Machine Learning for Climate Policy: Understanding Policy Progression in the European Green Deal

• URL: http://arxiv.org/abs/2510.16233v1
• Date: Fri, 17 Oct 2025 21:52:03 GMT
• Title: Machine Learning for Climate Policy: Understanding Policy Progression in the European Green Deal
• Authors: Patricia West, Michelle WL Wan, Alexander Hepburn, Edwin Simpson, Raul Santos-Rodriguez, Jeffrey N Clark,
• Abstract summary: We present a dataset of 165 policies, incorporating text and metadata.<n>We aim to predict a policy's progression status, and compare text representation methods, including TF-IDF, BERT, and ClimateBERT.<n>Using methods from explainable AI highlights the influence of factors such as policy wording and metadata including political party and country representation.
• Score: 37.115198171477985
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Quantifying Climate Policy Action and Its Links to Development Outcomes: A Cross-National Data-Driven Analysis [0.0]
  We develop a quantitative indicator of climate policy orientation by applying a multilingual transformer-based language model to official national policy documents.<n> Linking these indicators with World Bank development data in panel regressions reveals that mitigation policies are associated with higher GDP and GNI.<n>Disaster risk management correlates with greater GNI but reduced foreign direct investment; adaptation and loss and damage show limited measurable effects.
  arXiv  Detail & Related papers  (2025-10-20T11:12:30Z)
• Computational Analysis of Climate Policy [0.0]
  This thesis explores the impact of the Climate Emergency movement on local government climate policy.<n>I first built and configured a system named PALLM, using the OpenAI model GPT-4.<n>I validated the performance of this system with the help of local government policymakers.<n>I used it to conduct a large-scale analysis of current policy documents from local governments in the state of Victoria.
  arXiv  Detail & Related papers  (2025-06-13T00:13:30Z)
• AI-Driven Climate Policy Scenario Generation for Sub-Saharan Africa [0.005755004576310333]
  We use generative AI to simulate climate policy scenarios for Sub-Saharan Africa.<n>The project aims to create plausible and diverse policy scenarios that align with regional climate goals and energy challenges.<n>Our structured, embedding-based evaluation framework shows that generative AI effectively generate scenarios that are coherent, relevant, plausible, and diverse.
  arXiv  Detail & Related papers  (2025-05-24T13:38:17Z)
• Political-LLM: Large Language Models in Political Science [159.95299889946637]
  Large language models (LLMs) have been widely adopted in political science tasks.<n>Political-LLM aims to advance the comprehensive understanding of integrating LLMs into computational political science.
  arXiv  Detail & Related papers  (2024-12-09T08:47:50Z)
• Identifying Climate Targets in National Laws and Policies using Machine Learning [0.0]
  We present an approach for extracting mentions of climate targets from national laws and policies. We create an expert-annotated dataset identifying three categories of target ('Net Zero', 'Reduction' and 'Other') We investigate bias and equity impacts related to our model and identify specific years and country names as problematic features.
  arXiv  Detail & Related papers  (2024-04-03T15:55:27Z)
• Conformal Off-Policy Evaluation in Markov Decision Processes [53.786439742572995]
  Reinforcement Learning aims at identifying and evaluating efficient control policies from data. Most methods for this learning task, referred to as Off-Policy Evaluation (OPE), do not come with accuracy and certainty guarantees. We present a novel OPE method based on Conformal Prediction that outputs an interval containing the true reward of the target policy with a prescribed level of certainty.
  arXiv  Detail & Related papers  (2023-04-05T16:45:11Z)
• Climate Policy Tracker: Pipeline for automated analysis of public climate policies [3.3509104620016092]
  We use a Latent Dirichlet Allocation-based pipeline for the automatic summarization and analysis of 10-years of national energy and climate plans. We focus on analyzing policy framing, the language used to describe specific issues, to detect essential nuances in the way governments frame their climate policies.
  arXiv  Detail & Related papers  (2022-11-10T20:19:28Z)
• Building a Foundation for Data-Driven, Interpretable, and Robust Policy Design using the AI Economist [67.08543240320756]
  We show that the AI Economist framework enables effective, flexible, and interpretable policy design using two-level reinforcement learning and data-driven simulations. We find that log-linear policies trained using RL significantly improve social welfare, based on both public health and economic outcomes, compared to past outcomes.
  arXiv  Detail & Related papers  (2021-08-06T01:30:41Z)
• Supervised Off-Policy Ranking [145.3039527243585]
  Off-policy evaluation (OPE) leverages data generated by other policies to evaluate a target policy. We propose supervised off-policy ranking that learns a policy scoring model by correctly ranking training policies with known performance. Our method outperforms strong baseline OPE methods in terms of both rank correlation and performance gap between the truly best and the best of the ranked top three policies.
  arXiv  Detail & Related papers  (2021-07-03T07:01:23Z)
• Policy Evaluation Networks [50.53250641051648]
  We introduce a scalable, differentiable fingerprinting mechanism that retains essential policy information in a concise embedding. Our empirical results demonstrate that combining these three elements can produce policies that outperform those that generated the training data.
  arXiv  Detail & Related papers  (2020-02-26T23:00:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.