惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

D
Docker
cs.AI updates on arXiv.org
cs.AI updates on arXiv.org
C
Cisco Blogs
Scott Helme
Scott Helme
Know Your Adversary
Know Your Adversary
NISL@THU
NISL@THU
C
Cyber Attacks, Cyber Crime and Cyber Security
D
Darknet – Hacking Tools, Hacker News & Cyber Security
C
CXSECURITY Database RSS Feed - CXSecurity.com
S
Schneier on Security
I
Intezer
Spread Privacy
Spread Privacy
AWS News Blog
AWS News Blog
V
Vulnerabilities – Threatpost
Cloudbric
Cloudbric
V2EX - 技术
V2EX - 技术
Google Online Security Blog
Google Online Security Blog
L
Lohrmann on Cybersecurity
Recent Commits to openclaw:main
Recent Commits to openclaw:main
L
LINUX DO - 热门话题
S
Secure Thoughts
T
The Exploit Database - CXSecurity.com
博客园 - 【当耐特】
Recent Announcements
Recent Announcements
Security Archives - TechRepublic
Security Archives - TechRepublic
Stack Overflow Blog
Stack Overflow Blog
罗磊的独立博客
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
K
Kaspersky official blog
阮一峰的网络日志
阮一峰的网络日志
博客园_首页
Latest news
Latest news
B
Blog
F
Full Disclosure
大猫的无限游戏
大猫的无限游戏
博客园 - 叶小钗
L
LangChain Blog
GbyAI
GbyAI
Last Week in AI
Last Week in AI
S
Security Affairs
Apple Machine Learning Research
Apple Machine Learning Research
N
Netflix TechBlog - Medium
Security Latest
Security Latest
Vercel News
Vercel News
Y
Y Combinator Blog
G
GRAHAM CLULEY
S
Securelist
T
Troy Hunt's Blog
Hacker News - Newest:
Hacker News - Newest: "LLM"
雷峰网
雷峰网

DEV Community

Authentication Security Deep Dive: From Brute Force to Salted Hashing (With Java Examples) Why AI Systems Don’t Fail — They Drift Spilling beans for how i learn for exam😁"Reinforcement Learning Cheat Sheet" I Replaced Chrome with Safari for AI Browser Automation. Here's What Broke (and What Finally Worked) How Python Borrows Other People's Work The $40 Architecture: Processing 1 Billion API Requests with 99.99% Uptime Vibe Coding: A Workflow Guide (From Zero to SaaS) Most webhook security guides protect the wrong side. The scary part is delivery. Headless CMS for TanStack Start: Build a Blog with Cosmic EU Age Verification App "Hacked in 2 Minutes" — What Actually Happened Comfy Cloud’s delete function does not actually remove files Running AI Models on GPU Cloud Servers: A Beginner Guide Event-driven media intelligence with AWS Step Functions and Bedrock I scored 500 AI prompts across 8 quality dimensions — here's what broke How to Call Google Gemini API from Next.js (Free Tier, No Backend Needed) The Portal Protocol: Reclaiming Human Connection in the Age of AI How to Fix Your Team's Scattered Knowledge Problem With a Self-Hosted Forum Intro to tc Cloud Functors: A Graph-First Mental Model for the Modern Cloud Designing Multi-Tenant Backends With Both Ownership and Team Access I Built a Neumorphic CSS Library with 77+ Components — Here's What I Learned PostgreSQL Performance Optimization: Why Connection Pooling Is Critical at Scale Cómo construí un SaaS multi-rubro para gestionar expensas en Argentina con FastAPI + Vue 3 🚀 I Built an Ethical Hacking Scanner Tool – Open Source Project I Replaced /usage and /context in Claude Code With a Single Statusline A Pythonic Way to Handle Emails (IMAP/SMTP) with Auto-Discovery and AI-Ready Design I Collected 8.9 Million Polymarket Price Points — Here's What I Found About How Markets Really Move EcoTrack AI — Carbon Footprint Tracker & Dashboard Everyone's Using AI. No One Agrees How. 5 self-hosted ebook managers worth trying in 2026 Building Your First AI Agent with LangChain: From Chatbot to Autonomous Assistant Common SOC 2 Failures (Real World) Stop Vibe-Checking Your AI App: A Practical Guide to Evals How to Use SonarQube and SonarScanner Locally to Level Up Your Code Quality Your Next To-Do App Is Dead — I Replaced Mine with an OpenClaw AI Sign a Nostr event in 60 lines of Python using coincurve — no nostr-sdk, no nbxplorer, no rust toolchain ITGC Audit Explained Like You’re in Big 4 Patch Tuesday abril 2026: Microsoft parcha 163 vulnerabilidades y un zero-day en SharePoint Stop scraping everything: a better way to track competitor price changes Listing on MCPize + the Official MCP Registry while routing payments OUTSIDE the marketplace — how I kept 100% of my x402 revenue Building an AI-Powered Risk Intelligence System Using Serverless Architecture Why We Ripped Function Overloading Out of Our AI Toolchain Testing AI-Generated Code: How to Actually Know If It Works SaaS Churn Is Killing Your Business. Here Is What to Do About It (Without a Support Team) The Speed of AI Is No Longer Linear - And Self-Improving Models Are Why How to Implement RBAC for MCP Tools: A Practical Guide for Engineering Teams From Standard Quote to Persuasive Proposal: AI Automation for Arborists I built a CLI that scaffolds complete multi-tenant SaaS apps Axios CVE-2025–62718: The Silent SSRF Bug That Could Be Hiding in Your Node.js App Right Now The dashboard that ended our friendship Data Pipelines Explained Simply (and How to Build Them with Python) The Hidden Cost of AI Systems Nobody Talks About. undefined vs undeclared, and how typeof behaves Switching from file-based jobs to NATS/Kafka in Rust without changing code io_uring Adventures: Rust Servers That Love Syscalls Why Agentic AI is Killing the Traditional Database The POUR principles of web accessibility for developers and designers Quantum Neural Network 3D — A Deep Dive into Interactive WebGL Visualization How To Install Caveman In Codex On macOS And Windows Automation Pipeline Reliability: Why Your Workflow Breaks When Nobody Is Watching I Built an 'Open World' AI Coding Agent — It Works From ANY Folder From Freelancing to Product: A Tech Service Company's SaaS Transformation China's AI Giants: Adding Tencent Hunyuan & ByteDance Doubao to AI University (74 Providers) On the Vibe Coders and Their Lies clerk: Auto-Summarize Your Claude Code Sessions AI Weekly — 2026/04/10–04/17 | The Model Lockdown Is Here, but the Toolchain Is the Real Battleground AI 週報 — 2026/04/10–2026/04/17 模型封鎖潮來了,但工具鏈才是真戰場 Maybe this is how Open-Source apps are born... 🚀 Fine-Tune LLMs with LoRA and QLoRA: 2026 Guide tRPC v11 + Next.js App Router: End-to-End Type Safety Without the Boilerplate ShadCN UI in 2026: Why I Stopped Installing Component Libraries and Started Owning My Components SaaS Billing in React Server Components: Stripe + Supabase Without a Single `useEffect` Join our DEV Weekend Challenge — $1,000 in Prizes Across TEN winners! Submissions Due April 20 at 6:59 AM UTC. Implementing FSRS Spaced Repetition in Flutter + Supabase — Adding Memory Science to an AI Learning App "I Texted My Localhost From the Train — Claude Code Fixed the Bug Before I Got Home" I Built a Sales Prep AI and It Went Deeper Than Expected Design to Code #2: One JSON, Eleven Outputs Solving the 100M-Row Problem: A Summary Table Pattern for High-Volume Push Notification Logs Flutter Web With Wasm: What Actually Changes For Developers I Built 50 Royalty-Free Soundtracks for My Side Project in a Weekend Using AI Music Generation The Vibe Coding Security Checklist: 7 Things to Check Before You Ship Stop Letting Googlebot Guess Fix Your React App's SEO Right Desconstruindo o Streaming do LinkedIn: Como Criar um Engine de Extração de Vídeo de Alta Performance com HLS e FFmpeg (EDA Part-1) EDA (Exploratory Data Analysis) Explained With Real Life — Why Looking at Your Data Is the Most Important Step in Machine Learning Brand Relationship Management at Scale: Our 4-Touch Outreach System for 200+ Brands Why String.fromEnvironment() Might Return an Empty String in Dart JGuardrails 1.0.0 — Hardening Java LLM Apps Against Jailbreaks, Toxicity, and Prompt Injection Plan and Schedule a Full Week of Threads Content From One Claude Conversation Coding Cat Oran Ep3, Five Tables Changed Everything Updated: BFF Pattern I'm done watching freelancers get buried by 200 proposals. So I'm building the alternative. This is my first post BFS Algorithm in Java Step by Step Tutorial with Examples Tracking LLM Pricing Monthly: An Open Dataset for 22 AI Models How We Measure Content ROI on a Comparison Site: Revenue Attribution Without Perfect Data Introducing Nova AI Ops: The AI-Native Operating System for SRE Teams I built a free desktop video downloader for Windows — Grabbit How Talkie OCR Helps Vision-Impaired & Dyslexic Users Read the World Around Them VRCFaceTracking安装和iPhone面捕配置教程,有bug Even CrowdStrike Can't See Your Agents The Automation Gold Rush: What n8n Workflows and Claude Are Opening Up for Developers Right Now
How to Measure a Ship's CO Emissions From Land
VesselAPI · 2026-05-20 · via DEV Community

How to Measure a Ship's CO₂ Emissions From Land

Here's a question that sounds simple until you try to answer it: how much CO₂ did that container ship just emit on its way from Shanghai to Rotterdam?

A practical walkthrough of measuring vessel CO₂ emissions via API — what EEXI actually means, why fuel type changes everything, and how to verify a number before you trust it.

You can see the ship. You know roughly where it went, and roughly how fast. You can look up its size. And yet the honest answer — the one a regulator or a carbon accountant would actually accept — requires you to know things about that vessel that aren't printed on the hull. What fuel was in its tanks. What its engines were designed to do at three-quarters load. Whether its propeller has been polished this year. Whether the paint on its hull is the slick anti-fouling kind or the kind that's currently hosting a small ecosystem of barnacles.

This tutorial is about closing that gap. We're going to walk through how to ask our /emissions endpoint for the CO₂ output of a single vessel, and — more interestingly — how to understand what the API is actually doing under the hood. Because if you're going to make a decision based on a number, you should know where the number came from.

The thing everyone gets wrong

Most people assume ship emissions are calculated the way car emissions are: you burn X litres of fuel, each litre contains Y grams of carbon, multiply, done.

That's roughly right for a car. It falls apart for ships almost immediately.

The IMO does publish carbon factors for each marine fuel type, and they're as clean as you could want. For heavy fuel oil (HFO), the number is 3.114 grams of CO₂ per gram of fuel burned. For VLSFO — the blended low-sulphur product most large vessels burn today — it's 3.151. For marine gas oil (MGO), 3.206. For LNG, the direct combustion factor is 2.750. These aren't interchangeable. Submitting an MRV report with the HFO factor against a tank of VLSFO will fail verification, which is exactly the kind of thing I got wrong the first time I tried to reconcile a vessel's annual fuel mix against its reported emissions. The numbers look close. They aren't.

If you knew exactly how much of each fuel a ship had burned, you'd be done. The problem is that almost nobody outside the ship does. Aggregate fuel consumption is reported annually under the IMO Data Collection System; per-voyage data is collected by the EU MRV regime for vessels calling at European ports, but it isn't publicly accessible. If you want per-voyage emissions from the outside, you have to estimate them — working backwards from what the ship was designed to burn, and adjusting for what it probably actually did.

The starting point for that estimate is a three-letter acronym you'll see everywhere in this domain.

What EEXI actually is

EEXI stands for Energy Efficiency Existing Ship Index. The name sounds like it was designed to deter questions, so let's ignore it.

What EEXI actually is: a fuel economy rating. It's the mpg sticker on the window, except the window is a 300-metre container ship and the sticker is buried in a classification society database in Hamburg.

For most existing vessels of 5,000 gross tonnes and above — broadly, the ships big enough to do international trade and already in service when the regulation took effect on 1 January 2023 — the IMO requires a calculation that answers a single question: if this ship sailed at the speed corresponding to 75% of its maximum continuous engine rating, in calm weather, in a defined reference state, how many grams of CO₂ would it emit per tonne of cargo per nautical mile?

That's it. Standardised conditions, one number, comparable across vessels. A modern, efficient large bulk carrier might come in around 3 g CO₂/tonne-nm. An older, smaller one might be roughly double that. Container ships sit higher again because their capacity is measured differently. Lower is better, the way fewer litres per 100km is better on a car.

EEXI on its own doesn't tell you what a ship emitted yesterday. But combined with a known route, a speed profile, and a load factor, it lets you build a credible estimate without ever needing the captain's fuel logs.

The request

Let's measure something. Here's a minimal call for a vessel by IMO number:

curl "https://api.vesselapi.com/v1/emissions?imo=9395044" \
  -H "Authorization: Bearer YOUR_API_KEY"

Enter fullscreen mode Exit fullscreen mode

And here's the relevant chunk of the response, trimmed for clarity. The values are illustrative — they show the shape of the response, not certified figures for this vessel:

"emissions": {
  "eexi": {
    "attained": 6.82,
    "required": 7.84,
    "unit": "g_co2_per_tonne_nm",
    "compliant": true
  },
  "fuel": {
    "primary": "VLSFO",
    "eca": "MGO",
    "carbon_factor_primary": 3.151,
    "carbon_factor_eca": 3.206
  },
  "verifier": {
    "name": "DNV",
    "verified_at": "2023-04-12",
    "method": "approved_calculation"
  }
}

Enter fullscreen mode Exit fullscreen mode

The most important field here is probably the one you'd skip past on first reading: verifier. We'll come back to it. First, the things this response is telling you about the ship itself.

EEXI arrives as a pair — attained and required. EEXI is an efficiency index, so lower is better, and required is a ceiling: the maximum value the IMO will accept for this ship type and size. The vessel is compliant if attained sits at or below that ceiling. If it doesn't, the primary remedy is an Engine Power Limitation: a technical and administrative measure that caps engine output and brings the attained index down. Switching fuels operationally doesn't change EEXI itself — EEXI is a design-condition index, fixed at certification. Fuel choices affect the operational metric (CII) instead. A ship that can't demonstrate EEXI compliance can't get its certificate endorsed, which means in practice it can't legally trade. It isn't really a menu.

The fuel block reports two carbon factors because most large vessels switch fuels at ECA boundaries — the North Sea, the Baltic, the US and Canadian coasts, and (from 1 May 2025) the Mediterranean. VLSFO on the open ocean, MGO or another compliant blend inside the zone. For a voyage that crosses an ECA boundary, you need to know which fuel was burning where.

Why the verifier matters more than the number

An emissions number without a verifier is a rumour.

I mean this practically. EEXI calculations are prepared by the ship's owner or technical manager and submitted to a classification society — DNV, ABS, Lloyd's Register, ClassNK, Bureau Veritas. These bodies do the actual checking: engine specs, hull form, propulsion train, sea margin assumptions, the lot. They either approve the calculation or send it back. The number that survives this process is the only one that means anything in a regulatory context.

When the API returns a verifier block, it's saying: this isn't our estimate. This is the number a recognised classification society signed off on, with their reputation attached. If the verifier field is missing, or shows "method": "estimated", you're looking at a calculated approximation. Most APIs don't surface this distinction at all, which is how an estimated figure ends up in someone's audited disclosure.

For the EU ETS, which began applying to shipping in 2024 (40% of verified emissions surrendered that year, 70% in 2025, 100% from 2026), or for CSRD disclosures, or for any scope 3 reporting that has to survive contact with an auditor — the verified number is the one that matters.

From EEXI to an actual voyage

EEXI gives you grams of CO₂ per tonne-nautical-mile under reference conditions. To get the emissions of an actual voyage, the back-of-envelope multiplication is:

voyage_emissions = EEXI × cargo_tonnes × distance_nm × correction_factor

Enter fullscreen mode Exit fullscreen mode

This formula works, with friction. EEXI is calibrated to a defined reference state, which for bulk carriers and tankers is 70% of deadweight but for container ships is a TEU-based capacity figure — not a deadweight fraction at all. Check the IMO guidelines for the vessel class before applying a load correction. The cargo_tonnes you plug in should reflect actual cargo on board, because a container ship sailing 60% laden carries 60% of the cargo but burns considerably more than 60% of the fuel. And the formula degenerates on a ballast leg: zero cargo gives zero emissions, which is physically impossible. Ballast voyages need a displacement-based substitution or allocation back to the laden leg.

The correction factor is where the rest of the honesty lives. Real voyages aren't reference conditions. Heavy weather adds fuel. Slow steaming subtracts it — dramatically. For full-form displacement vessels, fuel consumption scales with speed at an exponent typically between 2.7 and 3.5, with the cube as the standard admiralty anchor. That non-linearity is why dropping container ship service speeds from around 21 knots toward 14 was, by most accounts, the single biggest operational lever the industry pulled in the last decade. Not technology. Just patience, and the willingness to leave port a day earlier.

For a rough estimate, a correction factor between 1.1 and 1.3 covers most container and bulk voyages in fair-to-moderate conditions. For anything precise, you want AIS-derived speed profiles for the actual voyage — which is the bridge between the /emissions endpoint and the /positions endpoint. Combine the two and the error band tightens, though the residual error depends heavily on voyage type and the quality of the load assumption.

What this still doesn't tell you

EEXI is a design-condition metric. It doesn't capture hull fouling — industry studies suggest six months of moderate fouling can add roughly 10% to fuel burn, and severely fouled hulls have been measured at 30% or worse. It doesn't capture weather routing decisions. It doesn't capture auxiliary engines running in port, which on a large container ship is not a rounding error. And it doesn't capture methane slip from LNG carriers, which deserves a paragraph of its own.

Methane slip is uncombusted natural gas escaping through the engine. It's a separate problem from the LNG carbon factor, and it doesn't show up in EEXI at all. Fossil methane carries a GWP₁₀₀ of roughly 30 per the most recent IPCC figures (the exact value depends on the assessment report and whether you include climate-carbon feedbacks), so a few percent of slip can erase the apparent climate advantage of switching from oil to gas. Two-stroke high-pressure LNG engines slip very little; some four-stroke low-pressure designs slip enough to matter. The API returns a CO₂-only figure and flags LNG vessels separately, because rolling slip into a single number requires picking a GWP horizon, and that choice is a political one as much as a scientific one.

So: where does that leave you? If you're filing EU ETS reports, the verified EEXI number is where you start and your auditor decides whether you need to go further. If you're building a product that surfaces emissions to end users, a well-bounded estimate is almost certainly fine and most users won't know the difference. If you're trying to prove in a contract dispute that a specific voyage exceeded a specific threshold — call a naval architect, not an API.

At sea, with no fuel pump to read and no tailpipe to sniff, the gap between what was burned and what we can prove was burned is wider — and stranger — than most people realise.