Climate change is one of the most pressing challenges of the 21 st century, and local and regional governments play a central role in shaping both mitigation and adaption. Beyond implementing policies, they also act as communicative actors, framing climate change for local communities. Yet, systematic and scalable evidence on how regional governments communicate climate issues remains limited. This paper develops a novel approach to studying local climate communication by applying modern natural language processing techniques to a large-scale text corpus, containing paragraphs from 397 German county websites. Using a SetFit few-shot learning classifier, we first identify climate-change-related content, which accounts for 3.74% of all county website text on average. We then employ transformer-based BERTopic modelling to explore eight coherent subtopics of climate communication, ranging from climate protection planning and energy consulting to sustainability education and mobility transitions. The geographic distribution of climate communication reveals pronounced spatial disparities. In regression models accounting for website content volume and topic co-occurrence, climate-risk indicators are only weakly and inconsistently related to climate communication. Instead, we find a persistent East–West divide, with East German counties substantially less likely to feature climate-related content overall and for selected themes. Communication also varies systematically with local socio-demographic context: older age structures are consistently associated with lower communication across the overall topic and all subtopics, while urban–rural differences are topic-dependent. Finally, determinants differ across subtopics, indicating that counties vary not only in whether they communicate about climate change, but also in which themes they emphasize.
Schütz et al. (Wed,) studied this question.
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