An Image-based Global Climate Sentiment Index

As the impacts of climate change on global economic and social systems deepen, identifying and quantifying climate sentiment has become crucial for climate policy design, risk monitoring, and cross-sector governance. To address limitations in existing studies—particularly their reliance on textual data, which introduces strong language dependence and limits cross-country comparability—this study incorporates visual information as the medium for sentiment measurement. Using the Google Images search engine, we collect approximately 3.19 million climate-related images from 19 representative countries over the period 2015–2024. By detecting positive and negative sentiment embedded in visual data, we construct a globally comparable, long-horizon, multi-frequency Climate Sentiment Index. This index transcends linguistic boundaries, substantially improving the accuracy and cross-cultural robustness of sentiment identification. It provides a valuable data foundation and analytical tool for understanding public climate attitudes, tracing risk transmission channels, and analyzing the formation of market expectations, with broad applicability across economic, social, and environmental domains.随着气候变化对全球经济与社会系统的影响日益加深，识别并量化气候情绪（climate sentiment）对于气候政策制定、风险监测以及跨部门治理而言愈发关键。为弥补现有研究的局限——尤其是其过度依赖文本数据所带来的严重语言依赖性，以及由此限制的跨国可比性——本研究引入视觉信息作为情绪测度的媒介。本研究借助谷歌图片（Google Images）搜索引擎，收集了2015年至2024年间来自19个代表性国家的约319万幅气候相关图像。通过识别视觉数据中蕴含的正负向情绪，本研究构建了可跨国比较、长时序、多频率的气候情绪指数（Climate Sentiment Index）。该指数突破了语言壁垒，大幅提升了情绪识别的准确性与跨文化稳健性。其可为理解公众气候态度、追踪风险传导路径、剖析市场预期形成机制提供宝贵的数据基础与分析工具，在经济、社会与环境领域均具有广泛的应用价值。