Emissions Factor Matching

Emissions Factor Matching uses AI to perform matching across activity data, spend categories, product records, and emissions factor libraries, enabling more consistent carbon accounting, by comparing units, geographies, supplier attributes, and calculation context to resolve likely factor matches, across ESG data collection and GHG inventory workflows.

Sustainability teams collect activity and spend records from many systems, often with inconsistent units, supplier labels, and category descriptions. Manual mapping to emissions factors is slow, inconsistent, and difficult to audit across repeated reporting cycles.

The AI performs matching across activity records and approved emissions factor libraries and produces suggested factor mappings with confidence, source context, and calculation notes. The output is reviewed inside carbon accounting and ESG data management workflows.

The primary metric is emissions factor mapping exception rate; the target direction is fewer unmapped records and lower review time per reporting cycle.

• •Activity data, spend data, product categories, units, geographies, and approved emissions factor libraries are available for the reporting boundary.
• •Carbon accounting or ESG reporting systems can store factor mapping suggestions, approvals, and audit history.
• •A controlled methodology exists for choosing emissions factors by activity type, region, unit, and reporting standard.