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 is also an opportunity to innovate! Using Artificial Intelligence (AI) and Big Data can help us efficiently address the negative effects of climate change on humans, ecosystems, and businesses.

To that end, we're seeing a surge in AI and Big Data solutions focused on climate change mitigation. These innovations are helping us better understand how climate change impacts the everyday world around us and providing us with the tools to tackle the issues more efficiently than ever before. From monitoring real-time carbon emissions to reducing natural disasters with predictive analytics, here are some examples of how AI and Big Data are helping us fight climate change.

It is possible to effectively identify the most suitable lands and soils through AI algorithms. Using data can help develop carbon capture, storage & sequestration services, as it gives out important insights to geo-reference and identify new deployment areas.

Using robots with machine learning software could help farmers manage a mix of crops more effectively. Algorithms could also 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.

The leading AI-based technologies that help ensure crop health are hyperspectral imagine and 3D laser scanning.  

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

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

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 through personalised nudging and compensate for gaps by using renewable energy to reduce waste and maximise efficiency.

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. 

AI-powered machines are far more efficient in automating the sorting process than human labour. This will be an effective and immediate advantage for AI in the long run.

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 through large-scale and multi-sourced data analysis and develop smart tools to help address these extreme events more effectively.

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 can also interpret their outputs and use them when scheduling renewable sources to the grid. 

Before we conclude, it is important to recognise one drawback of AI and big data in the sustainability sector. AI technology is predominantly led and owned by large institutions and developed countries. The unequal distribution of AI and access to big data could further exacerbate inequalities between the rich and poor, and neglect the developing countries where AI technology is most urgently needed for climate solutions. It is situations like this which a ‘fair and just’ transition to a more sustainable world is called for by activists and campaigners.

When used ethically, 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.