Emissions Modeling

AI-driven emissions modeling automates carbon footprinting and compliance reporting across scopes

Business impact

• Emissions accuracy — AI improves precision by integrating diverse data and vetted emission factors
• Reporting efficiency — Automated workflows reduce manual data handling and accelerate report generation
• Regulatory compliance — Aligns outputs with CSRD, IFRS, and other sustainability reporting standards

Data requirements

• Financial and procurement systems (Structured) — Provide operational and transactional data for emissions calculation
• Supplier surveys (Text) — Collect supplier-specific emissions and sustainability performance data
• Emission factor libraries (Numeric) — Reference vetted emission factors for accurate footprint estimation

AI methods and techniques

• Predictive AI — Used to estimate emissions based on historical and operational data patterns
• Generative AI — Automates report generation and contextualizes ESG indicators for stakeholders

AI models and model families

Proprietary local language models, GPT-4, Claude