Data Collection Methodology
Learn how Tracenable collects, standardizes, and validates corporate Climate Targets data through a five-step human-in-the-loop methodology.
Introduction
Accurate, comparable, and traceable Climate Targets 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 Climate Targets Data Collection & Standardization Approach
Defining the Schema through Research
We begin with a rigorous review of foundational frameworks, most importantly the Greenhouse Gas (GHG) Protocol, which provides the global foundation for defining emissions scopes (Scope 1, Scope 2, and Scope 3) and establishing consistent reporting boundaries: the same boundaries used in setting corporate climate targets.
Building on this base, we incorporate guidance from leading regulatory frameworks such as the EU CSRD (ESRS E1) along with voluntary standards including the Science-Based Targets initiative (SBTi), GRI 305, CDP, SASB, and TCFD. These references ensure that the schema reflects both the methodological rigor of global standards and the practical formats companies use when setting and disclosing GHG reduction targets.
Finally, we complement this theoretical foundation with empirical research, analyzing how companies actually set and report their climate targets: covering baseline and target years, scope coverage, reduction magnitude, and intensity metrics. This combined approach ensures that our schema captures the real-world diversity of corporate target disclosures while maintaining consistency, comparability, and scientific alignment across all sectors and geographies.
Capturing Climate Targets Disclosures at Scale
Corporate climate targets 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 climate targets data globally.
Extracting and Converting Disclosures into Structured Data
Climate targets 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 reduction targets-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 targets are absolute or intensity-based 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 reduction target data point is flagged with quality indicators, guiding our analysts in review. Two independent reviewers typically validate climate targets 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 reduction targets data that users can trust.
Rigorous Quality Assurance
Finally, our climate targets 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 reduction target 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 Climate Targets data comes from and how it is collected.
Standardization Guidelines - How disclosures are normalized into consistent Climate Targets 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.