Data Collection & Methodology
Learn how Tracenable collects, standardizes, and validates corporate Greenhouse Gas (GHG) Emissions data through a five-step human-in-the-loop methodology.
Introduction
Accurate, comparable, and traceable Greenhouse Gas (GHG) Emissions data requires more than simply aggregating figures. It requires structured methodology, reliable sourcing, and careful standardization. Tracenable’s approach combines automation, human expertise, and adherence to global reporting frameworks to deliver decision-ready emissions data you can trust.
Our Five-Step GHG Emissions Data Collection & Standardization Approach
Defining the Schema through Research
We start with a rigorous review of foundational references, most importantly the GHG Protocol, which defines the core elements of emissions reporting: Scope 1, Scope 2, and Scope 3 classifications, boundary-setting rules, and the seven recognized greenhouse gases (CO₂, CH₄, N₂O, etc.).
Building on this foundation, we incorporate requirements from leading regulatory frameworks such as the EU CSRD (ESRS E1), as well as voluntary standards like GRI 305, CDP, SASB, and TCFD. This ensures our schema reflects both global best practices and the disclosure formats companies are expected to follow.
Finally, we complement these standards with empirical research, studying how companies actually report emissions across sectors and regions. This combined approach allows us to design a schema that is granular enough to capture detail, yet flexible enough to apply consistently across thousands of companies worldwide.
Capturing GHG Emissions Disclosures at Scale
Corporate Greenhouse Gas (GHG) Emissions data can appear in many places: annual reports, sustainability reports, regulatory filings, standalone data spreadsheets, or hidden on a webpage deep in a company’s site. Our infrastructure is designed to capture all of it.
Through automated web monitoring and targeted expert retrieval, we ensure that no disclosure is overlooked. This comprehensive approach minimizes blind spots and provides the broadest possible coverage of corporate Greenhouse Gas (GHG) Emissions data globally.
Extracting and Converting Disclosures into Structured Data
Greenhouse Gas (GHG) Emissions disclosures come in many formats: PDFs, Excel annexes, HTML tables, and narrative text. Our AI-driven pipelines first convert raw files into a unified structure (e.g., PDF to markdown).
From there:
Computer vision to extract and parse tables, figures, and graphical emissions data.
Natural language processing (NLP) to detect emissions-related text, identify Scope and category, and extract quantitative values and units.
Classification rules to map disclosures into Scope 1, Scope 2, or Scope 3, and to identify whether metrics are absolute or relative measures.
The result: machine-readable, standardized data points that preserve traceability to the original disclosure.
Data Human-in-the-Loop Validation
AI brings speed and scalability, but human expertise ensures accuracy and context. Each extracted GHG data point is flagged with quality indicators, guiding our analysts in review. Two independent reviewers typically validate GHG emissions data, with arbitration applied where discrepancies remain.
This process allows us to:
Correct errors where AI may misclassify scope categories or emission types.
Preserve context from narrative disclosures, such as Scope 2 accounting methods or Scope 3 category definitions.
Continuously improve our extraction models through analyst feedback.
The outcome is audit-grade GHG emissions data that users can trust.
Rigorous Quality Assurance
Finally, our GHG Emissions dataset undergoes multi-layered quality checks:
Automated tests catch obvious anomalies (negative values, implausible spikes, inconsistent units).
Machine learning models detect statistical outliers through unsupervised methods and unusual time-series patterns.
Manual audits ensure nothing slips through the cracks.
This combination of automation and human oversight guarantees that every Greenhouse Gas (GHG) Emissions metric delivered is reliable, comparable, and ready for use in compliance, benchmarking, and research.
Learn More
To explore the methodology in detail, visit:
Data Sources - Where Greenhouse Gas (GHG) Emissions data comes from and how it is collected.
Standardization Guidelines - How disclosures are normalized into consistent Greenhouse Gas (GHG) Emissions dataset.
Calculation Logic - How missing values are inferred and totals are computed using transparent accounting rules.
Quality Assurance - The validations and controls that safeguard data integrity.