Global temperature rises, shrinking ice sheets, rising sea levels, ocean acidification, and an uptick in extreme weather events are just some of the climate challenges we face today. However, while humans have played a role in accelerating climate change, we are also part of its solution. Advances in AI and other advanced technologies promise a better future for our increasingly inhospitable world. With this in mind, let’s look at the ways AI is transforming the fight against climate change.

Protecting agriculture

Agriculture is paramount to our survival, but as our population grows rapidly, we need to find novel ways of securing our access to food. For example, according to the Food and Agriculture Organization (FAO), we will need to produce 60% more food by 2050 to feed the world’s population. At the same time, extreme weather events continue to make farming less predictable. Agricultural scientists are increasingly deploying AI in this area to achieve higher crop yields, monitor pest and disease levels, and track soils and plant health.

Another interesting AI application in this area is precision agriculture at scale. Monoculture farming (where farmers produce a single crop on a vast plot of land) dominates the modern-day agriculture landscape. However, this practice strips soils of nutrients, reducing its productivity over time. In addition, and perhaps more concerningly, farmers heavily rely on nitrogen-based fertilisers to combat this nutrient depletion. Unfortunately, these fertilisers can convert into nitrous oxide, a greenhouse gas 300 times more potent than carbon dioxide.

Machine-learning-powered robots could help farmers move away from monoculture farming by effectively managing mixed crops at scale. AI can also help replenish the soil by predicting what crops to plant and when, reducing dependence on fertilisers.

Predicting electricity consumption

The world’s electricity consumption has skyrocketed over the past half-century, with consumption more than tripling between 1980 and 2019, while the global population increased by around 75%. As we turn to more renewable energy sources, we will need a better way of predicting how much energy we need today and in the future. AI algorithms play a significant role here, forecasting energy demand, analysing human behaviour, and predicting weather patterns. With this critical information, we can make better decisions about the type of renewable energy to invest in and how to best meet the energy needs of a growing population.

For example, machine learning can analyse current and historical weather data to provide reliable predictions about what the weather will do in an hour, a day, next month, and so on. This hyper-accurate forecasting is vital in managing our energy systems, especially because renewable energy is often closely linked to the weather. We can’t harness as much solar energy with cloudy skies or as much wind energy with still air.

With this in mind, weather forecasting could allow power companies to increase their adoption of renewables while still utilising the help of fossil fuels for times when the weather doesn’t allow renewables to meet demand.

Similarly, artificial intelligence and machine learning play a pivotal role in grid management. These technologies leverage data analytics to predict energy consumption, helping energy companies manage the grid without any outage. By only producing as much energy as needed, we can help avoid waste and lower emissions.

Uncovering new materials

Scientists are under pressure to develop new materials that store, harvest, and utilise energy more efficiently. However, this process of uncovering new materials is usually slow and imprecise. But machine learning can help accelerate things by scouting, designing, and evaluating new chemical structures with desirable properties.

These AI tools work by examining the relationships between materials at a scale impossible by humans. And finding these new materials is vital to tackling some of the most pressing environmental challenges. For example, we could use machine learning to help develop highly efficient solar fuels that can store energy from sunlight. We can also leverage AI to find superior battery materials that make longer-range electric cars a reality.

Moreover, the role new materials play in transforming our lives can’t be overlooked. For example, most modern portable electronics today use lithium-ion battery technology, which was developed in the 1980s. AI could help us find the next generation of ground breaking materials that become a ubiquitous part of our lives.

Recycling

Recycling reduces the need for extracting (mining, logging and quarrying), refining and processing raw materials, all of which create significant air and water pollution. However, you’ve probably heard that we’re not all that good at recycling today. This is mainly because the responsibility for recycling tends to fall on individuals, and the average person is perpetually confused about what they can and cannot recycle. The result? Plenty of contaminated waste that ultimately ends up in a landfill.

However, AI systems can help tackle this issue by classifying and identifying 100s of different types of recycling. It does this by leveraging computer vision and advanced algorithms to identify objects based on colour, size, shape, and other properties. Once identified, the object can be sorted into the correct pile and moved through the recycling chain. One company making a splash in this area is AMP Robotics, whose AI can sort recyclables at 80 items per minute with an accuracy of up to 99%.

Supporting conservation efforts

Experts estimate that the loss of species we see today is between 1,000 and 10,000 times higher than the natural extinction rate. And as our planet gets warmer every year, it’s only becoming more uninhabitable for the many creatures that call this blue rock home. But with AI, we can capture and analyse large amounts of data about wildlife and ecosystems to determine landscape health and human impact on endangered populations. Armed with this information, conservationists can better understand animal behaviour, make better forecasts, and take proactive steps toward improving animal population health.

Final Thoughts

Tech and the environment must go hand in hand if we want to combat the climate crisis. Luckily, advancements in AI and machine learning are making this a reality, offering exciting solutions that improve food security, reduce the rising extinction rate, meet the energy demands of a growing human population, and more.

The header image is reproduced curtesy of pixabay (Link) and is CC0 Public Domain.