AI Scope 3 Emissions Tracking

AI-powered supply chain emissions tracking and Scope 3 carbon accounting

The Problem

“Accurate, auditable Scope 3 emissions across energy value chains”

Organizations face these key challenges:

Fragmented Scope 3 activity data across suppliers, traders, logistics providers, and customers with inconsistent formats and low response rates

High manual effort to map purchases, transport legs, and product sales to Scope 3 categories and appropriate emission factors, often with limited traceability

Material risk of misstatement from double-counting, missing data, and inconsistent boundaries (equity share vs operational control; joint ventures; traded volumes)

Impact When Solved

Automated ingestion and reconciliation of procurement, trading, and logistics data to produce audit-ready Scope 3 inventories with clear data lineageHigher-quality estimates via attribute inference and factor selection (region/technology/time-specific), with uncertainty scoring and anomaly detectionFaster reporting cycles and decision support for supplier engagement, low-carbon sourcing, and product strategy aligned to regulatory and investor expectations

Real-World Use Cases

AI in Energy Industry: Smart Grid Optimization and Energy Management

This is like giving the entire power system—power plants, grids, and large customers—a real‑time ‘autopilot’ that constantly predicts demand, reroutes electricity, and tunes equipment so you use less fuel, waste less energy, and keep the lights on more reliably.

Time-SeriesEmerging Standard

8.5

Artificial Intelligence for Energy Systems

Think of this as a playbook of AI tricks for running power systems—generation, grids, and consumption—more like a smart thermostat and less like a manual on/off switch. It applies machine learning to decide how much power to produce, when to store it, and how to route it so the overall system is cheaper, cleaner, and more reliable.