As technology continues to advance, AI is something people are increasingly embracing. It is already taking over automating repetitive tasks, enhancing productivity, and freeing up human resources.
With its potential in mind, it poses the question of whether it can also help us tackle the carbon challenge.
Firstly, it can contribute to optimising our energy consumption and reducing waste through AI-driven smart grid management, allowing us to achieve both goals.
These technologies include advanced data processing, communication, sensing, and control.
With AI, smart grid management can analyse real-time data from various sources to predict energy demands and detect patterns more accurately.
Smart grids also manage distributed energy resources such as energy storage systems and solar panels to balance the grid and prevent potential blackouts or overloads. Image: Unsplash
It could also be easier to optimise the allocation of energy resources to prevent waste and ensure supply meets demand.
AI can also optimise the deployment and operation of renewable energy systems by analysing weather patterns, maximising output, and forecasting energy generation so we can drive a stable energy supply.
Adopting Artificial Intelligence can also optimise the design and operation of carbon capture technologies to drive their cost-effectiveness and efficiency while also playing a crucial role in monitoring and predicting emissions.
Additionally, these advanced technologies can arrange to collect data from operations, including activities like IT equipment and corporate travel, and every part of the value chain, from securing materials from suppliers to downstream users of their products.
Leveraging AI in carbon management has great potential.
For instance, data analytics and predictive algorithms can help companies better understand and manage their energy usage, transportation, and manufacturing processes to model and measure their Scope 1 and 2 emissions accurately.
According to a study by the European Union, the use of AI and machine learning in energy management systems could result in energy savings of up to 15% in commercial buildings.
AI-driven applications can also contribute to managing Scope 3 emissions through supply chains by optimising connections and material usage between different stakeholders.
Experimental projects are looking at implementing blockchain-based carbon tracking systems.
Putting more effort into using predictive analytics is also an area of interest since it can model future carbon emissions and identify potential areas of improvement.
It means that based on previous data and patterns, AI can present different carbon footprint scenarios, allowing decision-makers to choose the one that best fits their strategic goals.
Some of the practical applications of AI-driven solutions can be found in the property market. Since most of the buildings that will be standing in 2050 are already built, decarbonising existing buildings is critical.
London-based company Mortar IO uses automated digital audits to help organisations comprehend how to achieve net zero for entire real estate portfolios in a matter of minutes rather than months.
A start-up, Eugenie.ai, with offices in the US and India, also uses AI's power to drive sustainable development through an emission intelligence platform created to help manufacturers in sectors such as oil, mining, metal, and gas decarbonise their operations.
Start-ups are not the only ones leveraging AI to tackle the carbon challenge.
The United Nations Environment Program (UNEP) is also utilising AI to help analyse and predict the concentration of carbon dioxide in the atmosphere, besides assessing changes in glacier mass and rising sea levels.
The International Methane Emissions Observatory (IMEO) is another tool used by the United Nations that uses AI to mitigate and monitor methane emissions.
On the other hand, experts and researchers warn that using AI has its own environmental footprint that cannot be overlooked.
Empowering the process of training AI and using it on a large scale requires a lot of energy, and global companies like Microsoft refuse to share relevant information regarding resources used in powering AI solutions.
Swedish researcher Anders Andrae has forecasted that data centres could account for 10 per cent of total electricity use by 2025.
Researchers at the University of Massachusetts, Amherst, found the training process for a single AI model can emit more than 626,000 pounds of carbon dioxide, which is about the same amount of greenhouse gas emissions as 62.6 gasoline-powered passenger vehicles driven for a year.
Ultimately, AI’s strength in helping us tackle the carbon challenge lies in its ability to learn by experience, collect massive amounts of data from its environment, recommend appropriate actions, and make connections that humans often fail to notice.
While it can be used as a tool to help us in our fight, it does still come at a cost to the planet since using these tools also has a notable carbon footprint.
Therefore, before we jump to AI as the ultimate solution to all our carbon problems, we must look at all aspects of its overall societal and environmental potential impact.