We address the challenge of efficiently extracting structured emission information, specifically emission goals, from company reports. Leveraging the potential of Large Language Models (LLMs), we propose a two-stage pipeline that first filters and retrieves potentially relevant passages and then extracts structured information from them using a generative model. We contribute an annotated dataset covering over 14.000 text passages, from which we extracted 739 expert annotated facts. On this dataset, we investigate the accuracy, efficiency and limitations of LLM-based emission information extraction, evaluate different retrieval techniques, and assess efficiency gains for human analysts by using the proposed pipeline. Our research demonstrates the promise of LLM technology in addressing the intricate task of sustainable emission data extraction from company reports.