Identifying and validating the strongest predictors of informed energy policy support across Europe

Widespread public policy support is crucial for efforts to decarbonize the energy system. Past research has proposed numerous variables as potential key predictors of support for mitigation policies but has failed to i) comprehensively evaluate and compare their predictive importance and ii) identify the strongest predictors within specific mitigation domains. We used machine learning models to identify the strongest predictors of energy policy support in informed citizens (Study 1), validate these results in a real-world referendum on renewable energy in Switzerland (Study 2) and test their generalizability to other climate mitigation policies across six European countries (Study 3). We identified affective responses, societal and environmental policy-impact beliefs, fairness perceptions and perceived trends in policy support over time as the strongest predictors of energy policy support. Using these predictors, we achieved high accuracy in predicting support for a real-world referendum as well as support for different climate mitigation measures across Europe.