Practitioners across industries increasingly use large language models to extract information from unstructured documents such as financial filings, legal contracts, and sustainability reports, yet typically rely on generic online recommendations for prompt design that lack empirical grounding in their target setting. We address this gap with a practical methodology for systematically varying individual prompt components (prompt structure, contextual information, and model selection) and measuring their effect on extraction quality. We use greenhouse gas emissions extraction from corporate sustainability reports as a case study of fixed numeric extraction from high-stakes PDF documents that vary widely in structure and presentation and are dispersed across company websites. Instantiated in a retrieval-augmented generation pipeline, our evaluation shows that best practices from industry-facing prompting guides do not always hold: a structured prompt incorporating roles, definitions, extraction rules, and JSON formatting unexpectedly underperforms a simpler question-style prompt, while the benefit of adding tabular context proves both prompt- and model-specific. We condense our evaluation procedure and findings into a practitioner’s decision guide for systematically testing design choices on task-specific data rather than adopting generic recommendations by default.

Automated extraction of corporate greenhouse gas (GHG) emissions from sustainability reports is critical for climate crisis governance, yet the choice of prompt design, context provision, and model in LLM-based pipelines is largely guided by informal best-practice recommendations rather than systematic evaluation. With **Climatextract**, we assess these choices in a RAG pipeline, comparing closed-source against open-weight models, a structured "best-practice" prompt against a simple question-style prompt, and text-only against text-plus-table contexts. We find that performance is non-monotonic across model generations, a structured prompt with roles, definitions, extraction rules, and JSON formatting is significantly outperformed by a question-style prompt, and tabular context helps only in a specific model-prompt combination. A cost-benefit analysis shows that performance gains are often disproportionate to cost increases.

Sustainability reports denote greenhouse gas emissions with heterogeneous unit strings, such as abbreviations, inconsistent formatting, and scale markers. In emission extraction pipelines, these units must be normalized to a common target, e.g. t CO2e, to enable reliable comparison and aggregation of emissions across companies, reporting years, scopes, and regions. We propose a hybrid normalization approach: If a raw unit string is not found in a curated dictionary, a machine learning classifier maps noisy unit strings to normalized emission-unit categories that can then be converted to the target unit. The classifier is trained using character-level TF-IDF, lightweight string features, and LLM-assisted data augmentation. On random-splits of the augmented dataset, an ensemble model achieves approximately 95% accuracy. Additional evaluation on rare real-world unit strings indicates that supervised fallback classifiers can handle out-of-dictionary variants in climate-related information extraction.

We present a research agenda focused on efficiently extracting, assuring quality, and consolidating textual company sustainability information to address urgent climate change decision-making needs. Starting from the goal to create integrated FAIR (Findable, Accessible, Interoperable, Reusable) climate-related data, we identify research needs pertaining to the technical aspects of information extraction as well as to the design of the integrated sustainability datasets that we seek to compile. Regarding extraction, we leverage technological advancements, particularly in large language models (LLMs) and Retrieval-Augmented Generation (RAG) pipelines, to unlock the underutilized potential of unstructured textual information contained in corporate sustainability reports. In applying these techniques, we review key challenges, which include the retrieval and extraction of CO₂ emission values from PDF documents, especially from unstructured tables and graphs therein, and the validation of automatically extracted data through comparisons with human-annotated values. We also review how existing use cases and practices in climate risk analytics relate to choices of what textual information should be extracted and how it could be linked to existing structured data.