Event URL: https://www.climatechange.ai/papers/neurips2022/60 »

To be effective, good policy interventions often need to be popular among citizens. The success of a given sustainability transition policy can then rest on what citizens think about it. Setting a price on carbon is a timely example. In Canada for example, despite the federal government's estimate that 80\% of households are receiving a cash surplus as a result of this policy, its popularity is evenly split. Previous work in various countries shows that public support is strongly influenced by ideology, with Canadian conservative leadership campaigning on abolishing the policy. Here, we ask what semantic structure underlies carbon tax opposition, with the hope of informing more effective messaging to increase support. To address this question, we use a large dataset of open-ended responses of Canadians elaborating on their support of or opposition to the tax. To capture the underlying semantics, we use the highly expressive structural topic model (STM), a standard implementation of which has been used to study carbon tax opinion in other countries. Unlike previous work, we focus on STM's ability to flexibly allow for topic mixtures. We have fit these models and find topics learned from oppose responses have higher semantic coherence. Focussing the inferred topic mixture weights, we find that oppose responses mix topics using weights that are less heterogeneous, lower-dimensional, and more (and more densely) correlated than those inferred from support responses. As a result, dislodging conservatives' opposition to carbon pricing, if possible at all, may require addressing multiple beliefs in tandem to overcome the putatively stabilizing, cooperative effect of the highly correlated topics raised when justifying that opposition.