We need better conversations on AI and the environment

AI is not one thing. Neither is its environmental impact.

We need to keep our eyes, our debates, and our policymaking focused on the problems we’re trying to solve. On the outcomes we’re trying to achieve and the society we want to build.

AI can be machine learning, Grok, or basic (mislabelled) data analytics and simple process automation software.

Its impact on sustainability can of course be environmental – its emissions, energy use, water consumption, effect on the local environment, or through the physical materials and supply chains that build datacentres and all manner of other infrastructure.

AI’s impact can also be social and economic – on employment, education, policing, for creatives, or on local community cohesion.

When considering governance, often the third pillar of sustainability (and the acronym ESG) AI opens up new questions of accountability from self-driving cars to corporate management.

I’m not going to rank these impacts. Or pretend that in one blog post I can fully account for their breadth and complexity. But as a starting point for our collective future conversations, here are some numbers, questions, and counterarguments to consider…

The whole technology industry, including chip manufacturing and electronics supply chains, is 2-3% of global emissions, based on an analysis combining sources like Our World In Data and the World Economic Forum. This is, it’s worth noting, a variable estimate depending on how you define “the sector” as is the case with all carbon accounting. The International Energy Agency (IEA) reports that AI is far less than the 0.5% of global emissions generated by all current datacentres, which provide storage and processing for our digital lives far beyond AI. Now that footprint will grow with the rapidly increasing number of datacentres before it comes back down as electricity is decarbonised. And given emissions saved now are especially vital in arresting global warming (vs emissions saved years and decades down the line) the construction of new gas and coal to fuel datacentres should be stopped. In the long run the energy transition is building critical mass: over $2 trillion will be spent this year on clean energy vs $1 trillion in fossil energy. Agriculture, steel, cement, and other sectors are more worrying as far as a successful transition to net-zero is concerned. But all this said, we must address both the now and long term.

Regarding electricity, it’s a major headache for planners and the grid. Numbers across NESO, DESNZ, and National Grid project datacentres as 2-3% of UK electricity demand in 2026. A recent London Assembly report cited that will grow to between 30-71TWh by 2050 (roughly between 1 and 3 Hinkley Point C nuclear plants) – an immensely variable number and likely supplied by heavily decarbonized electricity.

The impact of the physical infrastructure follows the concerns of the broader tech and infrastructure sector: security of critical mineral supply, refining ability, and human rights abuses in those opaque supply chains. Something we desperately need to address in legislation but not uniquely to AI.

Water is typically used in closed loop systems in the UK and this could easily be mandated in planning. Internationally it is more concerning but still presents a need for clear context as worldwide datacentre water withdrawal sits at around 0.1-0.2% according to the IEA… but it is often concentrated in water-scarce areas. In the UK, 64% of datacentres use less water than a typical leisure centre, suggests a TechUK report published in collaboration with the Environmental Agency. Compared with heavy industry and agriculture, AI’s water footprint remains minimal, although it still requires us to minimize specific hyper-local impacts.

Again, I’m not going to go into a major ranking exercise. Beyond also accounting for the social and economic impacts, the environmental positives and negatives alone need wide-ranging assessments by wide-ranging contributors.

AI’s negative impacts and positive spheres of influence must be better understood, debated, and put into policy so they can ultimately exist in a society where technology gives everyone a real platform to thrive and live their own unique version of a best life.

From a digital bill of rights, and our fundamental liberal principles such as providing that platform to allow meaningful freedom, fairness, and opportunity… liberals are so well placed to have these conversations over the coming months and years to create a vision and policy platform that can lead the debate and the direction of government.

Building on Brighton we’ll be gathering different parts of the party to expand a coherent and connected vision and policy platform for AI and technology more broadly – that all rolls up to a clear guiding point underpinned by the many pillars which give liberalism and the Liberal Democrats strength.

AI can be shaped to provide a platform for people to flourish. But it needs a liberal approach – and for the Liberal Democrats to take up the challenge.

 

* Josh works on climate and sustainability across research, consulting, convening, and advocacy, focusing on systems change and new economics. Josh is a member of the Lib Dem Federal Policy Committee (FPC). He is a former PPC and Cambridge City Councillor with the opposition portfolio for Climate, Environment, and the City Centre.

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4 Comments

  • “In the UK, 64% of datacentres use less water than a typical leisure centre”

    Yes, but that’s because in the UK we have virtually no datacentres running “generative AI”. As you say, AI covers a wide variety of marketing terms, but there’s a specific distinction with “generative AI” (chatbots and related, especially) which is that they are stunningly energy-inefficient ways of getting computers to do things.

    A typical UK data centre might draw a megawatt or so of power, if it’s a big one – and have cooling and water needs that aren’t all that large (a lot of the smallest ones might be entirely air-cooled and the water is just for the staff)

    But generative AI is talking up – because it’s really inefficient, because it needs it to run lots of copies if it’s to be wedged into every aspect of our lives and stop the Ponzi scheme funding it from collapsing – gigawatt-scale data centres, thousands of times larger than a typical UK one. Those are the ones which are the big problems for water use.

    Still – for those looking for victories for localism, community empowerment, etc. the news that Action to Protect Rural Scotland has managed to get a complete pause on large-scale data centre construction in Scotland is heartening, and many thanks to the Lib Dem MSPs for backing that in the Parliament.

  • paul barker 8th Oct '26 - 5:14pm

    I have seen figures suggesting that Data Centres currently planned would triple UK Electricity consumption, that is not remotely comparable with slowing our contribution to Global warming.
    Its now fairly widely agreed among Economists that The AI/Data Centre boom is already driving up Interest rates because of the colossal amounts of money being borrowed.
    Lets say No to any Data Centres that dont do something useful, Science or Medicine for example.

  • David Allen 8th Oct '26 - 6:30pm

    I don’t think the tech bros will want to – or have any viable way to – separate “useful” applications, like medicine or data storage, from “problem” applications, like chatbots.

    Why don’t we just make sure we tax these activities fairly? If ChatGPT needs a new reservoir and a massive electricity consumption to run their data centre, they should pay enough tax to compensate for the environmental harm they are causing. Then when they see what their tax bill is liable to be, maybe they will dial down their build programmes.

  • All that allowing (for instance) corporations to engage in environmental destruction for a fee, or a tax, or whatever you call it does is to make the government dependent on that destruction for revenue; it’s an encouragement, not a discouragement. Nor do corporations of such massive wealth find higher taxes a significant disincentive.

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