AI can help us fight climate change. But it has an energy problem, too

AI can help us fight climate change. But it has an energy problem, too

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 can improve healthcare, protect elephants from poachers, and work out how broadband should be distributed.

But it could be most valuable as a range of applications helping humanity fight our biggest threat – climate change. AI can strengthen climate predictions, enable smarter decision-making for decarbonising industries from building to transport, and work out how to allocate renewable energy.

AI’s relevance as a climate change fighting tool comes at a time when there are increasing ethical concerns linked largely to a data-hungry form of the technology called machine learning, where computer systems analyse patterns in existing data to make predictions and decisions. Machine learning applications have raised concerns about creeping public surveillance, intentional misuse, privacy, transparency and data bias that can lead to discrimination and inequality.

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.

‘We have to realise that AI is, in fact, a piece of software that we people design,’ said Virginia Dignum, a professor in social and ethical artificial intelligence at Umeå University in Sweden. We must be responsible for how we use AI, she says. ‘(It is) not some kind of magic that comes from outer space and happens to us. No. We make AI happen.’

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

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.

AI might be part of the problem, but it also has the potential to help us find solutions for climate change.

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.

Improving spatial use can help address issues such as urban heat islands, a phenomenon where urban environs built of steel and cement store heat and warm cities. ‘That’s a key problem of our future,’ he said.

Greening cities or using wind channel architecture to create ventilation are ways to help cities deal with extreme heat that can be guided by AI.

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.

Precisely because AI has so much potential, he also thinks its use should be combined with regulation, such as on not storing unnecessary data or constraining its use, so that it is targeted, efficient and doesn’t cause a new problem. However, he says that there’s currently not enough research into machine learning’s environmental impact. ‘There’s a lot to explore,’ he said.