How AI and Big Data can help reduce the impact of climate change

18 Jul, 2022

How AI and Big Data can help reduce the impact of climate change

Climate change is one of the most pressing problems of our time. It poses an imminent threat to the safety and well-being of people around the world. But it’s also an opportunity to innovate -- using artificial intelligence (AI) and big data to develop solutions that help counteract its negative effects on humans, ecosystems, and businesses. 

To that end, we’re seeing a surge in AI and big data focused on climate change mitigation. These two fields are not only helping us better understand how to tackle this issue, but they’re giving us tools to do so more efficiently than ever before. From monitoring carbon emissions in real-time to reducing natural disasters with predictive analytics, these are some examples of how AI and big data are helping us fight climate change.

Facilitate climate-friendly business models

It’s possible to identify the most suitable lands and soils through AI algorithms in a very effective way. An example of this is by using existing carbon sequestration action data and interpreting the massive data sets related to agriculture, meteorology, and geology in order to identify the most suitable locations.

Using robots with machine learning software could help farmers manage a mix of crops more effectively. Algorithms could also be used to help farmers predict what crops to plant at what time, improving the soil's health and reducing the need for fertiliser throughout the growing season.  



Optimise supply chains

There are several benefits to machine learning that can be realised in the food, fashion, and consumer goods supply chains, such as the reduction of inefficiencies and the lowering of carbon emissions. 


With better supply and demand forecasting, reducing the amount of waste generated during production and transport can be possible, and targeted suggestions for low-carbon products will help incentivise environmentally friendly consumption behaviour.

Improve energy efficiency

There has been a great deal of focus on machine learning supporting efficiency in power generation, whether it is tracking leakage, managing fleets or optimising routes. 

For example,

Deepmind AI

, a machine learning platform owned by Google, can predict wind patterns up to 36 hours in advance so that wind farms can be optimised accordingly. 

With the help of machine learning, you can gather the data to understand and anticipate energy generation. Additionally, it can help suppliers use resources more efficiently and compensate for gaps by using renewable energy to reduce waste and maximise efficiency. 


©Getty Images/MichaWolf

Renewable energy in Europe, ©Getty Images/MichaWolf

Waste Management

Artificial Intelligence plays a major role in the management of waste. One of the functions that it offers is the provision of intelligent garbage bins. With the help of IoT sensors, cities can keep track of the locations, time and frequency at which trash receptacles are available throughout the city, enabling them to extend and improve routes, timings and frequency of trash collection. 

It's safe to say that AI-powered machines are far more efficient than human labour in automating the sorting process. This will prove to be an effective and immediate advantage for AI in the long run. 

Monitor the environment

While grasses, trees, and plant life store carbon, deforestation causes it to be released again into the air. Deforestation and unsustainable agriculture are known for adversely contributing to climate change.

Many weather events have caused havoc across the globe in recent years, including instant floods in Indonesia, bushfires in Austria, cyclone Amphan in Bangladesh and India, and even green snow on the Antarctic continent.

To better prepare against the recurrence of such incidences, AI is being used to improve and predict weather events early on and also develop smart tools to help address these extreme events more effectively.

Improve predictions on electricity usage 

In order to rely on more renewable energy sources, utilities will need to predict how much energy is required, both in real-time and over the longer term, to make better decisions about energy use.

Although algorithms already exist that can forecast energy usage, the algorithms could be improved using AI to incorporate finer local weather and climate patterns, as well as household behaviour.

If efforts are made to make the algorithms more understandable, utility operators might also be able to interpret their outputs and use them when scheduling renewable sources to the grid.  

AI can help us address the impacts of climate change by providing real-time monitoring and predictive analytics that can prevent disasters before they happen. With Big Data, businesses also stand to gain insight into fluctuations in weather patterns based on geographical location and historical trends. Thanks to this knowledge, organisations can adjust their business models accordingly and become more resilient against the impact of climate change.