Carbon Digital Twin

Continuously ingests live operational data—such as telematics, IoT sensor feeds, and shipment milestones—to calculate the carbon footprint of every node and edge in the supply chain as events unfold. This moves reporting from a static, backward-looking exercise to a dynamic, forward-looking operational tool.

• Dynamic Ingestion: Connects to TMS, ERP, and IoT platforms via API.
• Instant Calculation: Applies GLEC Framework and ISO 14083 compliant emission factors on the fly.
• Live Visualization: Renders a geospatial heatmap of emission hotspots across the network.

Provides a risk-free sandbox to model the carbon consequences of strategic and operational changes before executing them in the physical world. Users can clone the current state of the network and manipulate variables to compare outcomes.

• Modal Shift Simulation: Test the impact of moving a lane from air freight to rail on total Scope 3 emissions.
• Network Redesign: Model the effect of opening or closing a distribution center on the Emission Intensity Index.
• Supplier Comparison: Evaluate the Carbon-Adjusted Total Cost of Ownership for different carrier bids side-by-side.

Decomposes the supply chain into a Supply Chain Carbon Graph, mapping every supplier, site, lane, and transport asset as a distinct node or edge. This allows for attribution of emissions to the most granular level, such as a single SKU or customer order.

• Well-to-Wheel Analysis: Captures emissions from fuel production through combustion.
• Activity-Based Costing: Links emissions directly to specific logistics activities, not just high-level averages.
• Hotspot Identification: Automatically flags the top 5% of emission-contributing lanes or products.

Leverages machine learning models trained on historical operational and external data to predict the future carbon footprint of planned activities. This enables proactive intervention rather than reactive reporting.

• Lead Time & Emission Prediction: Forecasts the carbon impact of a planned shipment based on predicted route, traffic, and weather.
• Volume Forecasting: Projects future emissions based on a Probabilistic Demand Forecast integrated into the twin.
• Budget Adherence: Alerts users when a predicted operational plan will exceed a predefined carbon budget or Science-Based Target trajectory.

Acts as the feedback loop for supply chain optimization algorithms. The twin provides the objective function (e.g., minimize CO2e) and constraint validation for engines that automate decision-making.

• Carbon-Aware Routing: Feeds real-time emission factors to a Dynamic Route Optimization engine.
• Load Consolidation Logic: Validates that a proposed Load Consolidation Algorithm output genuinely reduces total network emissions, preventing carbon leakage.
• Insetting Validation: Quantifies the emission reduction of an internal Carbon Insetting project to verify its impact on a specific product's footprint.

Establishes an immutable, cryptographically secured chain of custody for every emission data point from its origin to the final report. This transforms the twin from a simulation tool into a system of record for regulatory compliance.

• Immutable Ledger: Records the source, transformation, and calculation of each data point.
• Audit Trail: Provides a complete history for any reported figure to satisfy TCFD and CSRD assurance requirements.
• Factor Lineage: Tracks exactly which version of an Emission Factor Matching Engine was used for a calculation at a specific moment in time.

The foundational data structure that powers a carbon digital twin. It maps an end-to-end supply chain as a network of nodes (suppliers, warehouses, ports) and edges (transport lanes), with each connection enriched with a calculated carbon footprint.

• Enables the twin to identify emission hotspots visually
• Provides the topological model for running what-if scenarios
• Integrates real-time telemetry for dynamic recalculation

The Global Logistics Emissions Council Framework is the universal methodology for calculating and reporting logistics emissions across a multi-modal supply chain. A carbon digital twin relies on this standard to ensure its simulations produce consistent, auditable carbon accounting.

• Provides the calculation engine's rulebook
• Ensures comparability across different carriers and modes
• Required for ISO 14083 alignment

A comprehensive life-cycle analysis method that accounts for total energy consumption and greenhouse gas emissions from fuel production (well-to-tank) through to combustion in a vehicle (tank-to-wheel).

• Prevents the twin from ignoring upstream fuel emissions
• Critical for accurately comparing diesel vs. electric vs. hydrogen modalities
• Exposes hidden Scope 3 impacts in the energy supply chain

The execution counterpart to the digital twin's simulation capability. While the twin tests scenarios, the routing engine calculates the most fuel-efficient path in real-time by integrating traffic, topography, vehicle specifications, and emission factors.

• Consumes the twin's optimized model parameters
• Operates on live telemetry data
• Minimizes the carbon footprint of active shipments

A software component that automatically selects the most appropriate CO2e conversion factor from a managed database based on transport activity data. This engine provides the atomic emission calculations that the digital twin aggregates.

• Matches factors by mode, fuel type, distance, and vehicle load
• Eliminates manual spreadsheet errors
• Maintains an auditable chain of factor provenance

A marginal abatement cost curve (MACC) that visually ranks emission reduction opportunities by cost-effectiveness. The digital twin generates this output to guide decision-makers, plotting the cost per ton of CO2e avoided against total reduction potential.

• Translates simulation results into an executive decision tool
• Identifies the most capital-efficient decarbonization levers
• Separates cost-negative from cost-positive interventions