用个性化AI纠正气候行为误解，提升减排行动意愿。
Addressing Climate Action Misperceptions with Generative AI
- 用具备气候知识的定制化大模型提供个性化建议
- 参与者对减排行动效果的认知准确率显著提升
- 适合希望推动可持续行为改变的环保教育者
减缓气候变化需要行为改变。然而，即使关心气候问题的人群也常对哪些行动最能减少碳排放存在误解。我们招募了1201名关注气候问题的个体，研究与配备气候知识的大语言模型（LLM）对话，且该模型被提示提供个性化回应，是否能改善其对气候行动影响的认知，并提高采纳可行高影响力行为的意愿。实验对比了网络搜索、与非专业大模型对话及无干预三种条件。结果显示，只有个性化气候类大模型能显著提升对气候行动影响的认知水平，并增强采纳高影响力行为的意愿。尽管其在提升认知准确性上未优于网络搜索，但大模型提供个性化、可操作指导的能力，可能使其在激励实质性气候行为改变方面更具优势。
原文摘要 · Abstract (English)
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.
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