in Table 2. When

running a model including only the parameters capturing the dependency structure in the

network (triad closure and mean nodal degree, model not in Table 2), the model converges

quickly and both parameters are highly significant. However, both measures for goodness

of fit in this simple model, the AIC and the BIC (1621.4 and 1634.3 respectively), are

higher than in all models including additional parameters. This indicates that all presented

models include substantive effects and are an improvement over the baseline model. In what

follows, the various hypotheses will be further evaluated against the evidence provided by

the models. Table 3 provides a summary for the odds and the percentage change in the

odds for all significant effects of Model 4 in Table 2.12 These results are repeatedly used

in the following discussion. Models 1 and 2 of Table 2 are partial models, with Model 1

being the democracy model, and Model 2 the vulnerability model. Models 3 and 4 are full

models, yet the former includes all operationalized terms, while the latter only includes the

more substantial parameters for power. footnoteDyadic dependence models such as those

proposed in this paper are estimated using Markov chains Monte Carlo (MCMC) methods.

Hence, run length of the Markov chain is an important issue. For the partial models reveal

chains with a length of 300,000 iterations are sufficient to achieve convergence. However,

model diagnostics reveal that a run of about 3 million iterations is required to fully explore

Models 3 and 4. In what follows I discuss these models with a particular focus on Model 4,

as this is the model with the best goodness of fit according to the BIC.

Democracy: The results for a country’s democratic status indicate increased coordination

at similar levels of democracy, as H1a suggests. Although only significant at the 10% level

in the partial model, the significance increases in both full models and is thus corroborated.

The homophily effect of 0.30 in Model 5 of Table 2 translates to odds of 1.36, which implies

that countries sharing the same Freedom House rating have an higher likelihood to issue joint

12 The coefficients of the models are log-odds and must therefore be transformed into odds before theycan be interpreted further.

14

Table 2: Results of various ERGMs

Model 1 Model 2 Model 3 Model 4Democracy (main, pf) −0.38∗∗∗ −0.42∗∗∗ −0.44∗∗∗

(0.10) (0.12) (0.12)Democracy (main, nf) −0.15∗ −0.09 −0.19†

(0.07) (0.10) (0.10)Democracy (homophily) 0.24† 0.34∗ 0.30∗

(0.13) (0.15) (0.14)Vulnerability (main) 0.39∗∗∗ 0.30∗∗ 0.22∗

(0.10) (0.10) (0.09)Vulnerability (homophily) 0.00 0.13 0.13

(0.11) (0.12) (0.12)Emissions (main) 0.13∗∗∗ 0.15∗∗∗

(0.03) (0.02)Emissions (homophily) −0.11∗∗∗ −0.14∗∗∗

(0.03) (0.02)Delegation size (main) 0.12

(0.09)Delegation size (homophily) −0.24∗

(0.09)Negotiation group (homophily) 0.81∗∗∗ 1.01∗∗∗ 1.07∗∗∗ 1.02∗∗∗

(0.13) (0.13) (0.15) (0.14)Nodal degree −5.06∗∗∗ −10.32∗∗∗ −9.83∗∗∗ −8.29∗∗∗

(0.37) (1.09) (1.49) (1.02)Triad closure (GWESP) 2.65∗∗∗ 2.51∗∗∗ 2.30∗∗∗ 2.39∗∗∗

(0.29) (0.31) (0.30) (0.30)AIC 1521.24 1471.86 1350.19 1361.31BIC 1592.14 1516.98 1472.66 1445.11MCMC-Length 3e+05 3e+05 3e+06 3e+06N 4656 4656 4656 4656∗∗∗p < 0.001, ∗∗p < 0.01, ∗p < 0.05, †p < 0.10

15

statements than countries with different ratings. The coefficient indicates that two countries

from the same category are about 36% more likely to from a tie in the network than two

countries from different categories. This statement is valid for all three possible categories

free, partly free, and not free and supports H1a.

Next I discuss the main effect of democracy (H4b), which is the propensity of countries

to form ties based on their democratic status alone. As there are only three possible rating,

separate parameters are estimated in the models for each category, with free serving as the

baseline group. The effect of partly free is significant and negative in all models, hence

partly free countries tend to be less active than their more democratic counterparts. The

same is true for countries with the rating not free, yet the effect is smaller, on the fringe

of significance and in Model 3 even insignificant. The odds of partly free countries to form

a tie with a randomly chosen country are about 35% lower than for free countries. For

not free countries these odds are only 17% below those of free countries (employing Model 4

coefficients). This result is unexpected. According to theory, states labeled as not free should

be expected to be less active than partly countries. As it turns out, some particularly active

states such as China, Saudi Arabia, or Russia are in the not free segment, producing effects

which reveal that the not free countries are more active than those in the partly free group.

Power is already controlled for in the models, hence it cannot be the hidden driver behind

these unexpected results. Hence, H1b is partially contradicted, although evidence is found

that free countries are more active than countries from either of the two other categories.

Overall, the results for democracy are an indication that the public opinion, through the

audience costs that accrue in democratic countries, are an important factor in determining

the coordination behavior of states.

Table 3: Substantive effects of robust and significant parameters

Variable Odds Percentage changein the odds

Democracy (main, pf) 0.65 -35.42Democracy (main, nf) 0.83 -17.07Democracy (homophily) 1.36 35.52Vulnerability (main) 1.25 24.62Emissions (main) 1.17 16.76Emissions (homophily) 0.87 -13.45Negotiation group (homophily) 2.78 178.17

Effects of parameters reaching significance at least at the 10% level in Model 4. Column 2shows the odds associated with a unit change in the independent variable, and in column 3states the percentage change in the odds is given.

16

Vulnerability: The findings for vulnerability, both the main and the homophily effect,

are weaker than those for the democracy variable. Yet while there is still strong evidence in

favor of the main effect (H2b), the homophily effect (H2a) is insignificant across the models

and must be rejected. Vulnerability as main effect does exhibit significance at least at the

5% level in all models. The positive sign of the effect indicates that as vulnerability scores

increase, countries tend to grow more active and to become more involved in the negotiation

process. Specifically, a one point increase in the vulnerability scores, according to Model 4,

induces countries to be on average about 25% more active in coordinating their positions

with others. Highly vulnerable small countries, such as the SIDS, need a strategy to ensure

that their views are reflected in the negotiations. The rejection of the homophily effect of

vulnerability is an indication that such countries try to reach out to and coordinate positions

with more powerful countries to make their voice heard. This might be an indication that

audience costs are at work again, i.e. the force that compels governments of powerful (and

less vulnerable) countries to consider the plight of small, highly vulnerable countries. Tuvalu

is a good example for this sort of bargaining behavior. Overall, the country issued joint

statements with ten other negotiating parties, among them Argentina, Australia, China, the

EU, New Zealand, Norway, and Switzerland. Micronesia’s strategy is similar, the country

formed ties with inter alia Brazil, Egypt, the EU, South Africa, and the Philippines. Thus,

Jones’ (2007) notion that small, seemingly powerless countries “exploit” stronger negotiation

parties by benefitting from their strength is somewhat supported, and the notion that the

public opinion plays a role for the bargaining behavior of states is upheld.

Power: I will now also briefly discuss power and first turn to the homophily effect. Green-

house gas emissions, the proxy used for power, show a highly significant negative sign for the

homophily effect across all models, which implies that as the difference of emissions between

two countries increases, the likelihood of forming ties shrinks. More specifically, when the

gap between two countries’ emissions widens by 100 million metric tons, the probability of

forming a tie decreases by about 13%. In other words, countries very similar with regards to

their emissions, like for example Kenya (10.4 million tons of CO2 emissions) and Sri Lanka

(11.8 million tons), are expected to have a higher probability to form ties amongst them than

with countries emitting considerably more (or less), such as Algeria (111.3 million tons). In

the chosen example the difference between Algeria and the other two countries is almost

exactly 100 million tons. Hence, the chances to form ties are expected to be about 13%

higher between Kenya and Sri Lanka than for either of them with Algeria. The story for

delegation size is similar, although the significance levels drops somewhat. The coefficient of

delegation size in Model 3 indicates that an increase in the size difference of two countries’

17

delegations by 10 decreases the propensity to form ties by slightly more than 2%.

Now to the main effect of power. According to Model 4 of Table 2, an increase of

emissions by 100 million tons is congruent with a 17% increase in forming ties. To stick to

the example from above, although Algeria has a lower probability of forming a tie with Kenya

than Sri Lanka (due to homophily), the chances that Algeria coordinates with a randomly

chosen partner are about 17% higher than for either of the two others. The main effect of

delegation size tells a similar story, although the coefficient falls below significance in the full

model.

Finally, note that the effect of the control variable negotiation group is highly significant

across all models. The direction and size of the effect confirms that countries of a coalition

group tend to work much closer together than two randomly chosen countries, even after

discounting the purely group specific statements. Overall, coalition partners have an almost

three times higher probability of issuing joint statements than two countries from different

groups. Although not very surprising, this result shows that coalitions indeed play a pivotal

role in the climate change negotiations.

5 Conclusion

The results of the models described and discussed in this paper indicate that coordinated

behavior in the form of issuing joint statements during the climate change negotiations

strongly depends on countries’ interests and characteristics, and in particular that the public

opinion and democracy play a role in how ties are formed, and which countries work together.

ERGMs provide an ideal opportunity to empirically test hypotheses derived from the IR

literature on a network including a big number of the worlds’ countries. This novel approach

used in this paper, modeling country characteristics on a network of ties in the climate

change negotiations, helps to shed more light on the question why countries coordinate

their negotiation positions and how cooperation in a multiparty environment works. In

particular, the paper shows that a country’s power and its democratic status both play a

role in determining the coordinative behavior of parties to the UNFCCC. The statistical

models discussed above show that both homophily as well as main effects are important in

explaining the structure of the network formed in the climate change negotiations through

joint statements.

In the introduction of this paper I argued that explaining the structure of the network

formed during the UNFCCC negotiations is important to gain a better understanding of

regime formation. Coordination of negotiation positions reduces the complexity of the nego-

tiations and thus facilitates overcoming the Prisoner’s Dilemma. This paper demonstrates

18

that coordination indeed occurs and that the public opinion is one of the drivers of this co-

ordination. On the other hand, countries particularly vulnerable to climate change impacts

do not primarily seek partnership with other climate sensitive countries, but instead try

to voice their concerns by teaming up with often powerful and thus influential negotiating

parties, who cannot these weaker players due to audience costs. Thus, the overall network

shows clear patterns of coordination. On the one hand countries seek to maximize their

gains by influencing the outcome of the negotiations in their favor. On the other hand these

structures shed light on the inner process of regime formation, as clear battle lines between

democratic and non-democratic states and developed and developing countries (the coalition

groups).

To conclude, networks in international negotiations do not form randomly and the public

opinion is one important variable explaining why certain ties form, while others do not. Find-

ing alternative measures to capture the public opinion would be desirable, since the reliance

on proxies is prone to lead to mistakes. Yet, since other measures for the public opinion

regarding climate change are currently not available on a global scale, this first attempt to

measure the public’s influence on international climate change negotiations exhibits already

interesting results, and could be a first step indicating which direction future work might

take.

19

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Appendix 1: Various centrality measures

Country Betweenness Degree Eigenvalue1 China 0.22 0.07 0.062 Russian Federation 0.11 0.03 0.033 Saudi Arabia 0.10 0.05 0.044 South Africa 0.05 0.03 0.035 Mexico 0.05 0.03 0.036 Brazil 0.05 0.05 0.047 EU 0.05 0.04 0.048 Argentina 0.04 0.04 0.049 Australia 0.04 0.03 0.03

10 Japan 0.03 0.03 0.0411 Canada 0.03 0.03 0.0412 India 0.03 0.04 0.0413 Singapore 0.03 0.03 0.0314 Norway 0.03 0.03 0.0415 Bahrain 0.02 0.01 0.0116 New Zealand 0.02 0.03 0.0417 Kuwait 0.01 0.02 0.0218 Philippines 0.01 0.02 0.0319 Tuvalu 0.01 0.02 0.0220 Pakistan 0.01 0.02 0.0221 Thailand 0.01 0.01 0.0022 Micronesia 0.01 0.02 0.0223 Switzerland 0.01 0.02 0.0224 US 0.01 0.02 0.0325 Bolivia 0.00 0.02 0.0226 Oman 0.00 0.02 0.0127 Ethiopia 0.00 0.02 0.0228 Iceland 0.00 0.01 0.0129 Bangladesh 0.00 0.01 0.0130 Republic of Korea 0.00 0.01 0.0131 Egypt 0.00 0.01 0.0132 Gambia 0.00 0.00 0.0033 Venezuela 0.00 0.01 0.0134 Peru 0.00 0.00 0.0135 Barbados 0.00 0.00 0.0036 Algeria 0.00 0.01 0.0237 Antigua and Barbuda 0.00 0.00 0.0038 Belarus 0.00 0.00 0.0039 Belize 0.00 0.00 0.0040 Benin 0.00 0.00 0.0041 Bhutan 0.00 0.00 0.00

25

Country Betweenness Degree Eigenvalue42 Burkina Faso 0.00 0.00 0.0043 Burundi 0.00 0.00 0.0044 Cambodia 0.00 0.00 0.0045 Cameroon 0.00 0.00 0.0046 Chile 0.00 0.00 0.0047 Colombia 0.00 0.01 0.0148 Cook Islands 0.00 0.00 0.0049 Costa Rica 0.00 0.00 0.0050 Croatia 0.00 0.00 0.0051 Cuba 0.00 0.01 0.0152 Ecuador 0.00 0.00 0.0053 Gabon 0.00 0.01 0.0154 Ghana 0.00 0.00 0.0055 Grenada 0.00 0.00 0.0056 Guatemala 0.00 0.00 0.0057 Guyana 0.00 0.01 0.0158 Indonesia 0.00 0.01 0.0159 Iran 0.00 0.00 0.0060 Jamaica 0.00 0.00 0.0061 Kazakhstan 0.00 0.00 0.0062 Kenya 0.00 0.00 0.0063 Kyrgyzstan 0.00 0.00 0.0064 Liberia 0.00 0.00 0.0065 Malawi 0.00 0.00 0.0066 Malaysia 0.00 0.01 0.0167 Maldives 0.00 0.00 0.0068 Mali 0.00 0.00 0.0069 Marshall Islands 0.00 0.00 0.0070 Mauritania 0.00 0.00 0.0071 Nepal 0.00 0.00 0.0072 Nigeria 0.00 0.00 0.0073 Palau 0.00 0.00 0.0074 Panama 0.00 0.01 0.0175 Papua New Guinea 0.00 0.00 0.0076 Paraguay 0.00 0.00 0.0077 Qatar 0.00 0.00 0.0078 Republic of Congo 0.00 0.00 0.0079 Rwanda 0.00 0.00 0.0080 Saint Lucia 0.00 0.00 0.0081 Saint Vincent and the Grenadines 0.00 0.00 0.0082 Samoa 0.00 0.00 0.0083 Senegal 0.00 0.00 0.00

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Country Betweenness Degree Eigenvalue84 Sierra Leone 0.00 0.00 0.0085 Solomon Islands 0.00 0.00 0.0086 Sri Lanka 0.00 0.00 0.0087 Sudan 0.00 0.01 0.0188 Tajikistan 0.00 0.00 0.0089 Tanzania 0.00 0.00 0.0090 Togo 0.00 0.00 0.0091 Turkey 0.00 0.00 0.0092 Uganda 0.00 0.00 0.0093 Ukraine 0.00 0.00 0.0094 United Arab Emirates 0.00 0.00 0.0095 Uruguay 0.00 0.01 0.0196 Zambia 0.00 0.00 0.00

27