Scope3Trace: Evidence-Based Identification and Extraction of Scope 3 GHG Emissions from Sustainability Reports

2026-07-19

AI Summary

Scope 3 greenhouse gas emissions are challenging to analyze accurately due to sparse disclosure, heterogeneous formats, and a lack of traceable evidence. This work proposes the first dual-granularity framework for extracting Scope 3 emissions data at both organizational and building levels, featuring full traceability of supporting evidence. By integrating optical character recognition (OCR), large language models (LLMs), rule-based engines, and table reconstruction techniques, the framework enables end-to-end extraction of high-precision, interpretable Scope 1–3 emissions data from real-world ESG reports. Concurrently, the study introduces the first multimodal, evidence-annotated dataset designed to support reliable integration and transparent provenance tracking of heterogeneous emissions disclosures.

Abstract

Scope 3 greenhouse gas (GHG) emissions account for the majority of corporate carbon footprints, yet remain difficult to analyze at scale due to sparse disclosures, heterogeneous report document formats, and limited evidence traceability. Existing approaches typically rely on large language models to extract emissions information from ESG reports, but often lack explicit evidence grounding or depend on costly manual annotation and verification to ensure extraction reliability. To address these challenges, we propose Scope3Trace, an evidence-grounded information extraction framework designed to extract interpretable and traceable Scope 3 emissions information from real-world ESG and sustainability reports. The framework integrates a document information extraction pipeline that performs PDF collection and OCR parsing, LLM-assisted page localization and table reconstruction, and hybrid rule-LLM extraction of organization- and building-level emissions disclosures with evidence-grounded verification. Building upon this framework, we further contribute a dual-level, evidence-grounded, multimodal dataset comprising organization-level Scope 3 disclosures extracted from heterogeneous sustainability reports. Scope3Trace enables reliable extraction and transparent integration of heterogeneous sustainability disclosures, achieving high accuracy in extracting Scope 1-3 totals and category-level disclosures from sustainability reports.

Problem

• Scope 3 emissions
• sustainability reports
• evidence traceability
• information extraction
• GHG emissions

Innovation

• evidence-grounded extraction
• Scope 3 emissions
• LLM-assisted information extraction
• sustainability reporting
• multimodal dataset