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The dichotomy of artificial intelligence: fighting climate change vs increasing the carbon footprint

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Artificial intelligence (AI) technology can help us fight climate change – but it also comes at a cost to the planet. To truly benefit from the technology’s climate solutions, we also need a better understanding of AI’s growing carbon footprint, say researchers.  

AI is changing the way we work, live and solve challenges. It could be most valuable as a range of applications helping us fight our biggest threat - climate change. AI can reinforce climate predictions, enable smarter decision-making for decarbonising industries ranging from building to transport, and can work out how to allocate renewable energy.

 

It is part of a wider ethics debate in the EU about how to use AI for the benefit of human beings, the challenges that the technology poses and how best to tackle them.

 

Perhaps surprisingly, one issue that is only beginning to be discussed is the environmental footprint of AI. 

 

For one algorithm to train itself on whether an image shows a cat, for instance, it needs to process millions of cat images. The ecosystem for information and communications technology, of which data centres are a part, are comparable to aviation in terms of fuel emissions. ‘It’s a use of energy that we don’t really think about,’ said Prof. Dignum. ‘We have data farms, especially in the northern countries of Europe and in Canada, which are huge. Some of those things use as much energy as a small city.’ 

 

Swedish researcher Anders Andrae has forecast that data centres could account for 10% of total electricity use by 2025.

 

Although AI has been around for about half a century, the question of environmental impact – and other ethical issues - is only arising now because the techniques developed over decades can now be used in combination with an explosion in data and strong computational power, Prof. Dignum explains. ‘It’s time to start thinking about doing AI in a more environmentally friendly way,’ she said.

 

Professor Felix Creutzig, who leads a working group called Land Use, Infrastructures and Transport at Mercator Research Institute on Global Commons and Climate Change in Berlin, Germany, investigates ways to tackle climate change using data science. He is part of a group of international researchers advocating for more collaborative climate change solutions using machine learning. 

 

Prof. Creutzig sees vast AI opportunities to ramp up the applications for targeted climate change solutions at the street scale, or even building level, that can be applied in cities. Urban spaces are of particular concern, as they’ll be home to more than two-thirds of the world’s population by 2050 and are incredibly resource-intensive. ‘It’s cool to work with technologies and to invest in low-carbon technologies, but to achieve anything close to the 2 degree or 1.5 degree target (for limiting global warming) we must reduce energy demands drastically and can achieve that by improved spatial configurations,’ he said. Prof. Creutzig is employing a method called stacked architecture, which uses machine learning with traditional mechanical modelling to, for instance, obtain insights into how buildings behave when it comes to temperature or energy demand, to find the best design for low energy use and high quality of life. These can then inform urban planning and policymakers.

 

For Andrea Renda, head of global governance and digital economy expert at the Centre for European Policy Studies in Brussels, Belgium, and also a member of the expert group advising the European Commission, AI needs to be developed and deployed so it can meet society’s needs and protect the environment by saving more energy than it expends.

 

Read the full article here.