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  3. An Image-based Global Climate Sentiment Index.
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  3. An Image-based Global Climate Sentiment Index.

Related Experiment Video

Using Generative Art to Convey Past and Future Climate Transitions
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Published on: March 31, 2023

An Image-based Global Climate Sentiment Index.

Xiangyang Zhai1,2, Yunhan Zhang1,2, Shu Zhang1,2,3

  • 1Institutes of Science and Development, Chinese Academy of Sciences, Beijing, China.

Scientific Data
|June 4, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces a new Climate Sentiment Index using millions of images, overcoming language barriers to track global public opinion on climate change more accurately.

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Area of Science:

  • Environmental science
  • Climate change research
  • Social science

Background:

  • Climate change impacts necessitate understanding public sentiment for policy and risk management.
  • Existing methods relying on text data face language dependence and cross-country comparability issues.

Purpose of the Study:

  • To develop a globally comparable measure of climate sentiment.
  • To overcome limitations of text-based sentiment analysis by incorporating visual data.
  • To provide a robust tool for monitoring climate attitudes and market expectations.

Main Methods:

  • Collected ~3.19 million climate-related images from 19 countries (2015-2024) via Google Images.
  • Detected positive and negative sentiment within visual data.
  • Constructed a multi-frequency Climate Sentiment Index.

Main Results:

  • Developed a novel Climate Sentiment Index using visual data.
  • Achieved greater accuracy and cross-cultural robustness in sentiment identification.
  • Created a long-horizon, globally comparable dataset.

Conclusions:

  • The visual-data-driven Climate Sentiment Index offers a powerful, language-independent tool.
  • This index enhances understanding of public climate attitudes and market expectations.
  • Applicable across economic, social, and environmental domains for informed decision-making.