4 Ways Machine Learning can help in the fight against climate change
The COP26 summit has been a whirlwind of news as world leaders and the UN try to stop climate change. Headlines blurred as major greenhouse gas-producing countries and industries announced when they will reach carbon net-zero. In a surprising example, Indian Prime Minister Narendra Modi pledged to reach net-zero by 2070.
But how is India, the world’s fourth-highest carbon dioxide-producing country, going to do it? Will new technologies like artificial intelligence and machine learning help reach a greener future?
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It should be no surprise that our answer is a resounding, “Yes!” But we aren’t the only folks who think AI can help keep the temperature down. It only makes sense that the most emergent and promising technologies are some of the best tools to use in the fight against climate change. Another benefit: as varied as the impact of climate change is, so too are green applications of machine learning. Here are four areas where machine learning is helping the fight against climate catastrophe.
1. Improved Reusing and Recycling
Let’s start with the trifecta of 20th century environmentalism; Reduce, Reuse, and Recycle. The most effective way to manage the massive amounts of man-made plastics is to give them a second life. Effective waste management limits the amount of materials that reach the landfill and reduces pollution caused by plastic production.
But the reality is that plastic recycling is still a rather inefficient process. Missorted or soiled goods go straight to landfills, and sorting workers are routinely exposed to unsafe conditions. Overall, reducing the mess still proves to be quite messy.
However, using machine learning, we can optimize sorting, get humans away from the conveyor belt, and make the whole process more financially viable all in one fell swoop. While scientists search for the optimal way to implement neural networks that improve waste management, it seems very clear that machine learning is the best way to dig out of this pile of trash.
2. Helping Place Climate Refugees
An unfortunate part of preventing climate change is dealing with its current side effects, like population displacement. Just within the US, uncontrolled wildfires and historic hurricanes continue to drive many folks from their homes. And it’s not any better internationally.
Using machine learning, governments may be better able to ensure refugee success. Scientists from Stanford and ETH Zurich developed a re-housing algorithm that, using historical registry data from the U.S and Switzerland, improved refugee integration by 40 to 70%. This solution is born out of an unfortunate reality, but is a necessary step to improving how we live on our transformed planet.
3. Smart Cities
What if cities made green choices on their own? Or even the appliances in our home? It sounds like a stretch, but may prove easier than we think. The same way an iphone optimizes charging based on usage patterns, buildings, neighborhoods, and entire urban centers can do the same. Through fusions of deep learning and IoT, high population density areas may become the greenest parts of society.
4. Making Science Actionable
We know climate scientists of the world have reached consensus on climate change. But that’s not necessarily actionable information is it? Agreeing on the issue of climate change does little to prevent it. As we continue navigating climate change prevention, our success depends on accurate and effective information. So how do we get it?
As new technologies emerge, scientists are able to design and implement experiments that more clearly define the causes of climate change. In the case of this study, machine learning can help us more accurately identify and target the most impactful emitters of greenhouse gas emissions. Applications like this can show environmental impact in real time and help us make more informed choices about how to minimize our impact.
We are a group of scientists, engineers, and entrepreneurs with a vision for better AI. With backgrounds primarily in Machine Learning and Computer Vision, the Innotescus team understands the importance of having full control over and insight into data used to train Machine Learning models.
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