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

Bo Li1, Enxian Fu2, Shuhao Yang3

  • 1Chinese Academy of Environmental Planning, State Environmental Protection Key Laboratory of Environmental Planning and Policy Simulation, Beijing, 100043, China.

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This study quantifies China's climate policy stringency (CPS) from 1954-2022 using advanced text analysis and machine learning. The findings provide a robust dataset for understanding climate governance and its impacts.

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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.