Climate change policies are essential in responding to climate change. This study investigates 1310 Chinese national climate policies from 1992 to 2023 by applying deep learning models (ERNIE 3.0, BERT, and RoBERTa) and measuring climate policy intensity from three dimensions, policy objective intensity, policy instrument intensity, and policy stringency intensity. The subsequent analysis focuses on how policy intensity impacts on urban carbon emissions. The analysis of climate policy intensity reveals that policy intensity growth aligns with China’s developmental trajectory. Measurable policy objectives (rather than policy quantity), diverse instruments, and systematic integration of administrative constraints with actionable measures are associated with enhancing policy intensity for mitigation and adaptation efforts. Further analysis suggests that greater policy intensity reduces subsequent carbon emissions in cities, particularly in non-carbon intensive cities, where flexible, tertiary-dominated economic structures facilitate stronger policy responses through technological upgrades and energy optimization. The consequences also indicate that climate policy intensity plays a significant role in achieving carbon neutrality. This study contributes to climate policy evaluation by developing a quantitative framework integrating theoretical and empirical analyses, enhances climate governance research by highlighting economic structure’s role in policy responsiveness, and advances policy formulation by leveraging deep learning for empirically validated, targeted strategies. These findings provide insights for optimizing climate policy design to achieve carbon neutrality and demonstrate the broader applicability of deep learning models in climate policy research, guiding policymakers toward effective, context-specific climate governance strategies.
扫码关注我们
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号