New global climate policies must be deemed just in order to be effective. However, global climate policymaking is complex and subject to normative uncertainties, especially in relation to the distribution of resources, risks, and consequences. These diverging views are dependent on what is distributed and are ultimately based on moral rules and principles that prescribe when a distribution is morally just; distributive justice principles. Enhanced understanding of these distributive preferences is necessary to account for them in both policymaking and modelling. A bottom-up evaluation of stakeholder views in negotiations is a time-consuming but useful method to add to this understanding. This research evaluates the potential of using GPT-4o to perform this task, identifying distributive justice principles in High-Level Segment (HLS) speeches from UNFCCC COP. By identifying distributive justice principles—egalitarianism, utilitarianism, prioritarianism, sufficientarianism, and libertarianism—this research examines the moral foundations of climate policy decisions. Manual annotations of 51 HLS speeches created a ground truth dataset, revealing complexities and class imbalances in principles, with prioritarianism being most dominant. GPT-4o’s performance in identifying relevant sentences and distributive justice principles showed promise but struggled with consistency compared to human annotations. Despite limitations, the model demonstrated efficiency, highlighting its potential for pre-processing and classification tasks. The study underscores the importance of a nuanced, bottom-up understanding of distributive justice in climate negotiations, contributing to climate justice and IAM by offering theoretical insights and practical implications.