Large language models accurately predict public perceptions of support for climate action worldwide

TL;DR

This study demonstrates that large language models can reliably predict public perception gaps regarding climate action support across 125 countries, offering a rapid alternative to traditional surveys.

Contribution

The paper introduces the use of LLMs to accurately estimate perception gaps in climate support worldwide, highlighting their potential as cost-effective assessment tools.

Findings

LLMs, especially Claude, predict perception gaps with about 5 percentage points MAE.

Performance declines in less digitally connected, lower-GDP countries.

LLMs rely on structured reasoning, capturing social projection biases.

Abstract

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.