用机器学习分析欧盟气候政策推进情况，帮决策者看清关键影响因素。
Machine Learning for Climate Policy: Understanding Policy Progression in the European Green Deal
- 用文本与元数据构建165项政策数据集，比较TF-IDF、BERT等表示方法
- ClimateBERT在仅文本下表现最优（RMSE=0.17，R²=0.29）
- 加入政治与国家信息后，模型性能提升，可解释性分析揭示关键影响因素
气候变化需要有效的立法行动以减轻其影响。本研究探讨机器学习（ML）在理解欧洲绿色协议内政策从宣布到采纳的进展中的应用。我们构建了一个包含165项政策的数据集，涵盖文本与元数据。目标是预测政策的推进状态，并比较不同文本表示方法，包括TF-IDF、BERT和ClimateBERT。同时引入元数据特征以评估其对预测性能的影响。仅使用文本特征时，ClimateBERT表现最佳（RMSE=0.17，R²=0.29）；加入元数据后，BERT表现更优（RMSE=0.16，R²=0.38）。通过可解释AI方法，揭示了政策措辞及政治党派、国家代表等元数据的影响。结果表明，机器学习工具在支持气候政策分析与决策方面具有潜力。
原文摘要 · Abstract (English)
Climate change demands effective legislative action to mitigate its impacts. This study explores the application of machine learning (ML) to understand the progression of climate policy from announcement to adoption, focusing on policies within the European Green Deal. We present a dataset of 165 policies, incorporating text and metadata. We aim to predict a policy's progression status, and compare text representation methods, including TF-IDF, BERT, and ClimateBERT. Metadata features are included to evaluate the impact on predictive performance. On text features alone, ClimateBERT outperforms other approaches (RMSE = 0.17, R^2 = 0.29), while BERT achieves superior performance with the addition of metadata features (RMSE = 0.16, R^2 = 0.38). Using methods from explainable AI highlights the influence of factors such as policy wording and metadata including political party and country representation. These findings underscore the potential of ML tools in supporting climate policy analysis and decision-making.
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