Machine Learning for Climate Policy: Understanding Policy Progression in the European Green Deal
It addresses the need for effective climate policy analysis for policymakers, but is incremental as it applies existing ML methods to a new dataset.
This study applied machine learning to predict the progression of climate policies in the European Green Deal using a dataset of 165 policies, finding that ClimateBERT performed best on text features alone (RMSE = 0.17, R^2 = 0.29) and BERT with metadata achieved superior results (RMSE = 0.16, R^2 = 0.38).
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.