In recent months artificial intelligence has often been mentioned together with climate change. After all, why settle for one existential threat to humanity when you can have two?
The rapid advance of generative AI, in particular, has caused widespread consternation since late 2022. Several high-profile figures have argued that the technology poses a more urgent risk than climate change because it will be much harder to put the genius back on its feet. of artificial intelligence. bottle than it will be to decarbonise the world.
But is it necessarily right or helpful to pit the two against each other in this way? After all, the ominous prediction that global warming will exceed the 1.5C ceiling set by the United Nations in 2018 is actually the product of AI-powered models. While neural networks can serve as an effective early warning system, they surely have other useful applications in trying to solve the climate crisis.
Could Artificial Intelligence Boost ESG Analysis?
At the World Economic Forum meeting in Davos in January, Thomas Siebel, chairman and chief executive officer of software company C3.ai, suggested that artificial intelligence’s ability to “absorb huge amounts of data” and “extract signals from noise” would make it an important tool for tackling climate change. He suggested it could be used to more accurately assess companies’ progress towards ESG goals, such as reducing emissions.
Artificial intelligence insights can help evaluate the effectiveness of emissions reduction tactics
Siebel is not a solitary voice. Professor Somdip Dey, integrated AI scientist and founder and chief executive officer of Nosh Technologies, says: “There is a growing body of evidence suggesting that meeting ESG goals can have a positive impact on climate change.”
Of course, the sheer volume and complexity of ESG data being generated is likely to be daunting even for the best resourced companies. But this is where AI can help reduce the burden and ensure that all the correct metrics are monitored, so companies don’t have to work in the dark to meet their zero emissions goals.
AI can be used to “recognize emissions trends over time. The resulting information can help evaluate the effectiveness of reduction tactics,” says Dey. “And it can automate data capture, analysis and reporting. This frees up human resources, allowing more expertise to be channeled into developing and implementing reduction strategies.”
Design more efficient renewable energy sources
Artificial intelligence has already been applied in the generation of renewable electricity. Danish company Vestas Wind Systems is using the technology to make its wind farms more efficient by tuning individual turbines so that the air turbulence caused by their rotations does not disrupt the suction of the downwind turbines.
Working with technology partners Microsoft and Minds.ai, Vestas applied reinforcement learning to the challenge. This technique is a type of machine learning where systems teach themselves a task by learning from real-time environmental changes, imagining different scenarios and receiving rewards when the desired results are achieved. The system ran simulations in which it responded to a full range of wind conditions and automatically repositioned the wind turbines to optimize efficiency across the farm.
Is artificial intelligence too energy-hungry to help fight climate change?
AI’s potential as a weapon in the battle against global warming and climate change is tempered by one key factor, especially as we enter the GPT-4 era of generative AI: technology needs vast amounts of energy to work properly.
Can Artificial Intelligence Save the Rainforest?
AI has also been used in the global fight to prevent the conversion of ecologically important carbon sinks into agricultural land.
“We use several machine learning models to produce Global Forest Watch, an open source web application,” says Evan Tachovsky, global director of the World Resources Institute Data Lab. “Some models are trained on optical images and others use radar images, which can help us see through the clouds.”
They can detect tracts of forest that are being cleared by locating new agricultural plantations based on color, size, shape and pattern. “Our systems give us a near real-time view, with local accuracy on a global scale, of where deforestation is happening,” Tachovsky explains. “We can then issue warnings and provide data to the relevant public.”
And the hidden problems of artificial intelligence?
If these use cases are anything to go by, AI has many other applications in the fight to limit global warming and climate change. But bias is an ever-present challenge, a factor that must always be monitored if technology is to be truly effective. As a result, an entirely new discipline known as responsible AI is emerging.
“This is where machine learning is limited,” says Dr Kasia Tokarska, a climate data scientist specializing in the application of artificial intelligence. “A model of the climate system that obeys carbon, energy and water conservation can be trusted more than a purely ‘black box’ approach where you input data and get some results.”
Tokarska suggests that AI users should always be careful not to feed their systems with data derived only from observed events. This, she cautions, can lead to hallucinations, the term applied to highly inaccurate AI results. Instead, they should ensure that new events – for example new government policies regarding the environment – are also included in the AI data diet.
