J Anxiety Disord. 2026 Jan 30;118:103127. doi: 10.1016/j.janxdis.2026.103127. Online ahead of print.
ABSTRACT
Climate anxiety has emerged as a significant global psychological and social response to climate change, potentially shaping public engagement and support for climate-related technologies and policies. Here we develop a framework for analyzing online climate anxiety using social media data from China based on 177,232 geo-referenced Weibo posts from 2010 to 2024. The analysis began with the investigation of climate anxiety themes using climate-anxious dictionaries and machine learning methods. Next, the emotional intensity of climate anxiety was assessed through the semantic similarity-based scoring approach. Finally, statistical models were applied to measure the factors influencing climate anxiety. Four major findings are arrived. First, extreme weather events (52.36 %) and livelihood and resource insecurity (22.87 %) were the most discussed and concerning themes, with a notable increase in discussions during summer and autumn. Second, the intensity of climate anxiety has risen significantly. The average intensity increased from 4.42 during the period of 2010-2017 to 7.08 during 2018-2024, with a further notable rise to 7.49 in the more recent period from 2020 to 2024. Third, regions such as Beijing (8.70), Guangdong (8.31), and Zhejiang (7.94) exhibited the highest levels of climate anxiety. Fourth, the intensity of climate anxiety is associated with key demographic and regional factors. Specifically, younger individuals and those residing in climate-vulnerable or informationally developed regions exhibited stronger emotional responses. The framework provides a scalable method for tracking the spatiotemporal dynamics of collective climate anxiety online. The findings demonstrate that digital expressions of climate anxiety constitute a measurable indicator of public concern and carry significant implications for anticipating societal responses and designing targeted communication within climate governance.
PMID:41643241 | DOI:10.1016/j.janxdis.2026.103127
