Why carbon accounting is an AI-shaped problem
Carbon accounting is fundamentally a data transformation problem. You start with activity data — litres of diesel, kilowatt-hours of electricity, tonnes of purchased goods — and you multiply it by emission factors to get tonnes of CO2 equivalent. The maths is simple. The complexity is in the volume, variety, and inconsistency of the inputs.
A mid-sized manufacturer might have: 15 facilities burning 4 different fuel types (Scope 1), purchasing electricity from 8 different grids across 5 countries (Scope 2), and buying materials from 300 suppliers across 12 Scope 3 categories (Scope 3). Each source has different units, different reporting periods, and different levels of data quality.
This is exactly the kind of problem AI is built for: high-volume data processing with consistent rules, pattern matching across inconsistent formats, and systematic cross-referencing.
