Five ways AI can tackle Climate Change
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26/04/2022

As global leaders and countries are creating roadmaps, targets and policies to tackle the impending devastation due to climate change, there is also a big focus on using Artificial Intelligence (AI) to avert the incoming disasters. Governments, tech firms and investors are showing growing interest in machine-based learning systems that use algorithms to identify patterns […]

As global leaders and countries are creating roadmaps, targets and policies to tackle the impending devastation due to climate change, there is also a big focus on using Artificial Intelligence (AI) to avert the incoming disasters.

Governments, tech firms and investors are showing growing interest in machine-based learning systems that use algorithms to identify patterns in data sets and make predictions, recommendations or decisions in real or virtual settings.

Following the decisions at COP26 and the ongoing preparations for COP27, here are five ways where AI can be used to tackle climate change.

Efficient usage of electricity

The focus has now shifted to reliance on more renewable energy sources. But utilities will need better ways of predicting how much energy is needed, in real time and over the long term. Algorithms already exist that can forecast energy demand, but they could be improved by taking into account finer local weather and climate patterns or household behavior.

New materials can be discovered

Scientists need to develop materials that store, harvest, and use energy more efficiently, but the process of discovering new materials is typically slow and imprecise. Machine learning can accelerate things by finding, designing, and evaluating new chemical structures with the desired properties.

Building can become more energy efficient

AI controlled systems can reduce a building’s energy consumption by taking weather forecasts, building occupancy, and other environmental conditions into account to adjust the heating, cooling, ventilation, and lighting needs in an indoor space. A smart building could also communicate directly with the grid to reduce how much power it is using if there’s a scarcity of low-carbon electricity supply at any given time.

Improve adoption of Electric Vehicles

Electric vehicles, a key strategy for decarbonizing transportation, face several adoption challenges where machine learning could help. Algorithms can improve battery energy management to increase the mileage of each charge. They can also model and predict aggregate charging behavior to help grid operators meet and manage their load.

Make supply chains more energy efficient

AI can optimize shipping routes, it can minimize inefficiencies and carbon emissions in the supply chains of the food, fashion, and consumer goods industries. This will reduce production and transportation waste, while targeted recommendations for low-carbon products could encourage more environment- friendly consumption.

But AI alone cannot be the one-stop solution.  People, companies, governments must all come together to create long-term solutions with the help and integration of AI.

Some critics also warn that AI can be highly energy-intensive and environmentally damaging and that the tech could be a costly distraction from more effective ways of tackling climate change. The jury is still out on that.

(Banner image credits: commons.wikimedia.org)

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