Machine learning could play a crucial part in the ongoing fight against climate breakdown.
Effectively dealing with emissions is a climate change mitigation technique with tremendous potential. When we really can't get rid of emissions, minimizing their impact on the planet is often the next-best option.
Researchers have relied on machine learning to identify the best ways to convert carbon dioxide into methane, for example. They let the technology examine specifics like the size and chemical structure of the catalytic particles that make that process happen.
Prevention
Methane is a non-fossil-fuel energy source and is easy to store and transport. Machine learning fits into the picture because scientists have used it to analyze the X-ray signatures of catalysts that get collected during chemical reactions.
Early findings showed that certain catalysts can lower the temperature of the reaction between carbon dioxide and methane or make it more efficient.
People in many parts of the world have the luxury of thinking of climate change as something that might affect them "someday." Many farmers in Africa are not so fortunate. Statistics show that more than 95 percent of them are solely dependent on rainfall for irrigation. When climate change causes droughts, they'll face disastrous crop losses.
It's increasingly popular for companies to use machine learning for predictive maintenance. For example, the technology could warn about a failing pump before it breaks down and halts production. What if farmers could avoid crises as well by using high-tech solutions to make proactive decisions about climate change preparedness?
Researchers want to give them that option. They tweaked a machine-learning-based smartphone app that helps diagnose crop diseases. It now also makes recommendations about which drought-tolerant crops to plant and where to put them. The system incorporates dozens of data points related to Africa, with hundreds more gathered by the day.
Collaboration
Research shows that energy production and consumption collectively make up the largest source of greenhouse gas emissions worldwide. Many leaders around the world are taking action and committing to positive, time-based changes.
Mexico is one country that has recently achieved significant gains. Reaching those milestones required international collaboration, however.
Widespread energy creation or usage alterations to help the planet do take time and effort to plan. Moving in the right direction can also begin with individuals. Numerous technology companies offer machine learning products that reduce building-level energy usage.
One company has solutions that take data samples 8,000 times per second and claim to cut energy consumption by as much as 50 percent.
Since these options often have user interfaces that show energy efficiency trends and other relevant statistics, people can see the hard data confirming a product's effectiveness. Customers can also learn new insights, such as which hours in the day consistently demand the most energy usage or whether certain rooms make exceptionally high contributions to the overall amount of energy used.
Innovation
Scientists know that plants can cut carbon dioxide emissions by naturally absorbing them. What hasn't been so clear to them is the total effect of this over time, and how soaking up those emissions could affect the plant life later, such as by promoting growth.
A multinational research team used machine learning — along with statistical methods and satellite data — to get a better idea of the possibilities. They quantified the effects of elements like soil nutrients and climate characteristics on a plant's ability to take in carbon dioxide. The results showed that tropical forests — such as those in the Amazon and Congo — had the greatest potential for both regrowth and carbon dioxide uptake.
Another finding from the analysis showed that trees could remove six years' worth of emissions by 2100. But that outcome would only be achieved if we completely stopped deforestation activities. The use of machine learning could help climate activists encourage decision-makers to see trees as essential to slowing global warming.
There's no doubt that climate change is a major, pressing issue. These examples show that machine learning can help conquer it. Innovative technology does not replace human expertise and decisions, nor does it negative meaningful systems change, but it can complement it in some truly consequential ways.
This Author
Emily Folk is a conservation and sustainability writer and the editor of Conservation Folks.
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July 27, 2020 at 02:04PM
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Climate change and machine learning - The Ecologist
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