New Study Highlights Importance of Reliable Greenhouse Gas Emissions Data for Climate Crisis

Strategic Insights -

Researchers at LMU Munich's Department of Statistics have published a new study on sustainability research, emphasizing the need for reliable greenhouse gas (GHG) emissions data to address the climate crisis. According to the study, existing datasets are often fragmented, inconsistent, and lack transparent methodologies, making it difficult to obtain reliable emissions data. The researchers present a gold standard dataset containing emission metrics extracted from 139 sustainability reports collected from company websites.

Key Takeaways:

  • The study highlights the importance of reliable GHG emissions data for stakeholders addressing the climate crisis.
  • Existing datasets are often fragmented, inconsistent, and lack transparent methodologies, making it difficult to obtain reliable emissions data.
  • The researchers present a gold standard dataset containing emission metrics extracted from 139 sustainability reports collected from company websites.
  • The dataset acts as an intermediate step to validate and fine-tune models for large-scale extraction of emissions data from thousands of reports.
  • The study employs a Large Language Model (LLM)-powered extraction pipeline to automatically extract emissions metrics, which are then independently assessed by two non-expert annotators.
  • Reports with full agreement are directly considered gold standard, while discrepancies undergo expert review in two stages, with remaining disagreements resolved through in-person discussions.
  • The structured process ensures high data quality while reducing reliance on experts.
  • The dataset serves as a benchmark for human and automated annotation, with significant reuse potential for information extraction tasks in sustainable finance and other downstream tasks such as greenwashing analysis.
  • The study includes authors Jacob Beck, Anna Steinberg, Andreas Dimmelmeier, Laia Domenech Burin, Emily Kormanyos, Maurice Fehr, and Malte Schierholz.

Statistics:

  • The dataset contains emission metrics extracted from 139 sustainability reports collected from company websites.
  • The Large Language Model (LLM)-powered extraction pipeline automatically extracted emissions metrics.
  • Two non-expert annotators independently assessed the extracted emissions metrics.
  • Reports with full agreement were directly considered gold standard, while discrepancies underwent expert review in two stages.
  • The dataset serves as a benchmark for human and automated annotation, with significant reuse potential for information extraction tasks in sustainable finance and other downstream tasks such as greenwashing analysis.

Sources:

  • Addressing data gaps in sustainability reporting: A benchmark dataset for greenhouse gas emission extraction. Scientific Data, 2025,12(1):1-9. (Scientific Data - https://www.nature.com/sdata/.)
  • NewsRx. Department of Statistics Researchers Publish New Studies and Findings in the Area of Sustainability Research (Addressing data gaps in sustainability reporting: A benchmark dataset for greenhouse gas emission extraction). Global Warming Focus. September 15, 2025; p 57.