Probabilistic Forecasting of Climate Policy Uncertainty: The Role of Macro-financial Variables and Google Search Data
气候政策不确定性的概率预测：宏观金融变量与谷歌搜索数据AI 解读 ↓
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气候政策不确定性的概率预测：宏观金融变量与谷歌搜索数据
- 研究问题
- 宏观金融变量和公众搜索关注度能否帮助预测美国及全球气候政策不确定性，并揭示其变化所关联的主要驱动因素？
- 方法与思路
- 作者筛选宏观金融与搜索变量，用局部投影分析动态响应，再将预测变量纳入贝叶斯结构时间序列模型生成概率预测。
- 材料陈述的贡献
- 摘要称识别了若干主要影响因素，固定回归系数的模型预测表现较优，并将优势归因于变量选择及特征重要性分析所支持的信息。
- 阅读关注点
- 应核对局部投影的冲击识别、变量外生性、搜索数据的时间可用性，以及概率预测的样本外设计、校准和比较标准。
- 阅读边界
- 所据为 arXiv 预印本摘要，同行评审及期刊接收未核实；首创和预测优势是作者陈述，因果识别条件及数值结果未说明。
原文摘要
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.