Voyage Carbon Emissions Benchmarking
Shipment-level maritime emissions estimation and compliance analytics for LNG and broader vessel operations, supporting scenario analysis, decarbonisation planning, and regulations such as IMO CII and FuelEU Maritime.
The Problem
“Shipment-level maritime emissions estimation and compliance analytics for LNG and vessel operations”
Organizations face these key challenges:
Voyage, fuel, cargo, and vessel data are fragmented across multiple systems
Static emissions factors do not reflect vessel-specific operating conditions
Manual compliance calculations are time-consuming and error-prone
Regulatory rules vary by geography, fuel, vessel type, and reporting period
Impact When Solved
The Shift
Human Does
- •Collect voyage, cargo, fuel, and vessel data from separate records
- •Estimate shipment emissions using spreadsheets and fixed vessel assumptions
- •Interpret IMO CII and FuelEU rules for each voyage and reporting period
- •Compare route, speed, and fuel options manually for commercial decisions
Automation
- •No meaningful AI support in the legacy workflow
- •No automated data normalization across voyage inputs
- •No dynamic vessel-specific emissions modelling
- •No automated compliance monitoring or scenario generation
Human Does
- •Review scenario outputs and choose routing, speed, fuel, and cargo strategies
- •Approve compliance submissions and customer-facing emissions reports
- •Handle exceptions where voyage data, cargo facts, or regulations are unclear
AI Handles
- •Combine and normalize voyage, telemetry, cargo, weather, and fuel inputs
- •Estimate vessel- and shipment-level fuel use and emissions with uncertainty bands
- •Run route, speed, fuel, and cargo scenario comparisons and surface key tradeoffs
- •Apply IMO CII, FuelEU Maritime, and related rules to flag compliance risks
Operating Intelligence
How it works
AI runs the first three steps autonomously.
Humans own every decision.
The system gets smarter each cycle.
Who is in control at each step
Each column marks the operating owner for that step. AI-led actions sit above the divider, human decisions and feedback loops sit below it.
Step 1
Assemble Context
Step 2
Analyze
Step 3
Recommend
Step 4
Human Decision
Step 5
Execute
Step 6
Feedback
AI lead
Autonomous execution
Human lead
Approval, override, feedback
AI handles assembly, analysis, and execution. The human gate sits at the decision point. Every cycle refines future recommendations.
The Loop
6 steps
Assemble Context
Combine the relevant records, signals, and constraints.
Analyze
Evaluate options, risk, and likely outcomes.
Recommend
Present a ranked recommendation with supporting rationale.
Human Decision
A human accepts, edits, or rejects the recommendation.
Authority gates · 1
The system must not finalize routing, speed, fuel, or cargo strategy decisions without review by a chartering or operations manager [S2].
Why this step is human
The decision carries real-world consequences that require professional judgment and accountability.
Execute
Carry out the approved action in the operating workflow.
Feedback
Outcome data improves future recommendations.
1 operating angles mapped
Operational Depth
Technologies
Technologies commonly used in Voyage Carbon Emissions Benchmarking implementations:
Key Players
Companies actively working on Voyage Carbon Emissions Benchmarking solutions:
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