Uncovering Multi-Level Governance and Policy Idea Transfer in Energy Policy Using Topic Modelling on Large Policy Corpuses
The paper uses dynamic topic modelling to map the structure of this corpus and analyze trends over time at the three governance levels. Topic models are a family of machine learning methods that map word co-occurrence in documents to find word probability distributions that are, ideally, interpretable as topics to a human reader. Each document is a selection of words drawn from a mixture of topics. For example, the words “carbon” and “capture” might occur with a high probability in a topic, and that topic might then be interpreted as discussing carbon capture and storage technologies. Dynamic topic models also allow for the evolution of the word distribution, so that the prevalence of “greenhouse effect” might be overtaken with “climate change” as the vocabulary evolves.
This paper looks at the evolution of the topic structure and the words within topics in the three policy contexts, and evaluates whether the emergence of new issues and ideas happens first at the international, national or the local level, and whether the three levels are similar in the topics that are discussed and what vocabulary is used to discuss them. The analysis opens a novel vantage point into the relationships between formal and informal institutions and their development.