Supply Chain Carbon Graph

The foundational structure maps physical supply chain entities as nodes (suppliers, factories, warehouses, distribution centers, customers) and transportation links as edges. Each edge carries a carbon weight calculated by multiplying activity data (ton-miles) by an emission factor (kg CO2e per ton-mile). This graph-based approach, unlike tabular accounting, preserves the spatial and relational context of emissions, enabling path-specific analysis rather than aggregate reporting.

The graph traverses beyond direct Tier 1 suppliers to map Scope 3 upstream emissions across the entire value chain. By recursively walking the graph from a finished product node backward through component suppliers and raw material sources, the system calculates cumulative embodied carbon. This propagation uses a bill-of-materials explosion algorithm that respects the graph's directed acyclic structure, preventing double-counting when a single supplier feeds multiple downstream nodes.

Each edge's carbon calculation relies on an Emission Factor Matching Engine that selects the appropriate CO2e conversion factor based on contextual attributes:

• Transport mode: ocean container, air freight, truck, rail, barge
• Fuel type: diesel, LNG, sustainable aviation fuel, electric grid mix
• Vehicle utilization: empty running percentage, load factor
• Geographic corridor: country-specific grid intensity The engine queries a managed database aligned with the GLEC Framework and ISO 14083 standard to ensure audit-grade consistency.

The graph enables algorithmic identification of emission hotspots—specific nodes or edges that disproportionately contribute to the total carbon footprint. By running a weighted betweenness centrality analysis, the system surfaces chokepoints where a single high-emission transport lane or supplier dominates the footprint. Results are rendered as Sankey diagrams where the width of each flow line is proportional to its carbon contribution, allowing sustainability officers to visually prioritize abatement investments.

The graph serves as the computational backbone for a Carbon Digital Twin, enabling non-destructive scenario testing. Analysts can modify edge properties—such as switching a lane from air to ocean freight (modal shift) or consolidating multiple LTL shipments into a single FTL movement (load consolidation)—and instantly recompute the total network emissions. The system supports comparative diffing, highlighting the delta between baseline and proposed configurations in both absolute CO2e and Emission Intensity Index terms.

Every emission data point in the graph carries an immutable provenance record that traces its origin, transformation, and chain of custody. This is achieved through a verifiable credential model where each node and edge is cryptographically signed by the data owner. The provenance chain captures:

• Source: primary activity data, carrier-reported fuel consumption, or third-party emission factor database
• Transformation: any calculation, allocation, or estimation logic applied
• Timestamp: when the data was ingested and last updated This ensures the graph meets the evidentiary standards required for CDP disclosure and Science-Based Target validation.

The Global Logistics Emissions Council Framework is the universal methodology for calculating and reporting logistics emissions across multi-modal supply chains. It provides the standardized emission factors and calculation rules that populate the edges of a carbon graph.

• Ensures consistent carbon accounting across carriers and modes
• Aligns with ISO 14083 for regulatory compliance
• Enables apples-to-apples comparison of transport legs

A 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). This is the calculation logic that enriches each edge in the graph with a true emission value.

• Captures upstream emissions from fuel extraction and refining
• Prevents underestimation by avoiding tank-to-wheel-only metrics
• Critical for comparing diesel, electric, and hydrogen transport modes

A KPI that normalizes total carbon emissions against a business metric, such as grams of CO2e per ton-mile or kilograms of CO2e per unit of revenue. This is the primary metric visualized on a carbon graph's edges for hotspot identification.

• Enables performance comparison across different lanes and time periods
• Reveals efficiency outliers in the network
• Supports target-setting for carrier performance management

The computational process of quantifying indirect greenhouse gas emissions in a company's value chain. A supply chain carbon graph is the primary tool for modeling Category 4 (Upstream Transportation) and Category 9 (Downstream Transportation) of Scope 3.

• Maps emissions from purchased goods and distribution
• Identifies the largest emission sources across tiers of suppliers
• Required for Science-Based Target alignment and CDP disclosure

A software component that automatically selects the most appropriate CO2e conversion factor from a managed database based on transport activity data. This engine is the computational heart that assigns carbon values to each edge in the graph.

• Matches factors by mode, fuel type, distance, and vehicle load
• Maintains an auditable chain of factor provenance
• Updates factors dynamically as GLEC and ISO standards evolve

A virtual replica of a physical supply chain network that simulates the carbon impact of operational decisions in real-time. It uses the carbon graph as its foundational data structure for scenario testing.

• Stress-tests modal shifts without physical-world consequences
• Simulates the impact of load consolidation strategies
• Enables what-if analysis for network redesign and inventory placement