大型语言模型能准确预测全球对气候行动的支持感知
Large language models accurately predict public perceptions of support for climate action worldwide
AI总结：
大型语言模型能准确预测全球对气候行动的支持感知，为评估气候行动中的感知差距提供快速工具，尤其在资源丰富的国家可作为替代调查手段。
摘要：
尽管大多数人支持气候行动，但对他人支持的广泛低估阻碍了个人和系统性的改变。在本预先注册的实验中，我们测试了大型语言模型（LLMs）是否能可靠地预测这些感知差距。使用国家层面的指标和125个国家的公众意见数据，我们将四种最先进的LLMs与盖洛普世界民意调查2021/22数据和统计回归模型进行比较。LLMs，特别是Claude，能够准确捕捉公众对他人心愿为气候行动出资的感知（MAE约为5个百分点；r = .77），与统计模型相当，尽管在数字连接较少、GDP较低的国家表现有所下降。受控测试显示，LLMs捕捉了关键的心理过程——社会投射，具有系统性的向下偏差，并依赖于结构化推理而非记忆中的价值观。总体而言，LLMs为评估气候行动中的感知差距提供了快速工具，在资源丰富的国家可作为成本高昂的调查替代品，在未被代表的人群中则可作为补充。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
