Correcting misconceptions and shaping preferences about energy with reinforcement learning

Addressing the climate crisis requires citizens to make informed choices about sustainable energy policy, yet public debates are often shaped by inaccurate beliefs about the environmental and health impacts of different energy sources. Misconceptions about key features of energy sources, such as their greenhouse gas emissions, mortality rates, and contribution to electricity production, are particularly dangerous for this debate, as they risk systematically distorting public support for effective decarbonisation strategies. Here we investigate whether a brief reinforcement-based intervention can correct such misconceptions and influence societal investment preferences. Across seven countries, we reveal systematic misbeliefs, such as overestimating the emissions and mortality of nuclear power and underestimating the mortality of biomass, which were corrected by our intervention across all features and countries. Furthermore, correcting beliefs led to shifts in preferences towards objectively cleaner and safer energy mixes, and these changes were consistent across countries. Follow-up assessments indicated that most of our intervention's effects were detectable after 14 weeks. These results demonstrate a three-step actionable pathway, from behavioural feedback, through belief correction, to preference change, and suggest that short, reinforcement-based interventions could be a scalable tool for improving public decision-making in climate policy and other domains where misconceptions pollute the debate.