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This study provides the first comprehensive introduction to exponential random graph models (ERGM) in the Czech academic literature. In it we apply ERGM to a network of 68 organisations involved in the Czech coal policy subsystem. First, we summarise the major limitations of the statistical modelling of network data arising from the interdependencies among observations and explain principled solutions to them provided by ERGM. Next, we discuss ERGM's metatheoretical assumptions and their embeddedness within the broader context of social science research. We then introduce three types of relational mechanisms (endogenous, individual, and dyadic) opera-tionalised as specific configurations, which we illustrate through the empirical example of an expert information network. Following a descriptive analysis we apply ERGM, breaking it down into three main steps: simulation, estimation, and estimation assessment. We provide a detailed interpretation of the model's development and results, along with recommendations for building a model and solutions to convergence failure problems. One important finding is that one predictor of the exchange of expert information is ideological homophily, which reduces the potential of expertise to seek compromise solutions. We close with a discussion of the results and ERGM extensions to apply to more complex types of network data such as bipartite and multiplex networks and valued and longitudinal data. [ABSTRACT FROM AUTHOR]

The extent to which a policy actor is perceived as being influential by others can shape their role in a policy process. The interest group literature has examined how the use of advocacy tactics, such as lobbying or media campaigns, contributes to an actor's perceived influence. The policy networks literature, in turn, has found that network ties and occupying certain institutional roles can explain why actors are perceived as influential. When investigating what explains perceptions of influence, interest groups scholars have not accounted for network interdependencies and network scholars have so far not examined the advocacy tactics used by interest groups. This paper addresses the gap at the intersection of these two literatures by investigating the relationship between network ties, institutional roles, advocacy tactics and the presence of influence attribution ties in climate change policy networks. Exponential random graph models are applied to network data collected from the organisations participating in the national climate change policymaking processes in six EU countries that vary by the extent to which they are majoritarian or consensual democracies: Czechia, Finland, Germany, Ireland, Portugal, and Sweden. The results show that network ties and institutional roles are better predictors of influence attribution ties than advocacy tactics and that there is no pattern in the relationship between advocacy tactics and influence attribution ties across different institutional contexts. These findings suggest that because influence is primarily associated with structural factors (network ties and institutional roles) that more established policy actors are likely to have more influence, which may inhibit the need for a significant step change in climate policies. • This paper investigates what determines perceptions of influence in climate change policy networks. • Network ties and institutional roles are better predictors of influence than advocacy tactics. • Advocacy tactics rest on relational assumptions, and as such, can be fruitfully integrated with the policy network approach. [ABSTRACT FROM AUTHOR]

Collaboration between public administration organizations and various stakeholders is often prescribed as a potential solution to the current complex problems of governance, such as climate change. According to the Advocacy Coalition Framework, shared beliefs are one of the most important drivers of collaboration. However, studies investigating the role of beliefs in collaboration show mixed results. Some argue that similarity of general normative and empirical policy beliefs elicits collaboration, while others focus on beliefs concerning policy instruments. Proposing a new divisive beliefs hypothesis, we suggest that agreeing on those beliefs over which there is substantial disagreement in the policy subsystem is what matters for collaboration. Testing our hypotheses using policy network analysis and data on climate policy subsystems in 11 countries (Australia, Brazil, the Czech Republic, Germany, Finland, Ireland, Japan, Korea, Portugal, Sweden, and Taiwan), we find belief similarity to be a stronger predictor of collaboration when the focus is divisive beliefs rather than normative and empirical policy beliefs or beliefs concerning policy instruments. This knowledge can be useful for managing collaborative governance networks because it helps to identify potential competing coalitions and to broker compromises between them. [ABSTRACT FROM AUTHOR]

Scientifically informed climate policymaking starts with the exchange of credible, salient, and legitimate scientific information between scientists and policymakers. It is therefore important to understand what explains the exchange of scientific information in national climate policymaking processes. This article applies exponential random graph models to network data from the Czech Republic, Finland, Ireland, and Portugal to investigate which types of organizations are favored sources of scientific information and whether actors obtain scientific information from those with similar beliefs as their own. Results show that scientific organizations are favored sources in all countries, while only in the Czech Republic do actors obtain scientific information from those with similar policy beliefs. These findings suggest that actors involved in climate policymaking mostly look to scientific organizations for information, but that in polarized contexts where there is a presence of influential denialists overcoming biased information exchange is a challenge. [ABSTRACT FROM AUTHOR]