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Public support and political mobilization are two crucial factors for theadoption of ambitious climate policies in line with the internationalgreenhouse gas reduction targets of the Paris Agreement. Despite their compoundimportance, they are mainly studied separately. Using a random forestmachine-learning model, this article investigates the relative predictive powerof key established explanations for public support and mobilization for climatepolicies. Predictive models may shape future research priorities and contributeto theoretical advancement by showing which predictors are the most and leastimportant. The analysis is based on a pre-election conjoint survey experimenton the Swiss CO2 Act in 2021. Results indicate that beliefs (such as theperceived effectiveness of policies) and policy design preferences (such as forsubsidies or tax-related policies) are the most important predictors whileother established explanations, such as socio-demographics, issue salience (therelative importance of issues) or political variables (such as the partyaffiliation) have relatively weak predictive power. Thus, beliefs are anessential factor to consider in addition to explanations that emphasize issuesalience and preferences driven by voters' cost-benefit considerations.

Key predictors for climate policy support and political mobilization: The role of beliefs and preferences