Green AI

Most innovations nowadays are based on augmenting sustainability, and it makes sense with all the recurring climate change issues. While noble in theory, it is especially hard to achieve this vision of a sustainable future as the new trend is utilizing AI models to perform a variety of functions, and these AI data centers account for 2% of the world’s carbon emissions. Individuals in the tech space, however, have seen this underlying issue and are working towards a dream that seems impossible but might just come true if executed properly. That solution is Green AI.

Now, while the concept of Green AI is highly intuitive, it is often hard to grasp why these AI data centers are detrimental to the environment and what exactly is covered under this new domain. Green AI is designed with sustainability in mind, and this differs from most AI models today as they are hotspots for energy consumption. Due to most AI models often requiring immense amounts of computational power, a lot of electricity is needed, and most AI data centers rely on the standard electric grid. This electric grid runs on fossil fuels, so the more computational power needed usually translates to larger carbon emissions. 

While it is true that electricity can be generated through renewable approaches, fossil fuels remain as the predominant source of power in the US (56% of AI data centers) and globally (62% of AI data centers). It has been noticed in several reports that the US and major parts of Asia (China, Singapore, and India) primarily use natural gas and coal to power their AI data centers while European hubs have gravitated towards more renewable approaches. 

Additionally, providing computational power to these models aren’t the only contributions of AI towards harming the environment. AI data centers generate massive amounts of heat when performing calculations and algorithms, so this calls for cooling systems to ensure no overheating. These cooling systems are just another expenditure that requires electricity to function. Ultimately, the inevitable issue comes to light: financial viability. The government incurs significant costs by keeping these AI data centers actively running and computing. Many people perceive the AI models they use as simple additions to their lives, but in many scenarios, the setbacks far outweigh the benefits. It is impractical to abandon AI completely due to how integrated it is in society today, but what if we could improve AI?

Green AI incorporates techniques like quantisation and pruning to minimize the overall consumption of power. Quantisation is the process of utilizing low precision numbers to reduce the need for high computational power. While this technique isn’t too generally applicable to AI, there are certain cases where quantisation can benefit the user. Some examples are the AI models on one’s smartphone that would generally rapidly drain battery, but with quantisation, less power is used and therefore more power conserved within the phone. This technique also works with AI integrations in vehicles to optimize safety because cars usually need to make decisions in milliseconds, something that wouldn’t be possible if excessive computations were run. Pruning is also a notable Green AI practice. It is the process of leaving out unnecessary parts of an AI model. Not every AI model will need every feature, and gaining access to these features takes an immense load of computational power. It might be true that Green AI practices don’t speak too loudly, but surely, its effects will.

With the Green AI movement, data scientists and AI research scientists not  only find solutions to helping AI become more eco-friendly, but also increase AI output while minimizing input. Specialists in Green AI produce AI algorithms that require less computational power, which keeps the environment in a safer place while simultaneously reducing the expenditure for AI application. With this goal in mind, machine learning engineers will finally be able to work on AI applications that have been postponed or kept at bay due to lack of funding. Engineers in this space also design more efficient GPUs that provide a higher performance per unit of energy. 

The final piece of the puzzle is data centers in the US transitioning to renewable energy sources. While there are already countries in the world implementing this idea, it is important to note that the most active AI data centers in the world are in the US and China. Since these nations primarily use fossil fuels, efforts towards the Green AI movement will be lost unless there is advocacy against the energy consumption of these AI data centers. It won’t be instantaneous, but definitely rewarding. AI has become such an integral part of our lives, so before we abuse it to the point where the planet becomes inhospitable, we must change the way we use AI and foster a cleaner and more efficient AI future for all.

References:

Stanford Graduate School of Business. “What Point Do We Decide AI’s Risks Outweigh Its Promise?” Stanford Graduate School of Business, https://www.gsb.stanford.edu/insights/what-point-do-we-decide-ais-risks-outweigh-its-promise

Costa, David. “Making AI Sustainable by Design Is Key to a Better Future.” World Economic Forum, 5 Feb. 2026, https://www.weforum.org/stories/climate-action/designing-sustainable-ai-better-future/. Accessed 27 July 2026.

Bolón-Canedo, Verónica, et al. “A Review of Green Artificial Intelligence: Towards a More Sustainable Future.” Neurocomputing, vol. 599, no. 128096, Elsevier BV, June 2024, pp. 128096–96, https://doi.org/10.1016/j.neucom.2024.128096. Accessed 27 July 2026.

Quiz: