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The AI & Machine Learning community stands ready to help in the climate crisis battle

(Image credit: Image Credit: Geralt / Pixabay)

The world’s on fire.

For three days in August, 7 billion tons of rain fell on the peak of Greenland, which is just not the largest amount since records began 71 years back, but the first time we know that rain, not snow, fell on the country’s highest peak. Wildfires in Siberia broke another terrifying record for annual fire-related emissions of carbon dioxide, losing almost 19,300 square miles (500,000 square kilometers) of vegetation to the fires. And in the same month, the latest (sixth) scientific report from the Intergovernmental Panel on Climate Change sounded the emergency alarm yet again on the need for “strong and sustained reductions in emissions of carbon dioxide and other greenhouse gases” to try and save a common future for us all.

We’ve already lost $3 trillion through climate change in the last 20 years

We might have known this for a while, but the rate of extreme weather events and the accumulation of more and more data about the global emergency is now inescapable. It’s genuinely no exaggeration to say we’re in a fight for survival now. There are also more and more financial and business knock-on effects of all this that have already cost global economies uncounted billions, indeed more: Capgemini research shows that in the past twenty years, there were 7,348 major recorded disaster events claiming 1.23 million lives, affecting 4.2 billion people and resulting in approximately $3 trillion in global economic losses. 

For sure, we’re going to need to do a lot more than just recycle our soda cans or eat a little less meat per week (though we need to keep on doing all that, too); scientists are now talking of the need for serious, large-scale geoengineering to try and save us. Climate change should be on every organization’s agenda—but the IT world, which (rightly) gets criticized for its less than stellar record on exorbitant electricity consumption, as part of general economic activity (which is rising again), has a particular responsibility to help.

Why? Because we burn a lot of kilowatts, but also because a lot of smart people work in our world, many of whom are deeply concerned about the threat of anthropogenic climate change. As a global citizen and IT professional, I feel this concern too: and I also work in the AI (Artificial Intelligence) world. So I asked myself, what can AI and AI professionals do to help here: and this is what I found.

At the top level, AI provides powerful tools to researchers, engineers, chemists, biologists, town planners and policymakers—in short, everyone is trying to make a positive difference. All these people need the very best, most recent, most granular data to make their interventions or design remediation techniques, which will absolutely include carbon capture, Greener transport and new post-carbon industries and ways of living. But AI, or more specifically, machine learning, is also already lending a hand in a variety of practical ways and climate crisis use cases:

  • Better forecasting the supply and demand of power for the electricity grids and laying the foundations for more environmentally-sensitive smart grids
  • Reducing our dependence on harmful fossil fuel emissions through predictive maintenance
  • Enabling more accurate traffic predictions and optimizing transportation, so starting to cut down on car and truck pollution
  • Smarter, more efficient and less climate-impacting agriculture
  • And as a way to get more productive solar energy—among many, many other applications happening right now

‘AI-generated information could also help consumers and businesses to adapt towards more sustainable behavior’

The potential of machine learning in this space has already been called out by the EU, which has stated, in a report on the potential of AI to achieve its ambitious ‘Green Deal’ targets, noted that, “The transformative potential of Artificial Intelligence to contribute to the achievement of the goals of a green transition have been increasingly and prominently highlighted [due to its ability to] accelerate the analysis of large amounts of data to increase our knowledge base, allowing us to better understand and tackle environmental challenges,” especially around relevant information for environmental planning, decision- making, management and monitoring of the progress of environmental policies. And as Brussels also points out, AI-generated information could also help consumers and businesses to adapt towards more sustainable behavior, among other potential benefits.

That same study does point out the downside, or potential downside: AI could also negatively contribute via unforeseen consequences like automatically more efficient products might actually have the effect of causing users to give up control over their energy consumption and over-consume.

Yes—but we in the machine learning sector are very conscious of these issues, as are national governments and other legislators. But I am convinced that AI can, and should, play a central and positive role in helping put out the global ‘fire,’ and could also be used by companies to help start incorporating the impact of climate change into their future planning processes.

We all need to start helping. I know I will

In our own modest way, we’re ‘eating our down dog food’ ourselves at the company I work for, Our technology has been used for a number of positive climate projects and initiatives, including our work with a non-profit focused on wildlife conservation and research called Wildbook, which is blending structured wildlife research with AI, citizen science, and computer vision to speed population analysis and develop new insights to help fight the extinction of threatened species like the elephant.

Could we be doing more? Yes. And we need to. Could we all be doing more? Yes, and we need to. I believe that the climate emergency can be controlled, and a climate AI culture emerging between technologists, policymakers, domain experts, philosophers and the open-source community to optimize the design and deployment of helpful AI tools could really help.

Mark Bakker, Regional Lead Benelux,

Mark Bakker is Regional Lead Benelux for an AI technology software company. He is working with clients and partners to democratize AI across the Benelux.