Go to ACL 2024 Workshop ClimateNLP homepageClimate Policy Transformer: Utilizing NLP to track the coherence of Climate Policy Documents in the Context of the Paris Agreement
Climate policy implementation is pivotal in global efforts to mitigate and adapt to climate change. In this context, this paper explores the use of Natural Language Processing (NLP) as a tool for policy advisors to efficiently track and assess climate policy and strategies, such as Nationally Determined Contributions (NDCs). These documents are essential for monitoring coherence with the Paris Agreement, yet their analysis traditionally demands significant la- bor and time. We demonstrate how to leverage NLP on existing climate policy databases to transform this process by structuring informa- tion extracted from these otherwise unstruc- tured policy documents and opening avenues for a more in-depth analysis of national and re- gional policies. Central to our approach is the creation of a machine-learning (ML) dataset ’CPo-CD’, based on data provided by the Inter- national Climate Initiative (IKI) and Climate Watch (CW). The CPo-CD dataset is utilized to fine-tune Transformer Models on classify- ing climate targets, actions, policies, and plans, along with their sector, mitigation-adaptation, and greenhouse gas (GHG) components. We publish our model and dataset on a Hugging Face repository.