The AI hype machine rumbles ever onward, engulfing the UK government and spawning a thousand experts along the way. But it's not only good for contributing to the zombie-fication of the internet, AI is also quickly playing a starring role in climate breakdown too! There's a growing movement flagging the significant environmental costs of developing, training, and running models, particularly the incredible resources required to maintain the data centers associated with the largest models.

The first step for most organisations or individuals concerned about the impact of their forays into AI is to understand how bad it currently is. Baselines then provide a benchmark for looking at how effective their strategies for reducing it are. With this in mind, we've rounded up some of the available carbon footprint calculators below, to help you take that first step.

Most calculators are based broadly speaking on the type of hardware you're using, how long you'll be using it for, and **where the compute power is located that you'll be using. There are variations on this theme across all the different tools.

A quick note: the tools below all share a limitation that they are predominantly focused on the release of carbon dioxide into earths atmosphere. While this is a key greenhouse gas driving global warming, there are other emissions associated with global warming that are not included. The tools also don't cover the water usage of data centers, another significant climate impact which will only be compounded as the climate warms further. More on the water usage of AI here

We also see a split in enterprise vs individual project level estimates: project-level estimates typically calculate the one off emissions of training a model, while enterprise tools allow ongoing monitoring of the impacts of using a model as well.

Enterprise solutions for cloud compute carbon impact

You might be surprised to know that the three major cloud service providers offer a variety of different tools to estimate the carbon emissions of customers

Microsoft Emissions Impact Dashboard for Azure

Designed not just for your machine learning models, this PowerBI app calculates the carbon footprint of your cloud-based computing in Azure.

It's based on a Microsoft methodology validated by Standford in 2018 and includes emissions from Microsoft vendors and suppliers, as well as regional variations in fuel usage, "in line with ISOs". Although this is Microsoft marking it's own homework, and it only provides estimates, if you're already using Azure, this tool should help you track and potentially reduce, the emissions associated with cloud usage.

Link: https://appsource.microsoft.com/en-us/product/power-bi/coi-sustainability.emissions_impact_dashboard

Google Cloud Carbon Footprint Dashboard

Similar to Microsoft, Google will provide you with a lovely visual dashboard for tracking how your IT cloud operations are contributing to climate breakdown. Perfect for management of multiple projects across an organisation!

The biggest surface difference between the two is that Google claims to adhere to the Greenhouse Gas Protocol for emissions reporting instead of an unknown ISO. The documentation for the methodology is also easily accessible online, vs the random PDF file from Microsoft.

Amazon Web Services Carbon Dashboard

Not to be outdone, Amazon claims to meet Greenhouse Gas Protocol AND ISO14064 with it's carbon footprint estimates for Amazon Web Service customers. Notably however, it only includes on-site fossil fuels, and Amazon Web Service products electricity use in it's calculations. Google, in contrast incorporates a third category (the terminology used in the documentation is "scopes") which includes upstream emissions from data center equipment and buildings. So, while AWS might look like a lower carbon option, that's probably not the full picture.

Essentially though, which of the above you use depends on the enterprise cloud provider your organisation has gone with. They each have their benefits and drawbacks but most of them will give you some kind of overview that can be used to make management decisions about project cloud resources and use. If they're implmented before your company dives head first into the shallow end that is coporate AI adoption, you could also use them to demonstrate further what a bad idea that is climatically.

Individual Project Tools

Looking for tools at a different scale to the enterprise dashboards? There are a variety of different tools out there, mostly with some flavour of open license, that will integrate into your python project to provide tailored estimates

ML Co2 Impact

An open source (MIT license) project with a quick and easy website for calculating an estimate of carbon impact across cloud providers and hardwares - just select from the drop down options and hit the big red button. The data used to support all the calculations is on GitHub in case you'd like to interrogate it or add to it yourself, and the assumptions used to build the calculations are laid out clearly. There's also a python package to install if you'd like to integrate carbon estimations in your own coding workflow (say, alongside PyTorch, for example).

Bonus points for incorporating a LateX template for the researchers looking to include calculations and providing some tips on how to reduce the carbon impact of machine learning. Downsides include difficulty in monitoring emissions over time and at a scale larger than individual projects or work.

Website: https://mlco2.github.io/impact/

GitHub: https://github.com/mlco2/impact

CarbonTracker

Another open source (MIT license) python package developed by students in Denmark, this tool will estimate the carbon emissions for training your machine learning model, and even gives you the option to end training cycles early if the carbon emission predictions are too high. It'll automatically detect your location based on IP, then use that for part of the calculations of emissions.

CarbonTracker has been widely cited in academic papers (400+ times and counting) but as with codecarbon, it's not clear how well it plays with enterprise perspectives and demands. This is understandable, since it was developed by PhD students, but it's worth bearing in mind that you'll need to do some aggregation and parsing if you want a nice dashboard for your manager to point at in a meeting. The documentation also isn't great.

Website: https://carbontracker.info/

GitHub: https://github.com/lfwa/carbontracker

Want to get really nerdy about the tools mentioned above? "How to estimate carbon footprint when training deep learning models? A guide and review" (Bouza, Aurelie, & Lannelongue, 2023) is for you! It looks at seven different tools available in depth, so if the two mentioned above aren't quite what you were looking for, this paper probably has one that's a better fit. DOI: 10.1088/2515-7620/acf81b

Other options

For the non-technical folks out there, Deloitte has hopped on the AI bandwaggon with it's very user friendly AI Carbon Footprint Calculator. Is there a rigorous methodology described? Don't be silly, it's a big 4 consulting firm! It uses the latest weights and metrics, they promise. What it does come with are some limited multiple choice options in an easy to read format and a rating out of 10 at the end. This writer couldn't manage to get it give them a gold star as well, but you might have better luck. You also have the chance to enter their marketing funnel by downloading a personalised report with "recommendations".

This is honestly, probably best for senior managers or people trying to convince senior managers that using live streamed cloud data to develop autonomous flying cars with natural language processing might be a bad idea.

So once you've identified the carbon impact of your project, organisation, or idea to do weather prediction with chatGPT, what to do next? We'll cover this in a future article (Step 1: STOP PUTTING AI IN EVERYTHING), but in the meantime you can send your thoughts to contact@utaw.tech.

At UTAW, members are are organising and campaigning on issues ranging from tech complicity in genocide, employee surveillance and workplace discrimination, to climate breakdown. Join today and you too can get stuck into pushing for change across tech industry.