Your AI has a footprint.
Erase it
Connect your AI provider accounts. We measure your inference emissions using peer-reviewed research, then email you a one-click link every month to donate 1-3× the damage to climate charities you choose.
Connect your AI provider accounts. We measure your inference emissions using peer-reviewed research, then email you a one-click link every month to donate 1-3× the damage to climate charities you choose.
Add your OpenRouter, OpenAI, Anthropic, or Gemini account. Or just pick your subscription tier.
We compute your monthly CO₂e footprint using published energy-per-token estimates and cite every constant.
On the 1st of each month, we email you a one-click Every.org link pre-filled at 2× your damage. You click, you pay, you get a tax receipt. We never touch your money.
The film
The story
I use AI tools every day, for learning, building, and generally trying to get better at what I do. But the more I learned about the environmental cost of large language models, the harder it became to ignore: every prompt has a footprint, and those footprints add up fast.
I didn't want to stop using AI. The upside, upskilling, and productivity gains are real. What I wanted was a way to use it responsibly - to keep growing while giving something back to the planet in proportion to what I was taking.
The idea behind EcoDues is simple: calculate the actual damage your inference usage causes (in dollars, using the social cost of carbon), then donate twice that amount to the climate charities most likely to make a real difference. You keep the productivity. The planet gets a net positive.
This is a solo project built with a lot of curiosity and the help of AI tools themselves - Ironic. It's not perfect, and the methodology will keep improving as better data becomes available. That's why it's open source.
James Smith
j-m-s.dev100% Open Source
Every line of code — the emissions engine, the donation logic, the methodology — is public. Audit it, fork it, improve it. Contributors are always welcome and genuinely appreciated.