Addressing Climate Action Misperceptions with Generative AI

TL;DR

This study explores how personalized interactions with a climate-aware language model can improve perceptions and willingness to adopt high-impact climate actions among concerned individuals, showing potential for AI-driven climate behavior change.

Contribution

It demonstrates that personalized climate-focused LLM conversations can enhance understanding and motivation for impactful climate actions, outperforming generic web searches.

Findings

Personalized climate LLM increased knowledge about climate actions.

Participants showed greater intentions to adopt impactful behaviors.

LLMs can effectively motivate pro-climate behavior change.

Abstract

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.