[2602.22564] Addressing Climate Action Misperceptions with Generative AI

[2602.22564] Addressing Climate Action Misperceptions with Generative AI

arXiv - AI 3 min read Article

Summary

This study explores how a personalized large language model (LLM) can correct climate action misperceptions among climate-concerned individuals, enhancing their understanding and willingness to adopt impactful behaviors.

Why It Matters

Addressing climate change requires informed action. This research highlights the potential of generative AI in improving public understanding of climate actions, which is crucial for fostering effective behavioral changes necessary for climate mitigation.

Key Takeaways

  • Personalized LLMs can effectively correct misperceptions about climate actions.
  • Participants showed increased knowledge and intention to adopt impactful behaviors after interacting with the personalized LLM.
  • The study suggests that tailored guidance from AI can motivate pro-climate behavior change more effectively than general web searches.

Computer Science > Human-Computer Interaction arXiv:2602.22564 (cs) [Submitted on 26 Feb 2026] Title:Addressing Climate Action Misperceptions with Generative AI Authors:Miriam Remshard, Yara Kyrychenko, Sander van der Linden, Matthew H. Goldberg, Anthony Leiserowitz, Elena Savoia, Jon Roozenbeek View a PDF of the paper titled Addressing Climate Action Misperceptions with Generative AI, by Miriam Remshard and 5 other authors View PDF HTML (experimental) 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 effe...

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