Understanding AI’s Carbon Footprint: The Hidden Environmental Cost of Smart Technology
Understanding AI’s carbon footprint reveals how artificial intelligence affects energy use, emissions, and sustainability in the digital age.
The use of artificial intelligence (AI) has been widely recognised as a source of improved efficiencies, increased automation, and increased innovation across industries. AI applications are used almost everywhere that you can think of, from healthcare diagnostics to personalised online services – therefore, AI continues to be more integrated into our everyday lifestyle.
However, in addition to the rapid expansion of AI applications, there is another concern associated with the rapid growth – an increase in environmental impact due to the resource intensity of building and training AI systems.
It is now becoming increasingly important to consider the carbon footprint of AI, particularly with the increasing interest among governments, corporations, and academics regarding the contribution of digital technologies to global carbon emissions. AI systems may not seem “real”, but the majority of the resources supporting AI (data centres and computing power) are consumed in order to run the servers needed to operate these enormous AI systems.
Training AI models results in a high amount of electrical consumption. For example, the amount of electricity that it takes to train a single advanced AI model is similar to the amount that would be used by hundreds of average-sized homes in a full year’s time. The electricity consumption associated with AI systems is mainly derived from the operation of high-performance servers housed in large data centres that are making use of electricity.
The environmental impact of AI depends heavily on the source of electricity. For example, data centres powered by fossil fuels create significantly higher carbon emissions than those powered by renewable energy sources. As the number of AI applications continues to grow, there will continue to be an increasing need to address the carbon emissions associated with using the infrastructure to support them.
Why AI’s Carbon Footprint Is Often Overlooked
The environmental impacts of artificial intelligence (AI) are often overlooked because these impacts are indirect. Individuals using AI, whether it is in the form of search engines, social media recommendations or chatbots, do not directly interact with computer servers. However, all AI applications rely on processing data, which requires the use of electronic devices and generates CO2 emissions that contribute to global warming.
Understanding how the new generation of AI technologies has been made possible by the development of green data centres, which provide the physical foundations for digital technologies, can provide a valuable perspective on the relationship between the deployment of more energy-efficient computers and sustainable development.
As AI becomes more widespread, there is growing awareness among technology companies of the responsibility they hold to decrease AI’s carbon emissions and other environmental impacts. More businesses are purchasing next-generation computer chips that consume less energy than older generation processors, while others are moving their data centres to locations closer to renewable energy sources. Researchers are also creating smaller, more energy-efficient models of AI that use significantly less computer processing power and deliver comparable levels of performance.
In addition to the mitigation of greenhouse gas emissions from computing, organisations are increasingly focused on transparency in their energy and greenhouse gas emissions. Publishing standardised documentation of their energy consumption and emissions will allow organisations to measure and monitor their own emissions as well as use those same documents for measuring and monitoring the emissions of other organisations and the effectiveness of their greenhouse gas reduction efforts.
AI and Carbon Footprint: Key Insights
| Aspect | Impact |
| Model training | High energy consumption |
| Data centres | Major source of emissions |
| Renewable energy use | Reduces carbon footprint |
| Efficient algorithms | Lower computational demand |
| Regulation and reporting | Improves accountability |
Conclusion
As Artificial Intelligence (AI) becomes increasingly influential in the global economy, it will be critical to understand its carbon footprint on the environment. In addition to the benefits AI provides, there is also an environmental cost associated with its development and use.
The technology sector has an opportunity through improving energy efficiency, increasing the adoption of renewable energy sources and promoting responsible innovation to be able to develop and deploy AI responsibly so that the benefits of AI will not come at an unsustainable cost to the planet.
FAQ
What does the carbon footprint of AI refer to?
The carbon footprint of AI refers to the total amount of greenhouse gas emissions produced as a result of using energy to train, operate and maintain AI systems.
Why does AI require so much energy?
AI requires the use of high-performance computing hardware, and because these data centres must operate all the time, particularly during the training of a model, there is a large consumption of energy.
Can AI be made more sustainable?
Yes! By incorporating renewable energy into its operations, creating more efficient algorithms and improving the design of hardware used to power AI, it is possible to dramatically reduce the emissions produced from the development and use of AI.
Is the carbon footprint of every AI system similar?
No. Generally, larger models have greater energy consumption than smaller models or ‘optimised’ systems, which tend to have a smaller carbon footprint.
Should AI’s environmental impact be regulated?
Many believe that through regulations and the promotion of transparency, a balance can be established between the growth of AI technology and the goal of achieving sustainable development.
