Although most people support climate action, widespread underestimation of others' support stalls individual and systemic changes. In this preregistered experiment, we test whether large language models (LLMs) can reliably predict these perception gaps worldwide. Using country-level indicators and public opinion data from 125 countries, we benchmark four state-of-the-art LLMs against Gallup World Poll 2021/22 data and statistical regressions. LLMs, particularly Claude, accurately capture public perceptions of others' willingness to contribute financially to climate action (MAE approximately 5 p.p.; r = .77), comparable to statistical models, though performance declines in less digitally connected, lower-GDP countries. Controlled tests show that LLMs capture the key psychological process - social projection with a systematic downward bias - and rely on structured reasoning rather than memorized values. Overall, LLMs provide a rapid tool for assessing perception gaps in climate action, serving as an alternative to costly surveys in resource-rich countries and as a complement in underrepresented populations.

翻译：尽管多数人支持气候行动，但普遍存在的对他人支持程度的低估阻碍了个体与系统性变革。在这项预先注册的实验中，我们检验大型语言模型（LLMs）能否可靠预测全球范围内的此类认知偏差。利用来自125个国家的国家级指标与民意数据，我们将四种前沿LLMs与盖洛普2021/22年度世界民意调查数据及统计回归模型进行基准比较。LLMs（特别是Claude模型）能够准确捕捉公众对他人经济支持气候行动意愿的感知（平均绝对误差约5个百分点；相关系数r=0.77），其表现与统计模型相当，但在数字连接度较低、GDP较低的国家中性能有所下降。受控实验表明，LLMs能够捕捉关键心理机制——存在系统性低估倾向的社会投射效应，并依赖结构化推理而非记忆数值。总体而言，LLMs为评估气候行动认知偏差提供了快速工具，既可作为资源丰富国家中成本高昂的民意调查的替代方案，也可作为代表性不足群体的补充研究手段。