Maritime Carbon Price Planning
AI-driven platform for forecasting and modeling regulatory cost exposure in EU shipping fuels, quantifying how EU ETS and FuelEU Maritime impact the economics of fossil marine fuels versus RFNBOs to support compliance planning and fuel-switching decisions.
The Problem
“Forecast and optimize EU maritime carbon compliance costs across fuel choices”
Organizations face these key challenges:
EU ETS and FuelEU Maritime rules are complex and frequently updated
Manual spreadsheet models are slow to maintain and hard to audit
Fuel price, allowance price, and emissions assumptions change rapidly
Different vessel classes and routes have different compliance exposure profiles
Impact When Solved
The Shift
Human Does
- •Interpret EU ETS and FuelEU Maritime rules and decide which assumptions to apply
- •Collect vessel activity, fuel consumption, allowance prices, and fuel price inputs
- •Build and update spreadsheet models for routes, vessels, and fuel options
- •Review scenario outputs and choose compliance, procurement, and fuel-switching actions
Automation
- •Calculate compliance cost estimates from fixed formulas and entered assumptions
- •Aggregate historical price and emissions data into basic comparison tables
- •Generate static what-if scenarios for selected vessel and fuel combinations
Human Does
- •Approve policy interpretation changes before they affect planning assumptions
- •Set planning objectives, risk limits, and scenario priorities for vessels and routes
- •Review recommended fuel-switching, procurement, and decarbonization actions
AI Handles
- •Monitor regulatory updates and translate obligations into cost modeling assumptions
- •Normalize vessel, route, fuel, and emissions data and maintain scenario inputs
- •Forecast allowance prices, fuel prices, and compliance exposure across planning horizons
- •Identify break-even thresholds and rank vessel- and route-specific compliance pathways
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 change policy interpretation assumptions used for planning until a human reviewer approves the update [S1].
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 Maritime Carbon Price Planning implementations:
Key Players
Companies actively working on Maritime Carbon Price Planning solutions: