Addressing climate change effectively requires more than cataloguing the number of policies in place; it calls for tools that can reveal their thematic priorities and their tangible impacts on development outcomes. Existing assessments often rely on qualitative descriptions or composite indices, which can mask crucial differences between key domains such as mitigation, adaptation, disaster risk management, and loss and damage. To bridge this gap, we develop a quantitative indicator of climate policy orientation by applying a multilingual transformer-based language model to official national policy documents, achieving a classification accuracy of 0.90 (F1-score). Linking these indicators with World Bank development data in panel regressions reveals that mitigation policies are associated with higher GDP and GNI; disaster risk management correlates with greater GNI and debt but reduced foreign direct investment; adaptation and loss and damage show limited measurable effects. This integrated NLP-econometric framework enables comparable, theme-specific analysis of climate governance, offering a scalable method to monitor progress, evaluate trade-offs, and align policy emphasis with development goals.

翻译：有效应对气候变化不仅需要统计现有政策的数量，更需要能够揭示其主题重点及其对发展成果实际影响的工具。现有评估通常依赖于定性描述或综合指数，这可能掩盖减缓、适应、灾害风险管理及损失与损害等关键领域之间的重要差异。为弥补这一不足，我们通过将多语言Transformer语言模型应用于官方国家政策文件，开发了一种气候政策导向的量化指标，其分类准确率达到0.90（F1分数）。在面板回归中将这些指标与世界银行发展数据相关联后发现：减缓政策与更高的国内生产总值和国民总收入相关；灾害风险管理与更高的国民总收入和债务相关，但会减少外国直接投资；适应及损失与损害政策则显示出有限的可测量效应。这一融合自然语言处理与计量经济学的框架实现了气候治理的可比性主题专项分析，为监测进展、评估权衡取舍以及协调政策重点与发展目标提供了可扩展的方法。