AI and greener choices

The earth is heating up and AI isn't helping. It drives major increases in electricity use, water use and CO2 emissions. Yet, industry and governments alike seem keen to leverage the latest tech. Can we make greener choices?

Yesterday, I spoke at a conference on AI in government about sustainable choices. The presentation and slides were in Dutch, this post is a high level summary of what I covered.

As the presentation took place during a record-breaking heatwave, which meteorologists attribute to climate change, and there were some airconditioning-related issues, very little irony was lost on us.

Words matter

I started out talking about if AI is good or bad for sustainability and called it the wrong question. It's the wrong question, because both AI and sustainability are concepts that have many meanings.

AI can be understood in different ways:

• as a technology, there are many kinds; Microsoft alone has 78 products named Copilot, but also, there are many types of AI, like computer vision, natural language processing, generative AI and agentic architectures. Traditional vs generative can be a useful distinction in sustainability, because of the difference in energy impact.
• as an economic phenomenon (it isn't profitable yet, so it may get more expensive or introduce ads, and there are lots of financial stakes that explain why it's sometimes forced on consumers)
• as an infrastructure (behind the cloud metaphore, there are very real data centres that warrant closer inspection)

Sustainability is multi-faceted too. In the Web Sustainability Guidelines, we center our definition around three pillars that ultimately need balance between them: Planet, People, and Profit:

• Planet: its surface temperature rises due to emission of greenhouse gases that trap heat, and it has a finite amount of water of which only a small portion is suitable to drink.
• People: think of people, including children as young as 11 years old, who are working in cobalt mines in Congo for the batteries that power our devices, or of people (and hospitals) who had no electricity during last year's Spain heatwave, while data centres continued uninterruptedly.
• Profit: within our capitalist system, the needs of planet and people need to be balanced with profit, in order for anyone to successfully make the case for change.

So, there's some nuance about what the words mean, but despite that, it's worthwhile to try and reduce climate impact of our services. The pope said so, and in government, it's an increasingly a priority (like in the UK, where the Digital Service expanded their design guidance last year with principle 11: Minimise environmental impact) .

It's ok not to use AI

I shared five tips to make greener choices around AI, but couldn't help myself to add a bonus tip as tip zero: in many cases, not using AI for your project makes the most sense. Which is fine.

Many of us will have sat in meetings around a new idea or project, where someone put up their finger and said ”oh, maybe we can use AI for this”. The AI vendors primed us for this with hype.

Often, using AI, and especially generative AI, is like going to your local corner shop using a helicopter. What if you could solve the same problem with a couple of lines of Python, and Excel-sheet, people, or manually summarising?

I wanted to mention this specifically, because nobody wants to be the downer in the group and suggest not doing the thing that everyone is hyped about. But to be sustainable, we need to pick out just the cases where AI is absolutely necessary, and go without it for anything else.

Five tips to make greener choices

When using a specific type of AI actually solves your problem best, here's five tips to make greener choices.

Smaller models

When using generative AI, model size differs wildly. An example of a large model is DeepSeek v4 Pro, which has 1.6 trillion parameters (12 zeroes), while there are small models like Hugging Face's SmollLM that has 135 million parameters (6 zeroes).

A smaller model could save 30⨉ emissions of CO2 (and equivalent greenhouse gases), explain Luccioni, Jernite, & Strubell in their paper “Power Hungry Processing: Watts Driving the Cost of AI Deployment?”.

More specific models

A lot of generative AI uses so-called general purpose models, which are systems that know pretty much everything about the world. When hiring humans, we usually list a specific area of expertise. That's partially because there are no humans that know it all (just ones that think they do).

We should try to do the same with language models. If we're building a system that answers questions about taxes, we don't need to tell it what the capital of France is.

In “Power Hungry Processing”, Luccioni, Jernite, & Strubell explains this: “multi-purpose generative architectures are orders of magnitude more expensive than task-specific systems for a variety of tasks, even when controlling for the number of model parameters” (emphasis mine).

Be smart about data centre usage

In many applications of AI, the relevant software needs lots of computing: GPUs, CPUs, memory, and storage. This computing is often done in the “cloud”, or: data centres. The climate impact of data centres is widely seen as three components: electricity, CO2 emissions (and equivalent greenhouse gases), and water usage.

The International Energy Agency predicts the amount of data centres to double or triple over the next few years. The electricity used for this will be green, partially. But it won't all be green : gas and coal will continue to be a share of what powers data centres.

On an operational level, we could reduce the load on data centres by reducing how many tokens we use, via smarter prompting (shorter, more specific, more explicit, less repetition through variables). The paper Smarter, smaller, stronger by Perez Ortiz and Drobnjak details tests that show reducing a response from 400 to 200 words reduced energy consumption by 54%.

On a policy level, one tip I learned at the AI reporting event I attended, is to question how many 9's you really need. Data centres are convenient because they offer compute for rent and make it easy to scale. And they offer uptime, guaranteeing sometimes up to 99,99999 availability: five nines. More nines could mean less sustainability: some data centres will use diesel power or other unsustainable sources when they have a power cut. So to get to a higher uptime guarantee, they need to make such choices. What if we required less nines? What if we decided it's ok to have the AI thing not work for a couple of days a year?

Recognise greenwashing

Greenwashing is everywhere, and to make sustainable choices, we'll need to develop our skills to recognise it from afar. Sometimes it's easy to recognise, like an airline that said with them, you can “fly responsibly” (a Dutch court said this is misleading). EU Directive 2024/825 effectively forbids greenwashing, but companies still tend to do it.

Examples from The AI Climate Hoax include:

• “bait and switch”: often “traditional” AI has a much smaller footprint than generative AI; when companies point at how clean their traditional AI is while ignoring the effect of their generative AI, this is misleading
• use of (own) research with little evidence
• hidden emissions through clever bookkeeping (often leaving out Scope 3 or ‘embodied’ emissions)
• empty promises, like pointing at technologies that don't exist yet
• downplaying via selective shopping in data or data without context
• fatalism: “we'll reach those sustainability goals anyway, why bother trying”

Measuring matters

My last tip is around data collection. It's tricky to know how much energy data centres use, not the least because Big Tech successfully lobbied the EU to treat energy usage data as company secrets.

When you do get data, it often does not include Scope 3 emissions, which are estimated to be a very large part (I've seen percentages from 30 to 89%, and I am not sure what's right, other than that it is large).

There can also be a major difference in reporting: some companies do location-based reporting, others market-based reporting. Location-based means that you get information about how much a specific data centre is actually using, whereas with market-based, factors outside are taken into account, including certificates for carbon.

Location-based data is the most helpful if your team is trying to make optimisations, because it allows them to see the numbers go down (or up),where they would appear constant with market-based reports.

Lastly, I mentioned grid intensity: because there are many different power sources, and they deliver different amounts of energy depending on the time and location, grid intensity vary. For instance, if it's sunny and windy today at noon, the grid intensity will be low because there is lots of green energy available. At other times there's less.

Your application can use this difference, that constantly changes, by moving your computing to the right place at the right time. This is also called “carbon aware computing” and companies like Google already do it, to save emissions and costs at the same time.

Summing up

AI-related electricity usage, water use, and carbon emissions can be huge. That's a problem, but it's also an opportunity (cheesy but it's true). There is lots of low-hanging fruit that we can start addressing. As there's still so much to reduce, our efforts really can make a dent.

I really enjoyed giving this talk and hearing the audience comments afterwards. The main worry seems to be: how do I sell this within my organisation, and an answer emerged in the group: cost-savings and carbon-savings often go hand in hand.