Accurately forecasting Climate Policy Uncertainty (CPU) is essential for designing climate strategies that balance economic growth with environmental objectives. Elevated CPU levels can delay regulatory implementation, hinder investment in green technologies, and amplify public resistance to policy reforms, particularly during periods of economic stress. Despite the growing literature documenting the economic relevance of CPU, forecasting its evolution and understanding the role of macro-financial drivers in shaping its fluctuations have not been explored. This study addresses this gap by presenting the first effort to forecast CPU and identify its key drivers. We employ various statistical tools to identify macro-financial exogenous drivers, alongside Google search data to capture early public attention to climate policy. Local projection impulse response analysis quantifies the dynamic effects of these variables, revealing that household financial vulnerability, housing market activity, business confidence, credit conditions, and financial market sentiment exert the most substantial impacts. These predictors are incorporated into a Bayesian Structural Time Series (BSTS) framework to produce probabilistic forecasts for both US and Global CPU indices. Extensive experiments and statistical validation demonstrate that BSTS with time-invariant regression coefficients achieves superior forecasting performance. We demonstrate that this performance stems from its variable selection mechanism, which identifies exogenous predictors that are empirically significant and theoretically grounded, as confirmed by the feature importance analysis. From a policy perspective, the findings underscore the importance of adaptive climate policies that remain effective across shifting economic conditions while supporting long-term environmental and growth objectives.

翻译：准确预测气候政策不确定性对于设计平衡经济增长与环境目标的气候战略至关重要。气候政策不确定性水平升高可能延迟监管实施、阻碍绿色技术投资，并加剧公众对政策改革的抵制，尤其是在经济压力时期。尽管现有文献日益关注气候政策不确定性的经济相关性，但对其演变趋势的预测以及宏观金融驱动因素在其波动中作用的理解尚未得到探索。本研究通过首次尝试预测气候政策不确定性并识别其关键驱动因素来填补这一空白。我们采用多种统计工具识别宏观金融外生驱动因素，同时利用谷歌搜索数据捕捉公众对气候政策的早期关注。局部投影脉冲响应分析量化了这些变量的动态效应，揭示出家庭财务脆弱性、房地产市场活动、商业信心、信贷条件和金融市场情绪具有最显著的影响。这些预测因子被纳入贝叶斯结构时间序列框架，以生成美国和全球气候政策不确定性指数的概率预测。大量实验和统计验证表明，具有时不变回归系数的贝叶斯结构时间序列模型实现了最优的预测性能。我们证明该性能源于其变量选择机制，该机制能识别在经验上显著且理论上合理的外生预测因子，这一结论通过特征重要性分析得到验证。从政策视角看，研究结果强调了适应性气候政策的重要性，这类政策需在不断变化的经济条件下保持有效性，同时支持长期环境与增长目标。