AI and Sustainability: Bridging Innovation and Environmental Responsibility

Artificial intelligence is reshaping modern society, but this technological revolution comes at an environmental cost that we can no longer ignore. Although AI offers innovative ways to tackle climate change, it also contributes to carbon emissions and the consumption of resources.

The environmental impact of AI is staggering. Research published in Nature Sustainability reveals that implementing AI servers in the United States could generate between 24 and 44 million metric tons of CO₂ equivalent emissions annually by 2030 — comparable to adding 5 to 10 million cars to American roads. The United Nations emphasises that data centres hosting AI servers produce electronic waste, consume vast amounts of water, and use huge quantities of electricity, thereby fuelling greenhouse gas emissions. Golestan Radwan, UNEP Chief Digital Officer, states that we must ensure the net effect of AI on the planet is positive before it is implemented on a large scale. AI is what researchers call “double-edged technology”.

Supercomputers and AI models have their own carbon footprint, which varies depending on the type of AI and the training methods used. However, AI can also play a key role in reducing emissions through climate change modelling and smart grid design. Microsoft Research has demonstrated that AI-based systems can integrate renewable energy more effectively into stable electrical grids and reduce carbon capture costs by accelerating the discovery of new materials.

In this critical context, Graph Massivizer shows how innovation and sustainability can go hand in hand. The project aims to improve the efficiency of data analysis and reduce the energy impact of extract, transform, and load operations. It seeks to enhance data centre energy efficiency by a factor of two and reduce greenhouse gas emissions associated with graph-organised database operations.

The Data Centre Digital Twin use case, involving CINECA and the University of Bologna, exemplifies this vision by creating a digital graph representation of CINECA’s supercomputers. This representation allows system operations to be studied and understood and enables efficiency and sustainability to be optimised for the next generation of exascale supercomputers.

Recent research from the University of Bologna and CINECA showcases AI designed to support sustainable development. The ExaQuery project proposes an innovative ontology for operational data in HPC systems that organises and queries telemetry data more efficiently, reducing computational load. This knowledge graph-based approach facilitates the identification of complex relationships between hardware components, computational jobs and performance metrics, paving the way for intelligent resource optimisation.

Building on this foundation, researchers have developed a Virtual Knowledge Graph system that provides natural language access to heterogeneous IoT data in data centres. Combining Large Language Models with Knowledge Graphs achieves 92.5% query accuracy for this system, compared to 25% for traditional LLM-to-NoSQL approaches, while reducing latency by 85%. This breakthrough demonstrates that intelligent data organisation can dramatically improve accessibility and efficiency when managing complex telemetry systems.

As Graph Massivizer highlights, although data analysis and processing have a significant environmental impact, they can also be invaluable tools for achieving environmental sustainability. The key lies in using technology responsibly by focusing on efficiency, renewable energy and intelligent computational architectures. The message is clear: AI can and must be part of the solution to the climate crisis, not the problem. Projects like Graph Massivizer show that technology’s future can be powerful and sustainable if we make the right choices today.