Original
Health and Climate Risks: Can Policy Attention Persistence Enhance the Resilience of Aging Societies?
Abstract
ABSTRACT The allocation of policy attention serves as the foundational engine of government action. However, the contemporary risk societies render governance resilience increasingly dependent on its vital but under‐theorized temporal dimension: persistence. This study interrogates the conditions under which persistent policy attention becomes a prerequisite for effective governance. Integrating advanced machine learning‐based (Guided LDA) text analysis of Chinese local government reports, longitudinal health data (CHARLS), and a double threshold regression model, this paper examines the interplay between policy attention, its persistence, and climate risk. The findings demonstrate that the persistent allocation of policy attention is indispensable for fostering resilience in healthy aging outcomes, particularly within high climate‐risk environments. Ultimately, the study develops the Risk‐Oriented Attention Persistence Model, providing a novel analytical framework for diagnosing policy environments and guiding resilient governance strategies for complex, long‐term global challenges.
中文
健康与气候风险：政策注意力持续性能否增强老龄化社会的韧性？
摘要
摘要：政策注意力的分配是政府行动的基础引擎。然而，当代风险社会使治理韧性日益依赖于其重要但理论化不足的时间维度：持续性。本研究探讨在何种条件下，持续的政策注意力成为有效治理的前提。本文整合基于先进机器学习的（Guided LDA）文本分析，分析中国地方政府工作报告，结合纵向健康数据（CHARLS）以及双阈值回归模型，考察政策注意力、其持续性与气候风险之间的相互作用。研究发现，持续的政策注意力配置对于促进健康老龄化结果的韧性不可或缺，尤其是在高气候风险环境中。最终，本研究构建了风险导向的注意力持续性模型（Risk-Oriented Attention Persistence Model），为诊断政策环境、指导面向复杂长期全球挑战的韧性治理策略提供了一个新颖的分析框架。
关键词
政策注意力、注意力持续性、治理韧性、气候风险、老龄化社会、健康老龄化、地方政府、机器学习