Using Python to explore polycentric climate policy networks in European cities

Effective climate change mitigation requires collaboration between different types of actors and across political levels. At the local level, ‘polycentric networks’ comprising independent but overlapping actors, could be particularly influential. These complex systems are challenging to study due to the many actors involved, their dynamism over time, and multiple levels of interaction. More empirical study is needed to understand the factors that shape the creation, nature, and influence of polycentric networks. In particular, the significance of federalism, European Union membership, and a locality’s status as a capital or non-capital city, could each play an important role in shaping a local network, and its influence on local or national policy. I am halfway through a three-year investigation of polycentric climate governance, examining two cities in each of Germany, Sweden and the UK. The project is funded by the UK’s Economic and Social Research Council (grant ES/S014500/1). In this paper, I reflect, from a first-principles perspective, upon my methodological application of Python – a programming language – to analyse local climate policy networks. In particular, I focus on the challenges of integrating the concept of time, and interactions across levels, as well as my experiences of learning Python as a complete beginner. Alongside introducing Python and its utility for examining climate governance, networks, and public policy, I also present some early findings of my research from the project.