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<p>To conduct a thorough and profound analysis of carbon policy, this study introduces an innovative policy analysis framework that seamlessly integrates topic analysis, network analysis, and predictive modeling, collectively referred to as TNP. The application of TNP provides a comprehensive understanding of the intricate dynamics within China’s carbon policy landscape. Topic analysis delves into the categorization and evolution of key topics within China’s carbon policy. Meanwhile, network analysis explores the intricate relationships among policy topics and the dynamics of cooperation between different institutions. The implementation of the BERT-LSTM predictive model, renowned for its high accuracy, highlights that China’s future carbon policies need to holistically consider technological innovation, market mechanisms, social transformation, and government leadership. The results of this analysis underscore the effectiveness of TNP as a powerful new tool for policy mining and decision support. Its innovative approach, coupled with its ability to conduct multi-dimensional analyses and make accurate predictions, confers significant advantages in the examination of carbon policy and as well as policies spanning various domains.</p>

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Unveiling the complex tapestry of China’s carbon policy: an innovative topic-network-prediction framework

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月9日

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  • Mingyang Zhang,
  • Zhiqiang Xia

摘要

To conduct a thorough and profound analysis of carbon policy, this study introduces an innovative policy analysis framework that seamlessly integrates topic analysis, network analysis, and predictive modeling, collectively referred to as TNP. The application of TNP provides a comprehensive understanding of the intricate dynamics within China’s carbon policy landscape. Topic analysis delves into the categorization and evolution of key topics within China’s carbon policy. Meanwhile, network analysis explores the intricate relationships among policy topics and the dynamics of cooperation between different institutions. The implementation of the BERT-LSTM predictive model, renowned for its high accuracy, highlights that China’s future carbon policies need to holistically consider technological innovation, market mechanisms, social transformation, and government leadership. The results of this analysis underscore the effectiveness of TNP as a powerful new tool for policy mining and decision support. Its innovative approach, coupled with its ability to conduct multi-dimensional analyses and make accurate predictions, confers significant advantages in the examination of carbon policy and as well as policies spanning various domains.