Addressing Climate Action Misperceptions with Generative AI
This study addresses the problem of climate action misperceptions for climate-concerned individuals, aiming to improve their understanding and motivation for impactful pro-climate behaviors. This is an incremental contribution to the application of AI in climate communication.
This paper investigates whether a large language model (LLM) specialized with climate knowledge and personalized responses can correct misperceptions about carbon emission reduction actions among 1201 climate-concerned individuals. The personalized climate LLM was the only intervention that increased knowledge about climate action impacts and boosted intentions to adopt impactful behaviors, outperforming web searches, unspecialized LLMs, and no intervention in motivating behavior change.
Mitigating climate change requires behaviour change. However, even climate-concerned individuals often hold misperceptions about which actions most reduce carbon emissions. We recruited 1201 climate-concerned individuals to examine whether discussing climate actions with a large language model (LLM) equipped with climate knowledge and prompted to provide personalised responses would foster more accurate perceptions of the impacts of climate actions and increase willingness to adopt feasible, high-impact behaviours. We compared this to having participants run a web search, have a conversation with an unspecialised LLM, and no intervention. The personalised climate LLM was the only condition that led to increased knowledge about the impacts of climate actions and greater intentions to adopt impactful behaviours. While the personalised climate LLM did not outperform a web search in improving understanding of climate action impacts, the ability of LLMs to deliver personalised, actionable guidance may make them more effective at motivating impactful pro-climate behaviour change.