Yin Mengjiao, Zhu Mengmeng
School of Business, Wuxi Taihu University, Wuxi, China.
Front Public Health. 2025 May 15;13:1569903. doi: 10.3389/fpubh.2025.1569903. eCollection 2025.
This study explores the dynamic relationship between temperature changes and public negative emotions-specifically depression, anxiety, and loneliness. It introduces an innovative approach by integrating climate data with digital behavior metrics to provide objective insights into how environmental factors may influence mental health.
A dataset combining daily meteorological records and Baidu search indices from 31 provincial capital cities in China (2013-2023) was used. Search engine query data served as a proxy for public emotional states, avoiding social desirability bias commonly found in self-reported surveys. Panel fixed-effect models and heterogeneity analysis were employed to assess the impact of daily mean temperature (DMT) and daily temperature range (DTR). A "climate zone × season" framework was constructed to explore regional and seasonal variations. Threshold regression analysis was also conducted to identify nonlinear effects.
The results showed that for every 1°C increase in DMT, search indices for depression, anxiety, and loneliness increased significantly by 22.71%, 18.76%, and 19.59%, respectively ( < 0.01). Conversely, a 1°C increase in DTR led to decreases of 30.35%, 31.19%, and 15.41% in these indices ( < 0.05). Threshold regression analysis revealed that the adverse effect of high temperatures on loneliness became insignificant when DTR exceeded 14°C. Heterogeneity analysis highlighted significant regional and seasonal differences, particularly during cold seasons in severely cold zones and hot seasons in warm summer-cold winter zones.
The findings suggest that temperature fluctuations have a complex and regionally dependent impact on public mental health. The moderating role of climate characteristics and seasonal patterns underscores the importance of localized climate policies and mental health interventions. This study provides empirical evidence based on objective behavioral data, contributing to climate-related public health strategies and adaptive policy design.
本研究探讨温度变化与公众负面情绪（特别是抑郁、焦虑和孤独感）之间的动态关系。它引入了一种创新方法，将气候数据与数字行为指标相结合，以提供关于环境因素如何影响心理健康的客观见解。
使用了一个结合了中国31个省会城市（2013 - 2023年）每日气象记录和百度搜索指数的数据集。搜索引擎查询数据作为公众情绪状态的代理指标，避免了自我报告调查中常见的社会期望偏差。采用面板固定效应模型和异质性分析来评估日平均温度（DMT）和日温度范围（DTR）的影响。构建了一个“气候区×季节”框架来探索区域和季节差异。还进行了阈值回归分析以识别非线性效应。
结果表明，DMT每升高1°C，抑郁、焦虑和孤独感的搜索指数分别显著增加22.71%、18.76%和19.59%（<0.01）。相反，DTR每升高1°C，这些指数分别下降30.35%、31.19%和15.41%（<0.05）。阈值回归分析表明，当DTR超过14°C时，高温对孤独感的不利影响变得不显著。异质性分析突出了显著的区域和季节差异，特别是在严寒地区的寒冷季节和暖温夏凉冬地区的炎热季节。
研究结果表明，温度波动对公众心理健康有复杂且因地区而异的影响。气候特征和季节模式的调节作用强调了本地化气候政策和心理健康干预措施的重要性。本研究基于客观行为数据提供了实证证据，为与气候相关的公共卫生策略和适应性政策设计做出了贡献。
