Mitigating the Growing Carbon Footprint During AI Spring

Artificial Intelligence (AI) is a powerful tool that has the potential to revolutionize many industries, but it also comes with a significant environmental cost. The training and running of AI models require a lot of energy, and this energy is often generated from fossil fuels, which emit greenhouse gases. As AI technology becomes more widespread, its carbon footprint is only expected to grow.

Carbon Footprint of AI

AI models are run on computers, which require an enormous amount of energy to operate. This energy consumption can lead to carbon emissions, which are harmful to the environment. The MIT Technology Review states that more than 626,00 pounds of carbon dioxide equivalent can be omitted by training just a single AI model, which is approximately 5 times the lifetime emissions of an average private vehicle. There are two main types of carbon emissions associated with AI: operational emissions and embodied emissions. Operational emissions occur when electricity is used to power AI systems. Embodied emissions occur when the materials and components used to build AI systems are manufactured.

Sustainable AI

There are several things that can be done to reduce both the operational and embodied emissions. One is to use more sustainable forms of energy to power AI systems, such as solar and wind power. Another is to develop more efficient AI algorithms that require less energy to train and run, for instance, using more efficient hardware, such as specialized AI chips, developing new algorithms that are more computationally efficient, using AI to optimize the training and running of other AI models.  The tech industry also needs to be more transparent about the carbon emissions associated with AI by developing standards for measuring the carbon footprint of AI, so that users can make informed choices about which AI products to use.

Energy Efficient AI

Researchers in tech companies are working to reduce the carbon footprint of AI by developing new AI algorithms that are more energy efficient. One of the ways they are doing this is by using a technique called model distillation.

  • Model distillation is a process where a large, complex AI model is trained on a large dataset. This large model is then used to train a smaller, more efficient model. The smaller model can achieve similar accuracy to the larger model, but it requires less energy to run.

Tech companies are also developing new AI algorithms that are more efficient in terms of their computational requirements. This is being done by using techniques such as sparse matrix multiplication and low-rank approximation. These techniques allow AI models to be trained and run on less powerful hardware, which can lead to significant reductions in energy consumption.

  • Sparse matrix multiplication is a technique that can be used to speed up the computation of matrix multiplication operations. This can be beneficial for AI algorithms that require a lot of matrix multiplication operations, such as natural language processing and computer vision algorithms.
  • Low-rank approximation is a technique that can be used to reduce the size of a matrix without significantly reducing its accuracy. This can be beneficial for AI algorithms that require a lot of matrix storage, such as deep learning algorithms.


By taking steps to reduce the carbon footprint of AI, the tech industry can help to mitigate the environmental impact of this powerful technology. This is important not only for the environment, but also for the future of AI itself. If AI is to be used to solve some of the world’s most pressing problems, it must be done in a way that does not create new problems.

This article is drafted with the assistance of A.I. and referencing from the sources below:

The work described in this article was supported by InnoHK initiative, The Government of the HKSAR, and Laboratory for AI-Powered Financial Technologies.
AIFT strives but cannot guarantee the accuracy and reliability of the content, and will not be responsible for any loss or damage caused by any inaccuracy or omission.

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