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Guidelines for Good Data Sources

High-quality input data is essential to achieving accurate and defensible emissions calculations. While financial transactions and accompanying invoices form the backbone of carbon accounting under BeWo’s methodology, the manner in which these data sources are recorded and documented can significantly influence the precision of the resulting inventory. By following the guidelines below, organizations can enhance the reliability and clarity of their data, thereby improving the overall accuracy of emission estimates.

Financial Transactions: Clarity and Granularity​

1. Descriptive and Non-Contextual Entries:
When recording expenses in the General Ledger (GL), use clear and universally understandable descriptions rather than highly specialized internal codes or context-specific abbreviations. Generic descriptors enable both human reviewers and AI models to interpret the data more accurately.

2. Increased Granularity:
Consider creating more specific financial accounts to distinguish between categories that might otherwise be grouped together. For instance, maintaining separate accounts for electricity, heating, and water—rather than consolidating them under a single “Utilities” account—facilitates more precise emission factor application.

Example:
Instead of booking all energy-related costs to “Utilities,” break them down into:

  • Electricity (Account 4101)
  • Heating (Account 4102)
  • Water (Account 4103)

This delineation supports more accurate classification and estimation of emissions.

Invoices and Attachments: Quality and Relevance​

1. Retain the Actual Invoices or Receipts:
Storing scanned copies or digital versions of the original invoices and receipts is far more valuable than relying on bank statements or other financial summaries. The original invoice typically contains product-level details crucial for product-based or activity-based calculations.

2. Document Scanning and Image Quality:
The AI’s ability to interpret invoice content depends significantly on the clarity of the uploaded image or PDF:

  • Avoid obstructed, blurry, or incomplete scans.
  • Ensure proper lighting and focus, and avoid bundling multiple unrelated invoices into a single extended PDF.

3. Avoid Ambiguous Units and Descriptions:
Where possible, choose units and language that are straightforward and measurable. Complex formatting or line breaks within invoices can reduce the accuracy of data extraction and categorization.

Invoice Content: Structured and Informative​

1. Structured Tables:
Invoices presented in a clearly tabulated format, with each purchased item on a separate line and with identifiable units, improve the AI’s ability to interpret the data accurately.

2. Detailed Line Descriptions:
The more descriptive the invoice lines, the better. Instead of generic terms like “Material,” specify “Steel beams, 100 kg” or “Paper A4, 10 reams.” Granular detail supports more accurate matching to emission factors.

3. Clear Units and Quantities:
Using standard units (kg, liters, kWh) and avoiding ambiguous or proprietary abbreviations help ensure that the correct emission factor can be applied.

Item Registration in ERP Systems​

Organizations that maintain an item catalog or inventory management system can improve data quality by assigning meaningful attributes to frequently purchased materials or products. For example:

  • Product Descriptions: Provide informative, unambiguous labels.
  • Material Specifications: Include the type of material (e.g., steel, wood) or the production method if known.
  • Weight or Other Relevant Measures: Net weight per item can be particularly valuable for product-based or activity-based calculations.

Example:
If the ERP system knows that “Item 1234 – Steel Rod” weighs 10 kg per unit, it becomes straightforward to map this to a activity-based emission factor measured per kg of steel.

Recognizing Limitations​

While improving data sources significantly aids in achieving higher accuracy, certain limitations persist. Even with optimal invoice descriptions and ERP configurations, achieving high-tier estimation approaches (e.g., product-based or activity-based) is not guaranteed. The final quality of emissions calculations also depends on:

  • The availability and relevance of appropriate emission factors.
  • The AI’s capacity to interpret complex language or formats.
  • The inherent complexity of certain supply chains and product categories.

Consistency and Continuous Improvement​

Better data sources contribute to the consistency principle of the GHG Protocol. By applying structured, reliable documentation practices each reporting period, organizations facilitate year-over-year comparability and ease of baseline updates when new information arises. The platform’s features, such as the Data Optimizer, help maintain this consistency and streamline the process of reviewing and updating data as needed.

Learn more: Data Optimizer Overview

By adopting these guidelines for improving data quality—enhancing the clarity and specificity of financial transactions, preserving high-quality invoices, and leveraging item-level detail—organizations lay a stronger foundation for accurate emissions calculations. Over time, these efforts yield more reliable and transparent inventories, better aligning the reported results with the operational realities they seek to represent.