Policy networks and belief systems: the drivers of cooperation in international climate change politics 2001-2014

Abstract

In this paper, we analyze the drivers of political cooperation in international climate change policymaking. Specifically, we are interested in the stability and alteration of network features, policyrelevant belief structures and actor constellations over time as key drivers for cooperation in international climate change politics. Although nation states undoubtedly continue to remain the main actors in international climate change policy-making, we argue that the international climate change policy field today resembles a policy subsystem, a concept usually assigned to domestic policy arenas, involving a wide range of different types of actors (state and non-state actors from various levels) who regularly seek to influence policy choices around the climate change issue. To analyze and understand policy processes in the international climate change policy subsystem, we apply the Advocacy Coalition Framework (ACF). In doing so, we break new theoretical grounds as former applications of the ACF usually focus on domestic policy processes in Western Europe and North America. In line with most recent applications of the ACF outside this regional focus and with a particular emphasis on foreign policy issues, we further extend the theoretical scope of the ACF and explore the framework’s potential to better understand the policy process on a global policy issue such as climate change. In addition, to answer a common critique of the ACF we also include structural characteristics of the subsystem as driving forces for cooperation in our analysis. Empirically, we use political event data analysis to collect and systematize information on the international climate change policy process in a long-term perspective. Event data describes interaction patterns between various kinds of actors over time by encoding who did what to whom and when. In addition, we code for all the actors their key policy preferences and understand them according to the ACF as a function of underlying belief systems. Methodically, we apply a time dynamic network model (Temporal Exponential Random Graph Model, TERGM) that allows for a systematic testing of hypotheses on how and why network features, policy-relevant belief structures and actor constellations have evolved over time.