Tracking environmental policy changes in the Brazilian Federal Official Gazette

TL;DR

This paper develops automated NLP techniques and a curated dataset to track and classify Brazilian government actions related to environmental policies in the official gazette, aiding transparency and awareness.

Contribution

It introduces a novel approach combining domain knowledge and NLP models, along with a curated dataset, to classify environmental policy actions in the Brazilian Federal Official Gazette.

Findings

Best NLP model achieved an F1-score of 0.714

Created a high-quality annotated dataset in Portuguese

Demonstrated potential for scalable government action monitoring

Abstract

Even though most of its energy generation comes from renewable sources, Brazil is one of the largest emitters of greenhouse gases in the world, due to intense farming and deforestation of biomes such as the Amazon Rainforest, whose preservation is essential for compliance with the Paris Agreement. Still, regardless of lobbies or prevailing political orientation, all government legal actions are published daily in the Brazilian Federal Official Gazette (BFOG, or "Di\'ario Oficial da Uni\~ao" in Portuguese). However, with hundreds of decrees issued every day by the authorities, it is absolutely burdensome to manually analyze all these processes and find out which ones can pose serious environmental hazards. In this paper, we present a strategy to compose automated techniques and domain expert knowledge to process all the data from the BFOG. We also provide the Government Actions Tracker, a highly curated dataset, in Portuguese, annotated by domain experts, on federal government acts about the Brazilian environmental policies. Finally, we build and compared four different NLP models on the classfication task in this dataset. Our best model achieved a F1-score of $0.714 \pm 0.031$. In the future, this system should serve to scale up the high-quality tracking of all oficial documents with a minimum of human supervision and contribute to increasing society's awareness of government actions.