Carbon Accounting

Automate carbon emissions measurement and reporting to support sustainability and compliance

Business impact

• Carbon emissions — Accurate measurement and tracking reduce overall greenhouse gas emissions
• Regulatory compliance — Automated reporting ensures adherence to evolving environmental regulations
• Operational efficiency — Automation reduces manual effort and accelerates data processing and reporting
• Sustainability metrics — Enhanced data quality supports better sustainability performance monitoring

Data requirements

• Energy consumption records (Numeric) — Used to calculate direct and indirect emissions from operations
• Supply chain data (Structured) — Captures emissions from vendors and purchased goods for Scope 3 accounting
• Invoices and utility bills (Text) — Extracted via AI to automate emissions data input and validation
• Remote sensing data (LiDAR, multispectral) (Image) — Supports biomass and carbon stock estimation for environmental monitoring
• Real-time telemetry from devices (Numeric) — Enables dynamic energy consumption and emissions forecasting

AI methods and techniques

• Predictive AI — Forecasts emissions trends and identifies reduction opportunities
• Generative AI — Automates data extraction and gap reconciliation from diverse sources
• Symbolic AI — Applies rule-based compliance checks and emissions calculation standards

AI models and model families

GPT-4, Claude, Attentive Neural Processes (ANPs), Custom domain-specific ML models

Industries

Real-world evidence

9 documented case studies on record.

Companies using this: BT, GANNI, MAD Wealth, MVRDV, Microsoft, Olive Gaea, SKF, University Cambridge, Vizient Inc.