Measuring China's Policy Stringency on Climate Change for 1954-2022

Area of Science:

• Environmental Science
• Political Science
• Data Science

Background:

• Comparing climate policy stringency across time and jurisdictions is difficult due to undefined metrics and complex causalities.
• China's climate governance must balance environmental, economic, and social impacts for synergistic outcomes.

Purpose of the Study:

• To develop a quantitative framework for measuring China's policy stringency on climate change (PSCC) from 1954 to 2022.
• To create a robust, explainable dataset for analyzing climate governance and supporting causal inference.

Main Methods:

• An integrated framework combining lexicon analysis, text analysis, machine learning, and large-language models applied to multi-source data.
• Incorporation of Chain-of-Thought and SHAP analysis for robust and explainable measurement.
• Framing PSCC across mitigation, adaptation, implementation, and spatial dimensions.

Main Results:

• A comprehensive dataset quantifying China's PSCC from 1954 to 2022 has been generated.
• The dataset maps the Chinese government's evolving stringency on climate change.
• The methodology ensures effective, robust, and explainable measurement of policy stringency.

Conclusions:

• The developed dataset offers a valuable tool for downstream causal analysis in climate governance research.
• This quantitative approach addresses the knowledge gap in comparing climate policy stringency.
• The framework provides insights into China's multi-objective climate governance strategies.